# Komprise > Komprise is the leader in analytics-driven unstructured data management. The Komprise Intelligent Data Management platform gives enterprise IT teams a single place to analyze, classify, tier, migrate, and prepare petabytes of file and object data—across on-premises NAS, hybrid, and multi-cloud storage—without vendor lock-in, storage disruption, or ETL complexity. ## What Komprise Does Komprise powers the connection between unstructured data and AI. It solves three converging enterprise challenges: 1. **Cost & storage sprawl** — Most enterprise file data is cold and inactive yet consumes expensive primary storage and backup capacity. Komprise analyzes data across storage silos and transparently tiers cold data to lower-cost storage or cloud without user disruption, typically cutting storage costs by 70%. 2. **AI data readiness** — AI models require curated, contextual, compliant unstructured data. Komprise Smart Data Workflows and the Global File Index discover, classify, tag, and deliver the right data—with metadata enrichment and sensitive data filtering—so enterprises can feed AI pipelines accurately and safely. 3. **Data migration at scale** — Komprise Elastic Data Migration migrates petabytes of file and object data up to 27× faster than legacy tools, across any source and destination, with full analytics, SID mapping, and no downtime requirements. ### Core Platform Components - **Komprise Analysis** — Storage-agnostic analytics and visibility across NAS, cloud, and object data to understand what data exists, who owns it, how it is used, and what it costs. - **Transparent Move Technology (TMT)** — Proprietary technology that moves data to cheaper or cloud storage while preserving file paths, so applications and users see no disruption. - **Komprise Global File Index** — A searchable, metadata-enriched index of all unstructured data across storage silos, enabling fast discovery and curation for AI and governance workflows. - **Smart Data Workflows** — Automated policies to classify, tag, filter sensitive data (PII detection), and deliver curated datasets to AI pipelines or protected storage locations. - **Komprise Elastic Data Migration** — Fast, reliable, analytics-first migration of file and object data at petabyte scale, supporting NAS-to-NAS, NAS-to-cloud, and cloud-to-cloud scenarios. - **Komprise Data Experience (KDX)** — The unified UX across all capabilities: analytics, tiering, migration, and AI data preparation from a single platform with no lock-in. ### Key Differentiators - **Storage-agnostic** — Works across all major NAS, cloud storage, and object storage vendors (NetApp, Pure Storage, IBM, Nutanix, Azure, AWS, and more). - **No lock-in** — Data moved via Transparent Move Technology remains accessible as native files or objects; Komprise does not sit in the data path. - **Analytics-first** — Every action (tiering, migration, AI prep) starts with analysis so IT understands data before moving it. - **AI-ready data pipeline** — Purpose-built workflows for metadata enrichment, sensitive data detection, and curated AI data delivery. - **Enterprise SaaS** — Cloud-delivered management plane; no agents on storage arrays. ### Validated Customer Outcomes - U.S. Academic Health System: $4 million/year in storage savings with self-service tagging and showback - Katten Law Firm: $900K saved on storage with ransomware protection in Azure - Lummus Technology: 80% storage cost reduction with improved data lifecycle management - Boone County, Indiana: Managed 3,000% data growth by tiering to Azure - Major West Coast Research University: Petabytes of NAS data protected with cloud DR at a fraction of cost - Migrations to NetApp, Azure, AWS, and other platforms completed up to 27× faster than alternatives ### Industry Recognition (2025) - Inc. 5000 for the fourth consecutive year - Globee Gold, Enterprise Data Management Disruption - Stevie Award, Big Data Reporting & Analytics Solution - CRN Storage 100 and Cloud 100 - GigaOM Top Performer, Unstructured Data Management 2025 ### Key Partnerships Microsoft Azure (co-sell ready; Microsoft Azure File Migration Program partner), AWS, IBM, NetApp, Pure Storage, Rubrik, Nutanix, Qumulo, VAST Data, and other leading storage and cloud vendors. ## AI and Unstructured Data Management > Komprise's core thesis: AI success depends on access to curated, contextual, compliant unstructured data. Komprise provides the platform to find, classify, enrich, and deliver that data safely. ### Komprise Makes the Inc. 5000 2025 List For the fourth year in a row, Komprise earns a spot on the prestigious Inc. 5000 list of fastest-growing private companies in the United States. Komprise empowers IT teams with a full platform to manage unstructured data across storage for cost optimization, governance, and AI data classification and ingestion. Read the press release. ### Komprise Lands a Spot on Inc. 5000 for Fourth Year in a Row Unstructured data management SaaS expands use cases and customer adoption as enterprises invest in AI data preparation and AI data governance. Campbell, CA, August 12, 2025 – Komprise, the leader in analytics-driven unstructured data management, announces that the company has been named for the fourth year running to the annual Inc. 5000 list, the most prestigious ranking of the fastest-growing private companies in America. Komprise was selected based on its revenue growth from 2021 to 2024. Komprise Intelligent Data Management delivers full visibility into file and object data across storage silos so enterprise IT can reduce waste and right-place data for optimal cost efficiency. Komprise provides the fastest, most transparent platform for data tiering and data migration so that organizations can achieve the best ROI from hybrid cloud storage. The Komprise Global File Index and Smart Data Workflow Manager deliver a foundation to prepare and curate unstructured data for AI through metadata enrichment and sensitive data filtering. Komprise 2025 Highlights: In January, Komprise announced Smart Data Workflows for Sensitive Data Detection & Mitigation. This release allows IT to scan file shares for PII and/or use regular expressions (regex) and keywords to simplify and automate the process of finding and tagging sensitive data and moving it to protected locations. In March, Komprise updated its Elastic Data Migration technology with expanded automation and reporting features. Komprise received multiple industry awards and recognitions so far this year, including a Globee Gold in Enterprise Data Management, a Stevie Award for Big Data Reporting & Analytics, and the CRN Storage 100 and Cloud 100. View all here. In June, Komprise released a new survey: AI, Data & Enterprise Risk. The research reported on enterprise IT concerns with shadow AI, evolving AI data governance plans and IT infrastructure priorities. “Making the Inc. 5000 for the fourth year in a row is exciting for us as we navigate new AI use cases and data governance needs for our enterprise customers,” says Mike Munoz, CRO at Komprise. “Komprise Intelligent Data Management is a proven solution to help enterprises right-place data into the most cost-efficient storage. It brings insights on file and object data across storage and powers rapid, secure, cost-effective curation of data for AI.” For complete results of the Inc. 5000, including company profiles and an interactive database that can be sorted by industry, location, and other criteria, go to www.inc.com/inc5000. About Komprise Komprise powers the connection between unstructured data management and AI. Komprise Intelligent Data Management delivers a single platform to easily analyze, migrate, transparently tier and manage the lifecycle of petabytes of file and object data across hybrid environments. With Komprise, enterprise IT gains full visibility across silos to optimize storage, backup, ransomware and cloud costs. Komprise Smart Data Workflows and the Komprise Global File Index unlock unstructured data insights and access for AI.  www.komprise.com ### Why You Need Metadata for Smarter AI and Data Governance This interview originally appeared on Blocks & Files. "In the last few years, generative AI’s large language models require vector embeddings to perform semantic search, and such vectors are generated from unstructured data, from the content," writes Chris Mellor, editor of Blocks & Files. "Are vectors a kind of metadata? We explored these topics with Komprise CEO Kumar Goswami in an interview." Mellor: I could argue that the tokens and vector embeddings generated from a data item are metadata. What do you think about this idea? Kumar Goswami: Metadata and vector embeddings are complementary but related. Since vector embeddings are a computer-understandable representation of file contents (“the what”) while metadata is valuable information about the file that can go well beyond file contents (“the why”), you need both. For example, say you want a chatbot to answer questions based on the most recent product features but you want it to only use public facing documents and not confidential internal documents,  use metadata to exclude internal documents and non-final versions and run the vector embeddings and AI on just the right files. We are focusing on gathering and globally managing metadata to enrich and narrow down data while empowering other tools and processes to consume and process the data as a whole. For example, you can enforce AI data governance and improve AI data quality by using Komprise to cull the files fed to Nvidia NeMo for embedding and running inferencing. Mellor: Komprise says new tools can automatically analyze file contents and generate semantic tags at scale. What are semantic tags and how do they differ from vector embeddings?   Goswami: Vector embeddings are used to help AI understand the meanings of words in context while metadata provides semantic context for which files are relevant. For example, vector embeddings may help AI understand that the word “award” in the context of a research grant paper means getting a funding award and not winning a trophy. Metadata can be used to cull and curate all the documents related to a specific research topic by a specific researcher in a specific time frame to send to an AI agent that is helping write a grant application. Mellor: What tools exist that automate finding and analyzing metadata? Goswami: You need to index metadata across different storage and cloud environments and also act on it at scale. Komprise does both as our analysis extracts both system metadata and extended metadata such as sensitive data information into a global file index. This index retains the knowledge no matter where your data lives, and it does so without changing the original files. Komprise Deep Analytics helps you query and filter data based on this index and Komprise Smart Data Workflows allows you to search and feed the right data to the right AI process and retain its outputs as additional metadata. Unlike traditional ETL, you need an ongoing workflow solution to find the right data, get it to the right compute, run the compute either locally or in the cloud, and then repeat this process again. You can use any AI or vector embedding or processor to enrich metadata further on your data in Komprise workflows. A great example of this is our customer Duquesne University. Mellor: What AI tools are now available to extract pertinent information hidden in files and turn it into useful metadata that adds structure and context? How is the synthesis carried out? Goswami: Anything that looks at file contents and generates outputs can be used via APIs in Komprise to enrich metadata. You can use cloud-based services like Azure AI Speech to inspect audio or Salesforce Einstein to find particular purchase orders in your CRM, and then have Komprise tag the files. That is the beauty of iterative workflows. You can use any process or tool to distill relevant metadata once you have a systematic way to manage the workflow. Mellor: I understand Komprise thinks that automatic metadata from storage systems, while useful for basic operations, is just the start of a strategic metadata management program. Can you explain? Goswami: There are many types of additional metadata, some of which are shown below. You could have users manually apply additional tags based on their knowledge. And, you can systematically automate applying tags at scale based on the artifacts from other processes as we have explained in prior answers. Enriched metadata becomes part of the data stored and indexed by an unstructured data management system. Such systems must be able to handle the scale of billions of metadata tags and persist these tags wherever the data lives and moves, to be effective. Komprise can do this today. Contextual metadata: Project identifiers, geographical tags, departmental associations, and business context that give meaning beyond technical properties. Some of this information can be extracted from applications, some from headers in files, and some via APIs from related applications (like getting the account identifier for a proposal from the CRM system). Sensitivity metadata: PII, intellectual property, regulated data type and security classifications. This requires specialized tools to uncover and classify, as it involves analyzing file contents rather than just properties. User-based metadata: Manual tags, collaborative annotations and crowd-sourced insights that add human intelligence to data classification. While powerful, this approach faces scalability challenges as data volumes explode. AI-generated metadata: The newest and most transformative category. AI analyzes file contents and automatically generates contextual tags and classification insights at scale. Mellor: How can Komprise automatically identify and classify data based on business value, access patterns, and project requirements? Goswami:  Komprise offers automatic identification of sensitive data today in product, whether that is PII or keyword/regex search for a custom query. We also can work with any third-party AI tool to scan for different data types that uniquely identify data contents with tags that departmental users and data scientists need for projects. Culling and feeding the right data to AI is very important, regardless of whether the AI runs locally or in the cloud for three key reasons: a) it can be very costly to copy a lot of unnecessary data across environments, b) you don’t want to run expensive AI compute on irrelevant data or repeatedly on unchanged data, c) but most importantly, feeding the wrong data to AI could create data leakage and inaccurate results. Mellor: How can Komprise help data scientists understand data lineage and ensure compliance with governance requirements? Goswami: As Komprise moves the data to AI, it maintains an audit of what information was sent, and it tracks the lineage of where the data has been moved and where it came from. Increasingly, data governance is not just to comply with regulations but a corporate priority to prevent data leakage of corporate information. Komprise offers sensitive data detection and mitigation, orphaned and duplicate data search and deletion, and the ability to automate data management policies for different use cases. For instance, cold data tiering to immutable storage for ransomware protection or to ensure data that must adhere to regulations such as HIPAA and GDPR is stored and protected appropriately are great strategies to augment what cybersecurity teams are doing. You can set up a Deep Analytics query to identify these protected data sets (PII, PHI) and automatically act on them if they are not handled properly by confining them, sending them to compliant storage and deleting them per regulatory requirement timelines. Mellor: Komprise says sensitive data detection through metadata tagging for “PII” and other keywords helps find protected data that may be stored in non-compliant locations and secure it properly against cyberattacks. Can Komprise automate this process? Kumar Goswami: Yes! You can select the file shares and directories to search, and then Komprise will scan them for any data that is PII such as names, birth dates, user IDs, driver’s license, social security numbers, credit card numbers, addresses. You can also use regex/keyword search to find IP data or other data deemed sensitive to your organization that doesn’t fit any standard definitions and this could include EmployeeID, PatientID for example. You can then use a Smart Data Workflow to take additional actions, such as to confine the data sets for manual review for legal hold or deletion and/or automatically move them to secure storage. ### Preparing Unstructured Data for AI? Forget ETL This article has been adapted from its original publication on ITProToday. As AI transforms business operations, organizations need to focus on the data and, specifically, how to build efficient data pipelines to feed AI. The issue is that traditional data pipelines leveraging Extract, Transform, Load (ETL) were built for structured data and are fundamentally misaligned with AI's needs. ETL, which was designed for structured data from databases, no longer works in a world where 90% of data is unstructured and lives in files of many different formats and types. This data consists of documents, images, videos and audio files, instrument, and sensor data. This shift in focus from data analytics of the past leveraging structured data to AI of today that requires large amounts of unstructured data demands a complete rethinking of how organizations prepare data for AI consumption. The Unstructured Data Challenge The core problem with unstructured data is its inherent lack of a common schema. You can't take a video file, an audio file, or even three video files from three different applications and place them in a tabular format because they all have different contexts and different semantics. An MRI medical image and a marketing photograph may share the same file extension, but they require unique metadata structures and processing approaches. As well, the same document format might need entirely different preprocessing depending on whether it's being analyzed for legal compliance, customer sentiment, or research insights. To make unstructured data usable, safe and searchable for AI pipelines, organizations need to accurately enrich metadata in ways that don't require tedious, Sisyphean manual work. The metadata that storage systems automatically generate is limited: file type, creation date, author, modification date, size, last access date, and user ID. To enrich metadata, you first need a way to create a global file index of your unstructured data regardless of which storage or cloud houses the data. Once you have visibility, you can add tags manually with the help of departmental users who know their data and/or using AI and other automated tools. These new technologies — which can be standalone or exist within an unstructured data management platform — rapidly scan data sets and apply relevant tags describing their contents. This can identify sensitive data like personally identifiable information (PII) that must be excluded from AI workflows and add tags such as project code or research keywords that distinctly identify it for unique use cases. Komprise for Sensitive Data Management. As you catalog unstructured data, it is important to ensure that metadata can follow the data wherever it moves, avoiding the need to re-create metadata. Copying and moving unstructured data to locations for AI analysis is also time-consuming and expensive, and due to the size of the data, it can take weeks to months. As a result, you only want to move the precise data sets that you need, further highlighting the need for metadata enrichment and classification. Why AI Workflows Break the ETL Model Beyond format challenges, AI processing itself fundamentally differs from traditional analytics. With AI, the workflows become iterative and non-linear. For example, let's say you want Amazon Rekognition to look at images and tag them, run PII detection to find and exclude sensitive data and then send data to a large language model (LLM) like Azure OpenAI for chat augmentation. You now have three different AI processes working on the same data at different points. This creates an AI-feeding-AI scenario where outputs from one process become inputs for another. Traditional ETL  wasn't designed for this cyclical enrichment process. Additionally, AI introduces critical data governance challenges that are different from traditional analytics and unsupported by ETL, such as avoiding the exposure of sensitive data to commercial (external) AI services and maintaining clear audit trails of corporate data.  Finally, there is a need to keep a record of what metadata was AI-enriched versus AI-enriched and human-verified. Smart Data Workflows for AI A modern approach to AI unstructured data preparation requires rethinking the entire data pipeline. Rather than immediately moving data, start by building a comprehensive metadata index that spans all storage environments. This delivers intelligent curation that identifies the exact subset of data for AI processing based on content, context, and business requirements. A global metadata index should be designed to retain metadata and tags no matter where the data lives, so it is independent of your storage. This approach delivers significant advantages. In one real-world example, Duquesne University used Komprise and AWS Rekognition to first index and curate data to identify 10,000 relevant images out of three million files, cutting processing costs by 97%. Read the case study. Komprise Smart Data Workflows delivers an automated process for unstructured data preparation and mobility: Global metadata indexing and curation: Discover and select relevant data before moving it, integrating with AI processors as needed for rapid content analysis and tagging. User tagging: Allow end users to tag their own data since they know it best. Iterative enrichment: Store results as reusable metadata to avoid redundant processing. Built-in AI data governance: Automatically detect sensitive information and maintain comprehensive audit trails. There are several steps to follow on the path toward modern AI data preparation, including getting full visibility and analytics on unstructured data across storage silos, addressing data governance from the start, tracking AI data pipeline effectiveness with diverse use cases, and delivering departmental self-service capabilities for unstructured data classification. As AI becomes central to business strategy, the organizations that implement smart data workflows will gain significant advantages in agility, cost efficiency, and risk management. The question isn't whether your organization needs a new approach to unstructured data preparation for AI — it's how quickly you can implement one. ### Komprise Receives Gold Globee for Enterprise Data Management Disruption Enterprise customers use Komprise to eliminate unnecessary data storage, backup and ransomware costs and automate AI data pipelines. Campbell, CA — May 30, 2025 — Komprise, the leader in analytics-driven unstructured data management, announces that it has been named a Gold Winner for Enterprise Data Management in the 5th Annual 2025 Globee® Awards for Disruptors. Komprise was recognized for innovations in Automated Data Workflows for Unstructured Data AI Pipelines and Governance with Komprise Smart Data Workflows. Komprise, founded in 2014, is an industry pioneer in unstructured data management with Fortune 500 customers across data-heavy sectors such as healthcare, life sciences, financial services, media and entertainment, oil and gas and public sector. Its comprehensive SaaS solution delivers global visibility into file and object data across all storage, analytics to optimize decisions for data storage, high performing migration and tiering capabilities, automation for AI data classification and workflows, sensitive data management and ransomware defense. This year, the company announced sensitive data detection and mitigation capabilities as part of the Komprise Smart Data Workflows technology, along with an update to its Elastic Data Migration product. “Many enterprises are just realizing the need for independent unstructured data management technology, as their data environments become larger and more complex every year,” says Darren Cunningham, VP of Marketing at Komprise. “It’s wonderful to be recognized for this honor, as we continue to focus on helping enterprise customers optimize storage, backup and ransomware spending while they focus on preparing and securely delivering the right data sets to AI data pipelines, data lakes and analytics services.” See the full list of 2025 winners here: https://globeeawards.com/disruptor/winners/ About Komprise Komprise powers the connection between unstructured data management and AI. Komprise Intelligent Data Management delivers a single platform to easily analyze, migrate, transparently tier and manage the lifecycle of petabytes of file and object data across hybrid environments. With Komprise, enterprise IT gains full visibility across silos to optimize storage, backup, ransomware and cloud costs. Komprise Smart Data Workflows and the Komprise Global File Index unlock unstructured data insights and access for AI. www.komprise.com ### Komprise Unveils Sensitive Data Management Capabilities for AI Data Governance and Cybersecurity Komprise Smart Data Workflow Manager now finds and removes sensitive data from places it shouldn’t be to reduce the potential for costly AI data breaches. Campbell, CA--January 29, 2025 -- Komprise, the leader in analytics-driven unstructured data management and mobility, announces new sensitive data detection and mitigation capabilities to help organizations prevent the leakage of PII and other sensitive data to AI and reduce the risk of potentially ruinous data breaches. Komprise Smart Data Workflow Manager now includes detection for PII, regular expressions and keywords to simplify and automate the process of finding and tagging sensitive data and moving it to protected locations. IT teams dread the risks of sensitive data lurking where it shouldn’t be -- risks that are compounding as unstructured data grows explosively across all industries. GenAI popularity has made the situation more troublesome. Attempts to input PII into GenAI platforms represent over half (55%) of data loss prevention (DLP) events, followed by confidential documents (40%), according to 2024 research by Menlo Security. The global average cost of a data breach reached $4.88 million in 2024, according to IBM. Increasingly, storage administrators are responsible for data governance and compliance but lack ways to do this systematically across their data estate. The new sensitive data management capabilities of Komprise Smart Data Workflow Manager include: Standard PII detection: Select which PII data types to scan for such as national IDs, credit card numbers and email addresses. Komprise supports multiple classifications to identify multiple types of PII within any given file. Custom Sensitive Data Detection: Customers can find any text patterns in their data via both keyword and regular expressions (regex) search to identify specific data formats like employee IDs, machine or instrument IDs, product or project codes, or even PHI data like healthcare-system specific patient record IDs. Scans Sensitive Data in Place: Executes locally behind enterprise firewalls so sensitive data stays in place, unlike cloud-based data detection services. Remediate and Move: Once Komprise identifies sensitive data, users can set up a workflow to take appropriate action, such as confining the data or moving the data to a safe location. Pre-Process for AI Ingest: Sensitive data detection can be a pre-process step for an AI ingest workflow to eliminate sensitive data leakage to AI. Ongoing Workflow: Users can set workflows to run periodically, so Komprise automatically finds and acts on any new sensitive data for ongoing detection, tagging and mitigation, with full audit capabilities. Common use cases for the new sensitive data detection capabilities include: Prevent Unintended Data Leakage during AI Ingest: Data governance for AI remains a top priority. Komprise automates the workflow of identifying and excluding sensitive data from the data copied to AI, thus preventing human error and risky data leakage during AI ingestion. Sensitive Data Handling for Cyber-Resilience: Roughly 80% of data breaches involve sensitive data, according to Verizon’s Data Breach Investigations report and other sources. This is especially risky in regulated industries such as healthcare and finance where organizations face hefty penalties. By automating the process of finding sensitive data in non-compliant places and proactively remediating by moving this data to secure storage, organizations can reduce the risk of sensitive data breaches. AI Data Workflow Auditing: Komprise maintains a full audit record of all data processed by any workflow, such as a workflow copying data to a location for ingestion by an AI or ML system. "Komprise has continuously impressed me with their ability to add valuable features to the product,” says Jonathan Kowall, Director of Specialist Solutions Engineers at AHEAD. “With the new PII capabilities, storage admins get an extremely powerful feature on top of an already impressive tool. Storage teams are challenged to do more with less and features like these are why we are proud to partner with Komprise.” “The risk of sensitive data breaches is escalating and thereby paralyzing organizations from using AI,” says Kumar K. Goswami, CEO of Komprise. “We are pleased to systematically reduce sensitive data risks so that our customers can improve their cybersecurity and provide data governance for AI ingestion with the new sensitive data detection and mitigation capabilities in Komprise Smart Data Workflows Manager.” Availability Komprise Smart Data Workflows and the new sensitive data detection and regex search are currently in early access for customers and partners and will be generally available at the end of Q1 as part of the Komprise Intelligent Data Management Platform. About Komprise Komprise powers the connection between unstructured data management and AI. Komprise Intelligent Data Management delivers a single platform to easily analyze, migrate, transparently tier and manage the lifecycle of petabytes of file and object data across hybrid environments. With Komprise, enterprise IT gains full visibility across silos to optimize storage, backup, ransomware and cloud costs. Komprise Smart Data Workflows and the Komprise Global File Index unlock unstructured data insights and access for AI. Contact: Kevin Wolf kevin@tgprllc.com ### 5 Mistakes to Avoid When Refreshing Data Storage This blog has been adapted from its original version in eWeek. As data storage technology has evolved with more choice and options for different use cases—the flavor of today is AI-ready storage—determining the right path for a data storage refresh requires a data-driven approach. Decisions for new data storage must also factor in user and business needs across performance, availability and security. Forrester found that 83 percent of decision-makers are hampered in their ability to leverage data effectively due to challenges like outdated infrastructure, teams overwhelmed and drowning in data, and lack of effective data management across on-premises and cloud storage silos. Leveraging cloud storage and cloud computing, where AI and ML technologies are maturing fastest, is another prime consideration. Given the unprecedented growth in unstructured data and the growing demand to harness this data for analytical insight and AI, the need to get it right has never been more essential. Here are five ways to avoid suboptimal results from your data storage refresh. Mistake 1: Making Decisions without Holistic Data Visibility When IT managers discover that they need more storage, it’s easy to simply buy more than they need. But this may lead to waste and/or the wrong storage technology later. A majority (80%) of data is typically cold and not actively used within months of creation yet consumes expensive storage and backup resources. To avoid this common conundrum, get insights on all your data across all storage environments. Understand data volumes, data growth rates, storage costs and how quickly data ages and becomes suitable for archives or a data lake for future data analytics. These basic metrics can help guide more accurate decisions, especially when combined with a FinOps tool for cost modeling different options. The need to manage increasing volumes of unstructured data across multiple technologies and environments, for many different purposes, is leading to data-centric rather than storage-centric decision-making across IT infrastructure. Read the DCIG white paper: Data Management Must Replace Storage Management. Mistake 2: Choosing One-Size-Fits-All Storage Storage solutions come in many shapes and forms – from cloud object storage to all-Flash NAS, scale-out on-prem systems, SAN arrays and beyond. Each type of storage offers different tradeoffs when it comes to cost, performance and security. As a result, different workloads are best supported by different types of storage. An on-premises app that processes sensitive data might be easier to secure using on-prem storage, for instance, while an app with highly unpredictable storage requirements might be better suited by cloud-based storage that can scale quickly. This again points to the need to analyze, segment and understand your data. The ability to search across data assets for file types or metadata tags can identify data and better inform its management. Learn about the Komprise Global File Index. Also, less than 25% of data costs are in storage: the bulk of the costs are in the ongoing backup, disaster recovery and protection of the data. So, consider the right storage type and tier as well as the appropriate data protection mechanisms through the lifecycle of data. Mistake 3: Becoming Locked into One Vendor Acquiring all your storage from one vendor may be the simplest approach, but it’s almost never the most cost-effective or flexible. You can likely build more cost-effective storage infrastructure if you select from the offerings of multiple vendors. Doing so also helps protect you against risks like a vendor’s decision to raise its prices substantially or to discontinue a storage product you depend on. If you have other vendors in the mix, you can pivot more easily when unexpected changes occur. Using a data management solution that is independent of any storage technology is also a way to prevent vendor lock in, by ensuring that you can move data from platform to platform without the need to rehydrate it first. What is storage-agnostic unstructured data management? Mistake 4: Moving Too Fast A sense of urgency tends to accompany any major IT migration or update, storage refreshes included. Yet, while it’s good to move as efficiently as you can, it’s a mistake to move so fast that you don’t fully prepare for the major changes that accompany a storage refresh. Instead, take time to collect the data you need to identify the greatest pain points in your current storage strategy and determine which changes to your storage solutions will deliver the greatest business benefits. Be sure, too, to collect the metrics you need to make informed decisions about how to improve your data management capabilities. Mistake 5: Ignoring Future Storage Needs You can’t predict the future, but you can prepare for it by anticipating which new requirements your storage solutions may need to support in the future. At present, trends like AI, sustainability and growing adoption of data services mean that the storage needs of the typical business today are likely to change in the coming year. To train AI models, for example, you may need storage that can stream data more quickly than traditional solutions. Likewise, supporting cost-saving mandates might mean finding ways to consolidate and share storage solutions more efficiently across different business units. As organizations move from storage-centric to data-centric management, IT and storage architects will need to change the way they evaluate and procure new storage technologies. The ability to analyze data to make nuanced versus one-size-fits-all storage decisions will help IT organizations navigate many changes ahead – be they cloud, edge, AI or something else still on the horizon. What can Komprise Analysis do for you? ### Why AI Data Workflows Can Boost AI Plans Adapted from the original article on AI Business. With AI, comes an overarching priority to understand and properly leverage an organization’s vast data estate. Today, much of the petabyte-scale enterprise data store is not reused or even understood well enough to take advantage of the expanding array of free and low-cost AI tools available. This is unfortunate, as many use cases for AI are urgent. Consider the impact of an AI tool to quickly identify sensitive data and make sure that it’s being managed with data compliance could have. The Journal of the American Medical Association recently reported on an AI-based model in use at Stanford Hospital that predicts when a patient is declining and alerts the patient’s care team. Preparing for AI is the top business challenge for unstructured data management (57%), according to the Komprise 2024 State of Unstructured Data Management. The leading challenge in this effort is managing governance/security concerns (45%), followed by data classification and tagging (41%). Storage IT professionals have a prominent role to play in facilitating AI and big data analytics initiatives: They must deliver fast, secure and scalable storage infrastructure to support AI data workloads. Equally, they need to classify and deliver the right data to these tools to support the work of data scientists and other data stakeholders across the enterprise. Let’s consider the emerging concept of automated data workflows for AI. Feeding the right data to AI and enriching metadata classification using AI are prime opportunities for enterprise IT today. These processes require easy-to-configure AI data workflows, which benefit from systematic automation. To automate AI data workflows, you need to: Search and curate the right data: To create an AI data workflow, you first need a way to search across all your data estates which can be terabytes to petabytes of data to find the relevant data of interest. Manage data governance: When executing AI data workflows, it is essential to keep track of what corporate data was fed to which AI process so there is an audit trail. Similarly, it is important to enforce guardrails such as not sharing sensitive data with external processes. In the Komprise survey, AI data governance/security is the top future capability (47%) for unstructured data management, up from 28% in 2023. Cut AI costs by persisting results: Since most AI solutions have a pay-per-use billing model, it’s extremely important to avoid nasty surprises of high AI costs due to the same data being processed repeatedly. Therefore, having a global index that keeps track of the labels and tags from AI so users can search without having to run the AI process again on the same data is valuable. Leverage automation: The ability to automatically run the AI workflow on new data ensures that the AI is trained on the latest data without requiring cumbersome manual effort. Sample Use Cases for AI Data Workflows A workflow from the pharmaceutical industry could entail running a custom query across data silos to find all data for Project X using a data management solution. Next, the process could execute an external function on Project X data to look for a specific DNA sequence for a mutation. The data management software is configured to tag such data as “Mutation XYZ” and then moves only that new data set to a cloud AI service for analysis. Once the mutation data is no longer needed, the workflow finishes by moving it to a low-cost archival storage tier. The workflow could repeat with new data sets as often as needed. Taking this one step further, what if you could apply an AI tool to your data to rapidly segment and enrich the metadata with new tags? A data scientist may not know where all the data from a certain project resides and therefore cannot automate the process of tagging it. The scientist also needs to ensure that any files with PII are segregated so they don’t wind up in an external AI or ML tool for public access. An AI data workflow could integrate with a PII scanner to help in this regard. Or consider the application of Azure Bot Service, which allows developers to build and deploy intelligent chatbots and virtual assistants for customer service. An AI data workflow could analyze data from customer responses and then tag that data based on sentiment or customer issues and move it to a cloud data lake for future analysis. As the AI industry evolves and matures, we’re seeing a complexity barrier that could slow down the positive developments AI can bring to people, businesses and governments. Rising above these challenges requires extreme coordination between individuals across the organization – think chief experience officers, data scientists, security professionals, storage and data management experts, and IT infrastructure people, along with HR and legal – to avoid bad outcomes and ensure that goals are aligned. Data storage and data management leaders can contribute to this new age by connecting the dots between the unstructured data gold they manage and the best AI tools for the business. Developing and nurturing secure, intelligent AI data workflows is a sensible first step. Learn more about Komprise Smart Data Workflows. Read the Duquesne Amazon Rekognition AI Case Study. ### Metadata Management: Making Metadata Work for Your Organization This is the second blog in a two-part series on metadata management. Find the first blog here, which explains the role of metadata in unstructured data management. This metadata optimization content was adapted from its original version on Dataversity. To review, here are the top benefits of metadata (which is data about your data): Metadata brings structure to unstructured data, which is critical for search, data mobility, management, and analytics; Metadata supplies better insights on your data, such as: top data owners, top file types and sizes, and usage information such as last access date; It improves cost savings and decision-making for data storage; It supports compliance by tagging regulated or audited data sets; Users can find key data sets faster and move them to the right location for AI projects. Challenges with metadata management Metadata is massive because the volume and variety of unstructured data – files and objects – are massive and difficult to wrangle. Data is spread across on-premises and edge data centers and clouds and stored in potentially many different systems. To leverage metadata, you first need a process and tools for managing data. Managing metadata requires both strategy and automation; choosing the best path forward can be difficult when business needs are constantly changing and data types may also be morphing from the collection of new data types such as IoT data, surveillance data, geospatial data and instrument data. Managing metadata as it grows can also be problematic. Can you have too much? One risk is a decrease in file storage performance. Organizations must consider how to mitigate this; one large enterprise we know switched from tagging metadata at the file level to the directory level. Read more about data tagging with Komprise. How to optimize metadata for storage insights and savings While you can benefit from the metadata that your storage systems automatically create, an optimal plan will include curated or refined metadata that adds additional information to your files. Here are some metadata optimization considerations: Develop a holistic metadata strategy, which includes rules and guidelines for using, searching for, and customizing metadata. This can ensure that metadata does not get out of control and that it is used appropriately. A strategy may include policies for security and privacy, such as separation of duty. For instance, in a highly regulated business, users can tag the files they have access to, but only certain IT users should be authorized to execute action on the data once tagged. Your strategy should spell out goals and desired outcomes for metadata management. Create a tagging taxonomy and/or metadata catalog so users know when to use what tags. Decide on directory-/folder-level tagging versus file-level tagging. The former is easier to manage, as it reduces the number of tags you must create, track, store, and manage. For instance, you can collect all files related to one program within an integrated marketing campaign into a directory and use an unstructured data management system to automatically tag it as such. However, be diligent on directory contents to ensure that no errant files have landed in the directory and are now being inappropriately tagged. Enrich metadata with custom tagging: There are many use cases, from legal to research to marketing to product development, where it’s useful to add additional metadata tags to files. For example, a biotech company running an experiment in Munich and one in Palo Alto could create tags for each of those experiments so that later, a researcher wanting to run additional analysis could select the specific files from the specific location that she needs. Metadata enrichment is easiest using unstructured data management software like Komprise. Otherwise, you will need a database to store and track metadata tags and policies and all tagging is manual. This will require heavy manhours so consider if you have the staff to do it. Collaborate with data stakeholders: IT and storage managers don’t typically have insight on the data, but rather managing storage and file access. IT must rely on data scientists and data owners to tag data accurately. You will need a process for collaborative metadata tag management. Metadata management automation: It’s highly advisable to use automation where you can, given the volume and variety of metadata today. You can do this with your existing storage solutions, with data governance software such as master data management or data catalog software and/or using unstructured data management solutions. There are caveats: Storage solutions have some metadata features, but these are limited to the files in that system; you’ll need to maintain and integrate multiple metadata processes and tools across all storage. Further, file storage systems do not allow you to add or edit metadata to files. Depending upon your goals, consider a unified solution that looks across all data and metadata to centralize your efforts. Use tools that combine queries and tagging: Metadata management tools should not overuse tags and make users generate tags for information already available in metadata. This is cumbersome for users and leads to tag proliferation, tag conflicts, and scaling issues. As well, solutions should provide the ability to build and save queries that combine both standard and extended metadata. This query-plus-tag approach delivers efficient automation, scaling and minimizes manual effort for users. Final thoughts on metadata optimization As unstructured data volumes grow, IT and storage managers need to control the chaos and the costs – and that encompasses the metadata. The optimal metadata management and metadata optimization strategy includes close collaboration with business and security teams on data governance and analytics needs, tagging tools to enrich the metadata and automation to analyze and track it. With some effort and the right investment, you can reap the priceless benefits of greater data storage cost savings and long-term value from your mountains of unstructured data and metadata. ### Metadata & Its Role in Unstructured Data Management This article has been adapted from its original version on Dataversity. We live in a data-driven economy, but what lies beneath the data is hidden gold. Metadata, or data that describes data, delivers many benefits for storage and IT managers. Yet metadata is complex, vast, and distributed across hybrid cloud infrastructure. Understanding and strategically managing metadata as part of your overall data storage strategy has become central to optimizing unstructured data management and data governance practices across the organization. Explaining Metadata for File and Object Storage Metadata management includes both standard metadata that most storage systems create and track as well as extended attributes that are customized and specific. Standard metadata are system attributes such as: when the file was created, who created it, what type of file it is, its size, when it was last accessed, and when it was last modified. Advanced metadata is handled differently by file storage and object storage environments: File storage organizes data in directory hierarchies, which means you can’t easily add custom metadata attributes. Object storage lacks the hierarchical directory structure of file storage, but you can customize it. For instance, a clinical image file would only contain metadata such as creation date, owner, location, and size. But if it is stored as an object, a user can enrich the metadata with demographics such as patient’s name, age, and diagnosis. Ideally, metadata leverages both standard attributes and customized tags (by users or systems), which add context. For example, a metadata tag could identify a project, sensitive or PII data, demographics, location, or financial results such as quarterly sales. Read about how tagging works in Komprise here. Metadata Management Benefits for Unstructured Data Storage Why invest in metadata management for data storage? Firstly, metadata brings structure to unstructured data, which is critical for search, data mobility, management, and analytics. Below are some additional benefits of metadata management for data storage teams: Gain data visibility: Metadata supplies more information on your data, such as: top data owners, top file types and sizes, and usage information such as last access date. These basic file characteristics are a great starting point to help guide decisions, such as where to store the data based on its business priority or to answer questions, such as, “Who are the top data owners in a department?” As you enrich metadata, authorized users can segment and search for data based on keywords so they can reuse it, delete it, or move it. Improve cost savings and decision-making for data storage: Since metadata improves overall visibility and understanding of your data, you can ensure it’s always in the right place at the right time. For instance, set a policy whereby once a research project has concluded, all files tagged with the project name and data are archived – preserving costly, top-tier storage for your latest, most active data. Improve compliance: By tagging regulated or audited data sets, such as PII, IP, or FDA data, you can search across the enterprise to ensure sensitive files are stored according to compliance rules. You can expand this to include internal corporate policies, such as how to handle ex-employee or financial data or when to confine files for deletion. Improve search and workflows for AI/ML: Metadata management is becoming central to AI and machine learning initiatives, helping data owners and stakeholders find key data sets faster and move them to the right location for projects. With AI tools needing massive sets of the right kind of data for a project, the ability to automate this process will become increasingly vital to successful AI/ML outcomes. However, there are some challenges related to managing and using metadata. These include its volume, diversity in data types, over-tagging, and the wide distribution of data (and its metadata) across hybrid IT environments. All of this can make metadata unwieldy and un-useful. In the second blog in this series, we discuss these challenges in detail and suggest ways to corral metadata to benefit the broader organization. ### Top 5 AI Data Governance Tips for Unstructured Data There’s been much ado about the pros and cons of artificial intelligence over the last few months since the start of the ChatGPT era. Generative AI has hit the mainstream; new vendor solutions are cropping up daily and professionals from many different industries are giving it a test drive. Most of the data fueling these new tools is unstructured; success with generative AI requires a comprehensive unstructured data governance strategy. In this two-part blog series, we'll cover the expanding field of AI data governance. I’ll explain the data risks from generative AI and the role of unstructured data management in mitigating risks. With proper guardrails and the right tools, organizations can safely take advantage of these new AI solutions for a variety of use cases. A recent New York Times article interviewed doctors from different specialties about their experiences using ChatGPT to communicate more compassionately with their patients. One doctor used ChatGPT to write a letter in response to an insurer that denied paying for the off-label use of an expensive medication. After receiving the bot’s letter, the insurer granted the request. We are hearing of many other promising uses of generative AI – from marketing to operations and R&D. “In short, anything that people do with their natural intelligence today can be done much better with AI, and we will be able to take on new challenges that have been impossible to tackle without AI, from curing all diseases to achieving interstellar travel,” wrote Marc Andreessen. The Need for AI Data Governance Yet, this is not the whole truth. There are real dangers with AI and generative AI has brought this sharply to the forefront. Executives are rightly worried about the unintended outcomes of this new technology. Evidence of employees leaking corporate data into ChatGPT abound. People worry that it’s going to kill their careers, steal their identity, rob them of their financial assets, and worse: doomsday predictions abound. The reality is likely somewhere in the middle. Applications like ChatGPT seem intelligent and creative in a humanlike way: they are generating new content using pattern matching and are pretrained with large data sets, or large learning models (LLMs). The dangers lie within these LLMs, because there are many risks and unknowns with the data. Companies and organizations need to understand the data management issues that relate to generative AI. Let’s look at five key areas for AI data governance to consider across security, privacy, lineage, ownership and governance of unstructured data for AI --or SPLOG. SPLOG and Unstructured Data Management with Generative AI Security: Data confidentiality and security are at risk with third-party generative AI applications because your data becomes part of the LLM and the public domain once you feed it into a tool. Get clear on the legal agreements in place by the vendor as pertains to your data. There are new ways to manage this now: ChatGPT now allows users to disable chat history so that chats won’t be used to train its models, although OpenAI retains the data for 30 days. One way to protect your organization is to segregate sensitive and proprietary data into a private, secure domain which restricts sharing with commercial applications. You can also maintain an audit trail of your corporate data that has fed AI applications. Privacy: When you create a prompt for an AI tool to produce an output based on your query, you don’t know if the result will include protected data, such as PII, from another organization. Your company may be liable if you use the tool’s output externally in content or a product and the PII is discoverable. As well, since non-AI vendors are now incorporating AI tools into their solutions, perhaps even without their customers’ knowledge, the risk compounds. Your commercial backup solution could incorporate a pretrained model to find anomalies in your data and that model may contain PII data; this could indirectly put you at a risk of violation. Data provenance and transparency around the training data used in an AI application are critical to ensure privacy. Lineage: Today there is not much transparency with data sources in generative AI applications. They may contain biased, libelous or unverified data sources. This makes using GenAI tools circumspect when you need results that are factually accurate and objective. Consider the problem you are trying to solve with AI to choose the right tool. Machine learning systems are better for tasks which require a deterministic outcome. Ownership: The data ownership piece of generative AI concerns what happens when you derive a work: who owns the IP? As it stands today, copyright law dictates that “works created solely by artificial intelligence — even if produced from a text prompt written by a human — are not protected by copyright,” according to reporting by BuiltIn. As well, the article continues, copyrighted materials used in training AI models, is permitted under the fair use law. There are currently a batch of lawsuits under consideration, however, challenging this law. It will be increasingly important for organizations to track who commissioned derivative works and how those works are used internally and externally. Governance: If you work in a regulated industry, you’ll need to show an audit trail of any data used in an AI tool and demonstrate that your organization is complying. A healthcare organization, for instance, would need to verify that no patient PII data has been leaked to an AI solution per HIPAA rules. This requires a governance framework for AI that covers privacy, data protection, ethics and more. Data management solutions help by providing a means to monitor data usage in AI tools and create a foundation for unstructured data governance. In the next blog of this two-part series, I will describe two different pathways for using generative AI and how to adapt your unstructured data management and data governance practices accordingly: Curate Audit and Move (CAM), which manages feeding of corporate or domain-specific data to a pre-trained Large Learning Model for the best adaptation or; Use a pretrained LLM with prompt-based augmentation that you feed data to and manage across the SPLOG principles outlined above. ### Under the Hood: The Komprise Filesystem Walking Through the Komprise Architecture and How it Works Under the Hood. First, let’s look at how Komprise provides a redundant, transparent, hierarchical filesystem that bridges file and object data without superimposing a single namespace or metadata layer. Instead, Komprise delivers transparent file access to cold data from any of your sources. Let’s examine how cold data, identified by Komprise, is stored and managed on your cloud or on-prem targets. The Komprise solution includes a filesystem interface which is not merely self-serving vis-à-vis data ingestion, but also forms a vital cog that provides seamless and transparent access as the data temperature varies over its lifetime. Komprise Targets can be public/private clouds, LTO/archival devices or other NAS filers. Data moved to any of these targets looks to users and applications as if it were still on the original source and remains fully accessible, exactly as before. To understand how Komprise works and provides transparent access, watch our five-minute demo. Portal to Cold Data The Komprise Filesystem has been designed to address the following: Fidelity in preservation and representation of file metadata and namespace hierarchy Secure and bandwidth optimized for transport over WANs to public clouds (but not limited to just public clouds) Seamless, transparent translation between files and objects (key, value pairs) when object stores are the at rest targets Source filer NAS protocol agnostic Preserve data access on the target Provide a single namespace to all moved data without fronting all the metadata or data paths Transparent file gateway that bridges NAS, object and cloud storage Enable access to data from primary and secondary storage without lock-in Read how Komprise stitches NAS to cloud. Komprise addresses these with a patent-pending redundant, transparent, hierarchical filesystem that is not creating any new storage or metadata repositories. Instead, Komprise leverages the sources and targets themselves to create a redundant, overarching namespace. By leveraging open standards and creating a distributed design, Komprise is able to provide unified visibility and management without creating lock-in. To learn more about the Komprise architecture, read the Komprise Architecture Whitepaper. Workloads Supported The filesystem instances on the Komprise Observer Virtual Appliance are operationally transparent to users of the Komprise solution and entirely managed autonomously. Although not particularly having affinity to any particular workload, the following are what are typically expected over the lifetime of the Komprise management of data: Migration and Copy Cold and archival data transfer from the source shares to Komprise targets Client and application accesses Redirections from source share links or stubs Direct access over Komprise exported mounts Typically for browsing the directory hierarchies for a global view Off-grid cloud hosted read-only gateways for file based access in and from public clouds Recalls Policy based at folder granularity to have a level of control over egress costs from public clouds Availability and Scalability The filesystem instances running on various nodes of the Komprise grid serve up both migration and access traffic. They are also provisioned for dynamic load balancing across the Komprise Observer Virtual Appliance Grid, using a co-operative distribution algorithm. In concluding this post, it should be highlighted that we have built the filesystem to enable a variety of data management use cases, across a customer’s storage environment, without creating any lock-in. Open up possibilities beyond those permitted by pedestrian data mover applications, which are generic and require extensive customization and manual management to address each use case. Learn more about why organizations choose Komprise. Watch for more under-the-hood topics in this series.   Vikram Krishnamurthy is a Principal Engineer at Komprise and has been involved with the product design and development from the early stages. Prior to Komprise, he has worked extensively in the Storage industry with stints at IBM and NetApp. ### AI Puts A Shadow on Enterprise IT as Risks Get Real Are you subscribed to our monthly LinkedIn newsletter?  This month’s newsletter covers new research: The Komprise IT Survey: AI, Data & Enterprise Risk. Komprise surveyed 200 IT directors and executives at U.S. enterprise organizations of 1000 employees and larger.  The purpose of the survey was to discover how IT teams are preparing their unstructured data for AI and the challenges they are facing. The survey was conducted by a third party in April 2025. The survey covers: Shadow AI Risks Challenges & Tactics in Preparing Unstructured Data for AI IT Infrastructure Priorities Enterprise AI High Notes The amount of VC money funneling into AI ventures this year is nothing short of astronomical. [Note: Is any tech company today NOT an AI venture?] OpenAI: $40B Databricks: $10B Perplexity AI: $500M Lambda Labs: $480M Andreessen Horowitz: Raising $20B Fund For more big AI deals this year, check out Crescendo’s list. Amid big money and the never-ending hype, enterprises are starting to get real about AI. From prototype to production, there are a lot of steps and – plenty of concerns. Enterprise IT organizations are embroiled in the gargantuan task of managing and preparing their vast stores of unstructured data for AI pipelines. Investing in new IT infrastructure to support AI is foundational: the storage, compute and networking technologies for high performance and security.  Yet preparing and managing data for AI to support user workflows and governance is equally if not more paramount. Balancing these priorities effectively can help organizations deliver safe, optimized AI services for employees and customers. Read June 2025 Andreessen Horowitz report: How 100 Enterprise CIOs Are Building and Buying Gen AI in 2025 The Komprise IT Survey: AI, Data & Enterprise Risk found that nearly 80% of organizations have experienced negative data incidences with generative AI—with 13% resulting in financial, customer or reputational damage. The most common bad outcomes include false or inaccurate results from queries (46%) and leaking of sensitive data into AI (44%). Many surveys have indicated concern about these risks, but now those concerns are hitting the bottom line. Other trends identified include an overwhelming concern about “shadow AI”: nearly half are “extremely worried” about the security and compliance impact of unauthorized and unsanctioned use of AI tools. Much of the concern today centers on GenAI tools, which are free and widely available on the Internet. IT leaders shared their tactics for dealing with shadow AI, from using data management and AI discovery tools to implementing policies and training.  The survey also found that despite an uncertain economic landscape amid tariff price increases, enterprises are prioritizing developing the right IT infrastructure for AI. Key AI, Data & Enterprise Risk Report Statistics Shadow AI Risks The vast majority (90%) are concerned about shadow AI from a privacy and security standpoint, with 46% reporting that they are “extremely worried.” Most (79%) of IT leaders report that their organization has experienced negative outcomes from sending corporate data to AI, including PII data leakage and inaccurate or false results. Most (75%) are planning to use data management technologies to address risks from shadow AI, followed closely by AI discovery and monitoring tools (74%). Preparing Unstructured Data for AI The greatest challenge in preparing unstructured data for AI is finding and moving the right data to locations for AI ingestion (54%) followed by a lack of visibility into data across data storage to identify risks (40%). The top tactic for preparing data for AI is classifying sensitive data and using workflow automation to prevent its improper use with AI (73%). Nearly all (96.5%) are classifying and tagging unstructured data for AI, with a mix of manual and automated methods for doing so. More than half (56%) say that IT is moving data to AI processes for users manually, or with free tools, with 40% saying that users are manually copying data to AI on their own. IT Infrastructure Priorities Supporting AI initiatives is the top priority for IT infrastructure (68%), followed by 16% saying it is equally important as cost optimization, cybersecurity and core IT upgrades. Most IT leaders (45%) express a multi-faced strategy for investing in storage for AI, with equal priority to acquiring AI-ready storage, increasing capacity of existing storage and acquiring data management capabilities for AI. Top 5 Takeaways for AI in the Enterprise Generative AI is Hitting the Bottom Line: GenAI is now part of daily operations—and so are its risks. Many organizations have faced issues like inaccurate outputs or sensitive data leaks which in some cases bring financial and reputational damage. Without better controls, IT may see key AI initiatives shut down despite their competitive potential. Read the white paper. Shadow AI Risks Require New Tools: Shadow AI—unauthorized or unknown use of AI tools—poses serious privacy and security threats. Sensitive data can leak to public models, exposing PII and trade secrets. To combat this, IT will invest in tools for data classification and AI app tracking to prevent misuse and maintain visibility. Komprise for sensitive data management. Data Classification is Key to AI Readiness: AI needs unstructured data, yet this data must be precisely curated for accuracy, cost and security requirements. Current file systems lack rich metadata, making it hard to identify and secure the right data. IT will turn to automation to tag, enrich, and classify unstructured data, balancing access and protection. Komprise for unstructured data classification. AI Pipelines Demand Automation: Managing unstructured data at scale requires automation which can efficiently find, tag and move curated datasets into AI pipelines efficiently and monitor workflows. These tools index data across environments and support governance with auditing capabilities. Komprise for AI data workflows. AI Infrastructure Becomes Top IT Priority: AI infrastructure now outranks cybersecurity and cost control in IT budgets. Despite economic pressure, leaders are investing in fast, secure systems and AI-ready storage. These moves ensure data is prepared and protected for AI with proper compliance measures. Enterprise AI Risk Report Media Response TechNewsWorld writer John P Mello Jr covered the survey, interviewing several experts about the findings. The notion of AI introducing “security blind spots” was a hot topic. Said Melissa Ruzzi, director of AI at AppOmni: “The biggest risk with shadow AI is that the AI application has not passed through a security analysis as approved AI tools may have been.” Another expert noted that shadow AI extends beyond unapproved applications and involves embedded AI components that can process and disseminate sensitive data in unpredictable ways. Komprise COO Krishna Subramanian emphasized that shadow AI poses a much greater problem than shadow IT, which primarily focuses on departmental power users purchasing cloud instances or SaaS tools without obtaining IT approval. “Now we’ve got an unlimited number of employees using tools like ChatGPT or Claude AI to get work done, but not understanding the potential risk they are putting their organizations at by inadvertently submitting company secrets or customer data into the chat prompt,” she explained. "The data risk is large and growing in still unforeseen ways because of the pace of AI development and adoption and the fact that there is a lot we don’t know about how AI works. It is becoming more humanistic all the time and capable of making decisions independently.” --- Krishna Subramanian In TechRepublic’s coverage, Megan Crouse wrote about how to prepare unstructured data for AI: “A key component of using generative AI safely is making sure you know which data is exposed to the model. When preparing large amounts of company data to be fed into AI, 73% of IT teams approach it by classifying sensitive data, then using workflow automation to restrict its use by AI. Unstructured data management solutions that use tags and keywords can leverage those keywords to sort the data.” In closing, Subramanian remarked: “IT really needs to lead the charge on education, training and policies. They must go hand-in-hand. Employees need to understand the risks so that they can use AI safely and not expose sensitive and proprietary corporate data to public AI applications.” Komprise CEO Kumar Goswami discusses sensitive data management capabilities in Komprise, which help with AI data governance. The Time to Act is Now AI hype is unreal, but the risks are not. We expect to see more bad, publicly-reported outcomes from AI, at brands large and small. Preventing your company from getting in the news starts with an internal conversation and a line in the sand from every CEO. Your people are using AI regularly. Make sure they understand the risks and best practices. Create a policy on AI usage that managers should commandeer to their teams. Policies aren't easily enforceable: organizations will need the right technical tools and controls to track data movement to AI and prevent protected IP, customer and PII data from getting into public AI LLMs. The time to act is now. You can subscribe to our blog to receive new posts in your inbox as well as our YourTube channel. Be sure to also check out what's new by visiting our Resource Center. ### Law Firms: Contesting Growth and Risk with Unstructured Data This article originally published on Law.com. Global and national law firms face a growing dilemma: how to efficiently manage the vast volumes of unstructured data they accumulate over time—much of which cannot be deleted for legal or compliance reasons. From litigation documents to case files, contracts and discovery materials, legal institutions are witnessing explosive data growth, often exceeding 20% annually. This data sprawl presents some thorny challenges, particularly around storage costs, accessibility, and cybersecurity. For large firms, the inability to get rid of data stems from the unpredictable nature of legal cases. A closed case can suddenly reopen, making every piece of historical data potentially critical. Consequently, firms continue to store decades’ worth of data, even though most of it is rarely accessed and living on high-cost, on-premises storage. In today's world, unstructured data poses a more pressing problem: ransomware risk. Unstructured files become the weakest link most vulnerable to ransomware attacks given their sheer volume and given these documents are everywhere. Law firms are especially eager to reduce the risk of ransomware attacks due to the sensitive nature of their work and the need to protect client confidentiality. From litigation documents to case files, contracts and discovery materials, legal institutions are witnessing explosive data growth, often exceeding 20% annually. The Weight of Legacy Infrastructure Traditionally, firms relied on legacy infrastructure built on physical servers and network-attached storage (NAS) systems deployed at each office. However, as data growth accelerated, this model became unsustainable. Regular hardware upgrades were needed—often every 12 to 18 months—to accommodate the swelling tide of digital files, adding to the cost and complexity of IT operations. Over time, some firms have transitioned their infrastructure to regional colocation facilities, embraced virtualization, and are now moving workloads to the cloud. The move to cloud platforms, particularly Microsoft Azure, is popular due to compatibility with existing Microsoft-based systems which are common in the sector. Yet the transition to the cloud doesn’t necessarily translate to cost savings and easier management, especially when dealing with petabytes of unstructured data. The Rise of Intelligent Data Management Tools To modernize their environments for efficiency and simplicity, firms are turning to intelligent unstructured data management systems that can analyze, classify, and relocate data without disrupting user access. These platforms allow organizations to identify "cold" data—files that haven’t been accessed in years—and migrate them to cost-effective, secure cloud storage tiers such as Azure Blob. A breakthrough in this approach is the ability to tier data transparently: Instead of using traditional archiving methods that break links and create access issues, newer solutions leverage symbolic links. These links ensure that end users and applications can still access archived data as if it were stored locally. This not only reduces IT support hassles but also reduces the risk of workflow disruption: crucial in environments where quick access to files can make or break a case. Cloud data tiering is yielding dramatic financial benefits. By identifying and migrating more than 180 terabytes of stale litigation data to Azure, one global law firm recently avoided $400,000 in storage upgrades and expects to save up to $700,000 more through complete re-platforming. These savings are not just due to reduced hardware needs;  the firm can now purchase storage platforms with lower capacity requirements, thanks to the data offload. Read the case study. Ransomware Resilience Beyond cost efficiency, data tiering can also deliver a simple ransomware defense. With the legal industry increasingly targeted by ransomware, reducing the attack surface is a top priority. Immutable cloud storage—where data is write-once and cannot be altered—provides a formidable line of defense against attacks. Archived files stored in read-only cloud storage can’t be modified, giving firms an edge in data integrity and protection. This then allows a law firm to protect all their data by using the most advanced security tools to prevent and defend against attacks for high priority on-premises data while using immutable object storage in the cloud to protect non-mission-critical data at a more affordable price. Read how Komprise reduces ransomware costs and improves unstructured data protection. Data Workflows for AI As in many sectors that revolve around data-intensive work, AI has enormous potential in law. According to the American Bar Association, the primary areas where AI is being applied in the law, so far, include document review, legal research, contract and legal document analysis, proofreading and document organization. Research from Harvard Law found that 90% of firms interviewed believe that AI will free up time spent on more menial, manual tasks to help counsel deliver higher quality of service and strategic work. AI tools require vast amounts of unstructured data to deliver accurate, useful results. Data management tools can help prepare and efficiently deliver unstructured data to AI, which is one of the primary hurdles of putting artificial intelligence into production today. By integrating deep analytics and metadata tagging, IT can create automated AI data workflows that discover the right data for a project while also excluding and confining sensitive data that should not be ingested in an AI tool. This is especially valuable in law, where data governance policies and audits are non-negotiable components of standard operating procedure. Ultimately, intelligent unstructured data management is not merely an IT function—it is a strategic initiative that influences how a law firm operates, serves its clients, and prepares for the future. The ability to analyze and mobilize unstructured data to address storage growth, lower security risks, and support AI initiatives is fast becoming a competitive advantage. Learn more about AI data workflows. ### Komprise Survey Finds that Shadow AI is a Major Concern across Enterprise IT Ninety percent of IT leaders are worried about shadow AI and 13% have experienced financial and customer fallout. Campbell, CA — June 3, 2025 — Komprise, the leader in analytics-driven unstructured data management, announces the results of new industry research: Komprise IT Survey: AI, Data & Enterprise Risk. The survey showed IT organizations are concerned about shadow AI, with nearly half stating that they are “extremely worried” about the security and compliance impact of unauthorized and unsanctioned use of AI tools. Beyond concern, enterprises are seeing real-world impact from shadow AI. Nearly 80% of IT leaders say their organization has experienced negative outcomes from employee use of Generative AI, including false or inaccurate results from queries (46%) and leaking of sensitive data into AI (44%). Notably, 13% say that these poor outcomes have also resulted in financial, customer or reputational damage. To help combat the downside of shadow AI, most (75%) plan to use data management technologies to address risks from shadow AI, followed closely by AI discovery and monitoring tools (74%). Komprise surveyed 200 IT directors and executives at U.S. enterprise organizations of 1000 employees and larger. The purpose of the survey was to discover how IT teams are preparing their unstructured data for AI and the challenges they face. A third party conducted the survey in April. Download the report here. Key Statistics Shadow AI Risks The vast majority (90%) are concerned about shadow AI from a privacy and security standpoint, with 46% reporting that they are “extremely worried.” Most (79%) of IT leaders report that their organization has experienced negative outcomes from sending corporate data to AI, including PII data leakage and inaccurate or false results. Most (75%) are planning to use data management technologies to address risks from shadow AI, followed closely by AI discovery and monitoring tools (74%). Preparing Unstructured Data for AI The greatest challenge in preparing unstructured data for AI is finding and moving the right data to locations for AI ingestion (54%) followed by a lack of visibility into data across data storage to identify risks (40%). The top tactic for preparing data for AI is classifying sensitive data and using workflow automation to prevent its improper use with AI (73%). Nearly all (96.5%) are classifying and tagging unstructured data for AI, with a mix of manual and automated methods for doing so. More than half (56%) say that IT is moving data to AI processes for users manually, or with free tools, with 40% saying that users are manually copying data to AI on their own. IT Infrastructure Priorities Supporting AI initiatives is the top priority for IT infrastructure (68%), followed by 16% saying it is equally important as cost optimization, cybersecurity and core IT upgrades. Most IT leaders (45%) express a multi-faced strategy for investing in storage for AI, with equal priority to acquiring AI-ready storage, increasing capacity of existing storage and acquiring data management capabilities for AI. “As enterprises are starting to get real about AI as part of their business strategy, the cracks are starting to show,” says Krishna Subramanian, COO and cofounder of Komprise. “With most reporting that they have experienced negative and even damaging consequences from using corporate data with AI, it’s time to create the right AI data governance strategy.  Unstructured data management will play a central role by giving users automated tools for data classification, sensitive data management, data workflows and AI data ingestion.” Read the report: Komprise IT Survey: AI, Data & Enterprise Risk About Komprise Komprise powers the connection between unstructured data management and AI. Komprise Intelligent Data Management delivers a single platform to easily analyze, migrate, transparently tier and manage the lifecycle of petabytes of file and object data across hybrid environments. With Komprise, enterprise IT gains full visibility across silos to optimize storage, backup, ransomware and cloud costs. Komprise Smart Data Workflows and the Komprise Global File Index unlock unstructured data insights and access for AI.  www.komprise.com ### File Tiering Helps IT Leaders Control Risk and Costs This article describes how you can leverage Komprise Intelligent Tiering for Azure with any on-premises file storage platform and Azure Blob Storage to cut costs by 70% and shrink your ransomware attack surface. It was originally published on the Azure Storage Blog. Unstructured data plays a big role in today's IT budgets and risk factors Unstructured data, which is any data that does not fit neatly into a database or tabular format, has been growing exponentially and is now projected by analysts to be over 80% of business information. Unstructured data, commonly referred to as file data, has caught some IT leaders by surprise because it is now consuming a significant portion of IT budgets with no sign of slowing down. File data is expensive to manage and retain because it is typically stored and protected by replication to an identical storage platform which can be costly at scale. Why file data is factoring into CIO priorities Cost optimization is a top priority for IT, according to the 2024 Komprise State of Unstructured Data Management survey. This is because file data is often retained for decades, its growth rate is in double-digits, and it can easily be petabytes of data. Keeping a primary copy, a backup copy and a DR copy means three or more copies of the large volume of file data which becomes prohibitively expensive. On the other hand, file data has largely been untapped in terms of value. Businesses are now realizing the importance of file data to train and fine tune AI models. Smart solutions are required to balance the competing requirements of cost management and AI value. Why file data is vulnerable to ransomware attacks File data is arguably the most difficult data to protect against ransomware attacks because it is open to many different users, groups and applications. This increases risk because a single user's or group's mistake can lead to a ransomware infection. If the file is shared and accessed again, the infection can quickly spread across the network undetected. How to leverage Azure to cut the cost and inherent risk of file data retention You can cut costs and shrink the ransomware attack surface of file data using Azure even when you still require on-premises access to your files. The key is reducing the amount of file data that is actively accessed and thus exposed to ransomware attacks. Since 80% of file data is typically cold and has not been accessed in months (see Demand for cold data storage heats up | TechTarget), offloading these files to immutable storage through data tiering cuts both costs and risks. Hybrid tiering moves entire files from the data storage, snapshot, backup and DR footprints while your users continue to see and access the tiered files without any change to your application processes or user behavior. Unlike storage tiering which is typically offered by the storage vendor and causes blocks of files controlled by the storage filesystem to be placed in Azure, hybrid tiering operates at the file level and transparently offloads the entire file to Azure while leaving behind a link that looks and behaves like the file itself. Hybrid tiering offloads cold files to Azure to cut costs and shrink the ransomware attack surface: Cut 70%+ costs: By offloading cold files and not blocks, hybrid tiering can shrink the amount of data you are storing and backing up by 80%, which cuts costs proportionately. As shown in the example below, you can cut 70% of file storage and backup costs by using hybrid tiering. Shrink ransomware attack surface: Offloading cold files to immutable Azure Blob removes cold files from the active attack surface and provides a potential recovery path if the cold files get infected. When Komprise tiers to immutable Azure Blob with versioning, even if someone tried to infect a cold file, it would be saved as a new version. So you can recover files using an older version. Learn more about Azure Immutable Blob storage here. Assumptions Amount of Data on NAS (TB) 1024 % Cold Data 80% Annual Data Growth Rate 30% On-Prem NAS Cost/GB/Mo $0.07 Backup Cost/GB/Mo $0.04 Azure Blob Cool Cost/GB/Mo $0.01 Komprise Intelligent Tiering for Azure/GB/Mo $0.008 On-Prem NAS On-prem NAS +  Azure Intelligent Tiering Data in On-Premises NAS 1024 205 Snapshots 30% 30% Cost of On-Prem NAS Primary Site $1,064,960 $212,992 Cost of On-Prem NAS DR Site $1,064,960 $212,992 Backup Cost $460,800 $42,598 Data on Azure Blob Cool $0 819 Cost of Azure Blob Cool $0 $201,327 Cost of Komprise $100,000 Total Cost for 1PB per Year $2,590,720 $769,909 SAVINGS/PB/Yr $1,820,811 70%     Additional Benefits of Hybrid Cloud Tiering using Komprise and Azure include: Leverage Existing Storage Investment: You can continue to use your existing NAS storage and Komprise to tier cold files to Azure. Users and applications  see and access the files as if they were still on-premises. Leverage Azure Data Services: Komprise maintains file-object duality with its patented Transparent Move Technology (TMT), which means the tiered files can be viewed and accessed in Azure as objects, allowing you to use Azure Data Services natively. You can leverage the full power of Azure with your enterprise file data. Works Across Heterogeneous Vendor Storage: Komprise works across all your file and object storage to analyze and transparently tier data to Azure file and object tiers. Ongoing Lifecycle Management in Azure: Komprise can manage the data lifecycle in Azure; as data gets colder, it can move from Azure Blob Cool to Cold to Archive tier based on policies you control.     https://www.youtube.com/watch?v=3XI4-iv8HFw Global law firm saves $900,000 per year and achieves resilient ransomware defense with Komprise and Azure Katten Muchin Rosenman LLP (Katten) is a full-service law firm delivering legal services across more than a dozen practice areas and sectors, including Aviation, Construction, Energy, Education, Entertainment, Healthcare and Real Estate. Like many other large law firms, Katten has been seeing an average 20% annual growth in storage for file related data, resulting in the need to add on-premises storage capacity every 12-18 months. At the global law firm, data is growing exponentially annually but cannot be deleted. Katten needed a solution offering deep data insights and the ability to move file data as it ages to immutable object storage in the cloud for greater cost savings and ransomware protection. Katten Law implemented Komprise Intelligent Tiering to Azure and leveraged Immutable Blob storage to not only save $900,000 annually but also improved their ransomware defense posture. Read how Katten Law does hybrid tiering to Azure using Komprise. View this recent Komprise & Azure webinar: Cut costs, shrink ransomware risk and leverage AI for your file data with Azure and Komprise. Next steps Learn more and get a customized assessment of your savings at the Azure Marketplace listing or contact azure@komprise.com. ### Sensitive Data Management for AI Data Governance and Cybersecurity Nearly 60% of organizations experienced a ransomware attack in the last year, according to research by Sophos. Simultaneously, data security and privacy risks from using corporate data with AI are compounding; AI is also likely helping cybersecurity actors develop more sophisticated attacks in general. In the past, cybersecurity teams were on task to monitor and protect sensitive data, while IT infrastructure and storage teams focused on backups and recovery, meeting compliance requirements for storage and managing data access control. Yet in today’s environment, managers across IT and the business need to embed security into everything they do. Security technology is increasingly built into enterprise IT solutions including data storage, rather than existing as a separate, siloed discipline. This means that individuals managing data storage and data management must assume a larger role when it comes to protecting data. AI data governance is a growing, complex requirement, as is understanding the risks of data across the organization. Storage managers need to know how to find and protect the most sensitive data—such as personally identifiable information (PII), IP, internal research data and more. However, doing this is difficult: unstructured data lives in silos and most of the tools that storage managers use don’t have effective, scalable capabilities for sensitive data detection. This week, Komprise announced new sensitive data detection and mitigation capabilities for Komprise Smart Data Workflow Manager. A point-and-click interface to create, automate, monitor and audit AI and other custom workflows, Smart Data Workflow Manager now has two new scanners: PII Detection: Select which PII data types to scan for such as national IDs, credit card numbers and email addresses. Komprise supports multiple classifications to identify diverse PII types within any given file. Regex and Keyword Search: Find any text patterns across file and object data store silos via both keyword and regular expressions (regex) search to identify specific data formats such as employee IDs, machine or instrument IDs, product or project codes, or personal health information (PHI) data such as patient record IDs from specific healthcare IT systems. As we predicted, the role of storage administrator is evolving to embrace security and AI data governance. This begins with secure data visibility. Enterprise storage teams are becoming data stewards, especially as it relates to managing unstructured data access. In talking to Komprise customers and partners, these are some of the key attributes that make the new sensitive data management features attractive to data-rich enterprises: Komprise now includes built-in processors to search within file contents for specific information. Use Komprise Deep Analytics and our Directory Explorer to discover specific data sets that may contain sensitive data, across petabytes of data and all storage. All processing is done locally and securely. With Komprise, data never leaves the customer environment. Workflows execute locally behind enterprise firewalls so sensitive data stays in place, unlike cloud-based data detection services. The data that we find, we tag in our metadatabase, which is the Komprise Global File Index. Data classification brings context and structure to unstructured data without any modification to the original data. Don’t just find sensitive data but take action. Once Komprise identifies sensitive data, you can use the data mobility capabilities to take appropriate action, such as confining the data or moving the data to a safe location. This is the power of Komprise: Know First. Move Smart. Sensitive data detection is a critical first step for AI data pipelines. Sensitive data detection is a pre-process step for an AI data ingestion workflow to eliminate the leakage of protected data to AI services. Set it and forget it (with auditing): Users can set workflows to run periodically; Komprise automatically finds and acts on any new sensitive data for ongoing detection, tagging and mitigation, with full audit capabilities. In this video, we review the new PII detection and mitigation features of the Smart Data Workflow Manager:  Watch this short demonstration of a regex search in Smart Data Workflow Manager:  Komprise Smart Data Workflows and the new sensitive data detection and regex search capabilities are currently in early access for customers and partners. Komprise customers should contact their account team for more information. Read the Press Release ### 2024 Survey: The 5 Key Trends in Unstructured Data Management AI is no longer hype. Today the global Artificial Intelligence market is worth nearly $235 billion, with projections expanding to over $631 billion by 2028, according to IDC. Yet when it comes to implementing these technologies, it’s still early days, according to the fourth-annual Komprise 2024 State of Unstructured Data Management. Most (70%) organizations surveyed are still experimenting with these new technologies as “preparing for AI” remains a top data storage and data management priority for IT leaders. Many don’t have a specific budget for AI as IT departments are focusing on cutting costs and waste. The survey also uncovered the latest viewpoints on AI data governance, AI infrastructure plans, unstructured data management challenges and future needs. Here are the 5 top trends we uncovered in our latest industry survey.   1. Top Data Storage Priority: Cost Optimization For the fourth year in a row, IT directors say they will spend more on storage this year than the previous. In 2023, preparing for AI was the top data storage priority but this year, it is cost optimization during a tough economy. IT leaders, while certainly thinking about AI, are zoning in on workplace productivity and care most about making data accessible, highly available and easy to move as needed. 2. Unstructured Data Management Challenges: No Disruption & Data Classification As in 2023, moving data without disruption to users and applications is a top technical challenge (54%) for unstructured data management. IT leaders are always looking to deliver superior performance to their internal customers and avoid conflicts and unnecessary calls to the help desk. This means ensuring that data is easy to find and use after data tiering and migrations. Read about Komprise Transparent Move Technology. The second leading technical challenge is using AI to classify and segment data (48%), an emerging tactic to add structure to unstructured data so it can be discovered and leveraged for new value. AI-enhanced data classification is a highly efficient way to do this, but best practices are still emerging here. Read more in this blog.   3. Scrappy Times for AI Many IT leaders have plans for AI but first, they must select and/or build the stack of tools and infrastructure to host the programs. Getting ready for AI may involve upgrading storage and computing infrastructure, cleaning and preparing data, training or developing custom LLMs, acquiring new IT skills, and beefing up security tools. How organizations pay for all of this technology is unclear: only 30% say they will increase the IT budget for AI. Strategies will likely entail leveraging existing budgets – such as cloud, say 34% – to fund AI. To help, IT leaders can optimize data management for savings and be strategic with cloud spending to avoid waste. 4. Unstructured Data Management Evolves for AI Governance Unstructured data management solutions are maturing far beyond giving IT users a way to easily migrate and tier data to new storage and analyze and model costs. IT teams now want features supporting AI data governance and security, such as the ability to quickly find, tag and classify sensitive data and move it automatically by policy to secure storage where it can’t be ingested into GenAI. Other capabilities include creating AI data workflows which integrate data management tools with AI tools to find and tag sensitive data sets like PII across large data estates.   5. AI Strategies Focus on Infrastructure IT leaders are setting their sights today on creating “AI-ready data infrastructure.” There are many pathways for this--between procuring and developing internal technology, using cloud services or combining those strategies in a hybrid model. IT teams are split on developing AI models internally versus using commercial services and/or the cloud. AI technology decisions will need to factor in internal expertise and resources to support these new technologies, budget and data security concerns. Download the full report to see all the data and insights. ### Enterprise IT Builds AI Infrastructure on a Budget, Komprise Survey Finds The fourth-annual survey on unstructured data management finds the need to balance cost optimization with AI innovation and a focus on data governance and security across all areas. Campbell, CA, August 6, 2024 – Komprise, the leader in analytics-driven unstructured data management and mobility, announces the results of the Komprise 2024 State of Unstructured Data Management survey. The fourth annual survey finds that most (70%) of enterprises are still experimenting with AI and “preparing for AI” remained a top data storage and data management priority for IT leaders. Yet leaders said that cost optimization is an even higher priority this year and they are trying to fit AI into existing IT budgets. Only 30% say they will increase their IT budgets to support AI projects. Other highlights from the survey include a priority on building out the proper infrastructure and technology stack for AI by upgrading data storage and data management technologies and creating AI-ready infrastructure. Addressing security and governance gaps across unstructured data management, GenAI and the IT workforce and moving data without disruption are other top trends of 2024. As the startup community and established technology vendors alike rush to launch new products and capabilities for AI, enterprise IT is still laying the infrastructure foundation and determining the necessary economics to get started. The survey, conducted by a third party, gathered inputs from 300 global enterprise storage IT and business decision makers at companies with more than 1,000 employees in the United States. Download it here. Highlights of the Survey: Nearly 50% of enterprises are storing more than 5PB of unstructured data and nearly 30% have more than 10PB. The top priorities for data storage in the next year include cost optimization (54%), preparing for AI (51%) and investing in data management and data mobility (41%). As in 2023, moving data without disruption to users/apps is the top technical unstructured data management challenge (54%), followed by using AI to classify and segment data (48%). Prepping for AI is the top business challenge for unstructured data management (57%). Only 13% restrict what corporate data can be used in AI, while 31% have no restrictions for users, apps or data in AI. Nearly half (44%) are creating AI-ready infrastructure and 32% are building their own learning models. Only 30% are increasing the IT budget to support AI projects. The leading challenge in prepping data for AI is managing governance/security​ concerns (45%), followed by data classification and tagging​ (41%). The leading tactic to address AI data concerns is to upgrade data storage/data management technologies (53%). AI data governance/security is the top future capability (47%) for unstructured data management, up from 28% in 2023. Nearly 60% need more staff with skills related to security, compliance and sensitive data. “Our latest survey reveals a pivotal moment in enterprise IT as organizations grapple with the transformative potential of AI while balancing fiscal responsibility,” says Kumar Goswami, CEO and co-founder of Komprise. “IT leaders will also need to factor in critical data governance and security capabilities. Managing unstructured data strategically to optimize costs and use data workflows to enrich metadata is a great place to start an AI initiative.” Download the full report here. About Komprise Komprise is a provider of unstructured data management and mobility software that frees enterprises to easily analyze, mobilize, and monetize the right file and object data across clouds without shackling data to any vendor. With Komprise Intelligent Data Management, you can cut 70% of enterprise storage, backup and cloud costs while making data easily available to cloud-based data lakes, analytics and AI tools. www.komprise.com. Media Contact: Kevin Wolf, TGPR kevin@tgprllc.com ### Why Unstructured Data Management Matters This blog has been adapted from the original version on FastCompany. Read the 2024 State of Unstructured Data Management Report. A survey conducted by Komprise in 2023 found that 32% of organizations are managing 10PB of data or more. That equates to 110,000 ultra-high-definition (UHD) movies, or half of the data stored by the U.S. Library of Congress. As well, 73% of organizations are spending more than 30% of their IT budget on data storage. Now with AI, big data analytics, and digital processes dominating business strategies, we need to better leverage all this data. Unstructured data is the fuel needed for AI, yet most organizations aren’t using it well. One reason for this is that unstructured data is difficult to find, search across, and move due to its size and distribution across hybrid cloud environments. Enterprises have two main objectives in managing unstructured data: (1) the ability to quickly find, sort, and leverage it for AI projects and (2) control rapidly growing storage and backup costs. The benefits of unstructured data management for cost control and AI Unstructured data management solutions and strategies can help IT gain holistic visibility and a detailed understanding of enterprise data: How much data is stored and where, What types and sizes of files are most prominent, What are the costs to store and back it up, Who are the top owners, And other identifying characteristics such as metadata describing file contents. With this information, organizations can choose the optimal, most cost-effective storage for different data sets while also developing workflows to help departmental users find their data and move it to AI platforms as needed. Industry examples Let’s start with healthcare.  Roughly 30% of the world’s data volume is generated by the healthcare industry, and this will grow to 36% by 2025, according to research compiled by RBC Capital Markets. Clinical notes and records, medical images, digital pathology, and research studies are valuable sources of information to better inform personalized medicine and improve patient outcomes. AI is starting to enable more accurate, faster analysis of common scans, such as mammograms and colonoscopies and can help clinicians create holistic care plans through intelligent analysis of demographic and social data from patients with a particular condition. Generative AI solutions have been reported to reduce the paperwork burden of clinicians and improve communications between physicians and their patients. Healthcare organizations need to analyze and manage the complexity of data and file types while ensuring tight adherence to regulations governing its use and protection. Instilling the right policies and tools to analyze, discover, protect, and safely move data to the right locations where it can be anonymized and cleansed prior to analysis is a key strategy. The auto industry is another sector navigating technology disruption. It’s hard to drive down the road for more than a few minutes without seeing an electric vehicle, whereas two years ago they were still a rare sight. Electric and autonomous vehicles collect large quantities of sensor data, which helps the car adjust and take actions on the fly or issue alerts to the driver. The collection and analysis of this data is white gold for manufacturers to troubleshoot issues and improve their designs. Using an unstructured data management system, a car manufacturer could create a workflow like this: Find crash test data related to the abrupt stopping of a specific vehicle model; Use an AI tool to identify and tag data with “Reason = Abrupt Stop”; Move only the related data to a cloud data lakehouse to reduce the time and cost associated with moving and analyzing unrelated data; Move the unrelated data to an archival storage tier for cost savings (or delete it) once the analysis is complete; Imagine the implications for any manufacturer wishing to leverage the right machine data to avoid bad outcomes for its customers and to improve products faster than its competitors. Compliance & Regulations From industry regulations governing sensitive data to geolocation requirements responding to e-discovery requests, preventing ransomware, and managing data during an M&A or divestiture, the list of data compliance needs keeps growing. Holistic data governance is harder to achieve all the time given the volume of data, the prevalence of shadow IT, and the wide distribution of data. Consider data management solutions that support automated workflows for compliance: For example, a user could create a query to find all data related to a divestiture project and then, through an API, use an external application like Amazon Macie to identify PII data and tag it.​ Next, the system could automatically move the PII data to an object-locked cloud storage service where it cannot be modified or accessed. Growing assets of unstructured data can be both a gift and a curse. Enterprises of all sizes are dealing with the strain on budget and time to store, manage, and govern it all. Yet with intelligent automation, sound policies, and collaboration among top data stakeholders across the organization, IT teams can properly manage the data and leverage it for game-changing AI and analytics initiatives. ### AI Puts Unstructured Data in the Spotlight at AWS re:Invent 2023 At one of the most influential technology conferences of 2023, any guess what will be getting the headlines? Like all the hyperscaler cloud services providers, AWS has flooded the web with AI announcements and investments in recent weeks: Amazon aims to provide free AI skills training to 2 million people by 2025 with its new ‘AI Ready’ commitment AWS announces the general availability of Amazon Bedrock and powerful new offerings to accelerate generative AI innovation How Amazon's $4B investment in AI company Anthropic impacts healthcare Amazon's Jassy: We're "surprised" at growth of our generative AI business Announcing New Tools for Building with Generative AI on AWS This week at AWS re:Invent 2023, expect more of the same. Unstructured Data in the Spotlight at AWS re:Invent At Komprise, we’re happy to see unstructured data in the spotlight as foundational to every AI and machine learning announcement. After all, training AI requires unstructured data and AI also plays a huge role in enriching metadata with appropriate intelligence and context. We’ve been partnering with AWS on specific use cases leveraging AI and machine learning to deliver the right data to the right place at the right time as part of both a data storage and an ongoing data management and data services strategy. In 2022 we hosted a webinar together on leveraging AI data workflows in the automotive industry: Modernizing Unstructured Data Management in the Automotive Industry with AWS. Later in the year we published a blog post on the AWS Partner Network (APN) focused on detecting PII data and automatically tagging sensitive data using AWS machine learning with Komprise Smart Data Workflows: Using Amazon Macie with Komprise for Detecting Sensitive Content in On-Premises Data. The blog illustrates the power and simplicity of Komprise Intelligent Data Management in rapidly searching across silos of unstructured data, finding the right dataset, analyzing it with Amazon Macie to detect PII and then tag the data appropriately so any user can search and find sensitive data without having to run the entire workflow again. “Customers can easily extend the platform by creating customized smart data workflows for analyzing their data with other AWS artificial intelligence and machine learning services, including photo and video analysis using Amazon Rekognition, content-based searches across text documents using Amazon Kendra, and more.” Komprise has also worked closely with AWS on Smart Data Migrations. Download the white paper and read the APN blog post: Migrate from Multiple On-Premises Data Sources to AWS with Komprise. Unstructured Data Management for Generative AI Over the past few months, we’ve been discussing the enormous potential of unstructured data for AI, the need to “curate, audit and move” unstructured data and the importance of data governance for AI. In fact, the 2023 State of Unstructured Data Management report found that “the majority (66%) are most concerned about the data governance risks from AI, including privacy, security and the lack of data source transparency in vendor solutions.” Here’s Komprise cofounder and COO Krishna Subramanian talking to eWeek’s Editor in Chief James Maguire on this topic: Komprise President Krishna Subramanian on Generative AI and Unstructured Data: From the recent AI executive order in the US to leadership chaos at OpenAI, and innovations announced this week at AWS re:Invent, to say this is an unpredictable, rapidly changing space is an understatement. Organizations of all sizes are grappling with how to harness the power of AI without succumbing to its potential risks. Komprise was founded to change the way the world manages and moves data while maximizing unstructured data storage cost savings and value. Our role in AI and machine learning is to enrich unstructured data using the appropriate AI services while creating systematic data workflows with oversight and governance for responsible use of AI. We look forward to the exciting announcements at re:Invent and continuing to grow our integrations with AWS. Read the white paper: Unstructured Data Management in the Age of Generative AI. ### Getting Data Governance Right is Top AI Priority, Komprise Survey Finds The third-annual industry survey reports that preparing for AI is the leading data storage priority and unstructured data management challenge. Campbell, CA, September 12, 2023 – Komprise, the leader in analytics-driven unstructured data management and mobility, announces the results of the Komprise 2023 State of Unstructured Data Management survey. The third annual survey finds that IT and business leaders are largely allowing employee use of generative AI but the majority (66%) are most concerned about the data governance risks from AI, including privacy, security and the lack of data source transparency in vendor solutions. As the generative AI marketplace expands and executives push for departments to leverage new solutions for competitive advantage, the need for an unstructured data governance agenda is strong; IT leaders cannot forsake data integrity, data protection and risk faulty or dangerous outcomes from generative AI projects. To cope, enterprises are restricting the AI tools and/or data that employees are allowed to use, according to the survey. IT leaders are also pursuing a multi-pronged approach for mitigating risks of unstructured data in AI, encompassing storage, data management and security tools as well as internal task forces. The survey, conducted by a third party, gathered inputs from 300 global enterprise storage IT and business decision makers at companies with more than 1,000 employees in the United States and the UK. Download it here. Highlights of the State of Unstructured Data Management 2023 Survey: Most organizations (90%) allow employee use of generative AI yet 66% of organizations cited top data governance concerns of preventing security and privacy violations, lack of data source transparency leading to unethical, biased or inaccurate outputs and corporate data leakage into the vendor’s AI model; Preparing for AI is the leading data storage priority in 2023, followed by cloud cost optimization; The majority (40%) will pursue a multi-pronged approach to manage AI risk, encompassing storage, data management and security tools; Organizations managing more than 10PB of data grew from 27% to 32% this year, a 19% increase. Half of organizations are managing 5PB or more of data, similar to 2022; Nearly three-quarters (73%) are spending 30% or more of IT budget on data storage and protection, measurably higher than 67% in 2022; The top unstructured data management challenge is moving data without disrupting users and applications (47%), followed closely by preparing for AI and cloud services (46%); Most (85%) say that non-IT users should have a role in managing their own data and 62% already have attained some level of user self-service for unstructured data management; Monitoring and alerting for capacity issues and anomalies led the pack for important future unstructured data management capabilities (44%). “Generative AI raises new questions about data governance and protection,” says Steve McDowell, Principal Analyst/Founding Partner, NAND Research. “The Komprise 2023 State of Unstructured Data Management survey shows that IT leaders are working hard to responsibly balance the protection of their enterprise’s data with the rapid roll-out of generative AI solutions. It’s a difficult challenge, requiring the adoption of intelligent tools, such as those from Komprise, for managing an organization’s unstructured data.” “This year’s survey shows that in the blink of an eye, IT leaders are shifting focus to leverage generative AI solutions, yet they want to do this with guardrails,” says Kumar Goswami, CEO and Co-founder of Komprise. “Data governance for AI will require the right unstructured data management strategy, which includes visibility across data storage silos, transparency into data sources, high-performance data mobility and secure data access." Download the full report here. About Komprise Komprise is a provider of unstructured data management and mobility software that frees enterprises to easily analyze, mobilize, and monetize the right file and object data across clouds without shackling data to any vendor. With Komprise Intelligent Data Management, you can cut 70% of enterprise storage, backup and cloud costs while making data easily available to cloud-based data lakes, analytics and AI tools. www.komprise.com. Media Contact: Kevin Wolf, TGPR kevin@tgprllc.com ### Unstructured Data Management in Government: Balancing Between Opportunity and Risks This blog is part of an industry series on unstructured data management. Read the previous posts on life sciences and healthcare. The morass of issues and challenges facing government organizations are at a tipping point. Crime rates in many cities are up, a sluggish economy means that citizens have more needs while tax revenues are lower, budgets are tight, labor shortages persist and clean energy and sustainability mandates are on the agenda. On the IT front, agencies are facing multiple priorities to improve services and lower risks, including cloud modernization, AI-led decision-making, improved data sharing and integrated security strategies, according to Gartner, in its 2023 Government Technology Trends brief. “Not only is the current global turmoil and technological disruption putting pressure on governments to find a balance between digital opportunities and risks, it also presents solid opportunities to shape the next generation of digital government,” said Arthur Mickoleit, Director Analyst at Gartner, as quoted in the brief. “Government CIOs must demonstrate their digital investments aren’t just tactical in nature as they continue to improve service delivery and core mission impacts.” Digital initiatives begin with data and government agencies at all levels have a variety of data types due to the broad functionality government IT organizations deliver across their diverse constituencies. This trend has only grown with the explosion in new digital services spurred from Covid-19. Some of the more challenging data types for agencies to manage include CAD/CAM, geographical information systems (GIS), and bodycam surveillance video since the file sizes are so large. Common unstructured data management challenges in public sector Government and public sector data is stored in a variety of formats and systems that support specific programs, departments and agencies. When data suddenly explodes, due to a new construction project or law enforcement program, agencies are hampered by tight budgets and long procurement cycles for expanding storage infrastructure. At the same time, agencies must comply with strict data regulations around privacy, security and auditing. How does IT handle this increasingly complex and rapidly growing storage footprint without disrupting critical applications, users or the constituents they ultimately serve? How unstructured data management helps The right unstructured data management solution brings a unified interface for all data in storage across agencies, departments and other potential data silos. It allows government IT and storage managers to understand data and make better decisions, such as how to migrate and tier data to the cloud or set up data deletion policies. Intelligent guidance on managing relentless file and object data growth can significantly and continuously reduce storage and backup costs and accelerate cloud journeys. These solutions can also ensure the proper compliance measures are in place, such as for ransomware protection and the secure storage of PII data. Public-sector organizations can benefit from increased flexibility to adopt better, more efficient technologies when required: solutions like Komprise Intelligent Data Management, Komprise Analysis and Komprise Elastic Data Migration can ensure that data doesn’t get locked into any one storage or backup solution. Case in Point: Boone County, ID Protecting and storing data is mission-critical to local governments like Boone County, Indiana. The County Sheriff Department’s adoption of body and dash cameras for all its officers and vehicles resulted in a 3000% growth in evidentiary data. Using the ROI reporting from Komprise, the IT team built a five-year plan that projected 70% savings by using Komprise to transparently archive cold data into Azure Blob. Because data moved by Komprise still appears on Boone County’s SAN, there was zero disruption to users. “We were at an unsustainable data growth rate,” said Sean Horan, Senior Network Systems Specialist with Boone County’s contractor. “Now we can move large amounts of data to the cloud without any disruption to employees.” Komprise has customers across city, county, state and federal governments. We help solve common public sector challenges of widespread cost-cutting mandates, limited staff and budget resources, the need to modernize data management environments to meet mandates for better citizen self-service and reduce the risks and complexities of cloud migrations. Learn more here. ### Why Unstructured Data Management Matters: An Industry View The world of data is ever-changing in terms of its types, volumes, uses and risks. Understanding these differences is critical so that IT leaders, data analysts, data scientists and other data stakeholders can manage and use it effectively for new initiatives. The first distinction is unstructured data versus structured data. Structured data is presented in rows and columns, and typically stored in a database. Structured and semi-structured data can include transaction data, customer relationship data, back-office data, point of sale details, financial and claims data, or click-stream data from a website that is typically fed into a data warehouse and accessed, analyzed and shared via reporting and business intelligence tools. Unstructured data, which comprises at least 80% of all data in the world, does not follow a standard, identifiable structure. IT teams can’t easily store it in a relational database. And it is growing exponentially. There is an estimated 120 ZB of data in the world today, according to Statista. IDC expects data to grow to 175 ZB by 2025. To consider what that means, this Cisco blog gives a few analogies: “If each Terabyte in a Zettabyte were a kilometer, it would be equivalent to 1,300 round trips to the moon and back.” Unstructured data can include emails, documents, web files, audio and video files, genomics files, CAD files, images and instrument and research data. While unstructured data is harder to manage and costly to store, it is fueling the next generation of AI and ML technologies which are reshaping society as we know it. Unstructured Versus Structured Data https://youtu.be/aQVDhxE1-sE This unstructured data is growing particularly fast in certain industries. Here are some examples of common unstructured data types: Life Sciences: Imaging, genome sequencing, research Healthcare: Imaging, PACS, digital pathology Media & Entertainment: Post-production, animation, VFX, content delivery Government: CAD/CAM, GIS, bodycam surveillance Oil and Gas: Seismic data, compliance Transportation: Autonomous vehicles Financial Services: Claims data, call center recordings Legal Services: Contracts, court filings, transcripts, video files In this series, we look at several industry examples of unstructured data types, growth and data management challenges as well as the potential value this data can bring to its sector. The first post focuses on the Life Sciences sector, an $8-10 billion global industry with leaders including Eli Lilly, Pfizer, Johnson & Johnson, Merck and Abbvie. Here’s a teaser: Pharma and biotechs have been at the center of global innovation, with rampant revenue growth and demand fueled by the Covid-19 pandemic. Investments in cloud, AI and digital technologies have intensified, delivering groundbreaking changes in how companies develop, test and deliver products to market. Common file types in life sciences include: clinical images, genome sequencing and other instrument data, as well as research documents. These data types don’t work well with traditional data analytics tools; life sciences companies are increasingly moving research data to the cloud to leverage affordable and scalable processing and analytics services for research data, offered by the large cloud providers (CSPs). Here is a Pfizer case study on cold data tiering to AWS. In this unstructured data management by industry series we’ll cover: Common data management challenges, including data silos, poor visibility into data, cost optimization needs, continual change in regulations, and too much time spent on data preparation and deployment. Additionally, ransomware protection and the growing AI requirement for automating data workflows, right placing data, and ensuring the right data governance strategy is in place are all part of a comprehensive, storage-agnostic unstructured data management strategy. Read the Post:   Life Sciences and Unstructured Data Management Read the Post: Healthcare and Unstructured Data Management Read the Post: Automakers' Data Management Needs Span Safety, Performance and AI Read the Post: Unstructured Data Management in Government Read the Post: The Data Equation to Higher Eds Teknonic Shifts Read the Post:   Making A Case for Legal Unstructured Data Management ### 2022 Survey: The Top Five Trends in Unstructured Data Management This blog post reviews the 2022 State of Unstructured Data Management report. In August 2024, the 4th annual report was published: Enterprise IT Builds AI on a Budget, Survey Finds In August 2023 the 3rd annual Komprise Intelligent Data Management report was published. Download Now. Komprise announces the results of its second annual survey on unstructured data management, highlighting demand for cloud file storage, user self-service and big data analytics workflows. Have we reached the unstructured data tipping point? With global data volumes careening into the zettabytes, enterprise IT leaders are feeling the pains of complexity from data growth across hybrid data storage silos and the spiraling costs to manage it all. In our latest survey of IT leaders, the Komprise 2022 State of Unstructured Data Management, we uncovered some key trends as related to data growth, data storage, data management and unstructured data analytics. You can download the full report here. In the meantime, this blog covers some of the unstructured data management highlights: Unstructured Data Growth & Spending Trends More than 50% of organizations are managing 5PB or more of data, compared with less than 40% in 2021. Nearly 68% are spending more than 30% of their IT budget on data storage, backups and disaster recovery—similar to 2021. Nearly 70% said they would spend more on storage YoY, compared with 62% in 2021. Read: Why Data Growth is Not a Storage Problem Top Unstructured Data Management Challenges With data growth and spending accelerating, it’s no surprise that IT organizations are focusing more on unstructured data management. In 2022, 87% of IT leaders rate managing unstructured data growth as a top priority, up from 70% in 2021. Leading challenges in 2022 include moving data to the cloud without disrupting users and applications, the high costs of data storage and backups, hindered visibility into data’s characteristics and complying with laws and regulations. What are your top challenges with unstructured data management today? Trend #1: Unstructured Data Management User Self-Service When asked what benefits they expected from moving unstructured data to the cloud, the majority (56%) of IT executives were most interested in cutting costs. Yet the second-highest expected benefit (43%) is to improve self-service for end users and departments. In unstructured data management, self-service typically refers to the ability for authorized users outside of storage disciplines to search, tag and enrich and act on data through automation—such as a research scientist wanting to continuously export project files to a cloud analytics service. Watch a Demo: Giving Unstructured Data Insights to Line of Business Users Trend #2: Moving Unstructured Data to Analytics Platforms Unstructured data has been a missing ingredient in business intelligence. Yet now that machine learning (ML) programs rely upon large quantities of data and can deliver new insights from chats, texts, sensor data and multimedia files, IT’s job is to get the right data into the right platforms. A majority (65%) of organizations plan to or are already delivering unstructured data to their big data analytics platforms. Another top new and related approach for unstructured data management is the ability to initiate and execute data workflows. A legal hold workflow could be: find all data related to a divestiture project, execute an external function to identify PII data and tag it and then move the sensitive data to an object-locked cloud storage bucket. What new approaches to unstructured data management are you taking or plan to take? Read: How Storage Teams Use Komprise Deep Analytics Trend #3: Cloud File Storage Gains Favor As organizations seek to optimize data storage efficiency by moving more data to the cloud, new file storage options are attracting attention. Cloud NAS topped the list for storage investments in the next year (47%), followed closely by cloud object storage (44%). Large storage vendors such as NetApp have popular cloud NAS offerings alongside cloud-native offerings such as Amazon FSx and Azure Files. These services are ideal for active or “hot” data requiring high performance and response times; rarely-accessed or “cold” data can live on object storage which delivers significant cost savings for long-term storage. In the next 12 months, which of the following do you plan to expand? Read: Smart Data Migration for File and Object Data Trend #4: Unstructured Data User Expectations Beg Attention The largest obstacle to unstructured data management, more so than the high cost of storage, relates to user experience; organizations want to move data without disrupting users and applications (42%). What happens typically is that IT will move files to the cloud or secondary storage and then users cannot find the files. This creates conflicts between users and IT, lost productivity and business risk. Read: Komprise Transparent Move Technology (TMT): Digging Deeper Trend #5: IT and Storage Directors want Unstructured Data Flexibility A top goal for unstructured data management (42%) is to adopt new storage and cloud technologies without incurring extra licensing penalties and costs, such as cloud egress fees. As choice grows in hybrid cloud infrastructure, organizations may also wish to switch back and forth between technologies (such as cloud providers and cloud storage classes) as needed to meet shifting business goals and user requirements. Understanding proprietary requirements and “hidden fees” of storage technologies and cloud services prior to purchase is critical to avoid vendor lock-in. Independent data management solutions can also help navigate these tricky waters by offering a data-centric approach that works across all storage. What are your top objectives for improving your unstructured data management strategy? Read: Why Cloud Native Unstructured Data Matters Check out the full report here, to get all the statistics on unstructured data management trends. The 2022 Komprise unstructured data management trends report has appeared in the following publications: VentureBeat IDM TechBeacon TDWI Spiceworks The 2023 Komprise State of Unstructured Data Management Report is now available. Download Now. You can also read the 2021 report here. Download the 2024 Report ------------------------- ### Komprise Survey Finds 65% of IT Leaders Are Investing in Unstructured Data Analytics The second annual report on unstructured data management shows growing adoption of cloud NAS and demand for end-user and departmental self-service in the cloud. Campbell, California (August 30, 2022) – Komprise, the leader in analytics-driven unstructured data management and mobility, announces the results of its second annual industry survey. A key takeaway is the shift from driving storage efficiencies to delivering data services in the cloud including the ability to leverage file and object data in cloud analytics tools for new value and competitive gain. The Komprise 2022 Unstructured Data Management Report examines unstructured data management challenges and opportunities in the enterprise IT organizations. Participants reported on topics from how much data they are managing to cloud data priorities and future approaches to unstructured data management. The complete report can be found here.  According to the report, enterprises are storing more data than ever: more than 50% are managing 5PB or more of data, compared with less than 40% in the 2021 survey, and most are spending more than 30% of the IT budget on storage and backups. As a result, IT leaders are feeling pressure to manage data more granularly and cost-effectively; the survey shows that cloud file storage, followed closely by cloud object storage, are top areas of investment. Finally, the report found emerging use cases for unstructured data management as requirements grow beyond cost savings and into data lifecycle management, compliance and generating business value from data. Highlights of the Report: Unstructured Data Management Challenges and Trends: More than 50% of organizations are managing 5PB or more of data, compared with less than 40% in 2021. Nearly 68% are spending more than 30% of their IT budget on data storage, backups and disaster recovery. Cloud storage predominates: Nearly half (47%) will invest in cloud network attached storage (NAS), followed by cloud object storage (43%). On-premises only data storage environments decreased from 20% to 11.9%. The largest obstacle to unstructured data management (42%) is moving data without disrupting users and applications. Unstructured Data Management Goals & Plans: A majority (65%) of organizations plan to or are already investing in delivering unstructured data to their new analytics / big data platforms. The top goal (43%) is to adopt new storage and cloud technologies without incurring extra licensing penalties and costs. After cutting costs, the second highest expected benefit of cloud data migrations is to improve self-service for end users and departments (43%). The leading new approach to unstructured data management is the ability to initiate and execute data workflows (43%). Expanding use cases for unstructured data management include protecting sensitive data (63%) followed by allowing users to search and run analytics (41%) and enabling data deletion policies (35%).  “After cutting costs, IT leaders are motivated to improve self-service for end users and departments by moving more data to the cloud,” said Krishna Subramanian, Co-founder, COO and President of Komprise. “In unstructured data management, self-service typically refers to the ability for authorized users outside of data storage disciplines to easily search, tag, enrich and move data to new tools and services through automation. This delivers faster time to value for users looking to leverage vast unstructured data stores for new insights and revenue-generating activities, by using cloud data lakes and analytics platforms.” The Komprise 2022 Unstructured Data Management Report summarizes the responses of 300 enterprise storage IT decision makers at companies with more than 1,000 employees in the United States and in the UK. All respondents are IT director level or above.  About Komprise Komprise is a provider of unstructured data management and mobility software that frees enterprises to easily analyze, mobilize, and monetize the right file and object data across clouds without shackling data to any vendor. With Komprise Intelligent Data Management, you can cut 70% of enterprise storage, backup and cloud costs while making data easily available to cloud-based data lakes and analytics tools. www.komprise.com.  Media Contact: Kevin Wolf kevin@tgprllc.com -------------------------- ### Surveys Say: Data Chaos is Not Solved by Cloud There’s no lack of research on data and cloud these days, but what to make of all these surveys? We gathered some of the latest data points and found some conclusions to help IT and data storage leaders tackle difficult storage and unstructured data management decisions in the coming year. Welcome to the data economy Data runs our world, delivered by apps, devices, websites and snappy digital processes. But is it really working? Consider for a moment how much unstructured data we are generating and then compare that to how much is actually being used for analytics and decision-making — which by some accounts is less than 5%. By now, we’re all familiar with the oft-cited statistic from IDC Research that data volumes will grow from more than 70ZB currently to 175ZB by 2025. Keep in mind: this is just three years from today. Data generated at the edge is a notable contributing factor. In 2022, the market for the Internet of Things is expected to grow 18% to 14.4 billion active connections and by 2025, IoT Analytics predicts there will be 27 billion connected IoT devices. You can’t see data piling up like you might see garbage in a landfill. Data growth is a strangely invisible phenomenon — yet when it comes to the impact on IT budgets and staff, the pain is real every day. Also, the lack of data visibility and complexity in managing and analyzing unstructured data is a lost opportunity. Let’s take a look at some of the salient data points on data and data storage for some context: According to research by Dell, 43% of IT decision makers fear their IT infrastructure won’t be able to handle future data demands. Naturally, this concern is driving data migrations to the cloud. Cybersecurity Ventures predicts data stored in the cloud will reach 100ZB by 2025, or 50 percent of the world’s data at that time, up from approximately 25 percent stored in the cloud in 2015. With so many business applications now running in the cloud, thanks to SaaS maturity, moving unstructured data out of corporate data centers and to the cloud is the next logical step in enterprise cloud computing. Of course, a cloud data migration is not a foolproof cost-saving strategy, given the many variables at play — from selecting the right cloud storage to continually moving data to the right place, avoiding excess egress fees and getting rid of cloud resources when they are no longer in use. Respondents to the Flexera State of the Cloud 2021 Report estimate that their organizations waste 30 percent of cloud spend. Unsurprisingly, the Flexera study reports that optimizing the existing use of cloud (cost savings) is the top initiative for the fifth year in a row, followed by migrating more workloads to cloud and better financial reporting on cloud costs. Equally troubling is the lack of visibility as data growth explodes. Less than half (45%) have a strong understanding of data generated outside their team, according to IDC’s “Data Literacy: A Foundation for Succeeding in a Data-Driven World.” This lack of data visibility is not trivial, and it can hamper many initiatives — from security and compliance to decision-making, customer experience and innovation. A recent Accenture study revealed 68% of companies are not able to realize tangible and valuable benefits from data. Considering how much time and money is spent managing and storing all of this data, this is a poor ROI. Yet those organizations that can harness data, manage it appropriately across silos and understand it at a granular level can best leverage it for success. Organizations which take a data-driven approach to decision-making grow more than 30% annually, according to Forrester. Ten years ago, the cost and complexity of running big data analytics was a nonstarter for many organizations. But that is changing quickly, now that cloud-based AI and ML technologies have matured as well as data lakes and data lakehouses. The availability of more automated, scalable, affordable analytics platforms means that there is a lower barrier to entry and higher overall chance of success for many enterprises taking the leap into big data. No-code AI is no joke. Here's our analysis of what this research means for IT leaders striving (or being pressured from above) to monetize data and support departments in data-driven initiatives: IT organizations don’t have the infrastructure to keep up with the data deluge — they’ve got data swamps and “dark data” silos plus out-of-sight storage technology bills. Cloud data migrations are a smart idea to cope with strained data center storage capacity — yet there is far too much waste and not enough intelligence on best practices. Leading organizations want to leverage data to disrupt their markets and drive customer loyalty — Yet despite the plethora of advanced AI and ML services and technologies for rapid analysis and processing, data is stagnant and merely clogging the drain. If you’re managing data like you always have, it’s time to rethink that approach — With continual analytics on your data, you can understand it better, rather than treating it all the same. You can also find and move the right data to modern big data tools and analytics services — the tools which managers and researchers crave. Enter Komprise. Komprise was founded to help organizations take control of their unstructured data to both save money and make money. Komprise Intelligent Data Management delivers: Continual analysis of NAS and object data across on premises, edge and cloud storage, so you can move data to the right place at the right time for maximum data storage cost savings. Cost modeling so that IT can compare the data storage cost savings of moving data to different tiers of storage. Smarter cloud data management: Intelligent tiering of cold data to low-cost secondary storage by policy, retaining active or “hot” data on primary, high performing storage in the data center or the cloud. Smart Data Workflows and the Global File Index create automated workflows for all the steps required to find the right data across your storage assets, tag and enrich the data, and send it to external tools for analysis. Zero user disruption, full file fidelity and direct, native access to moved data. ------------------------------ ### Komprise Survey Finds IT Leaders Lack Insights for Hybrid Cloud Unstructured Data Management 56% of Enterprises Want to Store More Data in the Cloud, But Questions of Where & When Remain as Better Visibility is Top Priority San Jose, CA – August 24, 2021 – Komprise, the leader in analytics-driven data management as a service, today announced the results of new research: “Komprise 2021 State of Unstructured Data Management Report.”  The third-party survey  examines the challenges and opportunities with unstructured data in the enterprise with responses from 300 storage IT decision makers at companies in the United States and in the UK. The majority of organizations surveyed are managing more than 1PB of data and spending more than 30% of IT budgets on data storage and protection – a cost overhead that’s showing no signs of slowing down. Yet this unstructured data – application data, user documents, video and images, research files -- represents untapped insights for future business value. IT leaders realize that migrating data to the cloud can help cut costs and enable data monetization. The survey finds that they need analytics to help devise a cloud data management strategy for better planning, cost savings and support for cloud-based data lake and AI projects. Highlights of the survey: Unstructured Data is Growing as are its Costs 65.5% of organizations spend more than 30% of their IT budgets on data storage and management. Most (62.5%) will spend more on storage in 2021 versus 2020. Getting More Data to the Cloud is a Key Priority 50% of enterprises have data stored in a mix of on-premises and cloud-based storage. Top priorities for cloud data management include: migrating data to the cloud (56%) cutting storage and data costs (46%) and governance and security of data in the cloud (41%). IT Leaders want Visibility First Before Investing in More Storage Investing in analytics tools was the highest priority (45%) over buying more cloud or on-premises storage or modernizing backups. One-third of enterprises acknowledge that over 50% of data is cold while 20% don’t know, suggesting a need to right-place data through its lifecycle. Unstructured Data Management Goals & Challenges: Visibility, Cost Management and Data Lakes 44.9% wish to avoid rising costs. 44.5% want better visibility for planning. 42% are interested in tagging data for future use and enabling data lakes. “The Komprise 2021 State of Unstructured Data Management Report provides valuable insights into key customer priorities in unstructured data management,” said Krishna Subramanian, President and COO of Komprise. “The survey shows that enterprises want analytics and systematic data management to make the best decisions on cloud migrations and archiving. The end goal is to cut storage costs and create new value from unstructured data over time.” Download the State of Unstructured Data Management report.  About Komprise Komprise is the industry’s only multi-cloud data management-as-a-service that frees you to easily analyze, mobilize, and access the right file and object data across clouds without shackling your data to any vendor. With Komprise Intelligent Data Management, you are able to know first, move smart, and take control of massive unstructured data growth while cutting 70% of enterprise storage, backup, and cloud costs. www.komprise.com   Media Contact: Kevin Wolf, TGPR www.tgprllc.com kevin@tgprllc.com ### 16 Critical Things Every Business Leader Should Know About Ransomware With more and more data being stored digitally or in the cloud, ransomware has become a rising issue in recent years. While most people have heard of ransomware, business leaders may not always be aware of factors that can contribute to higher risk. ### How Storage IT Teams can Evolve with AI This blog has been adapted from its original version on ITOpsTimes. As artificial intelligence (AI) becomes deeply embedded in enterprise operations, IT storage professionals are being pulled into uncharted territory. Where once their focus was confined to provisioning, performance tuning, data protection and backups, today they are expected to orchestrate data services, ensure regulatory compliance, optimize cost models, and even help train AI models. Here are 7 key requirements and strategies for storage teams to adapt to the new world of AI:   1. Understand Your Own Data Landscape In the AI era, storage teams need granular metrics about their own environment: data about the data. Know where data resides, its access patterns, growth trends, duplication rates, and compliance status. Segment data by department, project, or data type to make smarter storage management decisions and improve data searchability and usability for internal customers. This often requires metadata enrichment or tagging and tools that can crack open files to supply additional context about the data. This supports accurate, efficient AI data ingestion.   2. Get Analysis for Smarter Spending Between storage hardware, backups, disaster recovery, and hybrid cloud capacity, enterprises invest millions each year.  IT organizations are now managing enormous volumes of unstructured (file and object) data: 20, 30, or even upwards of 50 petabytes (PB). But as unstructured data footprints expand, it’s clear that not all data can remain active or be treated equally. Treating everything as “hot” data drives up unnecessary costs, increases exposure to ransomware attacks, and clogs infrastructure needed for AI workloads. To address this, IT leaders are embracing transparent, automated data tiering strategies that operate across storage vendors.  Cold data can be shifted to lower-cost storage or cloud-based solutions transparently without user disruption. Some organizations even layer in “cool” storage tiers to retain a consistent experience while cutting costs. At the same time, chargeback or showback models allow departments to see how much data they’re using, how old it is, and who are the top consumers. Learn more about file tiering with Komprise.   3. Delivering AI-Ready Data with Context and Control Once a cost optimization plan and strategy is in place to handle the ongoing unstructured data deluge, it’s time to focus on preparing data for AI. AI systems can’t function without data and not just any data: contextual, curated, and compliant data. This has made data classification a top priority for storage teams. To restrict what AI bots can access, protect sensitive information and avoid redundant processing, IT organizations are prioritizing metadata tagging and automated data workflows.  Automated tools that allow end users to tag and classify their data are becoming essential. For example, researchers need to distinguish between data tagged “internal,” “sensitive,” or “public” to comply with governance policies. Power users such as analysts, data scientists and researchers also need easier ways to search across their data – such as project code, project name, and any other relevant keyword indicating the contents. Since unstructured data can easily span billions of files strewn across tens to hundreds of enterprise directories, efficiently classifying, searching and curating unstructured data is integral to AI. 4. Creating Tighter Connections with Key Stakeholders To meet the evolving demands of AI, storage professionals should aim to build relationships not only within IT but across the business. Storage teams now serve as trusted advisors to departments, researchers and IT peers, helping define data needs, governance policies, and infrastructure priorities. To be effective, storage IT practitioners can aim to gather details on enterprise objectives so they can align technical decisions with business outcomes, whether that’s cost control, regulatory compliance, or supporting cutting-edge research. 5. Redefining Metrics for the Modern Enterprise As AI workloads and cross-functional collaboration become standard, traditional storage SLAs may no longer be sufficient. Look into new data management metrics, such as: Top data owners by individual or department; Percent of non-compliant or orphaned data; Data classification completeness; Duplication reduction; Chargeback effectiveness. These KPIs help demonstrate value, encourage better data hygiene and align IT services with business needs. 6. Lock Down File Data Against Cyber Threats AI’s reliance on data makes storage a prime target for ransomware attacks. Offloading cold data to immutable storage in the cloud is one effective mitigation strategy. Immutable storage ensures that once written, data cannot be altered or deleted, effectively reducing the active attack surface by up to 80%. 7. Be a Partner in AI Infrastructure Building Training and deploying models often require high-performance compute: GPUs, TPUs, and advanced networking. Whether organizations choose to build their own environments or use cloud-based options, storage teams must be involved from the start to determine where AI should live (on-prem, cloud, or hybrid), how to manage data movement, and how to ensure security and performance at scale. Research has found that by 2025, half of all employees would need to reskill due to technology shifts. That prediction has arrived. For IT storage professionals, the shift is more than technical. It’s about protecting and furthering their careers and becoming trusted data services providers in the age of AI. ### Komprise AI Days: Unstructured Ignition in the Spotlight We launched Komprise AI Days, a new small-format networking event, on April 2 in Boston. It was a great opportunity to connect with customers, partners and prospects and discuss what enterprise IT leaders are thinking about now when it comes to unstructured data and preparing for AI. A highlight of the afternoon was a customer and partner panel that featured IT infrastructure leaders from Mass General Brigham, Yale University and DC Consulting. Here are the key trends that came out of the day: Get a handle on the massive unstructured data estate With many enterprises now storing 20, 30 or even 50 PB of data, everyone in the room agreed that data cannot all be treated the same and it cannot all be active. Avoid unnecessary costs and ransomware risks by offloading cold data, such as by setting up policies to automate transparent tiering to lower-cost storage and deploy a chargeback model (or showback) that provides departments and users with reports showing how much data they’re using, its age, and who are the largest consumers. Don’t buy more storage without getting analysis across silos to know how frequently data is being accessed and the costs for storing cold data. Storage pros are getting involved with AI Search-oriented AI bots or agents have emerged as the first use case, and because AI doesn’t do anything without data, storage teams have been tasked to deliver the proper data to these new AI services. One of the requirements is to deliver data classification to restrict what the AI bot can access. As research teams build out their models, IT’s role is to segregate and deliver the data services they need. While data owners will remain responsible for their own data, storage teams are recognizing they will play a bigger role in providing the appropriate guardrails for responsible use of data with AI, such as by automating data classification and tagging sensitive data. Researchers need help from IT to curate the right data for their projects. Metadata tagging came up frequently, especially as a requirement when working with research teams. Tagging will be important to avoid repetitive research processes, to be more efficient in managing unstructured data and improve the AI experience and lower costs. For example, organizations may want to be able to tag data as internal, high risk, or for public consumption. Read how Komprise supports data tagging for AI. Self-service data tagging will become more prevalent. Given that data storage teams don’t know the data beyond attributes like access time, growth and cost, IT needs to provide tools for end-user organizations to tag their own data for classification and to avoid repetitive search and curate processes. Komprise COO Krishna Subramanian discussed the Deep Analytics role in Komprise Intelligent Data Management. IT can set up users who can search across only the data they have permission to access and then tag their own data based on, for example: Grant information Project information If a project is active or cold Learn more about Komprise Deep Analytics. Meeting internal demands for AI with costs in mind. Storage leaders said they are looking to maximize the use of high-performance infrastructure economically so researchers can balance resources and “so everything isn’t custom.” One IT leader mentioned standardization: “Genomics is a good example. Once a data set is created, multiple researchers and labs will create copies from it. How do you find that data and clean it up so it doesn’t consume expensive disk space? How does the IT infrastructure team provide them with a central source that they can process?” AI requires flexibility, rapid data mobility. “This is our first foray of moving semi-live data to the cloud, and we need to figure out what characteristics to maintain as if on-premises, as we waterfall data from one tier to another tier,” said an IT director. “Everyone is talking about a deep archive. We instead chose a cool storage layer so that the user experience wouldn’t change.” Having the data near the compute is a top priority along with moving data quickly between hybrid cloud storage environments and across teams. “Everyone is talking about a deep archive. We instead chose a cool storage layer so that the user experience wouldn’t change.” Communicate frequently with departments during times of rapid change. Start with your “friendlies.” Leverage grassroots relationships in the organization, people whom you already have good working relationships with, when introducing change or new strategies. Initiate annual introductions of the IT infrastructure and storage team to departments and let them know what’s new and what’s on the roadmap. Make sure they know what you have to offer and understand what their workload will be to avoid surprises. You don’t want to risk a ransomware incident when a department director puts a server under their desk. Managing shadow IT and shadow AI is the work of diplomacy. “We tried for years to consolidate between central IT and shadow IT groups,” a CTO said. “Those groups were set up to meet specialized needs and it’s hard to let them go. We consolidated cloud services but many teams run their own backups and maintain smaller storage systems.” Attendees agreed that a consolidation strategy is only successful when it is part of the mission and comes from the CIO, rather than being a grassroots initiative. IT can further its case by being competitive internally .“Enterprise IT needs to prove that they are the gold standard in terms of technology and service. Ultimately the goal is to free up time and budget so departments, divisions, lines of business can focus on what they do best. Let’s give our subject matter experts to do it for them so they think of us first before going out and doing on their own.” Our next AI Days: Unstructured Ignition event is in Houston was on May 13th. Thanks to all of our customers, partners and prospective customers for making these great. Here are a few posts from the Houston event here and here. ### 6 Ideas for IT Leaders Amid Tariff Wars Since February, the U.S. Administration’s foray into tariff policy changes has created a whirlwind of unpredictable shifts and stock market turbulence. Foreign leaders across the globe are waiting to see what’s next. The past few months have seen a flurry of start-stop moves from President Trump, leaving business and IT executives in a holding pattern. In early July, Trump reignited the tariffs discussion with threats of up to 40% tariffs coming soon on many countries, depending upon whether countries agree to negotiating new terms. Levies on semiconductors, iPhones and other computing products are in the mix of fluctuating tariff policy. All this leaves CXOs, especially CIOs and CTOs, in a difficult position. Companies across all industries depend upon electronics to attain not only competitive advantage but day-to-day operations. IDC recently announced a bearish forecast for IT spending, downgrading its spending projections to 5% growth in 2025, rather than the 10% growth forecast previously. “The wave of new tariffs introduced by the US administration will drive up technology prices, disrupt supply chains, and weaken global IT spending in 2025,” the research firm stated in a blog post on April 4. So now what? CIOs can’t easily stockpile high-ticket items like servers, storage devices and networking equipment. Further, cost efficiency has been a mandate from above for several years; asking the CEO for another $2 million to prepare IT infrastructure and operations for tariffs may not play well. There is also the pressure on budgets for expanding cybersecurity protection and deploying AI. Here are a few strategies to consider as IT leaders balance fiscal responsibility with critical IT initiatives: 1) Reconsider technology modernization initiatives. If you have plans over the next 12 months to replace an aging and legacy infrastructure stack, such as adopting HCI or higher-performing Flash storage, this might be the time to delay or pare down plans--unless your enterprise can afford 20 or 30% higher prices for new hardware. 2) Re-evaluate cloud storage. While the major clouds (AWS, Google and Azure) manufacture some of their own hardware, they still rely upon global supply chains to source the computing, networking and storage equipment and components that power their datacenters and services. Yet the cloud giants can absorb tariff changes more easily than the average enterprise—whether through negotiations, volume discounts or other workarounds. Cloud repatriations have been on the rise in recent years due to high costs and mismanagement, but savvy IT leaders may reverse course if cloud infrastructure becomes a better deal now, comparatively. 3) Get insights on unstructured data before buying more storage. When IT managers discover that they are running out of storage capacity, the traditional response has been to buy more. But this may lead to waste and/or the wrong storage technology later. Most (80%) of data is rarely accessed (cold), yet consumes expensive storage and backup resources. By gathering insights on all data across storage, you can understand data usage and access patterns, growth metrics, data types and costs to make the right decisions. With a thorough analysis, your organization may be able to avoid purchasing new storage altogether through cold data tiering and right-placing data into the optimal storage for its current requirements. When IT managers discover that they are running out of storage capacity, the traditional response has been to buy more. But this may lead to waste and/or the wrong storage technology later. 4) Choose best of breed. IT organizations with just a few large IT vendors running their stack is not uncommon, as the giants continually expand their offerings to meet new needs of customers. But in times of supply-chain pressure, such as today, this may not be the smartest tactic. Having a multitude of vendors means they will work for your business and compete harder on pricing. This strategy may also give you the best cost and performance ratio for different workloads with some protection against a vendor raising its prices substantially or discontinuing an essential product. 5) Prolong the life of your infrastructure using software. Since new infrastructure is costing more for equivalent functionality, it is wise to see how you can delay hardware refresh cycles by getting more value from existing infrastructure. For instance, you can leverage older, slower storage infrastructure by using it as a cold tier for your high-performant storage. By using data management software to transparently tier cold files, you free up expensive storage capacity while also shrinking backup storage requirements. 6) Keep the future in mind. A cautionary note: despite growing concerns about tariff-induced inflation, IT leaders must always retain a strategic outlook. Making the best decisions for the business amid cost pressures should balance the need to develop an infrastructure that is AI-ready, secure across all areas with strong ransomware defenses, compliant with ever-changing global regulations, sustainable regarding energy constraints and flexible enough to quickly adapt to evolving business demands. Ensure that you make your data AI-ready with proper data analytics and data classification, as this is a first step for any AI data pipelines. Read more about how Komprise facilitates AI-ready data. The threat of new tariffs and a prolonged trade war has created great economic uncertainty in 2025. With careful planning using the steps outlined above, you can achieve cost savings while minimizing additional infrastructure spend by intelligently managing your cold data and continually right-placing all data in storage. ### Storage & Data Management Predictions for 2023: Living on the Edge Completing year-end projects may take precedence over planning for the coming year but first let's take a step back and think about the big picture. In times of global contraction, supply chain stressors and ongoing economic volatility, IT leaders may feel like they’re living on the edge as issues beyond their control swirl about. Yet by taking a cautionary approach to spending while also not missing opportunities to be strategic where it matters, enterprises should be able to travel the murky path ahead with confidence. Komprise executives share key trends which they foretell for unstructured data management and data storage in the coming year: implications of moving to data services, edge data management, multi-cloud caution, getting smarter about data migrations, and more. Get ready for new, business-oriented data services metrics. In VMblog, Komprise COO Krishna Subramanian writes about the evolution of data management metrics: “Storage teams have traditionally measured infrastructure metrics for capacity and performance such as latency, I/O operations per second (IOPS) and throughput. But given the massive data growth of unstructured data, data focused metrics are becoming paramount as enterprises move away from managing storage to managing data services in hybrid cloud infrastructure. New data management metrics look at usage indicators such as top data owners, percentage of "cold" files which haven't been accessed in over a year, most common file size and type, and financial operations metrics such as storage costs per department, storage costs per vendor per TB, percentage of backups reduced, rate of data growth, chargeback metrics and more.” Edge data growth will require intelligent edge processing. Kumar Goswami, Komprise CEO, PhD Computer Science, writes about edge trends for TDWI: “Explosive data growth along with consumer adoption of disruptive digital products such as self-driving cars is pushing demand for edge storage and consequently changing data management requirements to deliver visibility into edge data. This visibility will be instrumental in managing data in place at the edge through enrichment (such as data tagging) and extraction of just the right data sets for analysis. Smarter edge data management will avoid overspending on storing extraneous data in cloud data lakes and warehouses by filtering and deleting non-valuable data at the edge first. Edge analytics tools will quickly process the data without the need to send large files back and forth to cloud or on-premises data centers, saving time and money. The right edge analytics and data management program can deliver real-time insights to improve customer experiences or detect issues quickly, such as a manufacturing defect or a ransomware breach.” Cloud analytics becomes top of mind for unstructured data management In Spiceworks, Krishna Subramanian discusses this major trend: "The global AI software market is expected to reach a whopping $135 billion by 2025, at a growth rate outpacing the overall software market, according to Gartner. Technavio predicts that the cloud AI market will grow by over 20% in 2022. Unstructured data, which comprises at least 80% of all data generated, is the fuel needed to power modern ML engines. A majority (65%) of organizations in the Komprise 2022 State of Unstructured Data Management survey indicate that they plan to or are already delivering unstructured data to their big data analytics platforms. To meet these new requirements, IT organizations will need capabilities to efficiently segment and classify data, enrich it through metadata tagging and facilitate automated workflows to find and move the right data sets into cloud data lakes and analytics tools. Multi-cloud strategies will be less popular unless enterprises can manage cost and complexity. Komprise VP of Marketing Darren Cunningham predicts caution with multi-cloud deployments in an economically-uncertain 2023, as covered in ITProToday: “Many IT organizations today want the flexibility of using more than one cloud provider to balance the needs of costs and different workload requirements, as well as disaster recovery tactics such as replicating data to another cloud. Yet managing multiple clouds adds management and skills costs to ensure ROI. IT teams will need full visibility across all data assets, metrics to inform decisions, and the ability to move data between platforms and environments without excessive costs (such as cloud egress) and security risks. This will require tighter alignment and integration between storage/infrastructure and security/governance/compliance teams and tools and a storage-agnostic data management strategy.” The storage architect / engineer will take on data services. “We'll see more experienced individuals in these roles move on to cloud architect and other engineering roles while IT generalists/junior cloud engineers inherit their responsibilities,” predicts Krishna Subramanian in VMblog. “This is a challenging time for IT organizations in a hybrid model as there is still significant NAS expertise needed. Either way, the IT employees managing the storage function will need new skills beyond managing storage hardware. These individuals must understand the concept of data services--including facilitating secure, reliable governance and access to data and making data searchable and available to business stakeholders for applications such as cloud-based machine learning and data lakes. The new storage architect will frequently analyze and interpret data characteristics, developing data management plans which factor in cost savings strategies and business demands to create new value from data. This individual will interact regularly with departments to create and execute ongoing data management processes and plans.” Cloud data migration pains highlight the resurgence of enterprise IT silos. “Large-scale data migrations to the cloud, especially petabytes of file data that historically has been stored on expensive hardware platforms, will continue to be problematic for many enterprises,” predicts Darren Cunningham in ITProToday. “Migration issues — such as slow transmissions, data loss, and errors — not only derail timelines and add costs to projects but can sour the appetite for growing cloud spending. When it comes to file data migrations to the cloud, the complexity of network configurations — routing and security — has been underestimated. There are often technical bottlenecks in the way that haven't been investigated prior to migration. Storage and networking teams are often not on the same page, which causes perpetuation of IT silos, finger pointing, and missed deadlines. It's critical to spend more time in upfront assessment and testing of the network to prevent data migration complexities. IT executives will need to counteract silo tendencies and instead create processes for networking, storage, and security teams to work together closely for the common goal of moving large file data workloads safely and swiftly to the cloud without errors, data loss, or risks.” Unstructured data management expands with self-service. “Enterprise IT departments are drowning in data requests along with their daily responsibilities,” says Kumar Goswami in TDWI. “It’s time for end users and departments to play a greater role in managing their own files and data storage. With the appropriate security guardrails in place, storage professionals will benefit by sharing data management analytics with departments. By doing so, IT teams can collaborate more closely with departments to deliver data services while meeting cost savings and governance goals. For example, users can identify data sets with certain characteristics (such as project or age) to move to cloud storage to cut costs or support research initiatives. The democratization of unstructured data management will ultimately create tighter alignment and collaboration between IT and business units, which can only benefit the enterprise for the long term.” ------------------- ### Medical Group Transforms Data Management with Analytics in Mind This article was adapted from its original version on CIO.com. CIO Kevin Rhode is on a mission to sharpen and modernize the unstructured data management practice at his employer, Arizona-based District Medical Group (DMG). When he took the role of CIO of in May 2020, he faced his share of challenges. Not only was it the outset of the COVID-19 pandemic, but the nonprofit integrated medical group practice had just purchased five primary care clinics that had to be integrated into the existing organization. Rhode’s first step was to assess the organization’s disaster recovery, business continuity, backup, and data management capabilities so he could accelerate a cloud tiering strategy. Even without the addition of the new clinics, DMG data volumes were piling up fast and backups were slow and inefficient, taking 36 hours to complete on average. “My predecessor was really applications-focused and our applications team is top-notch,” Rhode says. “However, the infrastructure side was a little bit on the weak side, so I’ve spent the last two years rebuilding the infrastructure, really revamping the network and looking at how we’re configured, how our network works, and how we’re connecting our individual clinics and different sites together.” Introducing cold data storage To solve its data management and backup issues, Rhode says DMG needed to be able to quickly identify infrequently accessed “cold data” and push it to offsite storage, but then be able to easily pull it back into its IT environment if someone needed access. To facilitate this, Rhode turned to Komprise Intelligent Data Management, which enabled DMG to identify and tier all its data, moving any data that had not been accessed in the past two years or longer to the Wasabi Cloud data storage service, thereby freeing up half the space on DMG’s Windows file servers. The storage team also segmented data archived in Wasabi into buckets for each year. Anything exceeding DMG’s retention policy of 10 years gets deleted. “We try to keep the first three years of active data in the production system so that’s available to the docs,” Rhode says. “We do serve a children’s population, so a lot of times we’re looking way back in history for information. So, we have the archive linked into our platform so they can go as far back as they need to.” Previously, all data and reports stored within DMG’s internal data systems, including data dating back to the early 1990s, were sitting in drives that DMG was backing up every night, Rhode says. Rhodes estimates that the healthcare provider will save around $100,000 over three years by moving cold data off Windows servers to less expensive Wasabi cloud storage. The move has already reduced backup data by 5.5TB and speeded up the backup processes by 75%. Modernizing healthcare analytics With DMG’s data management and backup process retuned, Rhode could then focus on his overarching goal to modernize the healthcare provider’s analytics capabilities. The company’s 2020 acquisitions, while helping DMG grow, also meant the organization suddenly had a slew of new electronic medical records (EMRs) that were recording data in different ways. Much of his first two years has been spent integrating those clinics and standardizing on the Cerner EMR platform. With the EMRs finally integrated, Rhode’s team is now working with clinicians to perform a strength assessment of the Cerner platform and understand where the weak spots are. Reflecting on the experience, Rhode says his biggest takeaway is that while planning carefully for data migrations is absolutely necessary, that plan is just a guideline when it comes time to execute. “You have to be flexible enough to change that plan,” he says. “The complexity of our data, the complexity of all the interactions that have to happen on a daily basis, when you think about the number of people involved in just a single appointment, the complexity of the systems that we’re operating, it’s just amazing. In that complexity, what can you control?” Rhode advises identifying what you can control and setting aside the rest is key in overcoming migration challenges — and delivering business value. “At the end of the day, your job is to enhance the ability of providers to help patients,” he says. “Focus on what you can control and really work closely with the stakeholders within your environment.” Read the Case Study > ------------------ ### UK Spotlight: Covid, Brexit and the Untapped Value of Unstructured Data Martin Gibbons,EMEA Channel Director Martin Gibbons is Komprise Channel Director, EMEA, based in the London area. We asked him a few questions about doing business in the UK and Europe as Covid-19 slows down along with a pulse on what regional IT leaders and buyers are thinking about now. _______________________ Let’s begin with an economic outlook for the rest of the year, especially as pertains to Covid-19 recovery and re-openings? Martin: There is an air of optimism at last. In the UK it seems that we are coming out of the worst of Covid with more stability. The UK was quick and efficient to roll out vaccination programs and on the back of that, the UK government was quick to reduce restrictions on people, which means things are about six months ahead of other European countries. Germany and the Nordics are also in a strong recovery position today. IT organizations have spent a lot on security and edge computing so that their workforces could work from home. Those projects are now at fruition and organizations are looking at the way forward. _______________________ Have there been impacts of Brexit on the tech sector? Martin: We are yet to see how Brexit will impact the tech sector. The greatest impact is still Covid. One consequence is for US-based companies looking to set up shop in the UK, there’s been some thinking twice about that. Places like the Netherlands and Belgium have been able to take advantage of that opportunity. There don’t seem to be any IT staff shortages related to Brexit, although in the service industry where we have relied on migrant workers, along with farming, transportation and tourism, there’s definitely been an impact. _______________________ Let’s talk about IT trends and outlook in Europe? Martin: Security will keep hogging the headlines, particularly the topic of ransomware prevention and recovery from it. Cloud optimization is another big area, as is sustainability. Green practices are a much larger focus in Europe than in the US; this is definitely on the radar of customers, most of whom want to reduce their data center footprint. Another key trend is the drive for consumption-based pricing or pay-as-you-go. Buying just what you need is a compelling proposition and it will be interesting to see how partners in particular will adapt to this. Finally, the UK government is quite focused on the digital inclusion agenda--getting everyone connected. _______________________ We talk about data management overtaking storage management in North America. Is this concept also resonating in Europe? Martin: Without doubt all EMEA organizations recognize the value of their data and the need to consolidate it with third-party data to drive efficiencies and market differentiation. We are at the tip of the iceberg when it comes to data analysis, AI and machine learning. This is a different discussion to storage availability, application performance, data replication and disaster recovery; these concepts are a given now and it’s often difficult for storage infrastructure partners to differentiate. I do absolutely see data management and extracting the value of data overtaking storage management. _______________________ How about the trendy topic of data lakes and data analytics for unstructured data? Martin: Absolutely it's relevant and this is linked to the previous question. We haven’t scratched the surface of unlocking the potential of Komprise Deep Analytics with Actions and the Global File Index, which is like a metadata lake that brings structure to your unstructured data and taps into the value of cold file data. That’s because unstructured data has simply been too difficult and big to handle. In my view, unstructured data value and analytics will ultimately be the driver of adopting data management solutions, over analyzing file data to assess its age and usage. It’s exciting to consider how organizations will be able to find value in this data and be able to solve complex queries leveraging it. _______________________ ### Tech Data distribuirá las soluciones Komprise Intelligent Data Management Las soluciones de Komprise permiten a los clientes empresariales analizar, mover, gestionar y utilizar datos en cualquier lugar. "Sus soluciones ayudarán a nuestros partners a crear infraestructuras sólidas para gestionar y aprovechar el verdadero potencial de los datos de sus clientes", ha señalado Craig Smith, de Tech Data. ### Komprise: Comprehensive Data Management Komprise software does two massive things. It consolidates disparate file storage platforms in an organization, and it provides a quality set of metrics about that data. Imagine having files spread out across NetApp filers, Windows SMB shares, and Dell Isilon/PowerScale storage. Not too far-fetched for most enterprises. Providing unified access to and metrics from all of those storage systems into a single pane of glass is a neat trick. ### Under the Hood: 4 Pillars of the Komprise Architecture Revving up the Komprise Architecture with an Under the Hood Look at the Four Pillars As the Co-Founders, we spent a great deal of effort investigating the problems that storage IT faces in the presence of data growth. In formulating the Komprise Intelligent Data Management solution, we determined the system should serve as a data substrate that sits behind the storage and stitches the hot and cold data storage together. With this concept we could then provide migration of data and transparent access to cold data, without being in the hot data or metadata paths or fronting expensive storage. In this post, I will introduce the architectural pillars that drove how we built Komprise and show how those pillars helped satisfy the product requirements that we developed. Distributed Scale-Out Architecture The first architectural pillar is a distributed scale-out architecture, allowing Komprise to scale with data and storage. This pillar led to the grid of virtual appliances that run the data substrate and manage the storage. The grid of virtual appliances acts in a plug-and-play scaling model, so proofs of concept can become production and small deployments can become large deployments, all by simply adding more virtual machines. From terabytes to petabytes and even exabytes, from dozens of shares to thousands of shares, we have customers who have scaled their installations without re-architected deployments, specialized hardware, or centralized bottlenecks. Storage Agnostic & Non-Intrusive The second architectural pillar is to be storage agnostic and non-intrusive. Being storage agnostic means Komprise can work with a heterogeneous collection of storage systems, exactly the sort of storage layout that most needs the management and stitching of disparate storage together. By being non-intrusive, Komprise can cooperate with a variety of systems and layouts, of both data and storage, without imposing restrictions on deployment or storage architecture. Being storage agnostic, Komprise manages NFS, SMB, and object storage. It interacts through standard protocols to give the file-oriented view IT desires, while not requiring agents specific to each platform. Storage agnostic also means that Komprise does not compete with storage: Komprise partners with storage. Being nonintrusive means fitting into and managing data migration within any storage environment without impacting the architecture or performance. This leads to an all-software solution where customers can run proofs of concept on production storage and data without deployment changes around their critical systems. To see how you can simply connect Komprise to NetApp, Isilon, Windows File Servers and other NAS environments, watch the 5-Minute Demo. Redundant, Transparent Hierarchical Filesystem Another critical pillar underlying Komprise architecture is a redundant, transparent hierarchical filesystem. Without this pillar Komprise could not provide transparent access to data moved to secondary storage. In effect, Komprise paints the NFS or SMB from the primary storage over whatever secondary storage Komprise has moved or copied the data to, whether it is object store, cloud, or another file server. The movement is transparent, meaning that the original namespace continues to provide access to the data that is now sitting on secondary storage. Komprise is also a redundant filesystem in that you can access data from the secondary storage via Komprise through NFS or SMB protocols. To learn more, read our post Under the Hood: The Komprise Filesystem Distributed Search & Analytics The final architectural pillar is distributed search and analytics. In providing analysis at a shared level, Komprise sees and analyzes every file. Retaining the file analytics and allowing search and filtering, storage IT gains deeper insights and greater control over their data. Deep Analytics, currently in beta, distributes a file-analytics database within the same grid of virtual appliances, thus retaining the easy plug-and-play scaling that is found in the rest of the Komprise Intelligent Data Management solution. To learn more about the Komprise architecture, join us for a live webinar: Under the Hood: Four Pillars of the Komprise Architecture — August 2nd at 10am PT. Stay tuned for our next blog, where we will review the implications of these architectural decisions on actual customer deployments. ### Government IT Providers Grow Adoption of SaaS with Komprise Governement agencies are under pressure to do more, with less. With the growing use of technology, more data is being generated than ever before. Government Agencies are Under Pressure to do More, with Less With the growing use of technology, such as bodycams, more data is generated than ever before. Storage costs are climbing and backup of the growing data footprint is too slow and cumbersome. Government agencies are using Komprise to cut costs and shrink backups, by transparently identifying and archiving cold data to the cloud. In cases where IT acts as a central service provider, IT is able to cater to each department’s unique data management needs—using Komprise, to grow adoption of Storage-as-a-Service (SaaS). "Managing data growth within lean budgets is a key priority. By identifying cold data and managing it differently, we are able to do more with less." -Wendy Casesar, Director of Information Technology, Harris County Case Study Read about how a Government Service Provider and a Government Agency use Komprise Intelligent Data Management to transparently archive data and cut storage costs. Study 1: Government Agency Transparently Archives Cold Data to the Cloud & Shrinks Backup Costs Read Now Study 2: Government Service Provider Grows Adoption of Storage-as-a-Service Read Now ## Storage Cost Optimization and Data Tiering > Komprise analyzes and transparently tiers cold file and object data across storage vendors and clouds, typically reducing storage and backup spend by 70% without user disruption. ### Get Enterprise Data to the Cloud Without Losing Your Mind It was just a few years ago, post-Covid, that cloud repatriations were the talk of the town. But the cloud is hot again and we can thank widespread cost savings mandates and AI acceleration for that. Recent research from Flexera found that only 21% of cloud workloads were repatriated in the last year and organizations will spend 28% more on the cloud over the next year. Gartner backed up cloud popularity with its prediction that public cloud services will reach $723.4 billion this year, up from $595.7 billion in 2024. Yet according to Flexera, the reputable chronicler of cloud trends for more than a decade, organizations are exceeding their cloud budgets by 17%. Overspending isn’t surprising, given the ongoing complexity in executing large-scale cloud data migrations. Komprise Field CTO Benjamin Henry wrote about this topic recently in CloudTweaks. How important are cloud data migrations for enterprises today? Benjamin Henry: These days, with so much data being generated in enterprise by countless apps and systems, IT leaders need to adopt new storage infrastructure technologies frequently for cost, security, governance, performance and AI needs. Hybrid cloud is a common enterprise architecture to meet these diverse needs and retain flexibility, but that is also driving up complexity. Cloud migrations are at the center of many IT strategies today, but they can also result in performance bottlenecks, data loss and delays, compliance concerns and poor ROI. What makes migrating unstructured data such a pain in the neck? BH: Unlike structured data that fits neatly into databases, unstructured data lacks a consistent schema is spread across many locations and different environments and is used by various departments for diverse purposes. This makes large-scale migrations more than just a “lift and shift” operation. • Massive file counts and large volumes of small files can overwhelm traditional scanning and indexing processes, causing unexpected delays. • Network interruptions, file locks, or system errors can derail transfers and result in data loss. • Legacy tools or free utilities often fail to scale beyond a few hundred terabytes. • Limited insight into data usage means that IT may misplace cold data in expensive cloud storage or, conversely, store frequently accessed data in slower tiers. What’s the first step IT leaders should take? BH: In large, distributed organizations, data is often spread across silos, legacy storage, cloud storage, and even servers under desks or in closets, which makes full visibility elusive. Run a full discovery to understand what data you have, where it lives, who owns it, how often it's accessed, and what types of files you’re dealing with. That helps determine what needs to move, what should be archived, and what can be deleted. It’s also the foundation for setting priorities and building a phased migration plan. What are some risks that companies overlook? BH: The loss or corruption of metadata, file permissions, timestamps and access control lists is common. Those are often stripped or mishandled in basic tools. Another is underestimating the load on bandwidth, conflicts with network and security configurations and the time required to move billions of small files. Even something as simple as cut-over timing can cause issues. If users are still modifying files during the switch, you’ll have versioning conflicts or data loss. What features should your migration software include? BH: You want more than a copy and move tool. You need the ability to scan and index all unstructured data and then categorize it based on access frequency (hot, warm, or cold), file types, data growth patterns, departmental ownership, and sensitivity (e.g., PII or regulated content). Look for: • The ability to detect and confine sensitive data prior to a migration. • File-level tiering to right-size your migration and save on storage costs. • Full preservation of permissions and metadata. • The ability for users and applications to access data during a migration. • Included tools to proactively identify potential bottlenecks and other issues that derail migrations. A successful cloud migration avoids costly delays and data loss, while ensuring that your data is placed in the right storage tier the first time around. Are there tools built specifically for this kind of migration? BH: Komprise Elastic Data Migration is an enterprise-grade migration solution that includes deep analytics so you can plan migrations intelligently. The solution runs highly parallel transfers and uses dedicated WAN channels to avoid issues and delays with transferring large volumes of small files. It automatically preserves permissions and metadata tags, maps shares and retains data integrity. That kind of specialized tooling reduces manual work and is up to 25 times faster than legacy tools like Robocopy or Rsync. Read the latest update on Elastic Data Migration. What other best practices can you share to maximize the ROI of cloud data migrations? BH: Data migration isn’t a one-time event. You want tools that will deliver ongoing data lifecycle management, tiering aging data from high-cost hot storage to cooler, more affordable tiers as it becomes less relevant. By continually optimizing data based on its current use you have more predictable ROI and you can better leverage AI tools in the cloud with cloud-native data sets. You can set it and forget it with policy-based automated tiering. ### 5 Ways to Boost Unstructured Data Value: The Komprise Data Experience CIOs and IT leaders are grappling with petabytes of data scattered across hybrid cloud environments. They’re using several disparate technologies to move, store and backup this unstructured data. Yet, something’s missing. Enterprise IT teams often lack the control, insights and flexibility to make unstructured data a strategic asset – and not a liability. The IT landscape is shifting with the onset of AI. It is no longer adequate to store your file data on a resilient NAS in a “set and forget” fashion. Data must be optimized across its lifecycle for cost, classified for contextual search, moved quickly and without risk and disruption when required and governed appropriately for AI data workflows.     Consider the following when making plans for your data environment in the coming year: Are your storage and backup costs rising every year, with no end in sight?  Read more. Are you concerned about efficiently migrating data to new storage without incurring delays, extra costs and risk? Have you been burned in the past? Read more. Do you have adequate ransomware protection for all your data – even older file data which is rarely accessed? Do you fully understand your risks? Read more. Is your data classified and ready for AI, with data governance capabilities? Read more. Why your data deserves a better experience Legacy storage-based approaches for data management are siloed and reactive. They offer little visibility into your data and often involve costly rehydration processes, slow and troublesome migrations, and rigid vendor lock-in. Even worse, storage vendor data management tools fail to support the growing need for secure, governed AI data pipelines. The Komprise Data Experience (KDX) changes that. It provides an analytics-first, economic and non-intrusive solution for managing unstructured data across all your storage, backup, and AI environments, without lock-in. Here are 5 benefits of KDX, detailed in this paper. 1. Stop making storage decisions in the dark. Start with global visibility across silos. If you cannot see and understand your data across storage – whether that is on premises, at the edge or in the cloud – then you cannot achieve a holistic view to understand where data should ideally live. Komprise solves this with a Global Metadatabase (KMDB)—a high-performance, global file index that analyzes every file and object across your data estate. You can search, tag, and classify data in real time, and set automated policies to tier, move, or confine it—whether it’s on-prem or in the cloud. You can grant departmental users access to search and tagging features, for an even better strategy for data classification and lifecycle management. The system’s scale-out architecture ensures high performance, even at the petabyte level, with distributed workers that handle failures and network interruptions. 2. Stop overspending on data migrations. Start 25X faster migrations with lower costs. Unstructured data migrations are notoriously expensive and time-consuming with large data sets of diverse file types and sizes. Komprise delivers a rapid, scalable experience with Elastic Data Migration, offering 25x faster WAN performance and advanced planning tools like the Assessment of Customer Environment (ACE) to proactively identify and resolve migration bottlenecks. Our customers are using Komprise to right-place data through cold data tiering. This saves money and frees up capacity on your high-performance tier by ensuring that you only migrate active data to new storage. Tiering cold data (most of the data in enterprises today) to immutable object storage in the cloud, for instance, often mitigates the need to create and store copies for backup and saves exponentially on per-terabyte data storage costs. Watch the Komprise Migration best practice series. https://www.youtube.com/watch?v=bpNQGk4g17c&list=PLVrgEPwYrDWa98KeUeI82KMtC5skdYKm7&index=14   3. Stop paying the rehydration penalty. Start tiering data with no switching costs. If you’ve ever used a storage vendor’s tiering solution, you may have faced the dreaded rehydration penalty. When it’s time to switch platforms, you’re forced to pull all tiered data back onto the source, migrate that data to the new storage unit and then tier it again. If you don’t have enough capacity at the source, you’ll need to buy more before moving off the platform. That doesn’t make much sense. Komprise Transparent Move Technology™, was created to eliminate this problem. With Komprise, you can migrate the data directly to the new storage without rehydration because Komprise tiers the entire file without transforming it into proprietary blocks or stripping out its metadata into a proprietary layer.  You can switch vendors freely and maintain user and application access without any changes. On average, customers save 70% on storage and backup costs with Komprise transparent tiering. Read the paper. 4. Stop exposing file data to ransomware. Start shrinking the ransomware attack surface. Inactive unstructured data can be your weakest link when it comes to a potential ransomware attack because of its sheer volume, the number of users who have access to it and the long latency before a breach is detected. Storage-tiering solutions do not shield this cold data from ransomware attacks as the tiered files are still managed by the storage file system. Komprise addresses file data risk by moving cold data to immutable cloud storage, which prevents ransomware actors from accessing or modifying your data. You can set versioning on the immutable object storage to provide a pre-attack copy for restores.  With this defense posture, organizations can afford to protect all their unstructured data from bad actors-- not just the most critical data. Watch the video with Komprise CEO Kumar Goswami. Read the solution brief. https://www.youtube.com/watch?v=8pOkSL9b-mQ&t=8s 5. Stop sharing sensitive data with AI. Start with built-in AI data governance. As AI adoption accelerates, risks increase from inadvertent exposure of sensitive internal data like PII and IP to AI models and chatbots. Komprise gives control back to IT with Smart Data Workflows, which allow you to build automated data pipelines that search, tag, and move sensitive data to locations where it cannot be ingested by AI, while feeding the right data to AI pipelines. Watch the short overview below on Smart Data Workflows.  And watch the 5-minute demo on YouTube. https://www.youtube.com/watch?v=L_YnKYhlit0&list=PLVrgEPwYrDWZCOAaYK85V6wL7g4q8JNH_&index=8   The Komprise Data Experience empowers IT teams to shift from reactive storage management to proactive unstructured data management through: Global visibility across all storage and cloud silos; Smarter, faster data migrations; Flexible data tiering without vendor lock-in; Stronger data protection against ransomware; AI-ready workflows with built-in data governance. Got data? Get KDX. Read the white paper. Schedule a demo with our team today to learn more. ### 5 Ways to Make Safe, Ethical AI Decisions with Unstructured Data This article originally appeared in BusinessCloud. The rise of artificial intelligence and machine learning is reshaping how business executives approach decision-making. While data-driven leadership is a well-established concept, the integration of AI into this process introduces new complexities that require careful consideration. The challenge is no longer just interpreting structured data, but effectively managing and leveraging unstructured data, which includes everything from documents and images to sensor data and social media posts. With unstructured data now comprising at least 80% of all data in the world, it has become the primary fuel for AI. As AI tools become more accessible, they promise to revolutionize decision-making by automating data analysis and providing deeper insights on a much larger swathe of data. However, without careful planning and governance, AI can also introduce significant risks – such as false outputs or biased decisions – that could have serious consequences for businesses. Leaders must act swiftly and thoughtfully to ensure the ethical and effective use of AI in their organizations. To successfully integrate AI into decision-making processes while mitigating risk, IT executives need to take strategic steps to prepare the data. Check the following tips! 1. Understand and organize your data Before leveraging AI for decision-making, you need a clear understanding of the data available across your organization. This includes identifying the types of unstructured data – such as text files, images, videos, and more – and ensuring they are easily accessible and well-organized across a hybrid cloud environment. Data cleanup is the first priority: remove irrelevant or redundant information to reduce storage costs and security risks, particularly exposure to ransomware. Implementing a data indexing system will make it easier to search and apply AI effectively. 2. Classify and tag data for better access Once your data is cleaned up, it’s time to categorize it. By classifying unstructured data into meaningful categories and enriching it with metadata (tags and descriptions), data scientists and business analysts can quickly find the information they need for their AI projects. This step ensures that data sets are readily available for AI tools, and a global file index is key to avoid the inefficiencies of re-running AI processes unnecessarily. 3. Adopt AI data governance tactics Data security and governance remain top concerns when using AI for decision-making. Some data, particularly sensitive information like customer or financial records, must be kept from AI models unless it is anonymized. Other data, like IP and R&D data, also should be safeguarded from GenAI or other public AI tools to prevent exposing trade secrets outside of the organization. Given the massive scale of data organizations now manage, automated tools for managing and segmenting data are essential. These tools help ensure AI systems are working with the right data and that all outputs are traceable, accurate, and compliant with regulatory standards. 4. Prevent AI overload and bias With the influx of AI-powered tools, business leaders can easily be overwhelmed by data. Additionally, if the wrong data is fed into AI systems, there is a risk of perpetuating bias, which can undermine decision-making. To address this, business and IT leaders must agree on clear organizational goals for AI usage, prioritize high-value use cases, and select AI tools that align with these objectives. Training for executives on how to use AI tools safely – including evaluating the accuracy, bias, and completeness of outputs – is crucial to prevent errors and ensure the AI is being applied effectively. 5. Implement oversight and validate AI outputs AI’s ability to produce false or harmful results – whether errors or biased conclusions – requires human oversight. No matter how advanced the technology, there will always be a need for validation of AI outputs. Leaders should establish clear AI data governance frameworks that include regular review of AI-generated results by qualified personnel. Without proper oversight, businesses risk reputational damage or even legal liabilities. The goal is to ensure AI enhances decision-making without sacrificing accountability or transparency. AI’s integration into decision-making processes is still in its early stages, but its growth is happening rapidly. Business and IT leaders must act quickly to develop the necessary tools, processes, and governance frameworks to mitigate risks and unlock AI’s full potential. Without these safeguards, AI may fail to deliver on its promises, potentially leading to significant consequences for organizations that fail to act responsibly. Learn about Komprise for Sensitive Data Management and Komprise for AI Data Workflows. ### Komprise Elastic Data Migration Updates Enhance Automation & Reporting Data migrations can be one of the most complex and costly IT challenges enterprises face today. And when it comes to unstructured data, moving billions of files isn't just a technical hurdle – it requires careful planning, the right mapping of permissions, strong governance and automation. Too often, migrations exceed budgets and timelines due to manual processes and inevitable errors. At Komprise, we’ve always focused on simplifying unstructured data management, with an analytics-driven approach that is more cost-effective and transparent. Komprise Elastic Data Migration Spring 2025 delivers powerful new capabilities to further streamline and automate complex file and object data migrations. Smarter, Faster, and More Transparent Data Migrations Our latest enhancements tackle some of the most time-consuming aspects of unstructured data migrations head-on, reducing manual effort while improving compliance and governance. Automatic Creation and Mapping of Destination Shares One of the biggest challenges in data migrations is setting up the destination storage to mirror the source hierarchy. Traditionally, IT teams must manually create and map thousands of shares, often leading to delays and inconsistencies - especially in organizations where centralized storage teams support multiple departments with their own unique structures. Komprise now automatically creates and maps destination shares, preserving source hierarchies for full transparency and compliance. This automation eliminates tedious setup work while reducing the risk of misconfigurations. With this feature, IT can create multiple migrations simultaneously and customize them to run at a specific time. Watch demo on our YouTube channel. _______________________ Enhanced Chain of Custody Reporting Regulated industries and enterprises with stringent compliance requirements need complete visibility into their data movements. Komprise now automatically creates chain-of-custody reports which consolidate and track every file migration, computing checksums at both the source and destination and logging timestamps for full transparency. With these detailed reports, IT teams can easily provide evidence of successful migrations to auditors, compliance officers, and business stakeholders. Chain of custody reports can be generated and stored in a central location, such as a NAS file server or S3 buckets. Watch the demo on our YouTube channel. _______________________ Automated User and Permission Mapping IT teams often spend countless hours manually updating file ownership and group permissions when moving data. Our new automated security identifier (SID) mapping feature lets customers define policies that automatically translate and apply the correct permissions in the target storage. IT can also use this feature to define mappings for handling orphaned files during a migration. This not only saves time but also minimizes manual errors that could lead to compliance issues. Watch the demo on our YouTube channel. Transparent Share Mapping for Tiering Komprise Intelligent Data Management platform customers typically implement what we call a Smart Data Migration, whereby cold data is identified for tiering to lower-cost storage before a migration, dramatically reducing cost by ensuring the right data is at the right place at the right time. Komprise now supports transparent one-to-one mapping from file shares to object storage buckets, which supports data governance and compliance. This feature is also valuable for AI data workflows, as it ensures data lineage visibility. Expanding our Data Migration Partnerships Komprise Elastic Data Migration is the preferred solution for moving large-scale unstructured data workloads across hybrid and multi-cloud environments. From storage vendors to cloud hyperscalers to value added reseller (VAR) partners, our growing list of partnerships validates our leadership in enterprise data mobility. “With IT budgets tightening and enterprises facing increased costs due to supply chain and tariff pressures, IT leaders need ways to save money and optimize operations. Data migrations are often an expensive and high-risk undertaking, but Komprise is helping IT leaders take control with automation, insights, and governance-driven reporting.”--Komprise President and COO Krishna Subramanian Check out the following resources to learn more about Komprise Elastic Data Migration: • Guide to Unstructured Data Migration • Unstructured Data Migration Video Series Want to get started with Komprise Elastic Data Migration and Intelligent Data Management?  Schedule a demo today. ### Komprise Automates Complex Unstructured Data Migrations Komprise Elastic Data Migration enhances automation and reporting to lower costs and align IT and the business on unstructured data management goals. Campbell, CA–March 25, 2025 — Komprise, the leader in analytics-driven unstructured data management, expands its industry-leading Komprise Elastic Data Migration to automate the end-to-end process of enterprise migrations, bringing increased efficiency and ROI for IT managers overseeing complex, large-scale data migrations. Data migrations are problematic in enterprises, with many exceeding budgets and timelines--not only because migrating billions of files is complex, but also because the process is manual and error prone. Komprise Elastic Data Migration, which is already 25x faster than alternatives, is expanding its powerful analytics, fast data migration and flexible reporting capabilities to further simplify the process of complex migrations with high resiliency. Highlights of the Komprise Spring 2025 release: Automatic creation and mapping of destination shares: A core task in setting up migrations is configuring the destination share hierarchy and mapping data to it. This can be time-consuming when done manually for thousands of shares. Also, with many organizations using centralized storage teams to support multiple departments each with their own share hierarchies, data governance is complicated. Komprise eliminates these headaches with automatic creation of destination shares which directly map source hierarchies to the destination to increase transparency and minimize compliance risks. Automated user/permission mapping: The new security identifier (SID) Mapping feature automates the process of changing owner and group permissions when migrating files based on customer-defined mapping policies. Beyond saving time, SID mapping reduces the chance of manual errors to support data governance. Enhanced chain of custody reporting: Komprise now automatically generates consolidated chain-of-custody reports for the entire migration. Chain of custody is often required in regulated industries to serve as evidence of successful transfers. These reports list each file along with its checksums computed on both source and destination and the timestamps of each checksum. With this level of transparency in a consolidated report, IT can now easily share it with relevant stakeholders, including compliance teams and departmental leaders. Transparent Share Mapping for Tiering: Data migrations provide an opportunity to right-place data as you migrate. The Komprise Smart Data Migration approach gives customers insight into the cold data they can tier to secondary storage. This reduces the amount of data that IT must migrate to new storage. Komprise Intelligent Data Management also allows transparent 1-1 mapping from file shares to object buckets for data tiering. Data owners can see the original file data hierarchy represented in a similar structure in the destination object buckets. This is particularly useful when the data is used for AI data workflows as it provides transparent visibility into data lineage for AI data governance. Established Unstructured Data Migration Partnerships Prominent data storage technology and channel partners have adopted Komprise Elastic Data Migration as their preferred choice for moving petabyte-scale workloads, especially across hybrid IT environments. Azure: Customers have migrated petabytes to Azure through the Azure Migrate Program, which funds the use of Komprise for cloud data migrations, as highlighted in Microsoft VP of Azure Storage, Aung Oo’s session at Microsoft Ignite. AWS: Komprise, an AWS Advanced Tier Partner, is now prescribed by AWS for unstructured data migrations. “As IT organizations face price increases for new technology amid tariff-induced supply chain pressures, cost savings and cost avoidance are top enterprise strategies,” said Krishna Subramanian, COO and cofounder of Komprise. “IT leaders are looking for efficiencies across the board and data migrations can incur unnecessary extra costs and risks. Komprise is continually adapting to these new realities with features that help stakeholders align on which unstructured data to move, automation to set up migrations and detailed reporting for ongoing data governance.” Komprise Elastic Data Migration is available as a standalone software solution and is included in the Komprise Intelligent Data Management platform. Watch a demo of what's new. Unstructured data migration best practices Unstructured data migration guide Read the blog. About Komprise Komprise powers the connection between unstructured data management and AI. Komprise Intelligent Data Management delivers a single platform to easily analyze, migrate, transparently tier and manage the lifecycle of petabytes of file and object data across hybrid environments. With Komprise, enterprise IT gains full visibility across silos to optimize storage, backup, ransomware and cloud costs. Komprise Smart Data Workflows and the Komprise Global File Index unlock unstructured data insights and access for AI. www.komprise.com ### Komprise Earns Spot on CRN’s Cloud 100 List for 2025 Campbell, CA--January 21, 2025 — Komprise, the leader in analytics-driven unstructured data management and mobility, today announced that it has been recognized on the 2025 Cloud 100 list by CRN®, a brand of The Channel Company, for the fourth consecutive year. This prestigious CRN list spotlights 100 leading channel-focused cloud companies across five key categories: cloud infrastructure, management, security, software, and storage. CRN Cloud 100 companies demonstrate dedication to supporting channel partners and advancing innovation in cloud-based products and services. The list is the trusted resource for solution providers exploring cloud technology vendors that are well positioned to help them build cloud portfolios that drive their success. Komprise, founded in 2014, delivers a SaaS solution for enterprise IT organizations managing petabyte-scale unstructured data assets across hybrid cloud infrastructure. The company achieved numerous industry and business awards in 2024, including inclusion on the Inc. 5000 list of fastest-growing private companies. Additionally, Komprise advanced its capabilities for data workflow automation and AI data pipelines with the release of Smart Data Workflow Manager, an intuitive UI that allows enterprise data storage, departmental IT and research teams to set up AI data workflows including curating the right data set, configuring and tuning the AI service, defining the tags, scheduling and monitoring workflows in progress. Komprise has strong partnerships with storage providers such as Pure Storage, NetApp and VAST Data; cloud leaders including AWS, IBM, Wasabi and Microsoft Azure; and global channel companies such as Ahead, Technologent and WWT. The company’s customers span multiple sectors across the Fortune 1000, including healthcare, life sciences, financial services, media & entertainment, oil and gas, transportation, legal services along with higher education and public sector. "We are honored to be named again to CRN’s Cloud 100 list for the fourth year in a row,” said Kumar K. Goswami, CEO and co-founder of Komprise. “Managing unstructured data at scale is a huge challenge for enterprises, and we’re committed to providing solutions that make it easier to move, manage, and get value from their data. Whether it’s simplifying data migration, maximizing savings from transparent tiering, detecting and managing sensitive data, or powering AI workflows, we’re here to help businesses take control of their data and turn it into a strategic advantage." CRN’s Cloud 100 list will be featured in the February 2025 issue of CRN magazine and online at www.crn.com/cloud100 beginning January 21. About Komprise Komprise powers the connection between unstructured data management and AI. Komprise Intelligent Data Management delivers a single platform to easily analyze, migrate, transparently tier and manage the lifecycle of petabytes of file and object data across hybrid environments. With Komprise, enterprise IT gains full visibility across silos to optimize storage, backup, ransomware and cloud costs. Komprise Smart Data Workflows and the Komprise Global File Index unlock unstructured data insights and access for AI. www.komprise.com. ### 5 Considerations When Choosing a Storage Tiering Solution Data tiering is a well-known concept in storage circles, and was traditionally done to move data between the tiers of a single vendor's storage platform. This was all well and good when data volumes were manageable and when IT departments had a single vendor. But times have changed and today most IT teams have at least two storage vendors (such as, AWS or Azure) and too much data to keep on the expensive NAS. Data tiering needs have changed--and so have the strategies. Adopting a flexible data tiering solution that delivers the maximum ROI is paramount now--not only because storage budgets are stretched too thin but because intelligent data tiering supports feeding AI data pipelines and it also can improve your ransomware defense. Check out this case study with Katten Law to learn how they achieved multiple benefits with Komprise data tiering. Our latest white paper focuses on the benefits of hybrid tiering, an independent, storage-agnostic approach to tiering unstructured data across your entire hybrid storage infrastructure, including on-premises and cloud, file and object storage. While 90% of data created today is unstructured, according to IBM, the majority of this data becomes cold within months of creation, as users and applications tend to actively use unstructured data early in its life. This cold yet valuable unstructured data continues to occupy expensive storage, although it no longer has the same performance requirements. More importantly, it consumes expensive backup, disaster recovery and ransomware defense resources. The Data Tiering Mandate As a result, there is now a mandate to not only establish the right data tiering strategy to save and avoid unnecessary storage and backup costs, there is growing demand to gain value from this data for strategic AI and analytics initiatives. Most storage vendors have built in data tiering, or so-called "pools" based techniques, to move data within their operating systems. Storage-based data tiering and pools-based techniques to move data within a single architecture or operating system can deliver cost savings for system data such as snapshots. However, storage tiering introduces many limitations when it comes to flexible data policies, cost savings, ransomware defense, broader data access and unstructured data management needs. This paper reviews five considerations when evaluating data tiering options and presents a cost model that outlines the benefits of hybrid tiering as both a complement and an alternative to built-in tiering capabilities from data storage vendors. Reduce your ransomware attack surface. Avoid rehydration when refreshing your storage. Provide flexible data management polices while maintaining transparent access. Ensure innovation and choice with cloud native data access. Ensure your unstructured data is ready for AI. Additionally, with the Komprise hybrid tiering approach, you can change your underlying storage anytime and nothing changes. You are future proofed and not tied into any specific vendor’s storage with this standards-based approach. Download the White Paper Now For additional information on Komprise Transparent Move Technology (TMT), file-tiering benefits as well as best practices, be sure to check out the Guide to Unstructured Data Tiering. ### Top 10 Komprise Blogs of 2024 The top-read Komprise blogs in 2024 run the gamut from storage refresh, unstructured data workflows, unstructured data management, data classification, data migration, data tiering, industry use cases and cost saving measures. Be sure to subscribe to our blog to stay up to date on the latest trends in unstructured data management, AI data governance, AI data workflows, sensitive data management, storage cost savings. data tiering, data migration and unstructured data lifecycle management. 1. Best Practices for Data Management during M&A It would be nice if data management following a merger, acquisition or divestiture deal were as simple as moving a bunch of files from one storage location to another. But it’s not. To minimize risk and maximize efficiency, you need a data transfer plan tailored to the requirements of the business entities that emerge from the deal. Read Now   2. Five Mistakes to Avoid When Refreshing Data Storage Forrester found that 83 percent of decision-makers are hampered in their ability to leverage data effectively due to challenges like outdated infrastructure, teams overwhelmed and drowning in data, and lack of effective data management across on-premises and cloud storage silos. Avoid lock-in and restrictive solutions with these tips. Read Now     3. Why Unstructured Data Management Matters: An Industry View This industry series looks at the petabyte-scale challenges in different sectors, including healthcare, life sciences, media, government, oil and gas, transportation, financial services and legal. Read Now         4. Create and Manage Unstructured Data Workflows with Komprise According to a 2024 study by IBM, nearly half (45%) of companies report that advances in AI tools that make them more accessible are driving AI adoption. The research also found that only 34% are currently training or reskilling employees to work together with new automation and AI tools. There is a clear gap in skills needed to drive results from AI tools, which leads to our release of Komprise Smart Data Workflow Manager. Read Now     5. Metadata and Its Role in Unstructured Data Management Metadata, or data that describes data, delivers many benefits for storage and IT managers. Yet metadata is complex, vast, and distributed across hybrid cloud infrastructure. Understanding and strategically managing metadata as part of your overall data storage strategy has become central to optimizing unstructured data management and data governance practices across the organization. Read Now     6. Why File Storage Is Expensive and What to Do About It Most (94%) of IT leaders report their cloud storage costs are rising and 54% confirm their storage spend is growing faster than overall cloud costs. (Source: Virtana State of Hybrid Cloud Storage 2022) Nearly 30% of cloud spend is wasted and this year, cost savings overtook security as the top enterprise cloud challenge. (Source: Flexera, 2023 State of the Cloud). Read more on the reasons behind high file storage costs and how you can use data management technologies to maximize savings in a hybrid cloud environment. Read Now     7. Cracking the Code for Unstructured Data Classification System-generated metadata includes information about when the data was created, who created it, its type, its size, when it was last accessed and when it was last modified. This helps IT managers classify data by the department it belongs to and identify rarely accessed data as ready for archiving and tiering to lower-cost storage destinations. IT professionals can also search based on data types, such as video or medical imaging files, which may be consuming too much storage (and budget) and require action such as migration. For additional unstructured data classification, it’s important to enrich metadata using tools that can crack open file contents to search for keywords or data types. This is especially useful to find the right data sets for AI. Read Now     8. Future Proof Your Unstructured Data Data is taking up more budget, as it should, because organizations in all sectors are becoming data-driven operations. Yet you don’t want to waste money if you can avoid it. No matter what you’re doing with your data, every company should try and avoid vendor lock-in. The options to manage data are continually changing. At some point you will have to bring all the data back and rehydrate it before moving to another solution and this can be very expensive. Komprise can help you break lock-in and move file data to the cloud in its native format. You can switch vendors including Komprise, as you see fit. Read Now     9. Migrate to NetApp 27X Faster With Komprise Komprise allows you to analyze your multi-vendor NAS footprint across any NFS and SMB storage – across your data centers and clouds. You can analyze a mixed environment that has some NetApp, EMC Isilon, Windows File Servers, and any other NFS or SMB storage. Understand your data and its usage, and plan exactly what data you want to migrate to the cloud – both to NetApp CVO on AWS, Azure, and Google or ANF. Once you know what you want to move, you can choose to either migrate the data or replicate the data via Komprise into CVO or ANF. Read Now     10. Optimizing Pure Storage FlashBlade Komprise Intelligent Tiering for Pure Storage customers delivers several benefits that help them get more from their Pure investments. Those benefits include: different tiering policies for different types of data; eliminates rehydration; access data in native format; access data from any tier without going to the source. Read Now ### Komprise Expands Data Mobility Offerings for NetApp Customers Many of NetApp’s largest and most successful customers run Komprise Intelligent Data Management. In conjunction with NetApp INSIGHT 2024, where Komprise was a Gold Sponsor, we introduced new ways to optimize data migrations to NetApp storage technologies. Komprise supports all the latest NetApp storage releases including NetApp ONTAP, both on-premises and cloud, and NetApp StorageGRID. Komprise now has new capabilities for searching across shares and directories to help customers optimize their NetApp hybrid cloud storage environments. The Komprise Global File Index gives customers a single view of all NAS data to help identify which data sets to move to NetApp and instantly visualize savings and ROI. Komprise has moved hundreds of petabytes of file and object data to NetApp across diverse sectors including government, healthcare, life sciences, energy, transportation and financial services. Komprise Elastic Data Migration delivers the fastest migration technology for both SMB and NFS data, often 25X faster than common migration tools and with built-in reliability features such as checksums and automatic retries. Learn more about Hypertransfer. Learn more about NetApp Elastic Data Migration. Intuitive dashboards allow you to monitor and manage hundreds of simultaneous migrations and get status updates.   Komprise 2024 updates for NetApp customers Komprise customers and partners can register for the Komprise Technical Professional (KTP) training program. In the course we review core unstructured data management and mobility concepts, the Komprise Intelligent Data Management platform use cases and what's new, including our latest updates for NetApp including: Storage Insights: Customers can now use this unified console to view data and storage metrics at the directory and file level. This consolidated view of the data estate with instantaneous drill-down capabilities allows IT to pick and choose the right data for various use cases including AI, data migration to Flash and cost optimization. Watch a demo. https://www.youtube.com/watch?v=ysOLhyn1xMs Smart Data Workflow Manager: Komprise announced a point-and-click UI wizard to facilitate AI data workflows – from searching for the right data set, to configuring and tuning the AI service, to defining the tags and how frequently the workflow should run. This capability is valuable to help NetApp customers enrich and leverage their unstructured data for new uses in a faster, automated manner. Komprise Client Protocol (KCP) for SMB: Migrating SMB data is not only 25 times faster with Komprise, but also simpler, as the performance-optimized KCP for SMB runs entirely on the Komprise Observer and obviates the need for customers to deploy and maintain Windows Proxies. NetApp FPolicy update: Komprise supports all the latest versions of NetApp so customers can upgrade their NetApp environments knowing Komprise will automatically handle the latest features and updates, including for FPolicy. Updated NetApp REST API support: Customers can also upgrade their NetApp solutions knowing that Komprise will automatically use the appropriate API available for that version. Data Governance for AI with NetApp FlexPod: Customers can safely expose corporate data to Generative AI models running on the NetApp FlexPod for AI, using Komprise Deep Analytics to identify the appropriate datasets and Komprise Elastic Data Migration to copy the data. Komprise tracks the data being shared for corporate governance around AI. Miss NetApp Insight? Watch this Data on the Move Video.  Learn more about Komprise for NetApp. ### Komprise Named Finalist for AI Deployment in the 2024 A.I. Awards Komprise Smart Data Workflow Manager was recognized by The Cloud Awards’ new award for preparing data for AI and automating unstructured data workflows. Campbell, CA, September 18, 2024 – Komprise, the leader in analytics-driven unstructured data management and mobility, announces that the company has been named a Finalist for AI Deployment by The A.I. Awards. This new awards program launched earlier this year by established cloud computing awards body The Cloud Awards, recognizes excellence and innovation in the use or development of cloud artificial intelligence technologies and machine learning. The program features a wide range of categories and received entries from organizations of all sizes worldwide, including North America, across Europe, the Middle East, and APAC. “We’re excited to reveal the finalists of the inaugural A.I. Awards,” said James Williams, CEO of The Cloud Awards. “The program spotlights the incredible innovations taking place in the world of cloud AI all over the globe, and Komprise fully deserves its place amongst this year’s outstanding finalists.” Komprise received recognition for the Komprise Smart Data Workflow Manager technology, which helps IT and departments prepare data for AI, and is included within the Komprise Intelligent Data Management platform. Users can create automated workflows for all the steps required to find the right unstructured data across storage assets, tag and enrich the data and send it to external tools for analysis. A second use case of Smart Data Workflow Manager is to integrate with third-party AI tools to filter and enrich the metadata of unstructured data. This speeds up the process of curating the specific data set for AI projects and through automated tagging, the data is quickly discoverable for repeat processes. Komprise customer Duquesne University deployed Komprise Smart Data Workflow Manager with Amazon Rekognition to automate the process of discovering and tagging specific image files across its data estate, cutting the estimated time spent from 333 hours to less than two hours. Read the case study. “AI has tremendous potential across many industries but adding structure to unstructured data and efficiently moving precise data sets to AI tools is a manual, time-consuming effort,” says Kumar K. Goswami, Komprise CEO and Cofounder. “Komprise has delivered a cost-effective, systematic workflow to index data, run AI and ML and tag data to speed AI data workflows.” About Komprise Komprise is a provider of unstructured data management and mobility software that frees enterprises to easily analyze, mobilize, and monetize the right file and object data across clouds without shackling data to any vendor. With Komprise Intelligent Data Management, you can cut 70% of enterprise storage, backup and cloud costs while making data easily available to cloud-based data lakes, analytics and AI tools. www.komprise.com. Media Contact: Kevin Wolf, TGPR kevin@tgprllc.com ### Seth Georgion on Hybrid Cloud Data Management Seth Georgion is a Senior Sales Engineer at Komprise. His IT experience spans data storage, VARs and founder of an Artificial Intelligence as a Service (AIaaS) platform. Seth joined Komprise in May 2024. We caught up with him in between customer visits to learn more about his day-to-day at Komprise as well as the experiences that brought him here. Welcome to Komprise, Seth. Tell us more about your role. Thanks! I divide my time between three groups—internal, customers and partners. Educating and supporting partners is where my experience really matters, from my time working in the VAR world. I take the approach of solutions expertise not products, which helps in the field and with our customers. After the deal closes, I help with the transition to customer success and participate in all the follow-up meetings and handle the implementation. Why did you join Komprise? In my last job at EVOTEK, a Komprise partner, we were using Komprise at one of our customers. I wanted to switch from a VAR partner role supporting many products and focus on one platform to help solve a key problem regarding how enterprises manage a hybrid cloud environment. By hybrid, I mean managing two or more data storage platforms in two or more places. You need a storage-agnostic solution like Komprise Intelligent Data Management, which brings a unified view and analytics across the entire environment. This is powerful, because we don’t have to tell the story from a single vendor perspective. I was also impressed with Komprise because the software solution can go between protocols, from network storage to cloud storage, and it has a no lock-in approach with Transparent Move Technology. You’ve been a founder and a CEO of two different companies. How have those experiences shaped your career to date? My career has had some lucky breaks. I came from the customer side before I started my first company, Locust Storage, which was on the first wave of object storage. That experience showed me the power of how to change and disrupt markets. I went to EMC (now part of Dell) to work on new disruptive ideas in midrange storage. After that merger, I got to join up with three other EMC guys and we started an artificial intelligence company (Totem AI) for five years. That was wonderful. We built the first AI clouds for Medtronic and the U.S. Olympic Committee. I have a patent in artificial intelligence licensed to Medtronic which is in production with patient healthcare. Given your experience in both AI and storage, two areas that intersect with Komprise, how do you see the opportunities today for customers wanting to change the way that they manage data? I always focus first on the customer’s needs. I won’t shoehorn AI if it doesn’t belong. But when we find customers that want a way to start to engage, especially with generative AI, they need data pipelines to do it. It's one thing to come up with the idea that you have an algorithm you want to run. When you say I want to do this at scale across my enterprise, you need a pipeline to do it and that's part of the Smart Data Workflows pipeline. I suggest to customers that they build their own inferencing systems for AI. The goal is to make the AI model tailored for your business on your servers and in your cloud. A storage-agnostic approach to data management is again so important. Can you share any tales from the field? I was in a meeting with an executive at a large media company and was trying to talk to him about the potential of our technology. He was a tough one to convince. So I showed how once we move your data into a cloud service like AWS, you can just click on the file and it opens up directly. This was a moment of pure revelation. I was showing him this because we were talking about ransomware recovery. I said you can just click on the image and it opens right away. Every other vendor in this industry puts data in proprietary formats in the cloud. You can’t just click on it. And he was like, that's it. He wanted a quote. What is the most challenging—and conversely rewarding—part of being a sales engineer for a SaaS company? I have not yet walked into a first customer meeting where they didn't look at me like I was knocking on their door with a vacuum in my hand. You are following behind every other sales guy at every other vendor that has shown up there. That's very different than coming from a VAR where you have respect out of the gate because you have this long flowing relationship. The most rewarding part of the job is if you can get control of that room and turn your prospect into somebody who is willing to listen. And if it becomes a collegial thing in the end, and you can pull together a vision that is transformative for their business and they can see that too, it is amazing. What advice do you have for organizations that are struggling with growing unstructured data volumes and costs? I always encourage them to leverage the power of the hybrid cloud to manage costs while increasing performance. I want them to tier cold data up into the cloud on object storage (S3 protocol), so that they can reduce their on-prem footprint and drive those costs down dramatically. To increase data resiliency and performance, the savings can and should be partially reinvested back into the primary storage platforms. What do you enjoy doing in your free time? I used to race motorcycles—superbikes--but now I spend most of the time with my kids. ### Komprise Announces Full Support for VAST Data Migration & Tiering At Komprise, our mission is to help our customers manage their vast estates of unstructured data everywhere. We're excited to make our support of VAST Data migration and tiering official. Now, you can simply pick VAST NAS or S3 from the Komprise dropdown and start moving petabytes of data. Early this year Komprise announced support for VAST Data Platform (S3) as a Plan target. With our latest release, we’re expanding support of VAST as both a file and object platform. This means that customers can simply pick VAST from the Komprise data store dropdown, and they now have the full array of capabilities for managing all their VAST Data investments today and tomorrow: Connect to any VAST file shares to analyze usage and model plans; Migrate or copy data to and from VAST. Tier data to and from VAST Customers can use Komprise Intelligent Data Management to analyze and determine what data to migrate and then execute the migration 27 times faster than common solutions. Komprise is a simple, cost-effective solution to find and move petabytes of file and object data into VAST with many other benefits for storage-agnostic data management. Analytics: Komprise brings visibility across hybrid storage silos to help understand data usage, data growth and costs to plan for VAST data migrations and ongoing data management across the entire environment. Data Migration: Komprise Elastic Data Migration moves NFS and SMB files 27x faster than common migration tools. Start by pointing Komprise at your on-premises NAS (Windows, NetApp, Dell, Pure, Nutanix, etc.)​ to identify valuable data, eliminate expired data, assess ownership costs and identify which data sets to move​. Execute a Vast data migration by moving the right data with all permissions and security intact and with checksums on every file for extra assurance. Transparent Tiering: Komprise Transparent Move Technology™ intelligently tiers file data to avoid user and application disruption with Komprise Dynamic Links. Users simply access the file from its previous location and the links redirect to the target location in VAST. Cold data tiering can save on average 70% of annual storage and backup costs by preserving high-performing storage for your most valuable, active data sets. Smart Data Workflows for AI: Finding, enriching and moving the right unstructured data sets into AI tools is a key challenge for enterprise IT due to the size of data and its distribution across many silos. Komprise can help prepare and unleash data stored in VAST to make it usable for AI and then move precise data sets to new locations for analysis. Capabilities include integration with third-party AI services to enrich metadata for improved data classification and segmentation and a simple workflow manager that eliminates the need for specialized AI skills. Learn about Komprise Smart Data Workflows. https://www.youtube.com/watch?v=7OXOHWuUQes Learn more about Komprise for VAST Data. Read the Komprise Guide to Migration. ### Moving to Cloud: When to Migrate and When to Tier This blog was adapted from its original version on The New Stack. The cloud has had its ups and downs in the last year, but it remains a viable and increasingly vital infrastructure play for the enterprise. Moving your data and workloads the right way can cut costs dramatically and stage a platform for AI projects. With so many storage tiers now available, IT leaders need to understand the options while also navigating the choice: migrate, tier or do some of each? Compare Cloud Data Migration to Cloud Data Tiering First, let’s review the differences between cloud data migration and cloud tiering of files. Cloud data migration means taking data that is currently stored on-prem and moving it to a cloud storage service (like Amazon Elastic File Services or Azure Files) that makes the data instantly accessible from the cloud. Cloud data migrations may occur when it’s time to refresh storage and as part of an overall move-to-the-cloud strategy. Migrating data to the cloud has at least two purposes: One is to leverage cloud file systems and run applications in the cloud to achieve scale and on-demand pricing. The other purpose is to use the cloud as an offline archive, using low-cost object storage like Amazon S3 Glacier and Glacier Instant Retrieval. In contrast, cloud data tiering is the process of continuously offloading older, cold data that has not been accessed in months to cloud storage services. Tiering creates an “online archive” in the cloud in which files still appear to be on-prem and can be accessed by simply double-clicking on them. Archival storage in the cloud is (aka Glacier) is much less than standard S3 storage. Because tiering is continuously moving older data to the cloud, it reduces the amount of expensive, high-performance storage you need on-prem as well as the amount of backup storage required. This results in a reduction in storage costs by as much as 70%. Cloud Migration Considerations Pre-assessment of data: It’s important to use an analytics-first approach to identify what should be moved to the cloud and what should be deleted or archived. This will reduce cloud costs and migration times and ensure that you are choosing the right strategy for the right data sets at the right time. Pre-assessment of environment and network: Too often migration performance is poor due to bottlenecks in the on-premises infrastructure and associated network settings. Some migration solutions provide a tool that runs standard tests to identify bottlenecks within your environment. This can fundamentally improve the success of your migration project. Read more about Komprise ACE. Performance: Migrating large volumes of data, and especially lots of small files, to the cloud can be painfully slow, due to high-latency WANs–especially if migration depends on chatty network protocols like SMB to transfer data. Look for solutions designed to work over WANs and improve file transfer times, such as Komprise Hypertransfer. Network bandwidth limitations and outages can also impede the performance of data migration, and some file attributes or metadata can be lost in the data transfer process. Look for solutions that provide re-tries in the event of network issues and that perform a checksum test to ensure that all the bits of each file have been properly transferred. Read about Komprise Elastic Data Migration. Security: If you migrate data over the network, you’ll want to ensure the data is encrypted in transit to prevent eavesdropping. In addition, it’s important to configure proper access controls once your data is in the cloud to prevent data leakage or exfiltration. Cloud Tiering Considerations Block vs. file-level tiering: Block-level tiering is ideal for system data such as snapshots but has drawbacks when migrating regular user and application data. Because files are stored as proprietary blocks, they cannot be accessed natively from the cloud. Also, when you need to replace the on-prem file system, all the data tiered from it will have to be rehydrated and will require adequate capacity on the existing file server, followed by a migration of the rehydrated data to the new file server. Then you’ll need to tier the cold data back to the cloud. This can be daunting if you have tiered petabytes of data and it will be expensive due to egress fees and cloud API costs. File-level tiering in contrast, tiers the entire file which can be accessed natively from the cloud for use in AI and other cloud applications. Rather than rehydration, file level tiering, available in Komprise, will allow the tiered files to be accessible from the new file server without having to re-hydrate all the tiered data. This is a huge advantage that should not be overlooked. Transparency: Tiering should provide transparency so that users can access their data by simply double-clicking on what appears to be the file in the on-prem file server yet redirects to the location to which it was tiered. Transparency allows IT administrators to tier cold data automatically and continuously without disrupting their users and making them hunt for data that has been moved. Learn more about Komprise Transparent Move Technology. Bulk recall: When needed, tiering solutions should allow you to recall data en masse. If a revision of a project whose data has been tiered is needed, rather than restoring files as they are required, you should be able to recall all the files ahead of time for the best performance. Conclusion Cloud data migration is great if your goal is to reduce on-premises storage capacity, adopt new storage technologies and increase investments in the more flexible, on-demand nature of cloud storage. Data tiering is better in cases where you want to lower storage costs and capacity for data that you access infrequently — but which you may still need to recall on-premises in the future. ### Komprise Unstructured Data Management Solutions Now Available on IBM Cloud Marketplace We are excited to share that Komprise products are now available on IBM Cloud Marketplace. As unstructured data growth continues to explode, with IDC estimating 149 zettabytes will be created by 2024, organizations are struggling to contain data storage costs while realizing data value. Komprise Intelligent Data Management delivers one place to see and manage all your unstructured data, on-premises and in the cloud, without impeding hot data access. Holistic visibility means you can understand your data, manage costs and attract new value from your data. Komprise customers save an average 70% on storage, backup and cloud costs with intelligent data tiering, move to the cloud 27x faster with smart data migration and see a dramatic reduction in time spent preparing data for analytics workflows with our Global File Index. Komprise Elastic Data Migration allows IT teams to run, monitor, and manage hundreds of data migrations faster than ever at a fraction of the cost. Features include analysis across storage silos to plan what to move where for the best savings. Achieve the fastest, most reliable migrations for problematic SMB data sets with Komprise Hypertransfer, which is 25X faster than open-source tools like Robocopy. Migrate NFS data sets 27X faster than common tools like Rsync as well. Achieve full file fidelity and manage chain of custody reporting with checksums and integrity reporting per file. IBM Cloud Marketplace provides a valuable sales channel for IBM partners to buy and sell their enterprise applications to IBM Cloud clients worldwide. Through the marketplace, customers can unlock IBM Cloud’s trusted relationships, while gaining simplicity through single invoicing, seamless account integration, and a streamlined approach for deployment and management of their cloud solutions. Komprise Unstructured Data Management solutions join over 400 existing offerings on IBM Cloud Marketplace, increasing the delivery of Komprise’s services, driving product awareness, streamlining billing and metering processes, and opening access to enterprise clients running on IBM Cloud. Through the IBM Cloud Marketplace, customers can access Komprise Elastic Data Migration and Komprise Intelligent Data Management, the full platform which includes the migration offering, along with data tiering, Deep Analytics and Smart Data Workflows. Komprise has been working with IBM since 2017, offering enterprise customers managing petabytes of unstructured data a solution for tiering and migration to IBM Cloud Object Storage and IBM Spectrum Scale solutions. Simply pick your IBM target in Komprise, and the solution can automatically move data by policy. Moved data is accessed exactly as before from your source storage and users can access it as files or objects directly from IBM Cloud. Similarly, if data is moved to IBM Tape Storage via IBM Spectrum Scale, that data is still accessible from the Source NAS as well as from IBM Spectrum Scale. Komprise patented Transparent Move Technology (TMT) ensures a nondisruptive experience for users and the ability to readily access moved data without lock-in to the original storage. IBM bolsters its catalog of offerings on IBM Cloud Marketplace to empower 95% of the Fortune 500 using IBM Cloud and over 600,000 monthly active users with the solutions they need to support their digital transformations. Customers can find both solutions on the IBM Cloud Marketplace at these links: Komprise Intelligent Data Management Komprise Elastic Data Migration About Komprise Komprise is a provider of unstructured data management and mobility software that frees enterprises to easily analyze, mobilize, and monetize file and object data across hybrid cloud data storage without shackling data to any one vendor. With Komprise Intelligent Data Management, enterprise IT teams optimize enterprise storage, backup and cloud costs while making the right data available to analytics and AI tools. Learn more at www.komprise.com. About IBM Cloud Marketplace For more information on IBM Cloud offerings, visit https://cloud.ibm.com/catalog. ### The Rise of Data Services for Unstructured Data Management This blog was adapted from the original article on ITProToday. IT infrastructure leaders are now responsible for not only managing data, but also delivering data services. These data services include protecting data, managing compliance requirements, archiving data, managing data lifecycle and costs, and yes even deleting data when it's no longer needed. Data services responsibility also means making the right data easily available to end users and tools (such as cloud AI) and with cost optimization in mind. Here are a few proof points to consider: Gartner predicts that by 2026, large enterprises will triple their unstructured data capacity across their on-premises, edge, and public cloud locations, compared to 2023. Hybrid cloud and edge computing are now the predominant models for IT workloads, pushing data storage outside of the corporate data center. Shadow IT brought on by the cloud has complicated matters: How much data does the organization own and where does it reside? Finally, IT executives are seeing the imperative of efficiently curating the right data sets across their petabytes of increasingly hybrid, multicloud storage to feed new AI tools. Defining Data Services in Data Management and Storage Data services is a broad term that describes a range of activities typically provided by enterprise IT, such as: data processing, data integration, data security, data reduction, data protection, data storage, and unstructured data management. As relates to data storage and unstructured data management, data services involve the management of data throughout its lifecycle. Beyond primary storage, it covers analysis and reporting on data storage growth and costs including departmental showback, data usage, self-service file search and tagging, along with data mobility use cases such as data migration, data tiering, replication, and deletion. This new approach requires the ability to understand data usage and manage data independently of storage. Benefits of Data Services in Unstructured Data Management A data services approach helps IT and business teams in many ways, enabling: Holistic visibility and granular search across multiple storage systems and clouds; Analytics and insights on data types and usage for more accurate storage decisions; Automated, policy-driven actions based on that analysis; Reduced security and compliance risks; Full use of data wherever it is stored, especially in the cloud; User self-service access to support departmental and research needs for data storage, management, and AI workflows; Greater flexibility to adopt new storage, backup, and DR technologies because data is managed independently of any vendor technology. Data Services in Action To better understand the potential of data services, here are examples across higher education, biotechnology, energy, media & entertainment, and retail. Midsize university: A storage administrator can look across all the shares in the university to search for anomalies that pose risk: files belonging to people no longer with the university, sensitive files that aren't being stored in the right location, and old video files from the website team that are taking up a lot of capacity and are no longer needed. Medical device maker: A company with regulated products must regularly answer questions from auditors about its data: What is it, who owns it, and how is it used? Ensuring that data is being stored and protected according to various regulations such as HIPAA is imperative to avoid large fines and penalties. The company is also working to bring in "shadow" data from remote sites so that IT can ensure permissions and other protections are up to date. Using a solution that indexes all unstructured data is the key to accomplishing these critical compliance tasks. Read more about healthcare and unstructured data management. Life sciences: The central IT team at a global pharmaceutical firm has been tiering cold data to the cloud but now wants to give its research teams the ability to identify and tag their project files for later use. This approach flips the dynamic from IT having to police data storage to providing an analytics-based data service to the business. Read more about life sciences and unstructured data management. Energy: A global oil and gas services provider with data centers around the world is modernizing and optimizing its infrastructure. The company uses the cloud to deliver digital services to its customers such as managing data generated from equipment at the bottom of the ocean. By adopting a data services approach and toolset, the energy company tiered 85% of its data to far cheaper, archival storage in the cloud. A centralized data services strategy and tools help them easily move data from one platform to another, rather than using various point migration tools. Data services is now central to how IT operates — for cost savings, risk management and, flexibility. Read an energy sector case study. Retail: A national retail conglomerate went through a divestiture, shuttering a few of its brands and standardizing its IT infrastructure. The process resulted in a large quantity of zombie files from technologies no longer in use, such as Microsoft .PST files. A data services toolset allowed the organization to quickly find the unwanted files across all storage and delete them. With a simpler, more streamlined data environment, the company is in a great position to grow in its next stage of evolution. Entertainment: The storage director at a large Hollywood studio was investigating why certain shares were taking an excessively long time to back up. By running analysis on the data, the director discovered many old files belong to a handful of users. Once the users were made aware of the situation, they deleted the files, which improved backup cycles. The IT organization plans to give departmental data managers access to the data management solution. That way, they can view their own data and tag groups of folders or shares for data management actions such as data tiering to cold data storage or deletion altogether. This puts data owners in control of their data, while also helping IT meet its objectives. A Roadmap to Data Services There's no right way to transition to data services, but analysis is at the heart of the matter. Using data analytics and data management to understand data usage, data growth, and data storage costs across storage and cloud environments is a good start; Other core requirements include allowing data teams to search and tag data based on share-based access permissions. These tags can then inform central IT to execute automated policies, such as deleting project files that are more than three years old; Above all, data management and storage infrastructure experts will need to shift their thinking and practices from managing storage technologies to understanding and managing data for a variety of purposes. A data storage and data management infrastructure that supports flexibility and agility to shift with organizational data needs will allow IT to make the shift faster and with better outcomes for all. ### Avoiding Data Migration Chaos with Komprise ACE In 2022, Komprise unveiled a new program for customer onboarding called ACE (Assessment of Customer Environment) to analyze the expected performance of a customer migration or any movement of data--whether file to file migration, object to object migration, file to object migration, on premises or in the cloud. The ACE tool proactively identifies potential bottlenecks and other issues independent of Komprise Elastic Data Migration running in the customer’s environment and takes an hour or less of the customer’s time. There can be several hurdles living within the customer’s data center that can delay or break a data migration process. The larger the data set, the more stress to the network and that’s when things go wrong which normally don’t surface under a normal transactional load. ACE helps customers understand their topology and their environment to ward off preventable issues that can disrupt migrations. It’s like an insurance policy for your data. Common problems include: Network bandwidth, topology and configurations: For instance, a customer inadvertently did not choose the fastest, dedicated route for the migration traffic. WAN-based migrations can also run much slower than the negotiated speed of the circuit based on distance or everyday business traffic. Security systems and configurations: Firewall and other security tool settings can disrupt data traffic and may require whitelisting. ACE complements the analysis that Komprise Elastic Data Migration has always delivered. That includes seeing how many small and large files are in storage. Since file size affects migration performance, you can get an early sense of whether the migration will go quickly or not. Also, Komprise Analysis tells you if there are many directories containing just a few files, or if there are directories with more than 100,000 files. Those conditions will also slow down the migration. Komprise warns the customer of these things early before the migration begins. Even in cloud-to-cloud migrations, it’s important to plan ahead. If the migration takes place over the public internet, transfer times may be unpredictable and impose unplanned delays. Real-world ACE in action Here’s a recent example with one of our customers that was migrating 500 terabytes from the on-prem NAS in their corporate data center to a cloud-hosted solution. The customer was seeing transfer speeds of 3MB/s (24Mbps), which they expected to be closer to 63MB/s (500Mbps). Using ACE to identify potential bottlenecks, we first looked to see if the correct routes were used. Next we tested data movement in both directions by testing the topology such as firewalls, gateways and routers in the data path. The issue wound up being related to firewalls. The company had two firewalls: one on prem and one in the cloud, but the migration team had forgotten about the latter. We used trace routes to identify the second firewall and then added exceptions for that one as we had done for the first firewall. Voila: the stuck data started moving and the transfer speed improved drastically. In another case, a customer was experiencing delays despite having a a 10Gb WAN link. ACE discovered that the router was failing, because it only had a 1Gb line card. After the customer replaced the card with the proper size, the bottleneck went away. Here's how ACE works: ACE creates a configurable number of files of various sizes based on the specified number of threads. ACE uses these files to test source and target read, write, archive, recall, checksum and attribute copy operations, using any customer provided Linux or Windows Server. ACE starts with a round of pings and trace routes to determine basic connectivity and latency. Next, ACE tests how long it takes to read and write the various sized files on both source and destination along with checksums and attribute copy operations. Based on the number of threads and repetitions configured these tests can take as little as a few seconds. The resulting dataset helps identify and isolate issues with the following: Firewalls, routers, anti-virus systems, security appliances, WAN accelerators; External connectivity from the customer environment to the target storage system (e.g., slow link, excessive hops, asymmetric routes); Source and target storage system bottlenecks. ACE has proven its ability to prevent hours of tedious troubleshooting when things go awry during the data movement process. Walking through the various checkpoints is also valuable because it requires different functions within IT to communicate about data management—not a bad thing at all. As with any major IT project, there is measurable value in mapping out the challenges and limitations up front, so your team knows what to expect later. Ultimately, using an assessment process that includes ACE means customers will complete unstructured data migrations faster and encounter fewer issues while reducing the cutover process. Komprise customers can contact their account team for more information. Learn more about the Komprise Elastic Data Migration technology, including performance benchmarks, in this paper. Read the Komprise Guide to Unstructured Data Migration. ### Komprise Named a Fastest-Growing Company in North America for the Second Consecutive Year on the 2023 Deloitte Technology Fast 500™ Komprise continues to experience strong growth and customer retention as enterprise unstructured data management demands expand with the need for cloud cost optimization and preparing for AI. Campbell, CA, November 8, 2023 — Komprise, the leader in analytics-driven unstructured data management and mobility, today announced its inclusion on the Deloitte Technology Fast 500™, a ranking of the fastest-growing technology, media, telecommunications, life sciences, fintech, and energy tech companies in North America, now in its 29th year. Komprise grew 212% during the three-year period from 2019 to 2022. Komprise helps enterprise IT organizations save and make money on unstructured data, delivering analysis on data in storage and rich data lifecycle management capabilities including data tiering and archiving, data migration and policy-driven automated workflows. The Komprise Intelligent Data Management platform saves customers an average of 70% on data storage and backup costs by delivering full insights into file and object data across hybrid silos with the Komprise Global File Index, so that IT can always have the right unstructured data in the right place at the right time. Komprise Deep Analytics and Smart Data Workflows drastically cuts the time needed to prepare and move unstructured data to analytics, AI and other cloud services while delivering capabilities for AI governance. Komprise highlights of 2023 include: Announced a $37 million infusion of growth capital and strong results from 2022 during which the company doubled subscription revenues for a third consecutive year and achieved industry-leading net dollar retention (NDR) of 120%. Announced Komprise Analysis, a new standalone subscription for customers who want insights but are not yet ready to move data. Introduced Komprise Intelligent Data Tiering for Azure, the only Microsoft Azure Marketplace solution that gives customers access to file analysis and tiering to and within Azure. Announced new data governance and departmental self-service capabilities. Released the results of the third annual survey on unstructured data management, highlighting enterprise IT focus on cloud cost optimization, AI data governance and self-service data management. Released latest version of Komprise Intelligent Data Management, which includes Storage Insights, a new console combining storage-centric and data-centric metrics, Unveiled updates to Komprise Elastic Data Migration, supporting new use cases for IoT and data center and cloud consolidations. Komprise achieved several new industry awards/honors so far this year: https://www.komprise.com/about-us/recognition/ “We are thrilled to be recognized for the second year in a row on the Deloitte Technology Fast 500,” said Kumar Goswami, cofounder and CEO of Komprise. “Enterprise IT organizations are dealing with the perfect storm of massive data growth, the need to optimize data storage costs and the realization that there is untapped potential from unstructured data through AI and ML. At Komprise, our goal is to help our customers bring structure to unstructured data and deliver maximum cost savings and value.” “Each year we look forward to reviewing the progress and innovations of our Technology Fast 500 winners. This year is especially celebratory as we expand the number of winners to better represent just how many companies are developing new ideas to progress our society and the world, especially during a slow economy,” said Paul Silverglate, vice chair, Deloitte LLP and U.S. technology sector leader. “While software and services and life sciences continue to dominate the top 10, we are encouraged to see other categories making their mark. Congratulations to all the winners who show us how creativity, hard work and perseverance can lead to success.” About the 2023 Deloitte Technology Fast 500 Now in its 29th year, the Deloitte Technology Fast 500 provides a ranking of the fastest-growing technology, media, telecommunications, life sciences, fintech, and energy tech companies — both public and private — in North America. Technology Fast 500 award winners are selected based on percentage fiscal year revenue growth from 2019 to 2022. About Komprise Komprise is a provider of unstructured data management and mobility software that frees enterprises to easily analyze, mobilize, and monetize the right file and object data across clouds without shackling data to any vendor. With Komprise Intelligent Data Management, you can cut 70% of enterprise storage, backup and cloud costs while making data easily available to cloud-based data lakes, analytics and AI/ML tools. About Deloitte Deloitte provides industry-leading audit, consulting, tax and advisory services to many of the world’s most admired brands, including nearly 90% of the Fortune 500® and more than 8,500 U.S.-based private companies. At Deloitte, we strive to live our purpose of making an impact that matters by creating trust and confidence in a more equitable society. We leverage our unique blend of business acumen, command of technology, and strategic technology alliances to advise our clients across industries as they build their future. Deloitte is proud to be part of the largest global professional services network serving our clients in the markets that are most important to them. Bringing more than 175 years of service, our network of member firms spans more than 150 countries and territories. Learn how Deloitte’s approximately 457,000 people worldwide connect for impact at www.deloitte.com. ### Expanding File Data Migration Use Cases with Komprise From the beginning, Komprise has focused on delivering an analytics-driven SaaS platform to manage and mobilize file and object data. With its distributed, scale-out architecture, transparent tiering, and Global File Index, Komprise enables enterprise IT teams to easily analyze, tag and mobilize unstructured data across storage silos to gain insights, optimize costs and deliver greater value. With our Fall 2023 update, we introduced Storage Insights to unify data storage and storage management. Today we’re excited to announce major updates to Komprise Elastic Data Migration, which is available both as a standalone solution for analytics-driven data migrations and included in the Komprise Intelligent Data Management platform. Evolving Unstructured Data Management Requirements Data migration solutions have historically been designed for “like-to-like" migrations of similar sources and destinations with a prescribed cutover that requires some amount of downtime for end users and applications. In today’s hybrid cloud environments, there are several scenarios that do not fit neatly into this model, especially in healthcare and IoT sensor data use cases where instruments are continuously generating new data into your storage and quiescing for a cutover can cause unacceptable downtime. In our Elastic Data Migration 5.0 announcement, we highlighted three specific use cases that Komprise now supports: Pre-loaded migrations: Also known as a "jumpstarted migration" or "bootstrapped migration", this is a type of migration where an organization has performed the first iteration (the “baseline copy” of all the data) using a tool or mechanism other than Komprise (e.g., NetApp SnapMirror, Dell PowerScale SyncIQ). They then want to use Komprise to perform the ongoing synchronization of the source (which is a live file system, where users are adding, modifying, and deleting data) with the destination, and then finally perform cutover. The goal of a pre-loaded migration is the same as a standard migration, i.e., to "make the destination an exact copy of the source”, except the destination will initially be non-empty. “Zero downtime” or “warm cutover” migrations: A warm cutover migration will minimize the downtime experienced by end users and applications at the end of a migration, when the source is set to read-only to allow Komprise to copy over the last changes made on the source. By allowing end users and applications to use the destination (especially to add data) even while the final iteration is running, Komprise enables a "warm cutover" or "zero downtime" migration, providing the least impact to end users and applications. Consolidation migrations: Consolidation migrations enable multiple sources migrating onto one destination (e.g., to consolidate hardware, sites or data, due to acquisition, merger or to re-organize existing data). A simple consolidation migration may consolidate data into different subdirectories on the destination. Users can configure and run multiple standard or pre-loaded migrations into subdirectories, in parallel, as needed. In the general consolidation migration use case, multiple sources are migrated onto a single destination, merging their directory structures. The first source’s migration may start with an empty destination but could also start with a non-empty destination that already has existing data on it. For the second and subsequent migrations, the destination will always be non-empty. In a consolidation migration, Komprise will never delete data from the destination: Komprise will not propagate deletes from the source to the destination and will not delete files from the destination that do not have a match on the source. Next-Generation Unstructured Data Migration Komprise is focused on delivering the next-generation unstructured data migration solution. Komprise Elastic Data Migration includes valuable analytics that enables you to proactively plan migrations, understand before you migrate what potential issues may slowdown migrations, and prepare so the migrations run fast. We provide analysis so customers know how much data they have, how many are small files, how many per directory and other key data insights that help you plan your migration and determine how long it can take before you embark. And it's not just data insights. Since migrations depend not only on the profile of the data but also the network availability, Komprise includes a non-intrusive environment assessment called ACE (Assessment of Customer Environment) to help understand the expected performance and potential bottlenecks given the infrastructure and environment outside of Komprise, which could adversely impact a migration project. Those contingencies include security and network configurations, asymmetric routes, saturated routers, firewalls and security appliances, older, higher-latency storage systems and more. This knowledge helps iron out critical performance issues to speed up migrations before you begin. And it reduces the number of experts you need to diagnose environmental and performance issues. With every release, we’re expanding the core capabilities of the platform. Additional updates with Komprise Elastic Data Migration include: SMB Migration without Windows Proxies: With Hypertransfer, Komprise optimized WAN performance, especially for small file server message block (SMB) protocol migrations. With this release we’ve continued to optimize and simplify SMB deployments by eliminating the need for a Windows Proxy, which will simplify customer deployments and ongoing maintenance, as well as reduce Windows licensing costs for customers. Object-to-Object Migration: Similar to what Komprise supports for cloud data migrations, we now enable S3-to-S3 migration for objects between on-prem or cloud-based storage. Know First. Move Smart. Take Control. The benefits of an analytics-first approach that provides visibility into your network and your data are clear (know first). The ability to address more use cases with a proven, unified platform (move smart) and deliver greater ongoing data value (take control) continues to be our goal. Komprise Elastic Data Migration 5.0 has already received positive customer response and our partners continue to provide valuable feedback to ensure we’re reducing migration time and accelerating customer value. Attend the Webinar. Read: Top Considerations for a Cloud File Migration Read: Tips for a Clean Cloud Migration Learn more about Komprise Elastic Data Migration. ### Komprise Expands Data Migration to Address Unique Needs in Healthcare, IoT, M&A and Offline Data Transfers Komprise delivers capabilities for new unstructured data migration use cases as enterprise needs evolve. Campbell, CA – October 23, 2023 – Komprise, the leader in analytics-driven unstructured data management, today announces a major update to Komprise Elastic Data Migration, which expands to address specific use cases for large-scale file and object data migrations. The latest release includes three new options for customers that go beyond traditional enterprise migrations: zero downtime or “warm cutover” migrations for real-time and IoT data, pre-loaded migrations supporting non-empty destinations and consolidation migrations for IT teams to combine multiple data storage shares in a single migration job. Zero downtime/warm cutover migration: During a typical migration when the administrator is ready to perform the cutover to destination shares, the source shares and destination shares are set to “read-only” while the last changes made on the source are copied to the destination. This "cutover" period can be several hours. During this time, users or applications cannot write any data. This is a critical problem in many situations including where machine-generated data such as from medical imaging equipment, lab instruments or IoT sensors cannot cease the stream of data being generated and must be able to always write that data. Komprise addresses this situation by allowing users and applications to add, modify and delete data on the destination shares during this cutover period, eliminating this downtime. Pre-loaded migration: A pre-loaded migration (also known as a "jumpstarted migration" or "bootstrapped migration") occurs when the customer has performed the first iteration using a tool or mechanism other than Komprise and later selects Komprise to perform the ongoing migration of the source with the destination and the final cutover. Customers may start a migration with a free open-source tool, and then choose Komprise to finish it. They may have started with an offline data transfer technology such as AWS Snowball or Azure Data Box, after which they want to set up an ongoing migration using Komprise. As well, customers may have a mirrored copy of data using storage technologies like NetApp SnapMirror and want Komprise to finish the migration. Now, customers can complete the migration without having to move the data again. The Komprise migration process includes copying all the access control permissions, performing data integrity checks and creating necessary reports. Consolidation migration: In this scenario, a customer may wish to combine multiple sources in a single migration project to the same destination. This is common when dealing with consolidating shares and sites during an acquisition or a data center consolidation project, to jumpstart a cloud transformation or simply to reorganize shares or merge folders/directories into a new destination. Without this capability, an IT or storage administrator must manually reconcile merging data from multiple sources, which can be time-consuming and error prone. Komprise eliminates the additional effort by fully automating the process of consolidating multiple migrations to the same destination. “Komprise eliminates the dread of unstructured data migrations with analytics-first intelligent automation and the fastest WAN migration speeds for data center or cloud consolidations,” says Kumar Goswami, cofounder and CEO at Komprise. “Now, by delivering more options and flexibility, we enable faster ROI for our customers who rely on Komprise to move and manage petabytes of file and object data.” Availability Komprise Elastic Data Migration is available as a standalone solution and is included in the Komprise Intelligent Data Management platform. Learn more at Komprise.com/whatsnew. About Komprise  Komprise is a provider of unstructured data management and mobility software that frees enterprises to easily analyze, mobilize, and monetize the right file and object data across clouds without shackling data to any vendor. With Komprise Intelligent Data Management, you can optimize enterprise storage, backup and cloud costs while making the right data easily available to cloud-based data lakes, analytics and AI tools. www.komprise.com. ### Komprise Introduces Storage Insights to Unify Data and Storage Management Campbell, CA – Sept 21, 2023 – Komprise, the leader in analytics-driven unstructured data management, today announced the general availability of Storage Insights, the industry's first unified, consistent view of both data usage and storage consumption across vendors and clouds without having to contend with multiple consoles and multiple storage consumption definitions. Storage costs are continuing to grow unchecked; 73% of organizations are spending more than 30% of their IT budget on data storage and backups, according to a recent Komprise survey. At the same time, preparing for AI and optimizing cloud costs are top data storage priorities. Amid these pressures, IT is shifting from managing storage to delivering data services tailored to each department’s needs. With Storage Insights, Komprise customers can view new storage metrics, such as free and used space across vendors or shares with the highest volume of modifications indicating possible anomalous activity. This allows IT to quickly spot trends, drill down and execute plans and actions in one place to drive the best possible return on data storage investments. “The Storage Insights functionality will give us the ability to see our storage footprint across our hybrid cloud,” said Matt Madill, storage systems administrator at Duquesne University. “It’s a single interface that will show us important metrics like capacity usage in every storage location, which will save us a lot of time and ensure we make the right decisions for our departments and users.” Spot trends with global visibility across data and storage metrics Storage Insights gives administrators the ability to drill down into file shares and object stores across locations and sites, including relevant metrics by department, division or business unit, such as: Which shares have the greatest amount of cold data? Which shares have the highest recent growth in new data? Which shares have the highest recent growth overall? Which file servers have the least free space available? Which shares have tiered the most data? Track and manage what matters to your organization Track and manage what matters to your organization  Storage Insights includes over 25 columns that users can customize and filter to understand the current state of enterprise storage assets across sites; Users can see details on capacity, percentage of modified or new data and can filter by shares, status, data transfer roles, and more; Easily sort your shares and view by largest, most cold data, highest recent modified data, least free space, most and least data archived or tiered by Komprise and more; Dig into specific file servers such as NetApp, Dell EMC Isilon (PowerScale), Pure Storage, AWS, Azure, Windows, etc. to analyze system health and ensure maximum ROI and cost savings. From insight to action without disrupting the hot data path In addition to powerful analytics, Storage Insights leverages Komprise Transparent Move Technology to execute plans and move data without obstructing data access and with no disruption to users or applications. Examples of actions include: Tier cold data transparently from the shares that have the highest amount of cold data to cheaper storage; Identify cloud migration opportunities such as moving least modified shares or copying project data to data lakes; Spot potential security threats and ransomware attacks on data stores with anomalous activity such as high volume of modifications; Set alert thresholds to instantly see unusual activity or other data requiring fast actions such as storage nearing capacity. “As unstructured data continues to grow explosively, enterprise storage is becoming more distributed across on-premises, multi-cloud and edge environments, and often across multiple vendor systems,” said Kumar Goswami, cofounder and CEO of Komprise. “This latest release gives customers an easier, faster way to proactively manage and deliver data services across this complex hybrid IT environment while optimizing their data storage investments.” Availability Storage Insights is included in the Komprise Intelligent Data Management 5.0 platform release and available with Komprise Analysis and Komprise Elastic Data Migration. Komprise 5.0 also includes new prebuilt reports, including Potential Duplicates, Orphaned Data, Showback, Users and Migrations reports as well as many other platform updates. For more information visit www.komprise.com/whatsnew Register for the webinar. About Komprise Komprise is a provider of unstructured data management and mobility software that frees enterprises to easily analyze, mobilize, and monetize the right file and object data across clouds without shackling data to any vendor. With Komprise Intelligent Data Management, you can optimize enterprise storage, backup and cloud costs while making the right data easily available to cloud-based data lakes, analytics and AI tools. www.komprise.com. Media Contact: Kevin Wolf, TGPR kevin@tgprllc.com ### Komprise Named Winner in Data & Information Management by CRN The company was awarded a CRN 2023 Tech Innovator Award for Komprise Hypertransfer for Elastic Data Migration Campbell, California, July 17, 2023 – Komprise, the leader in analytics-driven unstructured data management, today announces that CRN®, a brand of The Channel Company, has named the company a Winner for the 2023 CRN Tech Innovator Awards. Komprise Hypertransfer for Elastic Data Migration, released in late 2022, garnered the top Data & Information Management honor by CRN. This annual award program showcases innovative vendors in the IT channel across 37 different technology categories, in key areas ranging from cloud to storage to networking to security. To determine the 2023 winners, a panel of CRN editors reviewed hundreds of vendor entries—including solution provider testimonials—using multiple criteria, including key capabilities, uniqueness, technological ingenuity, and ability to address customer and partner needs. Komprise Hypertransfer solves common performance issues that enterprises face when migrating data using the SMB protocol (such as Windows files) to the cloud: The technology optimizes the process by minimizing the WAN roundtrips, using dedicated channels to send SMB file data 25x faster compared to other alternatives. Komprise Hypertransfer strengthens security and defense against ransomware attacks by not accessing cloud file storage over the network during data migrations, since data transfers from source to target over private channels. “CRN’s annual Tech Innovator Awards acknowledge technology vendors committed to new and updated products that are creating the biggest opportunities for the solution providers and strategic service providers working on the from lines with customers. said Blaine Raddon, CEO of The Channel Company. “Congratulations to each one of this year’s CRN Tech Innovator Award winners. We are proud to recognize these best-in-class vendors that are driving transformation and innovation in the IT space.” “Enterprise cloud data migrations are complex by nature and a large migration of small files over the WAN is even more challenging,” said Kumar Goswami, CEO of Komprise. “A cloud migration project that could take months to complete will now take days and that’s a huge cost and time savings for our customers. It’s wonderful to be recognized by CRN for our work on Hypertransfer, which we built with the goal of making life simpler for IT teams and data storage architects managing and moving petabytes of data.” Learn more about Hypertransfer for Elastic Data Migration. The Tech Innovator Awards will be featured in the August issue of CRN and can be viewed online at crn.com/techinnovators. About Komprise Komprise is a provider of unstructured data management and mobility software that frees enterprises to easily analyze, mobilize, and monetize the right file and object data across clouds without shackling data to any vendor. With Komprise Intelligent Data Management, you can cut 70% of enterprise storage, backup and cloud costs while making data easily available to cloud-based data lakes, analytics and AI. tools. www.komprise.com. ### Unstructured Data Management & Migration Trends in 2023 Despite a difficult economy so far in 2023, it’s been an exciting time for data storage innovation and unstructured data management. There’s much at stake to leverage data for competitive advantage while still being as efficient as possible with cloud and infrastructure spend. We are seeing our customers’ needs grow and mature every day as they prioritize unstructured data management and mobility like never before. It’s not enough to move the right data sets to the cloud and leverage new technologies for a fast, efficient storage and backup footprint: As data keeps growing at exponential rates, organizations need the best storage cost optimization model possible; This requires an independent, data-centric model with full visibility and insights into file and object data assets; Doing so solves some intractable problems that enterprises have been dealing with for years. For instance, our CEO Kumar Goswami explained in Enterprise Storage Forum why organizations should stop trying to get rid of their data silos: “The answer is solutions that look across the data—search, classify, secure, visualize it in place—without forcing you to put all your data into one location or technology.” IT and business leaders are also seeing the need to prepare for new opportunities with AI, which relies on unstructured data. This requires governance and better collaboration with departmental stakeholders to better understand data priorities, usage trends and analytics needs. To that end, Komprise is delivering new capabilities to the market for analysis, easier reporting, secure self-service access for line of business IT teams and researchers, cloud tiering and more. Our vision and market execution were validated this year with our third external fundraising round. Komprise highlights of the year in progress Komprise raises $37M to help companies index, manage and transform data January: We started the year with major funding news, which was covered by TechCrunch. The $37M of growth capital from Canaan Partners, Celesta Capital, Multiplier Capital and Top Tier Ventures will be used to scale operations and extend market leadership, bringing the total funding to $85M to date. February: Soon after, we released results from 2022, in which we doubled annual subscription revenues for the third year in a row. Other 2022 results include: 50% growth in the Komprise Global File Index, which consists of hundreds of billions of files, providing customers a Google-like search across their entire data estate to find, tag and mobilize unstructured data. 120% net dollar retention (NDR) showing world-class customer retention and expansion, even during an economic downturn. 30% of revenues came from expansions, indicating strong customer satisfaction and loyalty. 200% growth in the number of organizations using Komprise for data migrations to a new NAS or for cloud data migrations. March: To help customers who want visibility and are not yet ready to move data, we announced Komprise Analysis as a standalone SaaS solution. It includes a new set of pre-built reports along with dynamic interactive analysis. The enhanced analysis and reporting capabilities are also included with Komprise Elastic Data Migration and the full Komprise Intelligent Data Management Platform. Read more about the features of Komprise Analysis in the blog. April: Komprise is a select vendor in the Azure File Migration Program, which launched in February 2022. The program offers customers access to Komprise Elastic Data Migration at no cost; we have seen the majority of these Azure customers go on to purchase our full Intelligent Data Management platform. We expanded this partnership in April with Komprise Intelligent Tiering for Azure. This exclusive offering is the only Microsoft Azure Marketplace solution that gives customers access to file analysis and tiering to and within Azure. Customers can acquire this without purchasing the full Komprise Intelligent Data Management platform.   May: Komprise Intelligent Data Management Spring 2023 is our latest product announcement, which brings our enterprise customers more powerful self-service access and stronger governance to meet changing enterprise IT needs. The Komprise Spring 2023 release includes: New Directory Explorer allows users to drill down into individual directories with a familiar browser interface. Komprise Deep Analytics now includes the ability to filter data using exclusions (e.g., “all data except .log files”) and then use these queries to create data management policies. Share-Based Access for Groups: Administrators now can assign group access to shares using Active Directory which automatically provisions data management access only to users in those groups. New Reports: Komprise has added more prebuilt reports to easily download and share: Showback, Orphaned Data, Query Summary and Potential Duplicates. Industry Recognition for Komprise Komprise has received several awards and recognition across business and technology organizations. We are always humbled to be recognized! Komprise Named Gold Winner for Information Technology Cloud/SaaS in the prestigious 3rd Annual 2023 Globee® Awards for Disruptors Bronze Stevie Award, Most Innovative Tech Company of the Year, American Business Awards Top Fastest Growing Storage Companies by StorageNewslettter CRN Storage 100 2023: Data Protection and Data Management Vendors Komprise File and Object Migration Momentum As we’ve worked with customers across multiple industries over the years, we’ve learned that no migration is the same; many of them are laborious due to the complexity of enterprise IT infrastructure. To ease this pain for our customers, we developed a non-intrusive tool called ACE (Assessment of Customer Environment). Our team deploys ACE at customer sites to predict the expected performance of a migration. The ACE tool proactively identifies potential bottlenecks and other issues independent of Komprise running in the customer’s environment; it’s been a great pathway for our enterprise customers that want to get to the cloud and modernize their storage environment faster and with better ROI. Komprise Migration Benefits   On that note, Komprise released Hypertransfer in late 2022, delivering 25 times faster migrations for problematic SMB workloads. Our resilient, rapid migration platform, Komprise Elastic Data Migration, is foundational to our work with Microsoft in the Azure File Migration Program. We have successfully completed more than 100 migrations for Azure customers so far since the program’s inception in 2022. AI and Data Management AI needs unstructured data Halfway through 2023, it seems as if we now live in an entirely different world than a year ago. Make no mistake – practical AI technologies have been years in the making but the mainstreaming of generative AI has thrown many industries and executives into a frenzy. It’s time to get ready for an AI-driven world. When it comes to data, enterprises need to be thinking about unstructured data and how they manage it because it’s the fuel needed to power new AI products and services. It’s firstly important to get visibility into the unstructured data stored across different storage technologies on-premises and in the cloud. What types of data do you have, who owns it, and what is its business value? But that is just the beginning. As our COO Krishna Subramanian explained in this recent eWeek interview, data management has a strong role to play when it comes to managing data governance and protecting IP and PII in commercial AI tools. She also discusses strategies in her recent blog: 5 unstructured data governance tips for AI. We’re bullish on the future of innovation in hybrid cloud file and object storage and unstructured data management. We believe that enterprises are gaining the upper hand in addressing infrastructure modernization and cost optimization. Automated, intelligent and unified solutions are evolving to take care of thorny, manual unstructured data management problems, paving the way for IT to deliver a rich array of valuable data services to stakeholders across the business. ### Komprise and HPE GreenLake for Unstructured Data Management & AI HPE is on a roll. In April, the company tapped VAST Data for file storage. In May, HPE announced strong earnings with significant growth for its GreenLake private cloud business. This week the company is hosting the HPE Discover Edge-to-Cloud conference in Las Vegas. Komprise is a long-time HPE partner. HPE resells Komprise Intelligent Data Management with GreenLake and Alletra so that enterprise customers can move the right data to the right HPE GreenLake storage tier. Cold data tiering helps enterprises cut 70% or more of NAS and backup costs. Here's a summary of the common Komprise for HPE use cases we see: HPE resells Komprise with its GreenLake and HPE Alletra offerings: Komprise analyzes and moves data to HPE GreenLake running Qumulo, Scality, VAST and Cloudian. Komprise Analysis for Storage Assessments: Komprise analyzes multi-vendor NAS (file) and object storage, both on-premises and in the cloud, to identify what data can move to HPE GreenLake and why with financial ROI and operations modeling. File and Object Migration to GreenLake for File Storage: Komprise migrates data to HPE Alletra systems running Vast, Qumulo and Scality. File and Object Tiering to GreenLake for File Storage: Komprise transparently tiers cold data to lower cost GreenLake tiers and to Scality-based systems or the cloud, cutting on average 70% of storage and backup costs. End users and applications access tiered data from the original location exactly as before. They can also access the moved files as objects directly from Scality or the cloud. Smart Data Workflows for AI/ML: Identifying the right training data sets is crucial to AI and ML success. Komprise creates a Global File Index on all the data it analyzes, so organizations can search and find the right data across all their silos using Komprise Deep Analytics. Users can set up a workflow in Komprise to continually find new data that fits custom search criteria and feed it to HPE Ezmeral for analytics processing. We’re excited to continue advancing our partnership with HPE GreenLake. Learn more about Komprise and HPE. https://www.youtube.com/watch?v=B6gx_peoCDE ### Why File Storage is Expensive and What to Do About It When it comes to managing data storage costs, it’s brutal out there. Data growth is outpacing the ability of enterprise IT organizations to cost-effectively manage it and cloud storage isn't the salve. These recent stats highlight the pains: More than 50% of enterprise IT organizations are managing at least 5 PB of data today and 73% are spending more than 30% of their IT budget on data storage, backups and disaster recovery. (Source: Komprise 2023 State of Unstructured Data Management. See chart below.) By 2026, large enterprises will triple their unstructured data capacity stored as file or object storage on-premises, at the edge or in the public cloud, compared to 2022. (Source: Gartner 2022 MQ for Distributed File Systems and Object Storage) 94% of IT leaders report their cloud storage costs are rising and 54% confirm their storage spend is growing faster than overall cloud costs: (Source: Virtana State of Hybrid Cloud Storage 2022) Nearly 30% of cloud spend is wasted and this year, cost savings overtook security as the top enterprise cloud challenge. (Source: Flexera, 2023 State of the Cloud) Current percentage of IT budget spent on data storage and protection. The High Costs of File Storage Most of the data enterprise organizations are generating and storing today is unstructured data – which is primarily file and object data. There are several ways to optimize file storage costs, using both tactics and tools. High file storage costs are pushing organizations to seek out lower cost places to store data, such as in the cloud. This recent article in Enterprise Storage Forum details cloud storage pricing among the top providers; pricing is competitive, but there are indeed differences that can impact your overall bill. However, it’s not enough to move data and park it there for life. Innovation in cloud and storage technologies means that there are always going to be new, better and more affordable options for data storage. IT organizations need intelligence on their data and the means to manage data continuously over its lifetime. Our new eBook, 8 Ways to Save on File Storage and Backup Costs, discusses the issues and provides advice on how to cut annual costs significantly while still modernizing your hybrid environment and meeting business and departmental needs for data access. Here are the top reasons why file storage is so pricey: Data hoarding: Enterprises often retain file data for decades because it contains useful information such as customer insights, potential research intelligence or machine learning training data which they may want to leverage later. Audits and compliance requirements, known and unknown, also support the long-term or never-ending storage of data. Yet we can’t keep all this data forever; most enterprise data is not in active use yet is typically stored on expensive storage units. One-size-fits-all storage: Whether due to historical or cultural norms or lack of understanding of data assets and costs or both, organizations are too often storing all or most of their file data on expensive NAS technologies when most of it doesn’t need that level of performance and availability. Lack of visibility: Without insight into data assets – how much data is in what storage, its rate of growth, types and sizes of files, top owners, data usage trends and so on – it’s difficult to make intelligent decisions about its management. Storage is only 25% of file data costs: When looking at the file storage costs, consider that most of the expense lives outside of the primary storage. IT teams need to protect data with backups and replicate it for disaster recovery and that means multiple copies of data to store, manage and secure. Deleting unused, zombie data is a first step to reducing storage waste and data storage costs, but there is so much more to consider when it comes to optimizing storage spend and delivering maximum unstructured data value. Read the eBook to learn about 8 Ways to Save on File Storage and Backup Costs. Komprise Intelligent Data Management delivers an analytics-first approach to file and object data management and mobility. Our SaaS solution first indexes all data across all storage to identify hot, warm and cold data and which departments or users are consuming the most space. You can see interactive ROI analysis of how much your organization can save based on different data management policies. Get started with Komprise Analysis. Know First. Move Smart. Save More. ### Komprise Named Gold Winner in the 2023 Globee® Awards Campbell, CA – June 2, 2023 – Komprise, the leader in analytics-driven unstructured data management has been honored as a Gold Winner for Information Technology Cloud/SaaS in the prestigious 3rd Annual 2023 Globee® Awards for Disruptors. The Globee® Awards, recognized globally as the foremost business awards and ranking lists, bestow these esteemed accolades to celebrate and acknowledge exceptional disruptors in every industry. See the complete list of 2023 winners here: https://globeeawards.com/disruptor/winners/  The Globee Awards for Disruptors recognizes and celebrates organizations and individuals who have made significant contributions in driving disruptive innovation across various industries. These awards acknowledge the trailblazers who have challenged the status quo, introduced groundbreaking ideas, and transformed traditional practices through their disruptive approaches. Komprise Intelligent Data Management is a solution that is revolutionizing the way enterprise IT organizations manage and mobilize unstructured data across all industries. Komprise customers, from enterprises including Pfizer, PacBio, Carhartt, Northwestern University, St. Luke's Heath System and Cadence are cutting on average 70% of annual storage, backup and DR costs from right-placing cold data into more cost-effective storage based on Komprise analysis and mobility. Komprise gives enterprise IT the power of holistic visibility of unstructured (file and object) data across all storage—from on-premises to the cloud. Komprise delivers a Global File Index of all this data and shows detailed analytics on data characteristics, such as data growth rates, access patterns, top data owners and top file types, to inform decision-making. IT and other authorized line of business and departmental users can easily search for the precise data they need and create automated data workflows to move data to the appropriate storage for archiving, AI and ML analytics projects, and/or compliance and security needs. “There is growing recognition that the right approach to unstructured data management is essential to data storage cost savings and unlocking data value in the enterprise,” says Kumar Goswami, co-founder and CEO of Komprise. “This award is continued validation of our vision of delivering a simple, transparent, software-centric approach to unstructured data visibility and mobility to feed AI, ML, analytics and other data workflows.” “Congratulations to all the remarkable disruptors who have been recognized and celebrated in the 3rd Annual Globee Awards for Disruptors,” says San Madan, Presidtent of Globee Awards. “Your relentless pursuit of innovation and your ability to challenge the status quo have set you apart as true game-changers in your respective industries. Your visionary ideas, groundbreaking solutions, and unwavering determination have not only disrupted the market but also inspired others to reimagine what is possible. Keep disrupting, keep pushing boundaries, and keep shaping the future. The world needs more disruptors like you. Well done!” More than 200 judges from around the world representing a wide spectrum of industry experts participated in the judging process. The judges are listed here https://globeeawards.com/disruptor/judges/ About the Globee Awards  The Globee Awards present recognition in nine programs and competitions, including Globee® Awards for American Business, Globee® Business Awards, Globee® Awards for Customer Excellence, Globee® Awards for Cybersecurity, Globee® Awards for Disruptors, Golden Bridge Awards®, Globee® Awards for Information Technology, Globee® Awards for Leadership, and Globee® Awards for Women In Business. For more information on the Globee Awards, visit https://globeeawards.com. twitter @globeeawards #globeeawards #disruptors Latest Komprise News: Komprise Automated Data Governance for IT and Simplifies Unstructured Data Access New Komprise Intelligent Tiering for Azure Slashes High File Storage Costs   About Komprise Komprise is a provider of unstructured data management and mobility software that frees enterprises to easily analyze, mobilize, and monetize the right file and object data across clouds without shackling data to any vendor. With Komprise Intelligent Data Management, you can cut 70% of enterprise storage, backup and cloud costs while making data easily available to cloud-based data lakes and analytics tools. www.komprise.com ### From Data Storage to Hybrid Data Lifecycle Management This article was adapted from its original version on Blocks & Files. On-premises and public cloud storage vendors can add data lifecycle management features to their products – but that only confirms the need for a global, supplier-agnostic data lifecycle management capability, said Komprise CEO Kumar Goswami in an interview with storage publication BlocksandFiles. Below are highlights from the interview: Blocks & Files: Komprise analyzes your file and object data regardless of where it lives. It can transparently tier and migrate data based on your policies to maximize cost savings without affecting user access. Can Komprise’s offering provide a way to optimize cloud storage costs between the public clouds? Kumar Goswami: Komprise enables customers to test different scenarios by simply changing the policies for data movement and visualizing the cost impacts of these choices. By running such what-if analysis, customers can choose between different destinations and then make their decision. Komprise also supports cloud-to-cloud data migrations. Komprise gives customers the analytics to make informed decisions for data lifecycle management and data mobility to implement their choices. Blocks & Files: How would you view AWS’s S3 Intelligent Tiering and the AWS Storage Gateway with integration between the cloud and on-premise, offline AWS-compatible storage. What does Komprise offer that is better than this? Goswami: AWS has a rich variety of file and object storage tiers to meet the various demands of data, with significant cost and performance differences across them. Most customers have a lot of rarely-accessed cold data. They can leverage highly cost-efficient tiers like Glacier Instant Retrieval that are 40 percent to 60 percent cheaper. On AWS, you cannot directly access the tiered data from the lower tier without rehydration. Nor can you set different policies for different data, because it automatically manages data within itself. AWS S3 Intelligent Tiering is useful if you have small amounts of S3 data, you don’t have file data, you don’t have analytics-based data management, don’t require policy-based automation and are worried about irregular access patterns. Komprise delivers data lifecycle management across AWS FSX, FSXN, EFS, S3 tiers and Glacier tiers. It preserves file-object duality so you can transparently access the data as a file from the original source and as an object from the destination tiers – without rehydration – using our patented Transparent Move Technology (TMT). Customers with hundreds of terabytes to petabytes of data want the flexibility, native access and analytics-driven automation that Komprise delivers. Blocks & Files: Azure’s File Sync Tiering stores only frequently accessed (hot) files on your local server. Infrequently accessed (cool) files are split into namespace (file and folder structure) and file content. The namespace is stored locally and the file content stored in an Azure file share in the cloud. How does this compare to Komprise’s technology? Goswami: Hybrid cloud storage gateway solutions like Azure File Sync Tiering are useful if you want to replace your existing NAS with a hybrid cloud storage appliance. Komprise is complementary to these solutions and transparently tiers data from any NAS including NetApp, Dell, and Windows Servers to Azure. This means you can still see and use the tiered data as if they were local files and you can access the data as native objects in the cloud without requiring a move to a new storage system. Blocks & Files: NetApp’s BlueXP provides a Cloud Tiering Service that can automatically detect infrequently used data and move it seamlessly to AWS S3, Azure Blob or Google Cloud Storage – it is a multi-cloud capability. When data is needed for performant use, it is automatically shifted back to the performance tier on-prem. How does Komprise position its offering compared to BlueXP? Goswami: NetApp BlueXP provides an integrated console to manage NetApp arrays across on-premises, hybrid cloud and cloud environments. So it’s a good integration of NetApp consoles to manage NetApp environments, but it does not tier other NAS data. Also, NetApp’s tiering is proprietary to ONTAP and is block-based, not file-based. Block-based tiering is good for storage-intrinsic elements like snapshots because they are internal to the system. However they cause expensive egress, limit data access and create rehydration costs plus lock-in for file data. To see the differences between NetApp block-based tiering and Komprise file tiering, please read our paper on tiering choices. Storage vendors stand to gain by having customers in their most expensive tiers. They are recognizing the demand and starting to offer some data management. But the storage vendor business model still derives from driving revenues from their storage operating system; they offer features for their own storage stack and tie the customer’s data into their proprietary operating system. Blocks & Files: There are many suppliers offering products and services in the file and object data management space. Do you think there will be a consolidation phase as the cloud file services suppliers (CTERA, Lucid Link, Nasuni, Panzura), data migrators (Datadobi, Data Dynamics), file services metadata-based suppliers (Hammerspace) ILMs such as yourself, and filesystem and services suppliers (Dell, NetApp, Qumulo, WekaIO) reach a stage where there is overlapping functionality? Goswami: As data growth continues, customers will always need places to store the data and ways to manage the data. Both of these needs are distinct and getting more urgent as the scale of data gets larger and more complex with the edge, AI and data analytics. The market across these is large: data management is already estimated at about a $18B market. Typically for such big markets, you will see multiple solutions targeting different parts of the puzzle. Our focus is storage-agnostic, analytics-driven data management. This helps customers cut costs and realize value from their file and object data no matter where it lives. We see our focus as broader than ILM. It is unstructured data management, which will broaden even further to data services. Blocks & Files: If you think that such consolidation is possible, how will Komprise’s strategy develop to ensure its future? Goswami: Komprise is focused on being the fastest, easiest and most flexible unstructured data management solution to serve our customers’ needs. To do this effectively, we continue to innovate our solution and we partner with the storage and cloud ecosystem. We have and will continue to build the necessary relationships to offer our customers the best value proposition. My cofounders and I have built two prior businesses, and have learned that focusing on the customer value proposition and continually improving what you can deliver is the best way to build a business. We are barely scratching the surface of unstructured data management and its potential. Think about the edge. Think about AI and ML. Think about all the different possibilities that no single storage vendor will be able to deliver. We are focused on creating an unstructured data management solution that solves our customers’ pain-points around cost and value today while bridging them seamlessly into the future. ### Cloud Cost Optimization Relies on Data Management In late April, the major public cloud computing providers reported earnings. AWS’ revenue growth of 16% lagged previous quarters and was lower than Microsoft’s reported cloud sales, according to Channel Futures. “Meantime, Amazon executives on April 27 said the seeming slowdown at AWS ties to cost optimization rather than to any direct spending cuts,” the publication added. Cloud cost optimization isn’t a new concept but it’s certainly gaining steam this year. There were hints at the beginning of 2023 of a cloud repatriation movement, but that hasn’t materialized in a notable way. Instead, enterprises are seeking to reduce waste and spend smartly in the cloud, as cloud spending has raised the eyebrows of many an executive in recent months. Cloud spending often represents more than 50% of the overall cost of revenue in hypergrowth SaaS firms and has significant implications on balance sheet health and market cap, according to Forbes. Silicon Angle reports that the cloud spending slowdown is a result of “cautious consumption patterns and aggressive cloud optimization, which is being promoted by the big three cloud vendors in an attempt to lock customers into longer-term commitments.” Proven ideas for cloud cost optimization There are many ways to optimize and reduce cloud spend. Common tactics include signing up for cost savings plans and other pricing promotions offered by the cloud vendors, monitoring spend with third-party tools and those offered by cloud providers, deleting duplicate and orphaned data, using cloud cost optimization software to look for additional efficiencies, and managing cloud sprawl and shadow IT through automated discovery and corporate policies. Data management is another proven method; it gives storage and IT managers a means to view data assets across all storage and right-place data in the most appropriate storage solution for current needs. This avoids data sitting endlessly on high-priced storage when it’s no longer active. Data management can also automate data movement between cloud storage tiers as it ages or as its usage and requirements change. Komprise for cloud cost optimization Komprise delivers a variety of metrics and reports to help you understand data growth, data usage and data storage costs. You can use those metrics to forecast savings of switching to different types of storage such as cloud object storage for deep archives. Read the Komprise blog on our new reports which users can download and shared with stakeholders. It’s also helpful to learn about the new metrics every IT director should be tracking in today’s hybrid cloud organization. Earlier this year, Komprise announced Komprise Analysis, an entry-level subscription offering to get started on unstructured data management. Read the white paper to learn more about all the ways Komprise Analysis helps you understand your data environment and plan for data lifecycle management. What’s New at Komprise Product News Komprise Intelligent Tiering for Azure: This exclusive offering is the only Microsoft Azure Marketplace solution that gives customers access to file analysis and tiering to and within Azure. Customers can acquire this without purchasing the full Komprise Intelligent Data Management platform. “Since Komprise tiers data to Microsoft Azure in native readable format and provides data workflows, customers can cut costs and leverage the full power of Azure services to address AI, big data, security and compliance use cases.” Learn more   Media Coverage Blocks and Files: Komprise Auto Tiers Files to Azure Blobs: “Pump the files up to Azure Blob storage and then they can be accessed by services such as Microsoft Purview and Defender for Storage, Azure Synapse Analytics and Azure AI.” Awards Top Fastest Growing Storage Companies in 2022: Komprise was included again in this prestigious list compiled by Storage Newsletter.   CRN Storage 100: Komprise was named a top vendor under the data protection category of CRN’s annual list. American Business Awards: Komprise was a Bronze Winner in the category for Most Innovative Tech Company of the Year. Events Check out our latest technical webinars: Upcoming: Reporting Update and Best Practices Hypertransfer for WAN Migrations Analysis Overview and Best Practices ### Introducing Komprise Intelligent Tiering for Azure In early 2022 we were thrilled to participate in the launch of the Azure Storage Migration program which offers Komprise Elastic Data Migration at no cost to migrate data to Azure. Later in the year we turbocharged our data migration solution with the introduction of Hypertransfer, a new way to migrate file and object data 25x faster than other tools. Today we're announcing a new solution with Azure for intelligent tiering. The partnership with Microsoft to accelerate the migration of file workloads to Azure is delivering great value for our joint customers. Over 100 customers are already migrating petabytes of data to Azure using Komprise through the program. Karl Rautenstrauch, Azure Storage Principal Product Manager, reviewed the highlights of the program’s first year and looked ahead at 2023: “The program and partnerships continue. Files and object flow. Risk and man hours decline. Unstructured data from all industries will continue to move to Azure Storage thanks to the Storage Migration Program. It will continue to fuel digital transformation and be used by virtual servers and desktops, containers, analytics, machine learning, and more. Value is unlocked and costs are saved by migrating the right data to the right platform and tiering with confidence.” Read Karl's new post on the topic of storage costs: The True Cost of File Storage: Why is File Data Expensive? Watch my interview with Microsoft’s Vamshidhar Kommineni and Komprise COO Krishna Subramanian to learn more about the program and get started with Komprise for Azure migration. Exclusive Intelligent Tiering to Azure Building on the success of this program, today we’re excited to announce the general availability of Komprise Intelligent Tiering for Azure. This new offering is the only Microsoft Azure Marketplace solution that gives customers access to file analysis and data tiering to and within Azure. Customers whose use case is primarily tiering and cost-efficiency can now acquire this without purchasing the full Komprise Intelligent Data Management platform. Komprise Intelligent Tiering for Azure is a smart way to move to the cloud and manage your data cost-effectively while there so you can continue to leverage cloud for innovation, flexibility and collaboration needs. Now you can analyze data across any on-premises and cloud storage, see cost models that show the potential cost savings of data tiering to and within Azure and then use Komprise to execute a plan. Azure customers can use Komprise Intelligent Tiering along with Azure File Migration to tier and migrate file and object data. The solution is available now on the Azure Marketplace and is MACC eligible. There are two use cases for Komprise Intelligent Tiering for Azure: Hybrid Cloud Tiering from On-Premises File Storage to the Cloud: Many enterprises already have on-premises file-storage network attached storage (NAS) environments and wish to optimize spend by tiering cold data to the cloud. Komprise Intelligent Tiering for Azure analyzes across any vendor NAS including NetApp, Dell, Windows Server, Pure Storage, Nutanix and others to identify cold data and transparently tier it based on custom policies to the appropriate Azure Blob and Azure File tiers. Read how Lummus Technology is saving roughly 80% on storage and backups annually by using Komprise to correctly identify cold data for tiering to Azure. Cloud File Data Lifecycle Management: Most enterprise IT organizations are in the midst of a digital transformation journey and are moving file data workloads to cloud file storage options such as Azure Files and Azure NetApp Files. Cloud file storage is highly performant and desirable for active, hot data but not economic for rarely-used data. By transparently tiering cold files in the cloud to cost-effective Azure Blob tiers, you can save 70% on your storage and backup costs without losing visibility or access to the tiered data from the file system. Long-Term Value in Azure Cloud Beyond data storage cost savings, data tiering can help IT organizations generate new value in the cloud by leveraging the many new data services available. Jurgen Willis, VP of Azure Specialized Workloads and Storage, had this to say about the new solution: “Every organization in the current environment is looking to do more with less while reducing cost. The rising cost of on-premises storage is a pain point that we are pleased to tackle in collaboration with Komprise. Since Komprise tiers data to Microsoft Azure in native readable format and provides data workflows, customers can cut costs and leverage the full power of Azure services to address AI, big data, security and compliance use cases.” ### New Komprise Intelligent Tiering for Azure Slashes High File Storage Costs with Exclusive Microsoft Azure Marketplace Offer Campbell, CA—April 27, 2023 – Komprise, a leader in analytics-driven unstructured data management and mobility, today announced an extension to its Microsoft collaboration with the availability of Komprise Intelligent Tiering for Azure. Most organizations are spending 30% or more of IT budgets on managing unstructured file data, which continues to grow rapidly. This exclusive offering is the only Microsoft Azure Marketplace solution that gives customers access to file analysis and tiering to and within Azure. Customers can acquire this without purchasing the full Komprise Intelligent Data Management platform. Organizations can accelerate their cloud journey and gain better ROI from cloud migrations by first transparently tiering data from any NAS that is cold and has not been accessed in months to Azure Blob Storage. This cuts an average 70% of costs on storage, backup and disaster recovery costs. Secondly, an organization with file data in Azure Files or popular cloud NAS platforms can use Komprise Intelligent Tiering for Azure to tier data to lower cost Azure Blob Storage automatically via easy to configure policies. Komprise Intelligent Tiering for Azure builds upon the success of the Azure File Migration program, which launched in February 2022 and gives customers access to Komprise at no cost to migrate data to Azure. “More than 100 enterprises are already using Komprise through the Microsoft Azure Storage Migration Program because of its simplicity and convenience,” says Krishna Subramanian, COO Komprise. “The new Komprise Intelligent Tiering for Azure extends this ease-of-use by allowing customers to use services such as Microsoft Purview, Microsoft Defender for Storage, Azure Synapse Analytics and Azure AI with data copied or tiered from on-premises. Azure customers can use their existing Azure contracts and utilize their Azure Consumption Commitments through this specially priced Komprise offer in the Azure Marketplace.” “Every organization in the current environment is looking to do more with less while reducing cost. The rising cost of on-premises storage is a pain point that we are pleased to tackle in collaboration with Komprise,” says Jurgen Willis, VP Azure Specialized Workloads and Storage. “Since Komprise tiers data to Microsoft Azure in native readable format and provides data workflows, customers can cut costs and leverage the full power of Azure services to address AI, big data, security and compliance use cases.” Pricing and Availability Komprise Intelligent Tiering for Azure is priced at $0.008/GB/mo based on an annual subscription. The solution is available today on the Azure Marketplace. Customers can easily upgrade to the full Komprise Intelligent Data Management platform to gain Smart Data Workflows and the Global File Index with Deep Analytics. About Komprise Komprise is a provider of unstructured data management and mobility software that frees enterprises to easily analyze, mobilize, and monetize the right file and object data across clouds without shackling data to any vendor. With Komprise Intelligent Data Management, you can cut 70% of enterprise storage, backup and cloud costs while making data easily available to cloud-based data lakes and analytics tools. ### Komprise Analysis Shines a Spotlight on Data Storage Costs New Komprise subscription arms storage teams with analysis and pre-built reports across storage and cloud silos to guide data management decisions. Campbell, CA—March 9, 2023 – Komprise, the leader in analytics-driven unstructured data management and mobility, today announced the immediate availability of Komprise Analysis to unlock insights and savings across all file and object data storage. Komprise Analysis is now available as a standalone SaaS solution for enterprises who want visibility first and are not yet ready to move data. It includes a new set of pre-built reports along with dynamic interactive analysis. The enhanced analysis and reporting capabilities are also included with Komprise Elastic Data Migration and the full Komprise Intelligent Data Management Platform. Stop Overspending on Data Storage With Komprise, customers can: Get insight in minutes: Simply point Komprise at all file and object storage including NetApp, Dell, HPE, Qumulo, Nutanix, Pure Storage, Windows Server, Azure, AWS and Google and see unified analysis in minutes of how data is being used, how fast it’s growing, who is using it, and what data is hot and cold. Forecast cost savings: Set different data management policies and customize cost models to interactively visualize expected savings. Address compliance, governance and security: Get insights to address data retention, data segregation and reporting for compliance and security use cases. Get a health check of storage and network topology: Understand file system and network topology performance bottlenecks before they impact data movement. Analyze at petabyte-scale: Scales across hundreds of petabytes with no performance impact to storage Share pre-built reports: A new Reports tab with a library of pre-built reports that can be easily viewed, downloaded and shared includes: Showback: See storage costs, potential savings and a breakdown by top shares, users and file types to foster better departmental collaboration and buy-in. Cost Savings: Understand projected cost savings and three-year projections for data growth of each data management plan. Data Stores: See all data stores across locations, vendors and clouds to easily sort across metrics like fastest growth, newest data, coldest shares. Orphaned Data: See obsolete, orphaned data and potential savings from its deletion. Duplicates: Identify potential duplicates across storage vendors, silos, sites and clouds. User Audit: Monitor user access and usage for auditing purposes. Easily upgrade to start moving data: Start with visibility and upgrade from standalone Komprise Analysis to Elastic Data Migration or to the full Intelligent Data Management suite depending on your use case or specific requirements. “Over the last year, Gartner end users report between 30% and 60% growth for their file data,” said Julia Palmer, Research VP at Gartner. “The steep growth of unstructured data for emerging and established workloads now requires new types of products and approaches that can address and consolidate an increasing number of business use cases with better agility, lower acquisition, operational and management costs.” "With increased pressure on budgets and costs, buying more storage to solve the massive data growth challenge is not sustainable,” said Paul Chen, Senior Director of Product Management at Komprise. “Komprise Analysis gives enterprise IT teams the visibility and information they need to cut costs with smart data tiering, data management and cloud data migration initiatives.” Pricing and Availability Komprise Analysis is available immediately. Pricing starts at $15k/year. Learn more at www.komprise.com/analysis. About Komprise Komprise is a provider of unstructured data management and mobility software that frees enterprises to easily analyze, mobilize, and monetize the right file and object data across clouds without shackling data to any vendor. With Komprise Intelligent Data Management, you can cut 70% of enterprise storage, backup and cloud costs while making data easily available to cloud-based data lakes and analytics tools. Media Contact: Kevin Wolf kevin@tgprllc.com Gartner Source: Modernize Your File Storage and Data Services for the Hybrid Cloud Future  https://www.gartner.com/document/4142399?ref=solrAll&refval=357928472 ### Komprise Doubles Revenues in 2022 and Recognized as Leader in Cloud Data Migrations and Unstructured Data Management Campbell, CA—February 24, 2023 – Komprise, the leader in analytics-driven unstructured data management and mobility, today announced that subscription revenues more than doubled for a third consecutive year. Komprise enterprise customers are distributed across healthcare, life sciences, biotech, media and entertainment, public sector, higher education, financial services, legal, energy, high-tech and other industries managing petabyte-scale unstructured data environments. Customers are using Komprise in several ways to enhance their data-driven operations, including cold data tiering for an average cost savings of 70%, 25 times faster cloud file migrations, cloud storage cost optimization, self-service data management and storage capacity planning through cross-silo visibility and actionable analytics. Other growing use cases include unstructured data tagging and automated workflows to right-place data for AI and data science initiatives. Komprise 2022 Highlights: Doubled annual subscription revenues for a third consecutive year. 50% growth in the Komprise Global File Index, which consists of hundreds of billions of files, providing customers a Google-like search across their entire data estate to find, tag and mobilize unstructured data. 120% net dollar retention (NDR) showing world-class customer retention and expansion, even during an economic downturn. 30% of revenues came from expansions, indicating strong customer satisfaction and loyalty. 200% growth in the number of organizations using Komprise for data migrations to a new NAS or for cloud data migrations. 100+ Microsoft customers migrating data to Azure using Komprise during the first 12 months of the Azure File Migration program, which funds customer use of Komprise. The company closed $37 million in new investment in January 2023. Recognized as the leader in the GigaOm Radar for Data Migration Tools 2022 and Coldago Report on Unstructured Data Management 2022, and covered in Gartner Hype Cycle for Hybrid Cloud Storage and Gartner Market Guide for Hybrid Cloud Storage Continued product innovation with Smart Data Workflows, Departmental/User Self-Service and Hypertransfer for Elastic Data Migration. Customer Trends in Unstructured Data Management & Storage Komprise internal research on its customers during Q4 of 2022 uncovered several trends which will likely persist throughout the coming year. Customers are looking to prioritize time and investment in the following areas. Reducing IT spending by pivoting to more of an OPEX environment and by deleting data that is no longer needed to reduce storage costs and complexity. Simplifying infrastructure, getting rid of legacy apps and software and data center consolidation to support business growth and IT modernization. Managing research workflows and the full lifecycle of data: Examples include, from a major university: enabling users to share data between labs and send some data to the cloud for processing, then bring it back on-premises. Use industry standards to move data easily between platforms. Externalize (tier) data off NAS: IT and storage managers want to tier cold data from across the business to cheaper, secondary storage to save money and free up primary storage capacity. "Every enterprise IT organization today is looking to cut costs as they modernize and simplify how their users can get faster access to data,” says Krishna Subramanian, COO and cofounder of Komprise. “This is why unstructured data management is becoming a must-have strategy for companies to be efficient, stop overspending on data storage, ensure successful migrations to the cloud and generate new value from data.” About Komprise Komprise is a provider of unstructured data management and mobility software that frees enterprises to easily analyze, mobilize, and monetize the right file and object data across clouds without shackling data to any vendor. With Komprise Intelligent Data Management, you can cut 70% of enterprise storage, backup and cloud costs while making data easily available to cloud-based data lakes and analytics tools. Learn more at www.komprise.com Media Contact: Kevin Wolf kevin@tgprllc.com ### Reduce Cloud Data Storage Costs & Optimize Cloud Spend in 2023 In part 1 of this two-part series, I reviewed the latest research and thinking on the state of the cloud; things have changed in a year. Here’s a quick synopsis of what has become a cloudy market for cloud infrastructure spending: Public cloud leaders’ earnings and near-term forecasts are soft, likely due to more conservative spending during an economic downturn; Enterprises are not giving up on the cloud and most will maintain or even increase spending this year; Cloud data storage costs are rising and repatriation is likely happening at some level; IT leaders will focus on optimizing cloud spend and reducing waste ASAP; CSPs may look to retain customers with better pricing plans and smarter tools to optimize and track spending and consumption. When planning your cloud strategy this year and forward, we don’t recommend a sharp pullback on the cloud. After all, hybrid cloud is still the future and a well-optimized cloud infrastructure is how organizations can be more agile, competitive and drive innovation faster than ever. However, during a time of belt-tightening, enterprise IT leaders need to sharply optimize cloud resources to save money, operate more sustainably and ensure value. The vast majority (83%) of companies are concerned about a recession in 2023, according to research by Spiceworks Ziff Davis. The survey also showed that most IT organizations aren’t planning to decrease spending but 43% will reduce non-essential spending. Consider this: cloud object storage tiers are 10 to 30 times less expensive than cloud file storage options. If you could transparently tier and offload cold data (which is often 80% of all data) to cloud object storage, you could reduce the storage cost of that data by 10x to 30x. Ultimately, the question is not about datacenter versus cloud: each model has pros and cons for different workloads and use cases. The prevailing issue is that if you do not efficiently manage data over its lifecycle, both options will be expensive. Don’t keep cold data on expensive, Tier 1 storage and backup resources when it can safely move to cheaper, secondary storage in the cloud. Komprise Brings Actionable Analysis to Reduce Cloud Data Storage Costs Komprise analyzes your file and object data regardless of where it lives. It can transparently tier and migrate data based on your policies to maximize cost savings without compromising user access. Continual analytics for the most cost-effective placement Komprise Analysis scans data across silos to deliver insights on usage, growth and costs. This gives IT a way to create a nuanced versus one-size-fits-all data management strategy. Many of our customers discover that most of their data is cold —  which guides decisions to move that data from NAS to cloud object storage. While developing life-saving vaccines in 2020, our customer, Pfizer tiered 2PB of cold data to AWS S3, saving 75% on  backup and data storage costs. Pfizer continues to tier and migrate data to object storage as it ages, so the savings are ongoing. Transparent cloud tiering Komprise employs file-based cloud tiering with Komprise Transparent Move Technology (TMT)™. The tiered file still looks like it is on the source as a file and users and applications can access it as before without disruption. However, Komprise translates the file and stores it as an object on S3 or Azure Blob in its native form. The user can access these objects natively from the cloud and as files natively from the source. This way, Komprise leverages the lower cost of object storage while providing full transparency. This transparency coupled with dramatic cost savings is what made it possible for Pfizer to tier data even while under pressure to develop the Covid vaccine. This is key to cost savings; customers share stories with us that before TMT, they could only tier a small fraction of their cold data because users and departments balked. There was too much risk to future access. Now with Komprise TMT, that risk is gone! Cloud data lifecycle management If your organization has an aggressive cloud strategy that entails moving large volumes of data, consider the Komprise approach for Smart Data Migration. Migrate the hot data to cloud file storage and the cold data to cloud object storage to optimize cloud spend. You can also configure Komprise to automatically move the files down to still lower cost tiers based on access patterns. Optimize file data search and workflows for AI A major roadblock with AI/ML projects is finding just the right data. The Komprise Global File Index and Smart Data Workflows help users search for certain files or tags across data silos with a simple Google-like experience. Then they can systematically enrich the data with more metadata and move it to AI and ML analytics platforms. This speeds up time to value for big data analytics programs. For example, a user can tag data collected from self-driving cars with information about driving conditions. Later, users can search these enriched data sets to analyze vehicle performance and safety. Get Your File Data AI-Ready! Organizations are seeing outcomes that simply weren’t possible until recently with cloud AI and ML. A cloud-based medical imaging system owned by Mass General Brigham uses AI-based diagnostics to dramatically speed test results, such as mammograms, according to VentureBeat. Cloud-based AI and ML will only grow and enterprises don’t want to be left behind. IDC forecasts that by 2025, nearly 50% of all accelerated infrastructure for performance-intensive computing (including AI and HPC) will be cloud-based. Final Thoughts: Know First. Move Smart. Developing a cloud strategy for the next 12 months won’t be easy. Many IT leaders are feeling pressure from above to cut extraneous costs and justify everything. Spending without a clear ROI and upon specious results won’t be popular. Komprise delivers an intelligent use of cloud resources in a non-proprietary method. This means your organization can continue its cloud journey with assurance and realize the following benefits: Stop Overspending on Storage: Cut significant costs from your cloud data storage spend, across a hybrid cloud or multi-cloud environment. Extend the life of your existing storage and leverage cost-efficient options instead of continuously buying more storage. Keep Your Users/Apps Happy: Invest in the cloud in a “future proof” manner which delivers long-term flexibility and a non-disruptive user experience. Be AI Ready: Gain a competitive advantage by leveraging automated actions and workflows to  discover and move the right unstructured data sets to cloud AI and ML tools and services. Read my blog on this opportunity for AI-enabled unstructured data analytics. ---------- ### To Cloud or Not to Cloud? The year 2023 has thus far been a rollercoaster. In some respects, there is positive news with inflation slowing and the IMF now forecasting 2.9% growth for 2023 – up from a 2.7% forecast in October. Yet there remains a persistent doom and gloom outlook given massive layoffs by large companies especially in the tech sector; the continuing war in Ukraine; ongoing supply chain concerns and energy shortages. There have been numerous predictions about the state of the cloud and enterprise demand in the coming months. In this two-part series, we summarize some of the latest news and research, and then share ideas and trends for IT organizations when it comes to cloud spend and cloud data management. The Cloud Bears The numbers don’t lie and earnings statements from the major cloud provider show that demand has cooled down. As reported on CNBC, Amazon’s cloud revenues grew by 20% in Q4, compared with 27.5% growth in Q3; Microsoft reported 31% growth from cloud (including Azure) in 2022, compared with 35% in 2021. In a report published last month, The Uptime Institute said that AWS figures represented "the slowest growth in its history." The Register also covered Google’s position: “Google's cloud growth was up to nearly 38 percent in Q3 of 2022, from 30 percent in Q1, this is down from a high of 58 percent in Q1 of 2021. While these figures would represent staggeringly positive growth in any other industry, among cloud providers they represent a mood change.” Many pundits have pointed to these earnings, with a general outlook of malaise for 2023. Yet what does that really mean? It’s hard to imagine companies pulling back massively on cloud plans, given the rapid momentum to modernize and disrupt markets through cloud computing which began with the pandemic. However, there is talk of repatriation-- indicating that in some cases, enterprises have been burned in the cloud and are looking to regain control by bringing workloads back in house. Of course, repatriation brings its own risks, costs and delays: staff time, potential data loss, egress costs and the potential need to buy more servers and storage appliances to house the apps and data on-premises. Let’s look at a more positive outlook for cloud:   The Cloud Bulls The Cloud Bulls Unsurprisingly, Microsoft CEO Satya Nadella expressed optimism for a quick return to normal: “At some point, the optimizations will end,” Nadella said on its recent earnings call per CNBC. “In fact, the money that they save in any optimization of any workload is what they’ll plough into new workloads, and those workloads will start ramping up.” Gartner’s forecast for 26.8% growth in cloud spend this year, compared with 25.9% in 2022, seems to support this view. TechTarget piled on by covering the findings of a recent survey from ESG: More than half of the 742 survey respondents (56%) reported public cloud infrastructure services spending would go up this year and 71% expect to develop and deploy cloud-native applications in 2023 -- an increase of about 11% from 2022. Only a small percentage of respondents--4% and 3%--expect public cloud spending on applications and infrastructure services to decline. Finally, a survey of US and UK IT leaders sponsored by Virtana found that 84% wanted a large portion of their storage to remain in the cloud – even though 54% say that storage is growing at a faster rate compared to overall cloud costs. Most respondents (69 percent) say that storage now takes up more than one-quarter of their total cloud costs, while 23 percent say it accounts for more than half. A Cloud Strategy for 2023 Here’s one way to look at the market as it stands now: Public cloud leaders’ earnings and near-term forecasts are soft, likely due to more conservative spending during an economic downturn; Enterprises are not giving up on the cloud and most will maintain or even increase spending this year; Cloud data storage costs are rising, and repatriation is likely happening at some level; IT leaders will focus on optimizing spend in the cloud and reducing waste ASAP; CSPs may look to retain customers with better pricing plans and smarter tools to optimize and track spending and consumption. The answer is not to turn away from the cloud—but to be more precise and analytics-driven with your approach. Insights from Enterprise Technology Research (ETR), reported in Silicon Angle, indicate that enterprises are targeting excess or wasteful cloud spend. Along those lines, “using lower-cost compute instances such as Graviton from AWS or Advanced Micro Devices Inc. chips and tiering storage to cheaper object stores or deep archive tiers” is a prominent strategy. We couldn’t agree more! In my next post I’ll review some specific cloud data management and cloud cost optimization strategies. Read part two: Reduce Cloud Data Storage Costs and Optimize Cloud Spend. ### How to Prep Unstructured Data for Cloud Analytics and AI This article has been adapted from its original version on TDWI. More than half of IT leaders report that their organizations are managing 5PB or more of data and most (68 percent) are spending more than 30 percent of their IT budget on data storage, backups and disaster recovery, according to the Komprise 2022 State of Unstructured Data Management. Update: The third-annual industry survey reported that preparing for AI is the leading data storage priority and unstructured data management challenge. Read a review of the survey reports in the definition of unstructured data management. Download the latest report: komprise.com/report Five petabytes is a lot of data (about 1.25 billion digital photos’ worth, for example) and much of it is unstructured, meaning that it doesn’t fit neatly into rows and columns in a database. This data -- such as log files, IoT sensor data, microscopic data, user documents and medical images -- is an untapped gold mine for the nascent field of unstructured data analytics. With advances in cloud computing, machine learning (ML), and AI tools, unstructured data analytics is now a prime opportunity. Today there are a multitude of cloud-based ML and AI services for different use cases -- from image and audio pattern recognition to personally identifiable information (PII) identification. Some interesting and valuable use cases for unstructured data analytics include medical insurance fraud detection, autonomous vehicle testing, malicious actor detection, precision medicine and customer sentiment analysis of call center audio files. Top Challenges for Unstructured Data Analytics The Komprise survey  showed that 65 percent of organizations plan to or are already investing in delivering unstructured data to their new analytics/big data platforms. To be successful in unstructured data analytics, you must jump through several hurdles compared to the relatively straightforward process of mining structured data in databases and spreadsheets. Industry analyst Doug Laney introduced the 3Vs concept in a 2001 MetaGroup research publication, 3D Data Management: Controlling Data Volume, Variety, and Velocity. When it comes to unstructured data, these challenges include: Volume of data. Because there is so much data in organizations today, you can’t feasibly or affordably analyze it or copy it all to a cloud service or big data platform. Efficiently finding the right unstructured data across on-premises, edge, and cloud silos and then moving it to an analytics tool is a prominent hurdle today.  We addressed these challenges with the 2022 release of Smart Data Workflows. Adding to this pain is the prevalence of duplicate data. A research group may have teams of people working on the same data set and therefore multiple copies exist across different file shares and geographic locations. Variety of data. Unlike structured data, unstructured data encompasses many different file types across video, audio, logs, lab notebooks, IoT and documents. Thus, understanding what types of files match with which data or cloud service is imperative so you’re always using the right tool for the right job. For example, looking for PII in documents is entirely different than finding all images that contain dogs. Different analytics techniques are needed to process different types of unstructured data. Velocity of data. Data is piling up fast and because of its speed and volume, you can’t often act on it fast enough to place unstructured data into the appropriate storage technology or data lake for analysis. What comes to mind is the iconic “I Love Lucy” episode where Lucy and Ethel fail at their candy factory job once the conveyor belt speeds up, leaving no time to wrap the chocolates and resulting in plenty of waste. Businesses need automation to manage unstructured data because it is impossible to manually handle the velocity, variety and volume of this data. Tagging and Automation Help Prep Data for Analytics Addressing the challenges of unstructured data volume, variety and velocity begins with real-time knowledge on key data characteristics. IT managers also need  knowledge of cloud infrastructure and the big data analytics ecosystem across data centers, the edge and clouds. Tactics may include: The ability to preprocess data at the edge so it can be analyzed and tagged with new metadata before moving it into a cloud data lake. This can drastically reduce the wasted cost and effort of moving and storing useless data and can minimize the occurrence of data swamps. Applying automation to facilitate data segmentation, cleansing, search and enrichment. You can do this with data tagging, deletion or tiering of cold data by policy and moving data into the optimal storage where it can be ingested by big data and ML tools. The Komprise survey found that the leading new approach to unstructured data management is the ability to initiate and execute data workflows. Adopting an unstructured data management tool that persists metadata tags as data moves from one location to another. For instance, files tagged as containing PII by a third-party ML service should retain those tags indefinitely so that a new research team doesn’t have to run the same analysis over again -- at high cost. Komprise Intelligent Data Management has these capabilities. Planning appropriately for large-scale data migration efforts with thorough diligence and testing. This can circumvent common networking and security issues that derail the timely completion of moving data from one place to another. Read this blog post for tips. In this economy, speed is a game changer. The faster you can feed quality data into your analytics platform, the faster you’ll get results and outcomes, and the less time you’ll spend doing it. Storage management and data management have finally converged, thanks to the demand for unstructured data analytics. ### Komprise Hypertransfer is Here: 25X Faster Cloud File Migrations According to Wikipedia, the concept of the “hyperloop” was first conceived in 1799 to address rail transportation challenges. Fast forward 200+ years later and it’s still an interesting idea that could eventually become a reality as the “fifth mode of transportation” after road, air, water and rail. When it comes to the transportation of unstructured data across a wide area network (WAN), there have also been challenges, resulting in slow, unpredictable, expensive and even failed cloud migrations. Some of these cloud file migration roadblocks include: Billions of (mostly small) files: Unstructured data migrations often require moving billions of files, the vast majority of which are small files that have tremendous overhead, causing data transfers to be slow. Chatty protocols: Server message block (SMB) protocol workloads—which can be user data, electronic design automation (EDA) and other multimedia files or corporate shares—are problematic since the protocol requires many back-and-forth handshakes which increase traffic over the network. WAN latency: Network file protocols are extremely sensitive to high-latency network connections, which are unavoidable in WAN migrations. Limited network bandwidth: Bandwidth is often limited or not always available, causing data transfers to become slow, unreliable and difficult to manage. [ Watch the webinar: Preparing for a File and Object Data Migration ] Read the White Paper > > 25X Faster SMB Migrations with Hypertransfer To address these cloud migration challenges, today Komprise is introducing Hypertransfer for Elastic Data Migration, which creates dedicated virtual channels across the WAN to accelerate cloud data migrations. By establishing dedicated channels to send data, Komprise Hypertransfer minimizes the WAN roundtrips, which mitigates SMB protocol chattiness and dramatically improves data transfer rates. Tests done using a data set dominated by small files show how Komprise accelerates cloud data migration 25x faster than other alternatives. Komprise already delivers 27x faster performance for NFS migrations.   Komprise Hypertransfer creates dedicated virtual channels across the WAN to accelerate cloud data migrations. Hypertransfer is specifically designed to address the slow transfer rate associated with small SMB files. Migrations that used to take 25 days to complete can now finish in about a day—but this is not merely about time savings. Consider that migrations take precious person-hours and sometimes professional service hours—you can now get to the cloud faster with a much lower investment. Shorter migration windows lower the risk of network outages and other transient errors that make migrations a headache. Komprise also has built-in capabilities to minimize errors and data loss, such as auto-retries and checksum processes to verify that the files transfer correctly. Point tools do not provide this verification and as such are not ideal for large scale, enterprise migrations. Additionally, Hypertransfer strengthens security for data migrations as all file communication passes directly from the on-premises Observers to the cloud Windows Proxies through the private Hypertransfer channel. No part of the migration goes directly from on-premises systems to the cloud filers, and therefore the cloud filers themselves do not need to be exposed to any systems or network outside the cloud if that is not desired by the customer. “Komprise made our data migration to the cloud as seamless as possible. While researching ways to move large amounts of data, we were confronted time and time again with limitations that made us believe the migration would never happen. Komprise was a sigh of relief for our entire team.” David Passamonte IT Manager at Molecular Pathology Lab Network, Inc. Azure Migration Program blog post Other Komprise Elastic Data Migration Updates With the latest release, Komprise administrators have more flexible data migration configuration settings to handle read-only sources and sources with access-time tracking disabled. There are several bulk recall UI updates and customers can now enable and disable data integrity checks to either ensure data accuracy or improve migration performance. Learn more about Komprise Elastic Data Migration. See what's new in Komprise Elastic Data Migration 5x. Join Komprise and Microsoft Azure for a webinar cloud file migration webinar: ----------------- ### Komprise Hypertransfer Migrates Data to the Cloud 25x Faster The latest release of Komprise Elastic Data Migration creates virtual channels to speed up problematic SMB data migrations across the WAN, solving a critical pain point for cloud adoption. Campbell, CA, December 14, 2022— Komprise, the leader in analytics-driven unstructured data management and mobility, today announced the availability of Komprise Hypertransfer for Elastic Data Migration, which accelerates data transfer to the cloud while strengthening cloud security. As enterprises migrate more file data to the cloud, IT organizations face many barriers which cause migrations to often take weeks to months. SMB workloads such as user data, electronic design automation (EDA) and other multimedia workloads contain lots of small files and are a particular challenge since the protocol requires many back-and-forth handshakes that increase administrative traffic over the network. Komprise Hypertransfer optimizes cloud data migration performance by minimizing the WAN roundtrips using dedicated channels to send data, mitigating the SMB protocol issues. According to recent tests performed by Komprise, Hypertransfer improves data transfer rates across the WAN by 25x over other alternatives for SMB datasets with predominantly small files. Komprise already delivers 27 times faster performance for NFS migrations. Komprise Hypertransfer also strengthens security and defense against ransomware by not accessing cloud file storage over the network during data migrations, since data transfers from source to target over private channels. “Enterprises need to move file data to the cloud to cut costs and unlock strategic value,” said Kumar Goswami, CEO and co-founder of Komprise. “With Komprise Hypertransfer you get measurably faster SMB cloud data migrations—shaving weeks off migration timelines while minimizing the chance for errors or data loss. Komprise Hypertransfer makes cloud migrations feasible.” Read the White Paper > > Komprise Elastic Data Migration is a software as a service (SaaS) solution available with the Komprise Intelligent Data Management platform or as a standalone product. Designed to be fast, easy and reliable with elastic scale-out architecture and an analytics-driven approach, it is the market leader in file and object data migrations. Komprise Elastic Data Migration ensures preservation of data integrity with access control propagation and file-level data integrity checks such as SHA-1 and MD5 checks with audit logging. With the latest release, Komprise administrators also have more flexible migration configuration settings to handle read-only sources and sources with access-time tracking disabled. Additionally, customers can now enable and disable data integrity checks to either ensure data accuracy or improve migration performance. Learn more at komprise.com/elastic-data-migration. About Komprise Komprise is a provider of unstructured data management and mobility software that frees enterprises to easily analyze, mobilize, and monetize the right file and object data across clouds without shackling data to any vendor. With Komprise Intelligent Data Management, you can cut 70% of enterprise storage, backup and cloud costs while making data easily available to cloud-based data lakes and analytics tools. www.komprise.com. Media Contact: Kevin Wolf kevin@tgprllc.com _______________________ ### Five Industry Data Migration Use Cases No enterprise data migration is the same — and since Komprise has been used by customers to migrate petabytes of data for many years now, we can say this with assurance. In fact, one of our migration customers uses Komprise on 85 PB of data! Unstructured data migration projects can be complex, especially because they often involve disparate source and destination technologies. Komprise routinely migrates petabytes of data (SMB, NFS, Dual) for customers in many complex scenarios: Disparate NAS Thousands of shares Migration jobs scheduled via API Regulated industries with stringent security needs that require access permissions to be translated, logs of MD5 checksums and more. Komprise Smart Data Migration for File and Object Data Komprise simplifies and eliminates this complexity and its associated costs without the traditional approach of heavy professional services with our Smart Data Migration. The tenets of this approach include: Simplicity: Komprise is analytics-driven so you can plan before you migrate. Komprise provides analytics of not just your data but also of your environment so you can determine what data to migrate, to where, what transfer rates the network can handle, and if there are any bottlenecks to address before starting the migration. We are the only solution that provides holistic analytics. Fast: Customers see 25 to 27 times faster migration speeds using Komprise over other approaches. Komprise also eliminates unnecessary migration overhead and costs by transparently migrating data that was previously tiered even by proprietary storage solutions such as Dell EMC Isilon CloudPools or NetApp FabricPool, without requiring rehydration. Efficient: Komprise identifies potential bottlenecks before you migrate to avoid delays, errors and other common migration issues. Komprise also transfers and verifies not just the data but also the permissions and the data integrity in each iteration. Cost-Effective: Komprise analytics help customers understand data so they know which data can migrate and tier to which class and which data should stay on-premises. This could show IT that 70% of their data hasn’t been accessed in over a year and could be tiered to object storage in the cloud while the remaining active or hot data moves to new NAS on-premises or file storage in the cloud. Here’s a look at some of the more complex file and object data migrations we’ve completed across multiple sectors and technologies. Data Migration Case Studies Industry: Healthcare File Migration Overview: A hospital group used API to migrate petabytes of SMB files from EMC Isilon access zones to Qumulo. Results: Komprise set up approximately 400 migration jobs via scripting using the APIs, and in a few weeks migrated 278 million SMB files spanning nearly 1500 shares. Details: The customer environment consists of EMC Isilon with multiple access zones and approximately 3000 shares for a total of roughly 4 PB of data. The organization was retiring older Isilon and moving data to Qumulo. Goals included simplifying operations with policy-driven data movement, analytics, and capacity management. Because of the number of shares and folders in the environment it was unrealistic to set up migrations one at a time via the UI, which led to Komprise recommending the API approach. Komprise for Healthcare and Life Sciences Industry: Customer Service File Migration Overview: Cloud contact center modernizes and consolidates multiple active shares to non-empty destination. Results: Successfully migrated 900 TB (1.2 billion files) across 135 shares to date. Details: The customer environment consists of NetApp 8 & 9 Cluster Mode and Pure FlashBlade. The customer wanted to migrate 1PB from NetApp to Pure Storage, where the source is active and destination is not empty, a non-standard NAS migration path. This is a common use case for customers undergoing modernization and consolidation. Komprise did not propagate deletes from the source to target. Industry: Semiconductor File Migration Overview: Semiconductor data center consolidation effort involves moving petabytes of data internally and cloud tiering to Wasabi. Results: Komprise migrated 2.5 PB and 4.6 billion files from NetApp to Pure and tiered 4PB to Wasabi. Details: Company was consolidating 30 data centers down to eight in a NetApp environment with 27 PB of total data. Komprise migrated 10 PB of active or “hot” data to Pure NAS while tiering 17 PB of cold data to the cloud. Komprise for Engineering and Semiconductor Enterprises Industry: Public Sector File Migration Overview: U.S. Government agency manages security via Komprise roles to migrate NFS and SMB files across sites. Results: Komprise migrated more than 126 TB of data (560 million files) from Isilon to Qumulo. Details: The customer environment consists of Pure FlashArray File Services and NetApp 7 with 500 TB, 475 shares, and a mix of NFS and SMB files. The project involved migrating data between strategic sites while adhering to stringent security and data encryption requirements. The customer leveraged Komprise Deep Analytics to allow authorized end users to tag data and IT to run the migrations. Komprise for Public Sector Industry: Media & Entertainment File Migration Overview: Large media company performed two migrations: moving object data from one cloud to another and file data from NAS to AWS. Results: Komprise migrated petabytes of StorNext data and 250 TB of cloud object data from Wasabi to Amazon S3. Details: The customer environment spanned Wasabi Cloud for object data, Quantum StorNext for file data. The project required Komprise to migrate 250 TB of data from Wasabi to AWS S3 and petabytes of data from StorNext to AWS. The customer was able to analyze its object and file data to assist with selecting the appropriate AWS S3 tiers for the target, simplified in one solution. Komprise for Media and Entertainment Komprise is a launch partner for the Azure File Data Migration program which offers Azure customers free use of Komprise to efficiently and reliably migrate unstructured data to Azure. Komprise has achieved AWS Migration Competency status. Komprise is used by Pure Storage Professional Services for data migrations. Data Migration Next Steps: Learn more about Komprise Elastic Data Migration, our scale-out data migration software as a service solution Learn more about Smart Data Migration Learn about Isilon Migrations with Komprise ### Predictions for 2023 Hybrid Cloud Storage & Unstructured Data Management Komprise November Update: Product, People and News Cooling markets and recessionary indicators are spawning a darker mood to the coming winter season in the Northern Hemisphere. It’s not all grim though: The U.S. economy posted its first period of positive growth for 2022 in the third quarter—beating forecasts at 2.6% while jobs and wages numbers are also looking strong, per CNBC. On the heels of the pandemic, we’ve been through rough waters before; organizations that assess their environment properly to innovate smartly and with the customer in mind should come out on top. Here’s our take on current trends in data management, storage and cloud infrastructure, along with the latest news at Komprise. Tech and Cloud Spending: Steady Now Bain predicts that 77% of companies will either increase or maintain their technology budgets in 2023, compared to last year when 90% of companies said the same for 2022, as reported in VentureBeat. Meanwhile, Gartner claims global IT spending will grow by 5.1% next year. Gartner analyst John-David Lovelock provided context on future spending: “However, as organizations look to also realize operations efficiency, cost reductions and/or cost avoidance during the current economic uncertainty, more traditional back-office and operational needs of departments outside IT are being added to the digital transformation project list.” Cost management will be front and center in planning, but not at the expense of growth and meeting new customer demands. Reports TechRepublic: “With the 2022 global inflation outlook  not showing signs that it will slow, more businesses are on the lookout for dependable means to cut down on the cost of running their businesses while still maintaining their capacity to scale and innovate according to demands.” In another TechRepublic article citing a Gartner study, the author observes: “Most CIOs have been investing in ways to improve operational excellence (53%) and customer or citizen experience (45%). Only 27% of CIOs cited growing revenue as a primary objective, and 22% answered improving cost efficiency.” Hybrid Cloud Storage Shifts to Data Services As reported in StorageNewsletter, Gartner talks about the need for enterprises to focus on hybrid cloud integration to address demands for seamless data services across edge, core data center and public clouds: “Hybrid cloud solutions are expanding from being providers of storage to providers of platform services, such as data insights, cyber resilience, life cycle management and data mobility across public cloud and on-premises deployments.” Other Gartner data points reported in the article include: By 2025, 60% of I&O leaders will implement at least one of the hybrid cloud storage use cases, which is a significant increase from 20% in 2022. By 2025, more than 40% of enterprise storage will be deployed at the edge, which is a major increase from 15% in 2022. Here at Komprise, our founders predict these IT trends for 2023: Many organizations are still catching up on post-pandemic priorities after spending the bulk of 2020 on remote workforce support and keeping lights on for customers, but tactical investing in key areas where value and customer impact are clear will be the end game. IT leaders will also focus investments through the lens of cost efficiency and lower TCO. Such tactics will include: Intelligent automation to help users and IT administrators reduce manual tasks and get insights faster. Cloud cost management, such as with strategic cloud storage tiering, will be important along with consolidating cloud spend across the enterprise. A shared services IT model will expand to better manage spend (as well as data governance) across departments. Departmental collaboration and visibility into data and IT assets across silos will be critical to meet efficiency goals and drive new value from data. Komprise News & Events Product News: Komprise Fall 2022 release announces the new Deep Analytics user profile. This new self-service feature allows line-of-business data owners and data specialists to view data usage, run queries and more, fostering collaboration with IT. In the Press Blocks & Files: Komprise tells users: Go do it yourself Among other articles, our product news was covered in Blocks & Files: “This is a tool for admin staff to give them the equivalent of night vision in a previously dark data environment and organize it so that access and storage costs are optimised.” Network Computing: Data Storage IT Careers Evolve with the Cloud Network Computing ran this story by our sales engineer Eric Platt on how storage IT careers are changing. “With hybrid cloud infrastructure dominating most enterprises, storage professionals need a deeper understanding of not only cloud technologies but networking and security configurations and protocols.” CRN: Komprise COO: Partners Can Drive Services Revenue Around Revamped Data Management Offering CRN interviewed our President and COO Krishna Subramanian on the unstructured data management marketplace: “Our competition honestly tends to be more of the siloed solutions. The cloud vendors and the storage players have some pieces of what we do. But most of them are also our partners. So we are an advanced tier partner with AWS. We also partner with [Microsoft] Azure.” Unstructured Data Management Videos Giving Data Insights to LOB Partners  Komprise Senior Director of Product Management Paul Chen’s demo on the new Deep Analytics user role for self-service is a quick five minutes of your time. Komprise for AWS Snowball  Our VP of Marketing Darren Cunningham interviews Ramesh Kumar of AWS about the partnership and integration between Komprise and the AWS Snow family of storage solutions. _______________________ ### Accelerating Petabyte-Scale Cloud Migrations with Komprise and AWS Snowball Last week Komprise VP of Marketing Darren Cunningham spent some time with Ramesh Kumar, the Head of Product and Solutions for AWS Snow Family of services at Amazon Web Services. The discussion focused on our newest integration to help customers migrate data to AWS using AWS Snowball. Ramesh kicked off the discussion with an introduction to the AWS Snow Family, its genesis and how it uniquely solves for customer data migration use cases. Darren reviewed the Komprise analytics-centric approach to what we call Smart Data Migration and introduced our Intelligent Data Management platform. The rest of the time was spent diving into use cases that are a good fit for Komprise and AWS Snowball. You can watch the full discussion here.  _______________________ What capabilities does Komprise bring to AWS Snowball? Ramesh: The key benefit is simplifying the migration process for customers. The primary use case where Komprise is a good fit for AWS customers is large multi-petabyte migrations with AWS Snowball, especially as these migrations require customers to use multiple Snowball devices to complete their data migration project. Customers can use Snow Large Data Migration Manager to plan their large data migration project and Komprise to manage the migration at their on-premises location. How does Komprise for Snowball work? Ramesh: Each Snowball Edge device can manage up to 80TB of storage, so customers segment their data to be copied to multiple Snow devices. These customers can use Komprise to automate and simplify their migration project using multiple Snowball devices. Komprise manages how much data is written to each Snowball, provides user feedback when any Snowball approaches capacity and when the capacity has been reached. Komprise also prompts the user to detach the full Snowballs and send them to AWS and attach the next Snowball device to Komprise, whereupon Komprise will continue the data copying operations automatically. With Komprise and Snowball, customers have an automated, efficient solution to move many petabytes of data offline into AWS. You don’t omit or duplicate; the Komprise software will manage the migration, tracking the data that was sent on each device, which reduces complexity for the customer. Enterprise customers require integrity and auditability. Komprise provides these customers with a detailed audit log showing where the data has moved. What are the primary benefits of Komprise and an AWS Snow solution? Ramesh: Firstly, if you need your file metadata preserved during migrations, Komprise and the Snowball solution provide the capability to migrate SMB and NFS data sets with corresponding metadata. Secondly, large migrations require using multiple devices in parallel. Komprise offers auto parallelization to accelerate migrations. Thirdly, you can plan, execute, and monitor your large migrations using the Snow Large Data Migration Manager. Komprise fits in nicely and helps simplify the execution. How do you summarize the better together message to AWS customers? Ramesh: Komprise makes migrations requiring multiple Snowball Edge devices easy and relatively seamless. What I mean by this is that Komprise keeps track of the data so you don’t have to. Once you have planned your large data migration project using Snow Large Data Migration Manager, you can use Komprise to start the migration and it will run in the background before connecting the next device. This means no more managing which data was transferred to each device, comparing checksum data, etc. Customers can move faster and focus on their day-to-day activities to run the business while reducing the friction that has delayed many of our customers from taking action. What’s the bottom line for Komprise and AWS customers? Ramesh: Komprise makes Snowball migrations easy and with visibility for migrations and it builds confidence: understanding the data you are moving and the progress of the migration. Komprise manages multiple Snowball devices through the entire process and streamlines this process. For a detailed discussion about the Komprise offering for AWS Snow Family solutions, read this white paper. Learn more about how Komprise works with all AWS storage solutions. ------------------- ### Cloud Data Storage Moves and Shifting Roles Komprise September Update: Product, People and News Back to school, back to business... and there are all kinds of evolutions happening in the data storage market too. Check out our take on the latest data storage and unstructured data management trends in our September update. Storage Industry: Changing Status Quo? The enterprise data storage market is mature and rigid, according to revenues for publicly owned storage companies, writes Chris Mellor of BlocksandFiles in his analysis of the major data storage vendors. While Micron, Dell, Western Digital, Seagate, HPE and NetApp are predictably the largest by far, it’s interesting to note the growth in the lower storage tier vendors: “All-flash array supplier Pure Storage is growing fast, at 30 percent year-on-year on its latest results, and so is cloud data warehouser Snowflake.” There’s always something new going on in the data storage and data management market with many upstarts (including cloud service providers) challenging the status quo. Cloud Storage Moves In September, AWS announced additional performance metrics for Amazon FSx for Windows File Server that include file server CPU and memory usage and storage disk throughput and IOPS. [Komprise supports analysis and migration of data to all the AWS Cloud NAS offerings.] Microsoft announced that immutable storage for Azure Data Lake Storage is now generally available. This means the data can’t be changed, deleted or otherwise modified, such as by those sneaky ransomware people. Mr. Mellor also recently covered storage news from Google: “Google Cloud has expanded upon its storage portfolio, augmenting existing services and launching a dedicated backup and data recovery service for the first time.” Google Cloud Hyperdisk, one of the new services, is “described as a next generation complement” to its Persistent Disk service block storage service, with different implementations for different workloads such as SAP HANA. Wanna be a data manager? As it turns out, this is quite a hefty job, according to Information Age. “Ultimately, the data manager is responsible for the design and management of a company’s data systems, which includes ensuring data is stored correctly and is secure, governed and meets regulatory standards.” But that’s not all. Data managers are also responsible for (at least in knowledge) data modeling, data mining, data cleansing and analysis, data storage best practices, and should understand data science and data movement and be excellent critical thinkers, the author expounds. Sounds like an ambitious job for the ambidextrous data IT professional! Komprise News & Recognition New Survey Report Last month, Komprise announced the results of our second annual industry survey: The 2022 State of Unstructured Data Management. The survey uncovered several new trends regarding cloud storage, user self-service, non-disruptive data access, big data analytics and more. VentureBeat's Take on the Survey The preeminent Silicon Valley venture capital investor publication covered the news: “The ability to initiate and execute automated data workflows for a variety of use cases is the leading new approach to unstructured data management.” Awards! Last month, Komprise received two honors: making the Inc. 5000 list for the first time and achieving Gold Winner status in the Globee Awards for New Product in Cloud, SaaS and Internet. Komprise and AWS Tackle PII Risks Komprise CTO Mike Peercy and AWS technical expert Girish Chanchlani lay out an important use case and technical steps for discovering and protecting sensitive personal data in this AWS blog. The New Unstructured Data Management Playbook In this TechBeacon article, Komprise COO & President Krishna Subramanian discusses common challenges in enterprises (how to move unstructured data without disrupting users, poor visibility into unstructured data, and legal constraints) with tips for how to address them and grow data value at the same time. Customer Story Pfizer’s director of hosting data solutions is interviewed in this industry publication about his organization’s shifting needs in data management and how Komprise is helping improve ROI from the cloud. Read here. -------------- ### Komprise Wins GLOBEE® - 10th Annual 2022 CEO World Awards® Komprise announced today that the company has been named a Gold Winner for New Product of the Year in Cloud Computing, SaaS or Internet, in the 10th Annual 2022 CEO World Awards®, by The Globee® Awards. ### Komprise Wins Globee® in the 10th Annual 2022 CEO World Awards® Komprise Intelligent Data Management selected for New Product of the Year in the Cloud, SaaS and Internet category. Campbell, CA– September 22, 2022 – Komprise, the leader in analytics-driven unstructured data management and mobility, announced today that the company has been named a Gold Winner for New Product of the Year in Cloud Computing, SaaS or Internet, in the 10th Annual 2022 CEO World Awards®, by The Globee® Awards. These prestigious global awards recognize CEOs, C-level executives and professionals from all over the world.  Komprise Intelligent Data Management delivers advanced analytics and patented Transparent Move Technology™ to help enterprises with petabyte-scale data challenges save significantly on data storage, backup and cloud costs by right-placing data into the optimal storage class. The Komprise Global File Index and new Smart Data Workflows allows IT users to easily discover, enrich and move unstructured data in native format into cloud analytics and processing tools so departments can generate new insights and business value from data.  Komprise customers include large enterprises across life sciences, healthcare, retail, public sector, higher education and financial services such as Pfizer, Northwestern University, AT&T, Pacific Biosciences, Cadence and Carhartt. In August, Komprise was included in the Inc. 5000 list of fastest-growing private companies. “We are proud to win the Globee and be recognized as an industry player in the cloud and SaaS category,” said Krishna Subramanian. “Unstructured data growth is reaching the tipping point in many enterprises and hybrid cloud infrastructure is complicating the process of managing it to save on storage while also supporting important digital initiatives. Our mission at Komprise is to deliver a new way—based on analytics, global search and tagging, and automated workflows--for storage IT professionals to manage data not storage technologies and thereby better support the business.”  Judges from a broad spectrum of industry voices from around the world participated and their average scores and inputs determined the 2022 award winners. See the complete list of 2022 winners here: https://globeeawards.com/ceo-world-awards/winners/ About the Globee Awards Globee Awards are conferred in nine programs and competitions: the American Best in Business Awards, Business Excellence Awards, CEO World Awards®, Cyber Security Global Excellence Awards®, Disruptor Company Awards, Golden Bridge Awards®, Information Technology World Awards®, Sales, Marketing, Service, & Operations Excellence Awards, and Women World Awards®. Learn more about the Globee Awards at https://globeeawards.com About Komprise Komprise is a provider of unstructured data management and mobility software that frees enterprises to easily analyze, mobilize, and monetize the right file and object data across clouds without shackling data to any vendor. With Komprise Intelligent Data Management, you can cut 70% of enterprise storage, backup and cloud costs while making data easily available to cloud-based data lakes and analytics tools. www.komprise.com.  Media Contact: Kevin Wolf kevin@tgprllc.com ### Anthony Fiore: Making Smart Storage Decisions on AWS Anthony Fiore is a Storage Specialist Solution Architect at AWS. His career in IT spans various infrastructure disciplines, including most recently as the lead cloud engineer at Tiffany & Company. We chatted with Anthony about storage trends in the cloud. How should a customer begin the journey of migrating data to AWS? We have three big buckets of storage at AWS: block, file and object. We work to understand where customers are today and their current needs and goals. Generally, they are looking to lift and shift data and run it in AWS just the same as before, which is helpful when closing a data center. Usually, in six to 12 months we start to talk about modernization and optimization. For example, if you are running a Windows File Server on Amazon EC2 you can use Amazon FSx for Windows. Customers love it because there’s no need to patch or maintain the file system. Some customers decide to use a managed file system right out of the gate on Amazon Web Services. Amazon FSx for NetApp ONTAP is also very popular with customers right now. We are hearing a lot more about file storage in the cloud lately. When would a customer want to pursue a managed file service like Amazon FSx versus Amazon S3? Customers often want to look at object storage like Amazon S3 but it’s tough to refactor their applications which is usually required. Typically, they will choose a medium like Komprise to bridge the gap between file and object storage. Otherwise, they are more constrained, and need to select file or object. They’ll look at their latency requirements and cost always comes up. Customers have a semblance of what they need but it’s not fully fleshed out. We can help them understand what they should be asking their businesspeople or security and IT folks as to what is important to them. Firstly, they need to determine if the on-premises application can even talk to object and usually it can’t. That’s where Komprise helps! Watch the short video interview with Anthony:  -------------- What are the common use cases for object versus file in AWS? For Amazon S3, the easy one is for backup storage. Another big one is data lakes. In Amazon S3, customers can store the data and use our analytics and ML services on top of it. We often talk about unlocking the value of the data. What if we aggregated data with other data sets and then we can begin to make business decisions on it. Amazon S3 is also great for cold data storage. Amazon S3 Glacier Deep Archive and Amazon S3 Glacier Instant Retrieval are great archival classes for cheap storage of data which customers don’t plan to access often. With file storage, we have a lot of different options for customers. From robust offerings like Amazon FSx for NetApp ONTAP, to Windows Native services like Amazon FsX for Windows, we have a file storage offering for every use case now. Some popular use cases we see are file sharing and collaboration, highly transactional workloads and persistent storage for containers. Is the notion of cloud native access and value becoming more important? If customers can have their cake and eat it too by having data on inexpensive storage on Amazon S3 but still access it in fairly real-time fashion when they want to, it’s a game changer. This opens us up to the art of possible. Twenty years ago, you’d archive data to tape and it sat there until someone says they wants to recover it. What I love about Komprise is the fact that everything is by default transferred to Amazon S3 in native file format and you can work with it in a data lake or run queries and you don’t need to use Komprise to get it. As part of a migration strategy, this is a powerful win-win message. What’s involved if you want to move data between storage tiers on AWS? First, you need to understand your data access patterns. We have seven Amazon S3 storage classes. The cost of storage gets lower as you get to cold storage such as Amazon S3 Glacier Flexible Retrieval but the cost of operations—from API or retrievals—goes up because Amazon S3 Glacier Flexible Retrieval is for archival. We want to get customers to the right storage tier based on their access patterns--but how many customers really know this, especially with the data sprawl from unstructured data? Komprise is valuable here by quickly analyzing all the customers data to show when files were last accessed so they can make the right business decisions on data storage. Their data can be in the optimal AWS storage from the very beginning and as things change over time, Komprise can help them execute on that too. What are the common migration challenges for enterprises and how does AWS help its big customers? Migration is one of our top initiatives. The AWS Migration Acceleration Program delivers an assessment to understand the customer footprint and do any POCs, then plan and do the actual migration bringing in external resources from our partners. This way, customers can lower their migration risk and costs and they don’t feel like they are on their own. Migration can be a scary word. Sometimes it’s a gut check. A customer may say, I want to migration to AWS in six months. This might be impossible if there is a lot of red tape; it depends on the agility of the customer and if they will be doing it on their own or getting outside help. We have over 200 services now that help with data migrations, such as the AWS Snowball family of devices that make it easier to move large data sets from on-premises to AWS. Learn more about how Komprise and AWS deliver value and maximize cost savings for the enterprise cloud journey. -------------------- ### How Storage Teams Use Komprise Deep Analytics Recently Komprise announced Smart Data Workflows, a systematic process to discover relevant file and object data across cloud, edge and on-premises datacenters and take action on it such as feeding data in native format to AI and machine learning (ML) tools and data lakes. At the core of this new capability are Komprise Deep Analytics and Deep Analytics Actions, which provide a searchable Global File Index (GFI) of all unstructured data. This way, customers can query and find just the data they need and then use Komprise data management policies to systematically operate on the specific data of interest. Increasingly our customers want to provide capabilities for departmental IT groups and power users to access and manage unstructured data. Here are some common Deep Analytics use cases we’ve seen storage teams adopt: Business unit metrics with interactive dashboards In many organizations, a central infrastructure/cloud team provides services to various departments. There is a need to provide insight on departmental data usage and growth, especially in a “showback” or “chargeback” model. Instead of IT teams manually generating and distributing reports to business units, with Komprise they can simply set up views for authorized departmental users to monitor and understand their storage spending directly in the Komprise dashboard. By providing easy analytics and reporting to departments of how much data they are using, what that data costs the company and who’s consuming it, departmental users become better informed to work with central IT on cost optimization. Business-unit data tiering, retention and deletion An organization may have a global retention policy that data in general should not be kept for longer than five years. However, some data may have shorter time frames because of its nature; for instance, research data such as images or genomics interim files may only be valid for weeks or months. Instead of having this temporary data around for the same five years as all other data, departments can now create Komprise Deep Analytics queries to identify data sets with exceptions—such as by tag or by data type or project name. Creating a Komprise Deep Analytics query. This data can have a different Komprise data management policy that tiers it more aggressively. Once the query and policy are set, Komprise will continuously find data that fits the criteria and act on it; neither the departmental users nor storage IT teams need to babysit each project individually. Conversely, some data may need to be retained for 20 years to meet regulatory requirements. Again, Komprise Deep Analytics queries can identify such data in partnership with the business users who have better insights into what data fits the criteria. Identifying and deleting duplicates Research data often ends up with multiple copies in different places. For instance, a dataset is generated by instruments and then copied in five different labs for analysis. The labs may copy the data further for multiple runs. When the project is complete, you can use Komprise to identify suspected duplicate files for review and possible deletion. This capability is helpful as a regular clean-up task to find and delete large, suspected duplicate files, which frees up valuable primary storage and cuts storage costs. Mobilizing specific data sets for third-party tools Enterprises use third-party tools or services for specialized data processing or analysis, but those tools may not be licensed for use in multiple locations or may only be available in the cloud. Finding the right data to feed these tools can also be a challenge. Data owners can leverage Deep Analytics queries to find specific data sets and then either tag the data sets or simply save the query for central IT to configure a policy to copy the data to wherever the tool or service can operate on it. Komprise automatically handles the file-to-object translation if the processing tool lives in the cloud, ensuring that the objects created are in native format and directly consumable by the cloud service. Using a Komprise Deep Analytics query for Smart Data Workflows. Using data tags from on-premises sources in the cloud Enterprises often don’t have a good system of searching historical unstructured data. Data tags are system-specific, so while users might have fastidiously tagged data in their electronic lab notebook or some source application, once the data is tiered to the cloud or other file storage, this tagging information is lost. Komprise addresses this limitation with a universal tagging approach that works across file storage and clouds. You can tag data in Komprise or by ingesting tags via API. Komprise retains the tags along with all the standard file metadata even as you move the data to cloud storage or across clouds and different storage architectures. This ensures that users can search for the same data regardless of where the data lives throughout its lifecycle. Read more about how Komprise data tagging works in this blog. Our customers often share feedback after they dig deeper into Komprise Deep Analytics and Smart Data Workflows: “Deep Analytics allows us to do fine-grained searches and get surgical about what we can find and archive. Komprise empowers our end users to archive data the right way via tagging.” “In higher education, we have people within shadow IT groups at our professional schools who want to use Komprise for research purposes. We don't have time in central IT to do searches for them.” “The most value we can provide to our users is to empower them to leverage Komprise to get their data to the cloud.” Deep Analytics Actions and Smart Data Workflows differentiate Komprise from any other unstructured data management platform on the market today, delivering data storage cost savings and greater data value extraction. In the coming weeks we’ll be announcing new functionality and interesting use cases to help our customers and partners understand what’s possible with a smarter approach to data migration, data tiering, data mobility and data management.   --------------------- ### Tips for a Clean Cloud File Data Migration Watch the video series with Ben Henry. As part of our Smart Data Migration webinar series, Benjamin Henry, chief customer success architect at Komprise, reviewed a series of best practices for enterprise IT organizations building a cloud data migration plan and data migration process for file and object unstructured data. Data migrations are a team sport. Having a checklist of what you need to know before you go will help ensure a faster, more effective cloud data migration process that meets your goals. Here's a summary of Ben’s recommendations: 1) Define Data Storage Sources and Targets  Develop a clear plan that reviews where you’ve come from and where you’re going. Understand point A and point B and don’t wait until the night before a migration to ensure this is clear. Discussion points: We see less storage vendor loyalty in today’s hybrid cloud world. Cloud native apps often need object storage. Are you prepared for this? Cloud NAS has matured. What is your plan? Do you still have requirements to keep data stored on premises? 2) Data Migration Rules & Regulations   Rules which you must adhere to are set by internal teams and may cover areas such as retention policy, legal hold and disaster recovery. Regulations are typically set by a governing body and typically involve fines if not followed, such as: HIPAA, SOX, GDPR. Discussion points: This is a good time to talk about partnerships within your organization. Partner with your legal and compliance teams. Get the data owners involved. Put it on the teams that care about the data so it’s not just on the shoulders of the storage team to establish the right data protection and unstructured data management strategy. 3) Know Your Unstructured Data: Data Discovery  When you do proper data discovery, you’ll understand your workloads and speed bumps. Do you have large files? Small files? Hundreds or thousands of shares? Bring other stakeholders into the data migration process and have them take on responsibility with the right data. As we say at Komprise: Know First. Move Smart. Discussion points: Dual vs. mixed protocols: What is your permission strategy and how will this affect your migration? Simplify and standardize: just because you’ve been doing things a certain way in the past doesn’t mean it’s the right way in the cloud. 4) Smart Data Migration. Know Your Topology  Define your path and understand what you have and will need for bandwidth, latency, firewalls, etc. when it comes to your hybrid cloud migrations. Discussion points: A core objective is how to avoid bottlenecks ahead of time. What ports need to be open? Once again, bring in security and network teams. 5) Before You Migrate Data: Test, Test, Test  Test sources and targets, topology and migrations. Consider whitelisting. Know the limitations of your current file storage systems and your future targets. Discussion points: Don’t leave any stone unturned. The iPerf tool doesn’t just measure storage performance. Use it to understand how long it will take to move data across the wire. The iterative error-log feature of Komprise Elastic Data Migration is an example of ensuring you’re working with the right tools. 6) Free Tools Cloud Migration vs. Enterprise  Ben has a lot to say about the challenges of free tools – from manual scripting and scheduling to MD5 checksums and audit logs to multi-protocol support. Discussion points: Enterprise solutions have centralized configuration and management and can tier and archive. They are multi-user. The list goes on. There’s a big emphasis on automating, reporting and supportability: dig into the Komprise Elastic Data Management white paper to learn more. 7) Data Migration Communication Plan  This is a theme throughout the session. Ben recommends red/yellow/green status updates on messages and cross-functional reviews. Discussion points: People want to hear the positives. Avoid the blame game and celebrate the wins. Be concise and consistent in your communication. Once again, hybrid cloud file migrations are a team sport so communicate early and often throughout the data migration process. That’s a lot of information and insight packed into a 30 minute webinar. You can watch the recording on our YouTube channel. See below for easy viewing:  Also, check out our Guide to Unstructured Data Migration! ------------------------ ### Komprise CEO Kumar Goswami: Milestones and Goals for 2022 Kumar Goswami, CEO and Co-founder of Komprise, shares his thoughts on recent accomplishments of the company and what lies ahead as customers expand use cases from unstructured data management savings to value through automating data workflows to cloud analytics, AI and ML tools. Komprise was just included for the first time in the Inc. 5000 list which recognizes the fastest growing private companies in the United States. What is the significance of this achievement? KG: We’re creating something new: a new way to manage your data. The timing is right. Organizations are drowning in data but old habits are hard to break. Many IT directors still simply buy more storage to address the issue. Others are trying to get to the cloud but don’t have the tools or the training to get the data there efficiently and in an optimized manner. What we’ve done at Komprise is make it easy. Our solution makes it easy to get insight into your data, see how fast it’s growing, who’s growing it and show you how you can save 70% of your storage cost by managing it correctly. Finally, we then help you extract value from all that data. It works across your data silos and different technologies, so you don’t have to fuss with it. That’s why, in spite of the economic turbulence from Covid, Ukraine War and inflation, we are still growing at a fast pace. That’s why, even though it is difficult to change old ways, once an enterprise starts using Komprise they continue to find new use cases and expand the use of our platform. Making the Inc. 5000 list is a fantastic achievement and I’m so proud of what we’ve been able to do as a company to get to this mark. What have been the most pivotal milestones for the company since its founding in 2014? KG: Our team has a background in distributed computing. One could say, starting with a scale-out design that can handle data at massive scale is pivotal. But for us that was natural. I think the first pivotal milestone was providing analysis and insight into data. Our goal was that in the first hour, we should be able to deploy our solution and analyze your data and leave you with insight which you’ve never had before. That became much more meaningful when we then allowed customers to use the product to act on that insight. For instance, from the insight you can say “OK, move data that’s more than six months old to the cloud.”  Another pivotal milestone was moving data transparently, using our patented Transparent Move Technology™ (TMT). Without using any agents, you can tier the data to the cloud and still access it from the original source as if it had never moved AND access it as a native object in the cloud to leverage cloud services like AI/ML cloud applications. This file to object duality, without agents, without getting in front of hot, mission-critical data is something no one else can tout. What do customers consistently say is the greatest value of using Komprise Intelligent Data Management? KG: Many customers say: “Where were you when we needed you? We’ve been looking for something like this forever.” We think differently and we came up with a way to solve a critical problem, managing massive volumes of unstructured data, in a simple, transparent way. Initially, the greatest value was quickly providing insight across disparate data silos. In a hybrid, multi-cloud IT infrastructure, delivering visibility across silos and demonstrating fast cost savings and ROI is critical. As customers take advantage of our Global File Index, Deep Analytics and our new Smart Data Workflows, we're seeing customers get really excited about unlocking the value of the massive amounts of unstructured data by delivering the right data to the right applications and analytics tools that are driving enterprise innovation. Komprise is a disruptive offering, introducing an independent data management layer in large enterprises where storage vendors have ruled the roost for decades. What are the challenges in selling a disruptive technology and influencing qualified prospects to make a change? KG: The challenge has to do with breaking old habits and old ways of thinking. Too many people think data is synonymous with storage. The world has changed and today there are many different storage tiers and technologies. AWS alone has 16 or 17 tiers of storage (including third-party offerings) and they don’t all work with one another. You need something independent of these storage tiers to properly manage your data through its lifecycle. Data when collected might be at some edge data center. Once it has been selected for processing, maybe it needs to move to fast storage. After that processing is done, if you just leave it there, you’re spending far too much money. You need to probably move it to a cheap cloud tier. Then, when that project is required again, you may need to bring it back again to a fast, tier-one storage. Finally, due to compliance or internal policies, there may come a time when you need to delete it. As you can see, data is independent of storage and its importance changes with time; data must be managed independently of the storage in which it currently resides. In the coming year, where will Komprise focus in terms of its product evolution and go to market strategy? KG: We started with features that provide insight across all storage and improve decision-making to cut costs. Now we’re providing workflows that can be created via UI and API to develop custom data management applications. This includes selecting a custom subset of your data across all your data silos--without having to worry that each silo may require different protocols to access the data--sending it for processing to a function you’ve developed which then tags the data and later uses that tag to determine just the data to feed an AI or governance application. Our notion is that you shouldn’t need to worry about where the data is located and how to access it. You simply tell us what data you want and what you want to do with it and we’ll take care of the rest. Because of this, creating applications that could take months to years, can be done in minutes. Where we’re going and what we’re providing our customers is incredibly powerful. Finally, what’s exciting to you about running a SaaS company in the crowded IT infrastructure space: what motivates you to come to work every day? KG: We are doing something different. We’re going to change the way the world manages their data. That alone is very motivating. And I’ll tell you, it’s not just me. The whole company is motivated by this. It’s a struggle to change how people think and traditionally do things. But it’s very motivating to be a part of an effort to change that for the better. Some days are hard. But then there are days when customers really see the light and appreciate what we’ve helped them accomplish. That’s so satisfying and that’s what makes Komprise tick. ---------------------------------- ### Smart Data Workflows Architecture: Cloud Field Day 14 When Komprise co-founder and CTO Mike Peercy goes to the whiteboard, people pay attention. Well, that’s been my experience and I certainly do. Mike once again got the colored pens out for Cloud Field Day 14 to walk through the architecture behind Smart Data Workflows. Chalk talk (sans chalk) with Mike from @Komprise at #CFD14 pic.twitter.com/v9YrPGVYWn — Ned is 🌮🏃🦖🦖 (@Ned1313) June 24, 2022 It all starts with data sources: cloud, edge, data center and these can reside in different sites, locations and networks. This is why Komprise supports multi-site deployments. When it comes to the cloud, the focus of the session, customers may have buckets, containers, object stores and cloud services such as natural language processing (NLP) and other analytics services. So the question is how do we get the right data from customer sources to these cloud services? That’s where Komprise comes in. Mike briefly explains the components of the Komprise architecture: Director and Observers and the Global File Index (GFI), a high-performance indexing service. The GFI is essentially a picture of all the data: it is the universe of the customer’s data, which is typically made up of a few billion files. Metadata that is exposed by NFS, SMB, object protocols is what we compile and place in the GFI.     Mike then reviews 3 Smart Data Workflow examples: 1) Data Enrichment: A Smart Data Workflow builds a policy to copy data to a storage bucket in the cloud, where this data is consumed by a cloud service which extracts more custom metadata. He describes the autonomous vehicle use case demo and how you can filter and feed AI and big data tools with the right unstructured file and object data in an automated fashion. At around the 10-minute mark there are some good questions from @bknudtson and @CraigRodgersms to clarify the role of Komprise as well as other possible use cases. @Komprise can be used as a combined migration/data sanitation tool, helping to move source data to a preferred location, bringing only relevant data potentially resulting in less overall storage used, contributing to overall cost savings by filtering out irrelevant data. #CFD14 — Craig Rodgers (@CraigRodgersms) June 24, 2022 2) Cloud Tiering: One of the main use cases for Komprise is Transparent Move Technology. Large files like .DAT files that you don’t need can move to the cloud for lower-cost storage. In this example, Mike creates a custom Deep Analytics query with specific parameters and sets up a data management policy to take action when these parameters are met. Here we get into a Komprise architecture discussion and review the power of file-object duality that Dynamic Links deliver as part of Komprise TMT. 3) Smart Data Migration: We encourage an approach where you know your data first, tier it to the right location and then migrate the hot data. This is done through the Komprise Director. The Observers do the work. The cost model is summarized in the Cloud Tiering Done Right session earlier in the day. The session wraps up with a lively discussion and questions about the Komprise business model, pricing and how to get started with a custom demo. You can watch Mike Peercy's Chalk Talk video below:  _______________________ ### Smart Data Workflows: The Evolution of Unstructured Data Management “Moving data to the cloud can help you optimize your infrastructure, but the bigger value is in leveraging the compute power and data services in the cloud,” remarked Komprise co-founder and COO Krishna Subramanian in our second Cloud Field Day 14 presentation. Subramanian defines unstructured data as any data we can access as a file or as an object that does not fit neatly into database rows and columns. These data sets are piling up in the data center, at the edge and in the cloud. An unstructured data management solution that can look across all your sources of unstructured data, provide an analytical view of this data, mobilize data and allow users to index, search and deliver only what is needed to data consumers is a smart strategy to modernize your data storage practice. In this session, Subramanian briefly introduces the Komprise SaaS platform and introduces our latest product update: Smart Data Workflows. Here’s an overview of what her session covered. You can watch the full session here: With Smart Data Workflows, IT users can create automated workflows for all the steps required to find the right unstructured data across storage assets, tag and enrich the data and send it to external tools for analysis. This eliminates manual effort in unstructured data management and helps organizations speed time to value from new cloud-native tools. With Smart Data Workflows, you can deliver only the right file and object data into a data lake: preventing the dreaded data swamp. Does Komprise alter the data? No. Data remains in native format. When a file is moved to an object store, Komprise does not “munge it up.” We call it file-object duality. Read about it in this post: Why Cloud Native Data Access Matters. The Power of Global Unstructured Data Visibility Krishna introduced the Global File Index, which is a unified view of your data without moving the data. Today enterprise IT organizations are flying blind. They don’t know what data is sitting where, who is using the data, how the data is growing and what the data is costing them. End users can’t find the data they need when they need it. With today’s data volumes, organizations must have full visibility to make good decisions. Once you have this data visibility, Komprise makes it actionable. This is where the magic happens. With billions of files and objects, analytics plus continuous mobilization is essential because data has a lifecycle and data management is not a one-time thing. Smart Data Workflow Use Cases Before the demonstration, Krishna reviewed a series of Smart Data Workflow use cases, including:  Legal Hold Search & Curate: Define and execute a custom query to find all data related to a divestiture project with Komprise Deep Analytics and the Komprise Global File Index. Execute & Enrich: Execute an external function to identify PII data and tag it. Cull & Extract: Move sensitive data to an object-locked cloud storage bucket and move the rest to a writable cloud bucket using Komprise Deep Analytics Actions. Manage Lifecycle: Move the data to a lower storage tier for cost savings once the analysis is complete.  Genomics Sequencing Search & Curate: Define and execute a custom query to find all data for Project X with Komprise Deep Analytics and the Komprise Global File Index. Execute & Enrich: Execute an external function on Project X data to look for specific DNA sequence for a mutation and tag such data as “Mutation XYZ”. Cull & Extract: Move only Project X data tagged with “Mutation XYZ” to the cloud using Komprise Deep Analytics Actions. Manage Lifecycle: Move the data to a lower storage tier for cost savings once the analysis is complete.  Autonomous Vehicles Search & Curate: Find crash test data related to abrupt stopping of a specific vehicle model with Komprise Deep Analytics and the Komprise Global File Index. Execute & Enrich: Execute an external function to identify and tag data with “Reason = Abrupt Stop”. Cull & Extract: Move only the related data to the cloud data lakehouse to reduce time and cost associated with moving and analyzing unrelated data using Komprise Deep Analytics Actions. Manage Lifecycle: Move the unrelated data to a lower storage tier for cost savings (or delete it) once the analysis is complete. Smart Data Workflow Demonstration Komprise CTO Mike Peercy delivered a demo related to autonomous vehicle data--because let’s face it, none of us will be driving in 10 years. The topic was also discussed in this recent webinar with AWS: A Modern Data Strategy for the Automotive Industry. Here is the flow: ENABLE SHARES with autonomous vehicles data for processing Show Deep Analytics (which is the UI for the GFI – Global File Index) Show query for crash reports in 2019 Show query with TAG : Stopped in traffic – there will be NONE Create Plan to analyze contents of 2019 files using LOCAL FUNCTION to tag matching files Activate the Plan to analyze and tag files Show query with TAG: Stopped in traffic – there will be MANY Questions from Cloud Field Day Delegates What options do customers have to scale up/out performance? Mike walked through the scale-out architecture of Komprise Observers in his Chalk Talk session. Observers are like virtual machines that reside next to the storage, whether in the cloud or on-premises and they scale out into a fault-tolerant grid. Learn more here. Can you add locations as you grow and easily manage that in an automated way? Yes. Mike explained the Komprise multisite capabilities, and discusses a central hub approach for Observers for multi-edge site deployments. How do you deal with encrypted content? The content of files is invisible to Komprise. So how do you classify a file if you don’t know what it is? Komprise only looks at the metadata that the storage systems show. Komprise does not look inside files, but can trigger a mechanism via the API for the customer to look inside the file and then tag and mobilize the data as needed. The tag that is being applied is now in the Komprise Global File Index. The actual file itself doesn’t change, correct? Correct. The tags are within Komprise and Komprise is not in the hot data path. The interaction between Komprise tags and cloud tags is on the roadmap. Pretty cool stuff. Learn more about automated unstructured data tagging. Metadata in Focus This led to an interesting discussion about metadata, initiated by @datachick Karen Lopez. A little #CFD14 light reading: Metadata. Everyone knows the basic definition... "data that describes data." But what does that mean, and how does it help systems and programs understand your data? Read on for a nice summary with real life examples https://t.co/hPqbCVBUlf — Chris Hayner (@hayner80) June 24, 2022 Subramanian clarified that Komprise does metadata-level find, search, curate, enrich, mobilize and lifecycle management. Komprise is enabling the workflow that calls an external function. Komprise calls an application or some cognitive service (as determined by the customer) via the API that does the processing. ... and in case you're wondering what the workflow looks like.... #CFD14 pic.twitter.com/hzSGONjiao — Ather Beg (@AtherBeg) June 24, 2022 Read about the first Cloud Field Day presentation here in the blog. ---------------------- ### Cloud Tiering Done Right at Cloud Field Day 14 We love to participate in the Tech Field Day sessions. Over the past few years we’ve gone from 100% remote presentations to hybrid to finally seeing the majority of delegates and the Gestalt IT team on-premises in Santa Clara in June. We always try to bring something new to the discussion and find ways to get out of just presenting PowerPoint slides. Last time Mike Peercy did a chalk talk deep dive of Komprise Transparent Move Technology™ (TMT) as part of our session, which focused on why unstructured data management must be independent from storage. So what was the focus of our Cloud Field Day 14 session? Here’s Krishna introducing the session: We saw Komprise give a great presentation at Cloud Field Day 14! Krishna Subramanian and the team talked about their data management software accessing data across file and object storage, their data tiering system, and showed off a demo! Check out the videos on our site! #CFD14 pic.twitter.com/gvG1OkK60a — Tech Field Day (@TechFieldDay) June 28, 2022   There were 3 sections to our Cloud Field Day 14 presentation: Cloud Tiering Done Right Introducing Smart Data Workflows Komprise Smart Data Workflow Architecture Chalk Talk In this post, I’ll summarize the key points in Kumar’s presentation. Cloud Tiering Benefits if Done Right Kumar Goswami, Komprise CEO and co-founder, kicked off the day by focusing on how to better manage growing volumes of unstructured data more cost effectively while extracting greater value from your data by leveraging the full potential of the cloud. He summarized the drivers for the massive movement to the cloud (digital transformation, data center consolidation, innovation, etc.) and the maturity of cloud NAS solutions (Qumulo, NetApp FSx ONTAP, NetApp Azure Files, etc.). He also highlighted the growing recognition that 80% of data is cold, which has made the importance of cloud tiering done right such an important topic in the enterprise. Cloud Tiering is hot. Doing Cloud Tiering right is essential to maximize #Cloud potential and business value. #CFD14 #DataManagement pic.twitter.com/bF41u9D97A — Komprise (@Komprise) June 24, 2022 Goswami discussed the importance of cloud-native data to maximize data efficiency, data access and data services. But the question always comes up: why not just use my storage vendor for cloud tiering? Storage-centric cloud tiering is not cloud native and limits the usability of the cloud; Storage-centric cloud tiering is good for storage efficiencies, not data efficiencies; Maximizing cloud use requires “Smart Data Migration”, which is data-centric not storage-centric. He goes on to share a specific cost and usability model that results in 10 times more savings by leveraging all the native cloud object and archive tiers with Komprise and gaining 95% greater access to cloud native data services. Smart file and object #cloud migration requires a data-centric approach. It's not just about cost. It's about value. Independent #DataManagement is required. #cfd14 pic.twitter.com/SvBELnu6rw — Komprise (@Komprise) June 24, 2022 Komprise provides an independent unstructured data management layer that works across on-premises file systems, object stores and cloud data storage environments. At this point in the presentation, we discuss how we define unstructured data and our Glossary of Terms gets a shout out from the delegates: thanks Karen! I think I've mentioned this before, but kudos to @Komprise for having a glossary of terms on their website. 💜https://t.co/vursbOTbvs#CFD14 #TeamDataOfficeHours #Glossary — Karen López 💉💉 💉 ❤️ (@datachick) June 24, 2022 To wrap up the first session, Kumar shared more details on Komprise market momentum, reviews our strategic partnerships and summarizes the impact we’re having on customers. You can watch the full presentation here: _______________________ ### Modernizing Unstructured Data Management in the Automotive Industry with AWS Recent years have seen ample disruption in the automotive industry – from self-driving cars to the rise of electric cars and an increasingly digital driving experience. Given these trends, we sought out Paul Baccaro, Storage Specialist at AWS, who works with Amazon’s global auto customers. In a webinar hosted by Komprise and AWS on June 28th, we discussed the new requirements for unstructured data management in the auto sector and the evolution of data lakes and ML services on Amazon to help automakers and related suppliers remain relevant and competitive in their markets. Komprise is an AWS partner for Migration and Modernization. Delivering the Right Data to the Cloud in the Automotive Sector Auto customers need data storage solutions that can scale while simultaneously incorporating all types of data, according to Baccaro. As in many industries, the variety of data has grown along with the velocity of new data creation from sensor data, images and streaming content such as video. This puts pressure not just on enterprise storage capacity but on the bandwidth required to serve data to end users. “The volume of unstructured data is so large that you can’t just move it all to cloud,” added Krishna Subramanian, President and COO of Komprise. “You need to filter the data across all storage and send the right data sets to the cloud while also deleting what you no longer need.” Komprise delivers a systematic, simple way to look at data across all platforms and data centers and consistently move data to the optimal cloud storage class and then apply the power of cloud processing on it. Otherwise, enterprises are just transferring costs from one area to another. Cloud data storage architecture is increasingly nuanced: having a solid understanding of your data and the various storage classes is critical to determining any savings from moving data off on-premises storage. The Self-Driving Car Data Deluge Calls for Unstructured Data Analytics Baccaro and Subramanian then delved into the demand for mining these massive data sets to meet the needs of new market segments. “By 2025, there will be 8 million autonomous or semi-autonomous cars on the road,” Baccaro said. “That is a very high number and so much of these vehicles’ operation is AI and ML-driven that it’s really creating challenges for our customers.” Autonomous cars will generate as much as 40 TB of data an hour from sensors. This is creating supreme urgency for car makers to manage their data differently than in the past. “They really need the ability to analyze and filter data at the edge,” Baccaro said. Giving customers the option and ability to save on infrastructure costs by sending critical data sets is key. The ability to intelligently tier data from on premises storage to the cloud or back again is another critical capability for automotive companies. Data Modernization: Bringing Industry Data to Cloud Analytics Platforms Finally, there is the exciting potential in using modern data lake and data lakehouse technologies to uncover new value in machine data. “This is a big shift for automotive companies,” Baccaro said. “Traditional analytics platforms had just a few sources of data such as a CRM or ERP and you would build out ETL to push the data into a data warehouse and run business intelligence on it. But over time data sources can change as well as the structure of data and every time you add or change the data, you then had to modify the ETL structure.” With data lakes, which are much more open and flexible, you can run machine learning models continuously on the data, query data directly on the data lake and process it there too without modifying the code, he explained. “Storage is the fabric of that and customers use AWS S3 for data lakes because of its scalability, security, availability and durability. S3 also delivers high performance for response time which is important for analytics.” Komprise adds value to S3 data lakes in how the solution manages unstructured data for cloud native access. During the demo, Subramanian showed the power of file-object duality that Komprise Transparent Move Technology™ provides so that users can access moved data as files from the original location but also leverage the same data as objects in AWS. Cloud-native access to data is a requirement for data to be ingested into data lakes and other analytics platforms such as Amazon Macie or Amazon SageMaker. “Our partnership with AWS helps auto customers maximize unstructured data value because Komprise delivers the ability to analyze and mobilize data to leverage all the AWS storage tiers at the right time and in an open fashion,” Subramanian said. “This means customers can save the most and use data fully in the cloud.” Recently, Komprise announced Smart Data Workflows, new functionality which allows IT users to create automated workflows for all the steps required to find the right data across storage assets, tag it for easier search and send it to external tools for analysis. You can watch the entire webinar on-demand. ---------------- ### Why Cloud Native Data Access Matters Unlock the Potential of Unstructured Data Cloud transformation is top of mind for our customers. As a result, hybrid cloud storage is where they are focused. When it comes to migrating data to the cloud, customers have some choices to make. They can work with their existing data storage vendors and adopt their cloud file storage options or they can choose a different path. As Komprise co-founder and COO Krishna Subramanian points out in our most recent Smart Data Migration webinar, this question is pivotal when enterprise IT organizations take a data-centric instead of a storage-centric approach to cloud migrations. Before You Migrate Data to the Cloud Remember: The cloud is not just a cheap storage locker; The cloud is an active platform with tremendous compute power; Users want to run new kinds of analytics on unstructured data. The bottom line: Don’t limit the potential of your data by locking data into a proprietary format. Cloud native data access is essential to unleash the potential of the cloud. Cloud native is a way to move data to the cloud without lock in, which means that your data is no longer tied to the file system from which it was originally served. By 2025, Gartner estimates that more than 95% of new digital workloads will be deployed on cloud-native platforms, up from 30% in 2021. In the webinar, Krishna reviews the importance of cloud native data access and maximizing the potential of your data. She summarizes it this way: Maximize Data Efficiency Leverage the full potential of the cloud and be able to use all tiers of cloud storage on your data. For example, Amazon FSx versus Glacier Instant Retrieval: there are significant cost differences. When you move your data to the cloud, and are considering cloud tiering, ensure you can take advantage of all of the efficiencies the cloud has to offer by moving data as it ages to lower-cost storage. Maximize Data Access When you move data in cloud native format, users should be able to access the data not only as a file, but also as a native object—which is necessary for leveraging cloud-native analytics and other services. Access to your data should not have to go through your file storage layer, as this incurs licensing fees and requires adequate capacity. Maximize Data Services Make it easy for your users to search and find the data they need and send it to data lakes and analytics services, most of which operate at the object layer. Cloud-native access ensures that your data can leverage these services. Why Storage-Centric Tiering is an Issue So why not take a storage-centric approach to cloud tiering? Storage-based tiering goes all the way back to hierarchical storage management days, where you could have different tiers or platters within a storage environment: SSDs, SATA drives, etc. and the storage operating system could move blocks of data to different tiers and it was transparent to users. Now many of these storage vendors are saying they can move blocks to the cloud and treat cloud object storage as a platter inside of their storage OS. While this may be good for some use cases such as snapshots, the data is not cloud native. You’re tiering blocks of data to a location in the cloud that remains in a proprietary OS, meaning this data has limited use in the cloud other than as an archive. Cloud file storage is great to store files, but it’s not your data management platform. Use the full power of the cloud with an independent data management platform. The rest of the webinar reviews the Komprise architecture, the Global File Index and dives into a demonstration that highlights the potential of cloud native data. I’ve embedded it below for easy viewing.  Read the white paper: Block versus File Tiering _______________________ ### Cloud Data Management for Life Sciences This article was adapted from its original version at HealthITAnswers. Like no other time in medical history, new technologies combined with industry collaboration are delivering groundbreaking opportunities for more accurate clinical decision-making and faster development of treatments for life-threatening conditions: Consider the speed at which major research institutions came together to develop and release Covid-19 vaccines, which wouldn’t have been possible without the cloud and digital tools for rapid research, testing, analysis, and communications. As another example, Internet of Medical Things (IoMT) delivers remote monitoring and diagnosis using wearable biosensors which monitor medications and deliver alerts on chronic conditions such as emphysema, multiple sclerosis, and diabetes. The flip side of medical technology innovation is that these devices and sensors are generating massive amounts of unstructured data – adding to the overall healthcare data deluge. Roughly 30% of the world’s data volume is being generated by the healthcare industry, according to RBC Capital Markets. The collection and analysis of quality data is vital to healthcare but the rising volume of unstructured data, data that doesn’t fit nicely into rows-and-column based spreadsheets and databases is stretching IT budgets. Life sciences: patient care innovation and competitive gain in the cloud One outcome of life-sciences unstructured data growth is an acceleration of data center consolidation and migration to the public cloud. Sixty percent of pharma executives have already made changes or have a plan in place to invest in cloud-based services to support their digital transformation efforts, according to PwC. Cloud-based artificial intelligence (AI) is particularly exciting. A report by Deloitte outlined several use cases including to integrate data and improve the workflow for clinical trials: “They can even use AI to generate insights from past and current trials to inform and improve future trials.” Yet the question is: which data sets should move to the cloud and how? Answering this starts with taking a closer look at the traditional life-sciences data infrastructure. Pharmaceutical companies, biotechs and research institutions frequently face the following data management issues, which drive up costs and impede R&D activities: Data silos hampering collaboration across teams and departments as well as audits. Data visibility issues with data spread across many different hybrid IT environments and disparate applications. Difficulties searching and securely accessing and using data exported into cloud-based data lakes and other new data platforms. Continual change in regulations, affecting data practices. Too much time–at least 50%– spent on data preparation and deployment, according to IDC. Read the case study: How Pfizer Uses Analytics to Cut Storage Costs by 75% Here are the leading considerations for managing life sciences data in the cloud: Analytics and segmentation: Before moving data to the cloud or buying more data storage, understanding data across all hybrid storage and usage/access patterns can direct optimal placement. A company may want to move clinical trials data to the cloud after the trial has concluded. This addresses compliance issues for storing data for the required time without clogging up expensive on-premises storage–which should be preserved for active, regularly accessed data. Analyzing and right-placing data can save significantly on storage costs, freeing up budget for R&D projects while ensuring that critical workloads have the appropriate protection and performance. Watch the Webinar: Komprise Unstructured Data Management for Healthcare and Life Sciences Data tagging for context and search: With scalable data lake and data warehouse technologies now commonplace, IT teams can move data into cloud services where data scientists can run machine learning and other processes on it. The trick is finding the right data. When moving data from clinical applications and instruments into cloud storage, contextual data is lost.  A data management platform which facilitates tagging can apply metadata such as project, disease type, instrument type and demographics to the files. That way when files are moved into the cloud the researcher can search on keywords and find what they need without manual digging. Read the Blog: Google-Like Search and Tagging for All Your Cloud Buckets, Objects and Files Driving collaboration between research data scientists and central IT: Research data specialists and IT directors will benefit by working more closely together. The former know what scientists are looking for while the latter understand the nature of cloud infrastructure and data analytics implementations so they can create the best technical foundation to meet these research requirements. The end goal is to make it more viable for everyday analysts to search across distributed data sets to find what they need so that IT can continually, through policy-based automation, move the right data to the right platforms for analysis. Read the Blog: Komprise Brings Data Storage Insights to Business Teams and Departments Ransomware defense: Privacy and security is a high priority for life science organizations, yet fewer than half (45%) have active ransomware protections in place to prevent breaches and data loss, according to research by Egnyte. One strategy is to move cold data to object-lock storage such as AWS S3 and eliminate it from active storage and backups. This allows organizations to create a logically isolated recovery copy while cutting storage and backup costs by up to 80%. Read the Blog: How to Protect File Data from Ranswomware at 80% Lower Cost Life-sciences organizations today have the tools and technologies to bring new and better products to market faster than ever before. Yet wrangling unstructured data is slowing down the process of leveraging new types of clinical data for research and creating a blind spot in data analytics. Rethinking traditional data management practices to fully leverage clinical and laboratory images, patient files including telehealth video, sensor data from wearables, research files and more is a must-have capability for life sciences leaders. ### Top Storage and Unstructured Data Management Terms to Know Komprise June Update: Product, People and News The landscape of data storage, unstructured data management and cloud are constantly in flux. Komprise sits right in the middle of all this change! Read on for some definitions of a few top terms in the space as well as updates on Komprise product news, events and media mentions. Also, check out our Data Management Glossary  for more definitions! What is NFS versus SMB? NFS and SMB are network file-sharing protocols, as defined here in our data management glossary: Network File System (NFS): The NFS protocol is one of several distributed file system standards for network-attached storage (NAS). It was originally developed in the 1980s by Sun Microsystems and is now managed by the Internet Engineering Task Force (IETF). Server Message Block (SMB): SMB is a network communication protocol for providing shared access to files, printers, and serial ports between nodes on a network. SMB is also known as Common Internet File Systems (CIFS). Microsoft partner Cloud Infrastructure Services delivers these comparisons: “NFS is unbeatable when it comes to medium sized or small files. For larger files, the performance of both protocols is similar. NFS is more appropriate for Linux users, while SMB is more appropriate for Windows users.” What is NAS? Network Attached Storage (NAS) is a storage device connected to a network that allows storage and retrieval of data from a centralized location for authorized network users and heterogeneous clients. These devices generally consist of an engine that implements the file services (NAS device) and one or more devices on which data is stored (NAS drives). The purpose of a NAS system is to provide a local area network (LAN) with file-based, shared storage in the form of an appliance optimized for quick data storage and retrieval. NAS is a relatively expensive storage option, so it should only be used for hot data that is frequently-accessed. What is Metadata? Metadata is data that describes other data, such as author, date created, date modified and file size. Metadata can be created manually or through automation and is useful in managing unstructured data since it provides a common framework to identify and classify a variety of data including videos, audios, genomics data, seismic data, user data, documents and logs. TechTarget describes several different types of metadata in this article. What is Cloud Tiering? Cloud tiering extends your current storage infrastructure to include cloud data storage resources. Cloud Tiering lets storage administrators set policies to move infrequently accessed data to lower cost storage. The result is better use of expensive primary storage with capacity only used by “hot data” requiring faster access. Cloud tiering can also save significantly on storage spending and protect cold data for disaster recovery, auditing and research needs. What is Data Tagging? Data tagging is the process of adding metadata to your file data in the form of key value pairs. These values give context to your data, so that others can easily find it in search and execute actions on it, such as move to confinement or a cloud-based data lake. Data tagging is valuable for research queries and analytics projects or to comply with regulations and policies. To learn more about data tagging with Komprise, read this blog. What is Unstructured Data Management? Unstructured Data Management is a category of software that has emerged to address the explosive growth of unstructured data in the enterprise and the modern reality of hybrid cloud storage. As further explained in ITProToday: “Unstructured data is more difficult to manage than unstructured data as it doesn't have a uniform format, even if the data source is the same. Indeed, managing it in the way structured data is managed is something of a novel idea, as it's only been feasible to mine it for information since big data analytics and AI have taken off.” Komprise Intelligent Data Management delivers a unique approach to this market segment, with a comprehensive platform that includes: data insight through analytics, data mobility, open standards, cloud native access and a non-disruptive user experience with Transparent Move Technology. What is a Data Lake? A data lake is data stored in its natural state. The term typically refers to unstructured data that is sitting on different storage environments and clouds. The data lake supports data of all types – for example, you may have videos, blogs, log files, seismic files and genomics data in a single data lake. Komprise COO Krishna Subramanian discussed tactics for cloud data lakes in RTInsights: “In the past few years, cloud-based data lake platforms have matured and are now ready for prime time. Cloud providers’ cheaper scale-out object storage delivers a platform for massive, petabyte scale projects that simply isn’t viable on-premises.” What is a Data Lakehouse? Since data lakes can become swampy and unusable with piles of raw unstructured data, a new type of architecture came to the forefront in 2021: the data lakehouse. As defined by Bernard Marr in Forbes: “Data lakehouses enable structure and schema like those used in a data warehouse to be applied to the unstructured data of the type that would typically be stored in a data lake. This means that data users can access the information more quickly and start putting it to work.” Not surprisingly, Databricks does a nice job of explaining the data lakehouse. What is a Data Fabric? “Data fabric is a solution that allows organizations to manage their data—whether it’s in different types of apps, platforms, or regions—to address complex data issues and use cases,” according to Dataconomy. “Data fabric aims to make an organization’s data as useful as possible – and as quickly and safely as possible – by establishing standard data management and governance processes for optimization, making it visible, and providing insights to numerous business users.” Datanami adds to the definition: “Conceptually, a big data fabric is essentially a metadata-driven way of connecting a disparate collection of data tools that address key pain points in big data projects in a cohesive and self-service manner. Specifically, data fabric solutions deliver capabilities in the areas of data access, discovery, transformation, integration, security, governance, lineage, and orchestration.” Komprise News Komprise Smart Data Workflows In May, Komprise announced new capabilities for its Intelligent Data Management solution. Komprise Smart Data Workflows is a systematic process to discover relevant file and object data across cloud, edge and on-premises datacenters and feed data in native format to AI and machine learning (ML) tools and data lakes. Learn More > > Komprise June Webinar Series In June, Komprise product experts will be discussing tips for Smart Data Migration across three different online events throughout the month. Read the blog to learn about them all and register at the same time! Learn More > > Komprise in the Media VentureBeat The tech investor’s bible covered our Smart Data Workflows announcement. Read Now > > eWeek Komprise director of product marketing wrote about data tagging and how it works in unstructured data management. Read Now > > Jaxenter Komprise President & COO Krishna Subramanian outlines a 5-step maturity model for unstructured data management. This is a must read for the modern storage team! Read Now > > HealthIT Answers The life sciences industry has been in the spotlight since the onset of Covid-19. In this article, Krishna Subramanian discusses the intersection of cloud maturity and unstructured data analytics: a perfect tipping point for innovation in life sciences. Read Now > > ------------------- ### Smart Data Migration for File and Object Data Many enterprises are contemplating or in the midst of a cloud data migration. Last week Komprise kicked off a webinar series focused on Smart Data Migration, to help IT and storage leaders navigate this complex set of processes for file and object cloud migrations. Smart Data Migration for File and Object Data Available On-Demand A “smart data migration” strategy for enterprise file data means an analytics-first approach ensuring you know which data can migrate, to which class and tier, and which data should stay on-premises in your hybrid cloud storage infrastructure. The first session of the series introduced the topic and provided a deep dive demonstration of the Komprise Elastic Data Migration solution. Watch the Webinar > > Preparing for a File and Object Data Migration Available On-Demand What you need to know before you go. In this interactive session we'll review the checklist for to prepare for a file and object data migration. Hear from the experts and learn best practices as you invest in a more Intelligent Data Migration, Management and Mobility strategy with Komprise. Watch the Webinar > > Read the Blog > > Cloud Native Access — What is it and Why Does it Matter? Available On-Demand Get maximum value from your file and object data in the cloud. No penalty. No lock-in. In this interactive session we'll discuss the growing importance of cloud native data access and dive into a demonstration of Komprise Transparent Move Technology™ and how we ensure you're getting maximum value from your file and object data in the cloud. Watch the Webinar > > Read the Blog > > File data’s time for the cloud has come As the host for the first webinar, I wanted to take a few minutes to summarize the key points behind this series. As summarized in the Enterprise Storage Forum this week: The Cloud Storage Market is heating up. Got Data? Data Storage Stats Cited in this Blog Include: IDC: Data volumes will grow from more than 70ZB currently to 175ZB by 2025; Cybersecurity Ventures: Data stored in the cloud will reach 100ZB by 2025; Flexera: 93% have adopted a multi-cloud strategy; But…. Dell: 43% of IT decision makers fear their IT infrastructure won’t be able to handle future data demands; Accenture: 68% of companies are not able to realize tangible and valuable benefits from data; Forrester: Organizations which take a data-driven approach to decision-making grow more than 30% annually. File data migrations are complex. File data can be petabytes of data and billions of files. Migrating this much data to the cloud takes time and can be disruptive. In the webinar we reviewed some of the differences between traditional storage data migrations and cloud data migrations. File and object cloud data storage solutions have matured and are increasingly being deployed in the enterprise. Amazon FSX for NetApp ONTAP, Azure NetApp Files, Qumulo and other solutions are gaining traction; having the right data migration and cloud data management strategy is essential. Unstructured Data Migration Options There are many unstructured data management and cloud data migration options. Some considerations include: Data-centric vs. storage-centric approach? Free tools or point tools vs. a platform? Lift and shift or archive first? Which tier? Which cloud? Which approach? It’s Time for Smart Data Migration We define Smart Data Migration as an analytics-first approach ensuring you know which data can migrate, to which class and tier, and which data should stay on-premises in your hybrid cloud storage infrastructure. Can you answer these questions today? What data do we have and where is it stored? What data sets are accessed most frequently (hot) and less frequently (cold)? What types of files and which comprise the most storage (image files, video, audio files, sensor data, etc.)? What is the cost of storing these different file types? Which types of files should be stored in a higher security level? (PII or IP data? Mission-critical projects?) Are we complying with regulations and internal policies with our unstructured data management practices? We hope you enjoy these 30-minute webinars, which include demonstrations of Komprise Elastic Data Migration. Be sure to also subscribe to our YouTube channel. ------------- ### Top Considerations for Cloud File Migrations This blog was adapted from the original article on ITProToday. The vast majority of enterprise data is unstructured — think audio and video files, medical images, genomics research data, electric cars and the digital exhaust of IoT products. As storage costs comprise more than 30% of IT budgets in most organizations, the cloud has become a cheaper, simpler alternative for unstructured file and object storage. A survey of U.S. and U.K. IT managers and directors found that more than half (56%) say that moving more data to the cloud is their top priority with unstructured data. Yet these cloud data migrations are fraught with complexity and risk. Moving large volumes of data to the cloud can result in errors and data loss. They also take an inordinately long time to complete — sometimes months — and may not result in predicted cost savings. For these reasons, enterprise IT teams may delay or forgo cloud file migrations altogether. The leading cloud data migration issues and decisions include: Deciding which unstructured data should move to cloud storage; Understanding the different storage tiers and when it makes sense to use lower-cost object storage tiers such as Amazon S3 Glacier Instant Retrieval or Azure Blob and when a higher-performing file storage option like Azure Files or Amazon FSx for NetApp ONTAP is ideal and the process for moving data between storage classes once in the cloud; Security — the configuration of cloud storage presents new challenges and complications especially when blending hybrid environments; If multi-cloud architecture is in place, deciding which cloud to use for which data and workloads; Understanding the potential uses of cloud-native services for machine learning and AI projects and considerations for successfully moving data into those services. Opportunity Abounds: But Which Cloud and Which File and Object Storage to Migrate To? As demand for cloud file storage has accelerated, the options for customers are changing continually. While this is great news, it's also confusing. The major cloud vendors have dozens of classes of file and object storage from which to choose, each with tradeoffs on cost and performance. Plus, there's always the risk of getting burned on cloud egress fees if users wind up needing to bring that data back out of the cloud more frequently than expected. The Analysis-First File Data Migration Strategy Typically, cloud file migrations are executed as lift and shift programs. IT organizations migrate entire file shares and directories to the cloud. You may not be able to get the best cost advantage of the cloud from a one-size-fits-all strategy and lift-and-shift strategies are often "set and forget". These moves don't account for long-term plans for unstructured data — such as making data in the cloud available for cloud-based machine learning and AI. With so much emphasis on data as a strategic lever for competitive advantage and operational efficiencies, it makes sense to institute an analysis-first approach to migrations. Start by getting visibility into data usage and growth — across on-premises, edge and clouds — to understand not only your overall data profile but the requirements of different data sets. Strive to answer questions these unstructured data migration questions: What data do I have and where is it stored? What data sets are accessed most frequently (a.k.a. hot data) and which are rarely accessed (a.k.a. cold data)? What types of files do we have and which comprise the most storage: a.k.a. image files, video or audio files, sensor data, text data. What is the cost of storing these different file types? Which types of files should be stored in a higher security level — a.k.a. those containing PII or IP data or belonging to mission-critical projects? Benefits of an Analytics-First Cloud File Migration Approach Cost savings: Based on analysis of unstructured data before you migrate or backup, you may decide to first tier 60% of the data to archive storage in the cloud (like AWS S3 Glacier), and then migrate the remaining 40% to cloud file storage. This can cut down your cloud storage bill significantly. Faster migrations with lower risks: By first analyzing data and then migrating or moving by workload, data type or other key value, you can also be more agile: you'll break a massive and disruptive task into smaller bites which is faster and less risky. An added benefit of granular data sets is the ability to pivot to use new cloud resources as they become available with ease. Comprehensive data lifecycle management: With regular analysis running on your data assets, you can continually optimize data over its lifecycle — from expensive hot storage to lower-priced warm storage to cold (rarely if ever accessed) storage and then eventually, deletion. Migrating Data for the Endgame: Native Cloud Analytics The major cloud providers now have dozens of services which go far beyond hosting and storage into IoT, DevOps and data lakes. Cloud providers are investing billions into quantum computing, AI and ML to give customers powerful analytics capabilities they'd otherwise need to build and support internally at a high price. IT organizations will need to carefully consider the data management tools and platforms they are using to tier and migrate data into the cloud, so that they can easily access and move data elsewhere as needed to leverage cloud-native analytics tools. Storage vendors may implement proprietary data formats that prevent direct access to data tiered or migrated to the cloud outside of their own appliance. This approach locks customers into a static storage strategy and prevents access by cutting edge analytics and AI/ML services that access data via open APIs. Accelerating file data migrations to the cloud can bring a host of benefits, from cost savings and automation to using cloud tools to uncover hidden insights from massive volumes of unstructured data. Maximizing ROI from these migrations requires a nuanced, analytics-based approach using open unstructured data management tools and processes. This will right-place data into the appropriate storage class based on age, usage, compliance needs and/or business priority and allows IT teams to easily move the data again and again as new enterprise storage innovations come to light. ----------------------- ### Komprise Automates Unstructured Data Discovery with Smart Data Workflows Komprise Intelligent Data Management Platform automates the process of finding, tagging and delivering file and object data to cloud services and big data analytics platforms. Campbell, CA – May 19, 2022 – Komprise, the leader in analytics-driven unstructured data management and mobility, today announced Komprise Smart Data Workflows, a systematic process to discover relevant file and object data across cloud, edge and on-premises datacenters and feed data in native format to AI and machine learning (ML) tools and data lakes. Industry analysts predict that at least 80% of the world’s data will be unstructured by 2025. This data is critical for AI and ML-driven applications and insights, yet much of it is locked away in disparate data storage silos. This creates an unstructured data blind spot, resulting in billions of dollars in missed big data opportunities. Komprise has expanded Deep Analytics Actions to include copy and confine operations based on Deep Analytics queries, added the ability to execute external functions such as running natural language processing functions via API and expanded global tagging and search to support these workflows. Komprise Smart Data Workflows allow you to define and execute a process with as many of these steps needed in any sequence, including external functions at the edge, datacenter or cloud. Komprise Global File Index and Smart Data Workflows together reduce the time it takes to find, enrich and move the right unstructured data by up to 80%. “Komprise has delivered a rapid way to visualize our petabytes of instrument data and then automate processes such as tiering and deletion for optimal savings,” says Jay Smestad, senior director of information technology at PacBio. “Now, the ability to automate workflows so we can further define this data at a more granular level and then feed it into analytics tools to help meet our scientists’ needs is a game changer.” Komprise Smart Data Workflows are relevant across many sectors. Here’s an example from the pharmaceutical industry:  1) Search: Define and execute a custom query across on-prem, edge and cloud data silos to find all data for Project X with Komprise Deep Analytics and the Komprise Global File Index. 2) Execute & Enrich: Execute an external function on Project X data to look for a specific DNA sequence for a mutation and tag such data as "Mutation XYZ". 3) Cull & Mobilize: Move only Project X data tagged with "Mutation XYZ" to the cloud using Komprise Deep Analytics Actions for central processing.  4) Manage Data Lifecycle: Move the data to a lower storage tier for cost savings once the analysis is complete. Other Smart Data Workflow use cases include: Legal Divestiture: Find and tag all files related to a divestiture project and move sensitive data to an object-locked storage bucket and move the rest to a writable bucket.  Autonomous Vehicles: Find crash test data related to abrupt stopping of a specific vehicle model and copy this data to the cloud for further analysis. Execute an external function to identify and tag data with Reason = Abrupt Stop and move only the relevant data to the cloud data lakehouse to reduce time and cost associated with moving and analyzing unrelated data. “Whether it’s massive volumes of genomics data, surveillance data, IoT, GDPR or user shares across the enterprise, Komprise Smart Data Workflows orchestrate the information lifecycle of this data in the cloud to efficiently find, enrich and move the data you need for analytics projects,” says Kumar Goswami, CEO of Komprise. “We are excited to move to this next phase of our product journey, making it much easier to manage and mobilize massive volumes of unstructured data for cost reduction, compliance and business value.” Visit here to learn more about Komprise Smart Data Workflows. About Komprise Komprise is a provider of unstructured data management and mobility software that frees enterprises to easily analyze, mobilize, and monetize the right file and object data across clouds without shackling data to any vendor. With Komprise Intelligent Data Management, you can cut 70% of enterprise storage, backup and cloud costs while making data easily available to cloud-based data lakes and analytics tools. www.komprise.com. Media Contact: Kevin Wolf, TGPR www.tgprllc.com kevin@tgprllc.com ### The 4 Steps to Managing Data in a Hybrid Cloud This article was adapted from its original version on The New Stack. In a hybrid cloud environment, you often wind up with tool sprawl, making it hard to manage disparate environments. Simplify this by getting data visibility so you can apply rules and policies to keep IT and data assets under control. Data management is hard enough when your data lives in a single data center or cloud. But when you opt for a hybrid cloud strategy, you face a whole new level of complexity when it comes to tracking, securing and governing your data. The main reason why is that in a hybrid cloud model, you have many more data vendors, tools and protocols to contend with than you would when all of your data lives in a single environment. You might, for example, have some data that lives in local file systems on on-prem Windows and Linux servers. Meanwhile, you also host some data in an NFS or SMB file share running on your corporate network. At the same time, you use a cloud-based object storage service like AWS S3 or Azure Blob Storage. You might have other storage solutions, such as NetApp, in the mix to boot. Not only does each storage vendor or protocol in a scenario like this involve a different storage location, but it also entails an entirely independent set of tools for identifying, managing, backing up and protecting data. Securing data on a Linux file system requires you to use Unix tooling to set file permissions, for example, whereas with cloud-based data, you’d use your cloud vendors’ access management framework, like AWS IAM. The bottom line is that determining where your data lives — let alone managing it effectively — requires you to juggle a disparate set of tools when you have a hybrid cloud strategy. You have to navigate a variety of data silos and master numerous protocols and platforms to keep your data secure and enforce governance policies. A Better Approach to Hybrid Cloud Data Management You can’t erase the siloed nature of data in a hybrid cloud. It just comes with the territory. What you can do, however, is to take steps to simplify and streamline the way you work with data across the various silos that exist within a hybrid cloud. There are four key practices to follow in this regard: Achieve full data visibility—The first is simply to know which data you have via the creation of a global file index. After all, you can’t govern data very effectively if you don’t know where it exists or which protocols or platforms it depends on. Building a data index that identifies all your data across the various assets in your hybrid environment can ensure you know where your data resides at all times. Some storage vendors can index their storage platform only. This is proprietary and limited to that silo, so IT would need to integrate the indexes manually along with any data stored in the cloud. Build for accuracy—The second step toward better hybrid cloud data management is ensuring that your data index is continuously updated. It’s very likely that your data architecture changes constantly. You may move data from one location to another within your hybrid environment, for example, or introduce new types of data services. It’s critical that your data index remain flexible and scalable so that it can reflect these changes as they occur. Your index needs to support new data formats, storage locations, protocols and so on so it can keep adapting with your business. Operate by rules and policy—Third, strive to deploy an actionable data management strategy. You should be able to write policies that define how data should be managed based on attributes you define and then enforce those policies automatically across your hybrid environment. Consider an organization that needs to delete data of a certain type (such as ex-employee or ex-customer data) after a set period of time to meet compliance requirements. Instead of attempting to meet that rule imperatively — which would mean going out and finding the data and then deleting it manually — the organization can adopt a declarative approach wherein it writes a policy that says “when data is tagged with [insert attribute here], delete it after one year.” Then the rule would be continuously enforced across the environment, regardless of where exactly the data is stored. Maintain excellent user experience—Finally, the best hybrid cloud data management practices should enforce data governance rules without disrupting user access and/or the way that your workloads operate. They shouldn’t slow down performance or cause application errors even as they move data around, modify access controls and so on. There’s no denying that hybrid cloud architectures make data management inherently more complex. With the right approach, however, it’s possible to manage that complexity in a way that ensures both efficiency and consistency, no matter how many data silos, tools or protocols exist within your cloud environment. Unstructured Data Management _______________________ ### Smart (and Free) File Data Migration to Microsoft Azure Komprise recently announced that we’ve been selected for the Microsoft Azure File Migration Program, which is an exclusive new program giving Azure customers access to industry leading file-migration at no cost. Let’s face it, enterprise data migrations are always stressful. And when you factor in moving data to the cloud, that stress and risk compounds. It’s hard to know which files to move, when and where (to which cloud storage class) and the typical migration process is slow and error-prone. “If you take the same approach with the cloud as with on-prem migrations, which is taking entire volumes of data and moving them, that is not the right approach for cloud,” says Steve Pruchniewski in a recent Komprise webinar with Azure about the new program. “You’ll end up putting data on the wrong storage tier. Cloud data migrations require that you take a minute to see what data you have and then you can move to the cloud in an agile way and move data over time. In a way, it’s the end of migrations.” What are we announcing with @Azure? File Migration with Free Best-of-Breed Software! https://t.co/DPEAPw1tMr #Microsoft #datamanagement #datamigration @Kloud_Karl @CaitiePossum @webscalesteve pic.twitter.com/f6zeCcE2YY — Komprise (@Komprise) April 7, 2022 Komprise is a select partner working with Azure to help customers migrate file data to the cloud in simpler and cost-effective way. “I recognized a gaping hole in our story to help customers onboard to Azure and that is unstructured data migration,” says Karl Rautenstrauch, Principal Program Manager for Storage Partners at Microsoft. “Not only did we not have a tool for this but we have trained customers to expect migration to be free. Our Komprise partnership solved both problems—an intelligent, scalable migration solution and you get it for free.” _______________________ About Komprise for Azure Komprise Elastic Data Migration eliminates the cost and complexity of managing file data by providing analytics-driven data migration to Azure without creating any vendor lock-in. Some of the benefits of our approach include: Visibility: Analytics across existing NAS (NetApp, Dell, Windows) to identify which data sets to migrate and to which tier of Azure; Mobility: Systematically migrate files 27 times faster. Komprise Elastic Data Migration scales elastically according to the distribution of your shares, directories and files; Value: Ensure full data integrity by migrating all file attributes and permissions with full MD5 checksums on every file Of course, Azure customers will have the opportunity to upgrade to the full product, Komprise Intelligent Data Management, which means they can transparently tier across Azure Storage platforms, cutting 70% of cloud costs. With Komprise cloud tiering, unstructured data is tiered transparently, allowing users and applications non-disruptive access. Organizations which take advantage of the full platform also benefit from the Komprise Global File Index to query, tag and move the right data to the right place for AI, ML and data processing. Using Komprise and Azure together can deliver the best ROI for customers as well as a pathway to monetize data through moving data to cloud analytics platforms such as Azure Databricks and Azure Machine Learning. The steps are to analyze your data and understand what you have. Then, look at the possibilities for moving data into Azure, set a plan and make granular moves which you can refine at any time. _______________________ Program Details The Azure File Migration Program offers free software licensing, an onboarding session, and limited access to support after selecting the Azure-sponsored offer from the Azure Marketplace. To learn more about this program, visit the Azure Tech Community Blog. Also, read the Komprise Data Migrations blog for best practices in using Komprise for moving on-premises file data to the cloud. Watch the on-demand webinar:  Accelerate Data Migrations to Microsoft Azure with Komprise. _______________________ ### Komprise Spring 2022 Update: Deep Analytics, AWS Storage, Migration Expanding Deep Analytics Use Cases for Unstructured Data Management Cloud-based analytics and AI and ML applications are opening the doors to leverage unstructured data in new ways while also demanding changes to unstructured data management platforms. Data lifecycle management is imperative today, given the volume and complexity of data assets in storage — but this process of finding, moving, copying and replicating data to maximize value over time can be overwhelming for traditional IT infrastructure and enterprise data storage teams. The analytics-first approach of Komprise Intelligent Management gives customers the means to understand their data and ultimately uncover new insights from massive volumes of file and object data. Since our launch of Deep Analytics Actions in October 2021, we’ve added a number of important new features, updates and enhancements to the Komprise Intelligent Data Management platform. As always, our customers and partners are encouraged to review the documentation site for details. In this post, I’ll summarize the salient updates. Deep Analytics Actions – Adding Copy and Confine Functions Komprise Deep Analytics Actions gives users the ability to create powerful queries for granular data sets; those queries can then serve as criteria for a Komprise data management plan. Komprise delivers a unified, Google-like search across on-premises and cloud data storage silos, which we call the Global File Index. This allows users to feed custom data sets into automated plans for ongoing tiering or copy into machine learning pipelines. Komprise delivers data tagging through the UI with automated tagging capability through the API, such as from AI/ML applications. Komprise now supports these additional actions for Deep Analytics Actions: Copy: Now users can copy specific data sets to NAS, cloud NAS, and object storage targets. Make a copy of data for collaboration with other business units or external partners while ensuring the source data is not modified. Feed analytics and AI/ML pipelines with specific data sets.Migrate specific data sets to new storage such as cloud NAS. Enable caching by copying data to other locations for lower latency access by applications or users such as for Content Distribution Network or similar use cases. Watch a demo. Confine: Find and move specific data sets for deletion. Configure data management policies to expire data sets according to compliance/ regulatory requirements. Locate ex-employee or other obsolete data for deletion. Data owners can query the Komprise Global File Index to find precise data sets and automate policy driven actions such as copy, tier or confine.   Data consumers and/or line of business teams can create queries to find data sets. These new features are now options in your policy-driven Komprise data management plan. The actions are systematically and automatically applied at the interval specified. You can keep remote copies of your data synched with the original copies and confine data as it ages and meets the criteria. Staying Current with AWS Innovation To meet customers’ changing use cases and requirements, independent data management platforms must support new AWS features. As an AWS Migration and Modernization Competency partner, Komprise gives customers the flexibility and power to use the best of the cloud. Unlike storage-based “cloud gateways” or other proprietary data management technologies that limit storage classes and cloud features, Komprise ensures that customers can rapidly adopt new AWS features and reduce costs. Komprise now supports two additional S3 storage classes: Amazon S3 Glacier Deep Archive - Amazon S3’s lowest-cost storage class is ideal for long-term retention of data that is accessed infrequently enough that users can tolerate access times of up to 12 hours. The common use case is when customers must comply with regulations to retain data for many years, such as in government or healthcare. Komprise customers can choose to move or copy data directly to Amazon S3 Deep Archive or have Komprise move it over time through data lifecycle management policies. Amazon S3 Glacier Instant Retrieval – This new Glacier service is 80% less expensive than S3 Standard-IA with the same fastaccess and throughput. The one caveat is you are charged higher fees than S3 for data retrieval. This is ideal for data that is accessed no more than once a quarter but requires fast access for applications and users—for example medical images from non-active cases or research from completed projects. Komprise makes it simple for customers to find data sets that haven’t been accessed for six months or more and tier that data to Amazon S3 Glacier Instant Retrieval to slash storage costs, while not affecting user experience because of our Transparent Move Technology™ (TMT). _______________________ _______________________ Support for AWS GovCloud U.S. Government customers and their partners that must adhere to compliance mandates and strict security standards can now leverage Komprise to move data to AWS GovCloud storage. AWS GovCloud provides specialized AWS Regions designed to meet the isolation and security needs of federal customers and partners. Read more about Komprise AWS GovCloud here. Speed Up Migrations with Migration Iteration Error Log Many customers delay data migrations because of the hassle of untangling complex permissions and other issues. While Komprise can't promise to eliminate these issues altogether, we can make it easier to find and address the problem with the new Migration Iteration Error Log. By unifying error messages for all migration failures for each iteration into a central log, Komprise helps you find and correct problems between migration iterations. This means your team can discover and resolve issues faster and thereby complete data migrations in fewer iterations. Customers can visit our support portal for release notes, how-to articles and documentation. To see a high-level overview of our latest updates visit: komprise.com/whatsnew. _______________________ ### Automated Data Tagging with Komprise Tag and Enrich Data with Custom Workflows at the Edge, Data Center and Cloud In the previous post we discussed how data mobility is now the key challenge for deriving value from unstructured data via data analytics and AI/ML processes. A significant amount of time in the data analytics workflow is consumed finding and moving data rather than the analytics or AI/ML processing of that data. Here we discuss how to bring data analytics services to your unstructured data to enrich and refine data sets. One useful data analytics service is tagging, where data can be tagged with custom, user-defined tags to enable easier future searches. Tagging can also be used to combine related data that are logically or temporally separated into one result set using a common tag or tags. You can use tags stored in applications and make them available no matter where your data moves. You Can Execute Data Tagging Operations Either at the Source of the Data or in the Cloud Data tagging using Komprise is flexible and customizable. Using APIs, you have the freedom to apply tags to data wherever it’s most convenient for you. So why tag data locally, at the edge? Network restrictions — Limited bandwidth, long latency, and costs may make sending massive data sets over the internet problematic. In such cases, searching and narrowing just the right data that needs to be sent to the cloud can speed up data analytics. Security — Some customers may not be able to send data over public networks, so processing should be performed at the point of generation. Speed — Depending on your workload, it may be faster to bring compute to your data versus your data to compute Application-specific metadata — Custom or industry-specific applications, such as a Lab Information Management Systems (LIMS), will have their own set of metadata for files: for example, a device ID in microscopy image files. This metadata will only be available by querying the applications at the local data center. Regardless of the reason, tagging data at the edge or data center has many benefits and is the first step in enriching your data set before sending it to the cloud for further analysis. Clinical Research Unstructured Data Tagging Example As a case in point, we will use the compute power of the Komprise Observers. These key components of the Komprise architecture are virtual appliances that run in VMs deployed adjacent to your data and which analyze and transfer data by custom-defined policies. When you point Komprise at your different file and object repositories, Komprise automatically indexes all the standard metadata and creates a Global File Index. Users can enrich data with custom tags, which Komprise maintains as your data moves according to your policies. No matter where your data goes, your tags persist and you can search for them across clouds with Komprise. To illustrate how you can run any custom function and create custom data workflows using Komprise, we will use a simplistic example of taking a set of files from a Komprise query on the Global File Index, running a standard Linux-based search tool to find files that contain a specific term and tagging them systematically through a workflow. Our environment has a data set that includes files in Abstract Syntax Notation format (*.asn) , which is an International Standards Organization (ISO) data representation format for data interoperability that is commonly used in life sciences. We will query the Komprise Global File Index to first find files with extension ASN in directories with “coronavirus” in their name, and then use a standard text search tool run a full text search of these files for “Netherlands.” The files returned by the query will be tagged with “Country=Netherlands” via the Komprise API. Here are the file and object data tagging steps: Step 1: Deep Analytics query First, we use the Komprise Deep Analytics interface to query the Global File Index and find the ASN data that we will refine. For this demo, the data set is small, just 8.9 GB and about 1400 files across a few file shares; in a production environment the data set could easily be petabytes and billions of files stored across many data centers and clouds, across any file storage. _______________________ Step 2: Select your file We save a query to select “file type: asn & directory = “coronavirus” and name the query “ASN files under coronavirus”. This refines our data set to just 46 files. We could have Komprise move or copy the results of this query to the cloud if we wanted to further analyze this. But, we actually want to refine the data further as we are only interested in Coronavirus ASN files that have studies done in the Netherlands. _______________________ Step 3: Create a Komprise tag Now we create a new query named “Researched in Netherlands” that looks for a tag with name “country” and with the value “Netherlands”. When we run the query at this point, it returns 0 results; the ASN files have not yet been processed and tagged with a location value. _______________________ Step 4: Create a Komprise Plan Next, we create a Komprise Plan that will invoke the text search function to inspect and tag the selected files. To be efficient we only want to process the files identified in our previous query “ASN files under coronavirus” so we use that specific query for our source. This will enable us to run our search function against this specific data set. The Observers will now extract these select files to search for the term “Netherlands” and apply the tag “country: Netherlands”. _______________________ This plan will run on a scheduled basis to continually find new data that meets the criteria we specified. _______________________ Step 5: Review your result set Returning to the Deep Analytics function within Komprise, we can now re-run our query looking for files tagged with “country: Netherlands” Now we see five files that meet our criteria. The researcher can take action on this specific data set in a clinical study, for example, and copy this precise data set to the cloud to run AI/ML processes. This same set of tags could be used for other purposes, such as to enforce GDPR data locality rules or data retention policies. The combination of Komprise Intelligent Data Management with APIs and the customer’s applications provide an open framework to drive innovation and derive greater value. How Does This Work? We used a text search application that extracts data and stores it in an open format (JSON). We then used the Komprise API to apply this data to each file as a tag and make it available in the Komprise Global File Index. Komprise uses the industry standard Swagger UI interface to guide customers use of the API. How Do You Create Systematic Data Workflows Across Repositories? While this was a specific scenario, Komprise provides an Intelligent Data Management and Mobility framework via an API that can interact with advanced data analytics applications, across industries and disciplines, such as life sciences and manufacturing. The ability to use Komprise to search, find, apply tags and then take action makes it possible for customers to get faster value from enriched data sets. You can use this to build your data pipelines with any custom workflows. Once you design your queries and set the policies in Komprise, it automatically and continuously executes your data pipeline. In this example, as new ASN files are created, Komprise will automatically find them, tag the ones with Netherlands and update the query results. You can also have Komprise move these results or copy them to the cloud for further data analytics. In our next blog we team up with AWS to discuss processing and tagging data with AWS cloud data services. Read the blog post: Using Amazon Macie with Komprise for Detecting Sensitive Content in On-Premises Data   _______________________ ### Google-Like Search and Tagging for All Your Cloud Buckets, Objects and Files Data is Piling Up in the Cloud, Data Centers and on the Edge How do you easily search across these various data silos to find the data you want to analyze further and feed AI/ML applications? Searching and prepping unstructured data is hard: data scientists spend an estimated 80% of the time finding, cleansing and organizing the data, not in the analysis. Unstructured data (such as audio, video, images, genomics data, IoT data) is typically stored as files or as objects, both in file storage and cloud. It has no common structure and can easily be billions of files and objects strewn across many buckets, accounts and file stores. Customers need a way to search across all storage and then mobilize and use the data through systematic data management. Komprise, an independent data management platform, delivers a Global File Index across all unstructured data along with a scale-out architecture, which means that customers can quickly find and act on specific data sets and set up automated policies. Think of it as a Google-like search across all your data repositories along with the ability to automatically move or execute actions on the results. To enhance and improve the search, customers need data tagging capabilities which enrich and refine data by adding metadata. Why Unstructured Data Tagging Matters Metadata makes it easier to find and manage data and take action. This is where data tagging comes into play and it’s a core feature in Komprise Intelligent Data Management. Tagging adds additional metadata to your file data in the form of key value pairs. These values give context to your data, allowing it to be easily found or associated with a project, study, or classification. Example of tags: Country = US, Project ID = 123, HIPAA = TRUE Tagging helps you become agile with your data: the ability to quickly find the exact files you want out of a sea of potentially hundreds of billions of files and then send data sets to analytics tools and data lakes on-premises, at the edge or in the cloud. These tags can be applied either by data stewards / owners who may have intimate knowledge of the data and its business value or programmatically by analytics applications via API. This is valuable for research queries and analytics projects or to comply with regulations and policies. Global Analytics & Search across silos with Komprise   Examples of how tags can be leveraged with Komprise Intelligent Data Management A common scenario for end-user tagging is self-service in the Storage-as-a-Service (STaaS) model. Check out the Komprise best practices series here for demonstrations and discussion. Here are some common unstructured data tagging use cases: Mergers and Acquisition: Recently two regional banks entered into a merger agreement. Part of this process involved moving massive amounts of data to different data centers and clouds. By tagging data sets with values that indicate the bank of origin and categories, the newly formed company can efficiently process and manage the data over its lifecycle. Edge-to-Cloud: Lab instruments often generate terabytes of data which are stored in a NAS file system. This file system can simply be used as a daily cache and the data can be tagged and automatically tiered to the cloud as new data lands every day. The benefit of this approach is that lab data is available in the cloud, tagged, and thanks to Komprise Transparent Move Technology™ (TMT), natively accessed as objects. This means that users can import it for analysis with any cloud data analytics service and at a dramatically lower storage cost. Improving Customer Support: A technology company used a machine learning program to run sentiment analysis on call center recordings. The results, such as customer satisfaction scores, are recorded to each audio file with a tag. Now employees can find relevant audio recordings for training and improve support efficiency. Medical Imaging: A healthcare system may want to run machine learning on medical images and then tag image with diagnosis codes. Researchers can now quickly find images by diagnosis to support clinical projects. Read the white paper. Legal Hold: Legal discovery applications can find documents and file data related to litigation and then apply tags with the case ID. This data set can then be copied to immutable storage with retention policies to ensure the evidence is not altered or deleted. Automotive: Data collected from self-driving cars can be tagged with information about driving conditions. Once tagged these data sets are useful to replay scenarios or generate synthetic data for further training. See a demo. You may have some tags generated in industry specific applications such as Electronic Lab Notebooks (ELN) or Lab Information Management Systems (LIMS), but these tags do not propagate to the cloud and cannot be used outside of the specific application. Komprise provides a way to create, use and search based on standard metadata and tags no matter where your data lives, and maintain the information as data moves from one  repository to another. You can also execute additional functions on data and enrich data with tags, which then persist as data moves from one system to another. Komprise Smart Data Workflows provide a a simple point-and-click UI wizard to set up an automated data workflow: easily search across on-premises, edge and cloud data storage silos to find the data you need, execute Komprise or external AI functions on a subset of data and tag the data with additional metadata. ​Move only the data you need, build custom AI data workflows, and manage the lifecycle of unstructured data intelligently. In our next blog we’ll dive into how you can process and tag your data where it lives without moving it to the cloud. We’ll include a use case where we copy a data set to the cloud for analysis. In both cases the data will be processed and tagged and with policy-driven automation. Read the VentureBeat article: How to create data management policies for unstructured data. _______________________ ### Ukraine Impact, Cloud Data Migrations & Object Storage Komprise March Update: Product, People and News Thinking about tech news with the tragedy unfolding in Ukraine can feel trivial – but it’s also telling how ingrained tech is in our lives and world affairs. Protocol posted about how major tech vendors including Google, Samsung and Microsoft are responding by pulling out of Russia. Blocks and Files covered the storage vendor aspect of this trend. Here’s our take on intriguing IT infrastructure and data trends reported in the trades, along with our own latest news. Chip Shortage Potentially Worsened by Ukraine Crisis The Russian invasion of Ukraine is likely to create havoc on the semiconductor chip market. Both Ukraine and Russia are key sources for neon gas and palladium, both of which are needed in the manufacturing of semiconductors, as reported in Crunchbase. The U.S. neon supply is largely from Ukraine/Russia, while Russia also is a key supplier of palladium, providing about 33 percent of the worldwide supply, according to Techcet. Russia could choose to ban sales of these materials to North America and other regions in a quid pro quo move to President Biden’s March 8th ban of Russian energy imports. _______________________ Cloud Storage Maturity Brings Up New Security Risks The year 2021 saw massive adoption of cloud technology as business moved operations from on-premises. Gartner predicts that global spending on cloud services is expected to reach over $482 billion in 2022, up from $313 billion in 2020. Along with that move came new security challenges as infrastructure teams adopted cloud-based technologies and blended with on-premises solutions. Not surprising, this presents some security challenges. A report from CybelAngel found that cloud storage leaks grew by 150% in 2021 from 2020, as covered in StorageNewsletter. Pauline Losson of CybelAngel remarks: “The huge growth in cloud adoption and organizations’ increasing reliance on outsourcing development work means that all risks are, in effect, moving to the cloud.” _______________________ Object Storage Comes of Age Cloudian's CEO and CTO discussed the evolution of object storage and its dependence on the widespread adoption of the S3 API, in TechTarget: "What we're seeing is a lot of people simply cannot store data on the traditional storage at the same rate, at the same cost and with the same way to manage. When you get to enough of a scale, object is really the only way." _______________________ Untangling AWS S3 Choices Choosing the right classes storage depends upon a number of variables including access frequency, user requirements (such as how fast retrieval times must be) and file types. A Cloud Guru published a thorough analysis of the AWS storage decision tree including a helpful infographic. Here’s just one consideration: “If you have objects that you need to retain for business reasons, and absolutely have to be on-hand for immediate access, S3 Glacier Instant Retrieval is spot-on for your needs. If it can wait even a few minutes, S3 Glacier Flexible Retrieval (formerly just S3 Glacier) can continue to save you heaps, and even more if hours or days are an option, where you may use S3 Glacier Deep Archive.” _______________________ Komprise News Azure & Komprise File Migrations Once you’ve decided on cloud storage targets, it’s time to address the migration process—which can be slow and risky if data is lost or results in access issues. In February, Microsoft announced a new program: the Azure File Data Migration service and Komprise is one of just two vendors chosen for the launch. Azure customers can use Komprise Elastic Data Migration to simplify and improve data migrations to Azure, at no cost. Read our blog interview with Azure's Storage Guru Karl Rautenstrauch. _______________________ Kompriser Spotlight Larry Dabrow, director of North America channel sales at Komprise, was included in the CRN 2022 Channel Chiefs list. Prior to Komprise, Dabrow worked in senior channel and sales roles at Dell, EMC, FalconStor and Ingram Micro. _______________________ Komprise Honored by Storage Magazine Komprise was named to the list of top data storage and management tools of 2021, by SearchStorage and Storage Magazine. _______________________ Komprise in the Press VentureBeat: “With data growing at an unprecedented rate, comprising 30% or more of the overall IT budget on its storage, now is the time to hunker down on the idea of data management policy automation.”—Randy Hopkins, VP, Global Systems Engineering Enterprise Storage Forum: “It’s time to manage data across silos, across edge data centers and clouds. They are not going away, and no one storage vendor can help customers avoid silos altogether.”—Kumar Goswami, co-founder and CEO of Komprise The Patent Lawyer: “It was only after the big problems were solved that we went for the patent. This was helpful because we did the hard work to get to the top of the mountain and then knew exactly what to patent.”—Kumar Goswami CRN: “There’s a huge blind spot when it comes to unstructured data management, both on the optimization of the data and in the extraction of value from that data. That‘s the problem we’re solving.”—Krishna Subramanian _______________________ ### Azure’s Storage Guru on Customer Adoption and File Data Migration Interview with Karl Rautenstrauch, Principal Program Manager, Storage Partners, Microsoft Azure Interview with Karl Rautenstrauch Principal Program Manager, Storage Partners, Microsoft Azure _______________________ You’ve been working in storage-related roles for many years not only at Microsoft but also NetApp and Blue Cross Blue Shield. Explain the evolution of enterprise storage management and technologies over the years? KR: I’ve been working in this industry for 24 years and over all this time, the problems have stayed the same. Thankfully, the solutions have changed and become much more attainable for customers. One of the biggest problems has remained managing the always growing amount of data. Twenty years ago, there was a big push to use tools to remove stale content and store it on a lower cost platform where it is no longer polluting the production environment and complicating data protection. An unfortunate truth is, the more data you have to move between platforms, the more painful and risky the process becomes. The tools were designed to mitigate that, but they were very expensive and complex and the choices of where you could move the data were not that much less expensive – and still required data migration at the end of each platform’s life. _______________________ In the last five years, two things have changed for the better: the rise of SaaS platforms and cloud storage. SaaS platforms offer less for customers to manage and the cost benefits of multitenancy. Cloud storage brings the end of data migration. End of life is the problem of the cloud provider and platform level migrations are now a thing of the past. These developments have really changed the game. _______________________ What’s exciting for you about working in a partner-facing role at Azure? KR: I have one of the best jobs at Microsoft. I get to see innovation across the industry, meet partners frequently and learn new ways to solve problems. This means I can bring the best available solutions to our customers to solve very real problems. I get to choose the partners from across the globe that I work with too. This has taken me to places where I had never imagined I would go, such as Romania. _______________________ Why is storage a cool space right now? KR: I always say that storage is not the sexy in IT but it’s never dull and it’s always in demand. Data is and will remain the lifeblood of any company in any industry. If you are adept at managing, protecting and sharing that data securely you are a hot commodity. Being a storage and data specialist is one of the best career decisions you can make. The same innovation we’ve seen in phones we’ve seen in storage. There is always faster, bigger and easier tech emerging in storage, such as--ways to get infinite copies of data to developers in seconds. I’m seeing an endless series of innovations that make it never boring. _______________________ Let’s go back to Azure. What are some top challenges or barriers that enterprise customers have today in adopting cloud storage? How is Microsoft helping alleviate those pain points? KR: In storage, it’s all about understanding the right platform to use for a particular application workload. It’s rarely easy. The only time it’s easy is when you select a service where there is no choice of storage like Azure SQL. But otherwise, you need to decide what remains on disk storage, what should be on shared file storage such as NAS in the cloud and what can move to object storage. That can be very stressful. And then you need to determine how long it will take to move data and afterward, verify that everything did indeed move – or did I miss an alert that something went wrong? Azure has built guidance for customers which clearly outlines our platforms capabilities to make it easier for customers to decide which platform to use. And we’ve done a fantastic job of solving how to move virtual machines and databases. Now, we’re offering simple automated solutions in the unstructured data management space so customers can embark on a risk free and simple migration. This is a very strategic initiative for us. _______________________ Read the Solution Brief You’re referring to Microsoft’s new program for file data migration, which has enlisted the partnership of a select group of ISVs including Komprise. Can you talk about the genesis for this program and how it will help customers? KR: We are 100% customer driven. This program was born from customer conversations and escalations with moving large amounts of file data to Azure. The tipping point came about a year ago. We had a large strategic customer in the energy space and watched them struggle with a file migration, even with their massive and skilled staff. Throwing bodies at the problem didn’t make it go away. You need the right technologies. We chose to partner rather than build. We had existing relationships with best-of-breed companies like Komprise. The key thing to solve was what we in fact created--customers expect migration tools to be free. _______________________ Through our partnership with Komprise, we can bring elastic data migration to our customers for free and meet their expectations for cloud data migrations. Learn more about the Komprise for Azure File Data Migration program and read Karl’s blog with the Azure perspective. _______________________ What do you see as the market differentiation with Komprise? KR: Ease of use equaling time to data. I get frustrated when I have to go deep into a manual. Not once did I need to open Komprise documentation or reach out to a peer at Komprise to get clarity on how to perform an operation. Within 15 minutes of using the software, I felt I could pass a certification test. That gave me comfort that our customers could be up and running quickly and wouldn’t have difficulty completing what is typically a time-intensive task. _______________________ On a personal level, what do you most enjoy doing outside of work? KR: I am an avid runner. I’m training for a 15K right now. I have three boys who span middle school to college. I love spending time with them. I’m an avid sports fan. I will watch any football or hockey game. I also love to build things, like messing around with home automation. Finally, I spend a lot of time researching off-the-grid living-- for the next stage in life. _______________________ _______________________ ### Komprise Partners with Microsoft to Support Microsoft Azure File Data Migration Program The Azure File Data Migration service brings Komprise Elastic Data Migration to Azure customers at no cost. Customers can now migrate file data to the right Azure tier for cost savings and to drive value from their data. Campbell, CA— February 8, 2022– Komprise, a leader in analytics-driven data management and mobility, announces that it has been selected for the Microsoft Azure File Migration Program launched today by Azure. The new program gives customers access to industry leading file-migration at no cost and complements the Azure Migrate portfolio which customers use to automate and orchestrate the migration of servers, desktops, databases and web applications to Azure. Komprise is one of a select few Microsoft ISVs chosen for this exclusive program. Get Started Migrating on-premises applications such as file workloads, high-performance computing (HPC) and analytics requires identifying and migrating tens of terabytes to several petabytes of file data stored on NAS appliances and other on-premises storage to the right tier of Azure Files, Azure NetApp Files and Azure Blob Storage. Customers often guess at which data to migrate and to where and these migrations can be laborious, error-prone and slow when moving large data sets. Komprise Elastic Data Migration eliminates the cost and complexity of managing file data by providing analytics-driven data migration to Azure without creating any vendor lock-in: Provides analytics across existing NAS (eg NetApp, Dell, Windows) to identify which data sets to migrate and to which tier of Azure; Systematically migrates files 27 times faster.  Komprise Elastic Data Migration scales elastically according to the distribution of your shares, directories and files; Ensures full data integrity by migrating all file attributes and permissions with full MD5 checksums on every file; Customers will have the opportunity to upgrade to the full product, Komprise Intelligent Data Management, which means they can transparently tier across Azure Storage platforms, cutting 70% of cloud costs. Data is tiered natively, allowing users and applications nondisruptive access.  Organizations can leverage the Komprise Global File Index to query, tag and move the right data to the right place for AI, ML and data processing. “By working closely with the Microsoft Azure Storage team, we can help enterprises accelerate their path to the cloud for file and object data and make their data available to cloud-based data lakes and AI services without locking their data to proprietary storage technology,” says Krishna Subramanian, President and COO of Komprise.  “Moving data to Microsoft Azure Storage needs to be fast and easy for our customers,” said Jurgen Willis, Vice President, Optimized Workloads and Storage, Azure. “We are excited to work with Komprise on delivering a valuable service so that our customers can more easily and reliably move file data from expensive on-premises NAS devices to the cloud native storage services on Azure.” The Azure File Migration Program offers free software licensing, an onboarding session, and limited access to support after selecting the Azure-sponsored offer from the Azure Marketplace. Get Started To learn more about this program, visit the Azure Tech Community Blog. Also, read the Komprise Data Migrations blog for best practices in using Komprise for moving on-premises file data to the cloud. Webinar: Accelerate Data Migrations to Microsoft Azure with Komprise About Komprise Komprise is a provider of unstructured data management and mobility software that frees enterprises to easily analyze, mobilize, and monetize the right file and object data across clouds without shackling data to any vendor. With Komprise Intelligent Data Management, you can cut 70% of enterprise storage, backup and cloud costs while making data easily available to cloud-based data lakes and analytics tools. www.komprise.com.  Media Contact: Kevin Wolf, TGPR www.tgprllc.com  kevin@tgprllc.com ### Efficiently Manage Amazon Cloud NAS (AWS EFS & FSx) with Komprise NAS in the cloud is seeing rapid adoption as enterprise IT organizations are increasingly moving their SMB and NFS workloads to AWS managed file services. These fully managed NAS offerings reduce your administration overhead while optimizing your cloud spend to save the most and at the same time, help your organization make money from file and object data. Komprise supports data migration and data tiering to all AWS NAS offerings, making it easier to right-place your data in the optimal storage class. Planning for AWS Cloud NAS Migrations As you consider migrating your NAS workload to AWS cloud NAS ask yourself these questions: How will I get my data to the cloud with minimal disruption while ensuring data integrity? How can I place data on performance and archive tiers to optimize cloud costs? How do I know which data sets make the most sense to move to AWS and is there data I need to delete or retain for legal and other reasons before doing a migration? How can I leverage AWS services to drive value from my data with cloud analytics and AI/ML? This is where unstructured data management that goes beyond point migrations and storage-level tiering is crucial: Analytics: Tear down storage silos by analyzing your current file workloads on any NAS platform and then migrate granular data sets to the optimal AWS storage class; Flexible Policies: Tear down team silos by empowering data owners to work with storage infrastructure teams to create policies; Simplicity: Blend AWS managed file services into your current infrastructure without introducing complexity; Leverage file and S3, Glacier classes: Continually optimize your data placement in the cloud as data ages and access patterns change, to take advantage of lower cost or higher performance storage; Reliable Automation: Get a systematic way to make data available for AWS analytics and AI/ML workflows. Komprise Intelligent Data Management gives you analytics to understand your data and its performance characteristics. With this knowledge you can create granular data management policies for Komprise to migrate, tier and move data from any NAS device to the optimal AWS storage class or from high performance SSD storage to cost-effective S3 object storage. Komprise Support for AWS Managed File Services AWS has multiple cloud NAS offerings, each with its own features, characteristics, and price structure: Amazon FSx for NetApp ONTAP: Feature-rich enterprise NFS and SMB file storage Amazon FSx for OpenZFS: Best for current users of ZFS file system or other Linux-based file servers (NFS) Amazon FSx for Windows File Server: Fully managed Windows File server (SMB) Amazon Elastic File System (EFS): Supports the Network File System version 4 For more information on these offerings, consult this AWS article. Komprise Elastic Data Migration for AWS Komprise’s proven data migration technology has received AWS Migration and Modernization Competency Certification. The crucial difference compared with other migration point tools is the performance and advanced analysis provided by the Komprise Global File Index. Komprise gives you the visibility into your data to understand its requirements and the ability to place your data on the right storage class: Hot data on high performance managed file services in AWS and cold data on lower cost Amazon S3 object storage such as Glacier Instant Retrieval and Amazon S3 Infrequent Access. Optimize AWS spending and user experience with transparent tiering As a savvy storage architect you just read the last sentence and asked, how do I put cold files on Amazon S3 and not disrupt my NAS applications? Glad you asked! Komprise’s patented Transparent Move Technology (TMT) uses industry standard symlinks* with Komprise Dynamic Links to non-disruptively maintain access to tiered files for users and applications. In other words, users access their files from the same place as before. Each storage class on AWS has distinct pricing and there are ample differences. The high-performance SSD tiers are measurably more expensive than archive class such as Amazon S3 Glacier Instant Retrieval. The flexibility to use the optimal storage for each data set and to leverage S3 classes like Amazon S3 Glacier Instant Retrieval can save you up to 70% on your storage costs. Make your data work harder with AWS analytics After moving data, employees may want to use AWS analytics and artificial intelligence (AI) and machine learning (ML) on the data. Komprise TMT enables file-object duality – meaning that while your file is available on the original NAS device it's also accessible via the Amazon S3 API without going through Komprise, which is a key requirement for many AWS services. The transition from on-premises to the cloud is an opportunity to up level your capabilities from storage admin to cloud data architect. An agile stance in the cloud puts you in control of costs and lets you collaborate with your line of business teams. You can move file workloads transparently to the cloud to support AI, machine learning and cloud analytics initiatives. Komprise is an AWS Advanced Technology partner. Learn more about Komprise for AWS. * Symlinks: As of February 2022, Amazon FSx for Windows File Server does not support symbolic links required for Komprise Transparent Tiering. ### Storage Tiering and Data Fabrics on the Rise Komprise January Update: Product, People and News   January: back to school, back to work, back to ignoring your New Year’s resolutions! Setting reasonable, positive goals you know you can attain is always a smart move. Whatever your plans are for 2022, we at Komprise hope that you have a healthy, safe and prosperous year. Here’s our take on intriguing IT infrastructure and data trends reported in the trades, along with our own latest news. _______________________ Where’s the Love for Shadow IT? This AWS blog says what we already knew: shadow IT is here to stay. So what to do about it? The author has some useful tips including this one: Leverage cloud offerings to standardize and make “common use” services such as file storage, productivity and collaboration applications and BI available for use. _______________________ Cloud and Sustainability Go Hand-in-Hand, According to Futurist Bernard Marr “Most of the tech giants will spend 2022 implementing measures and innovations aimed at helping them achieve their net-zero carbon aspirations. Amazon, the world's biggest cloud company, is also the world's biggest buyer of renewable energy and also has 206 of its own sustainable energy projects running worldwide, generating around 8.5GW per year.” _______________________ Big Money for Cloud Security Google Cloud is acquiring Siemplify, an Israeli startup specializing in incident-response automation, for a reported $500 million. Google’s cybersecurity plans include “expanding zero-trust programs, helping secure the software supply chain and enhancing open-source security,” according to Calcalist. _______________________ All About Storage Tiering Storage Newsletter ran a nice overview of the storage tiering market, listing use cases, benefits, and top providers. Use cases are frequently changing for these solutions. Our own Steve Pruchniewski had these thoughts which were quoted: “The economics of tiering to cheaper storage in the data center or in the cloud are clear, but customers want to be able get value from that data as well. If you can move your data to the cloud so it is transparently accessible from the original location and accessible in native object storage format, you can take full advantage of cloud services and compute. You can run cloud-native services, such as analytics or AI and ML workflows on that data.” _______________________ The Rise of Data Fabrics As reported in this review of big data trends in Datamation: “One of the most interesting developments of 2021 was the rise of data meshes and data fabric. Interest in data fabrics grew thanks to its ability to provide a common layer for data access, discovery, transformation, integration, security, governance, lineage, and orchestration.” Unifying data silos to support easier decision-making is becoming a top priority in the data management space. _______________________ Komprise News Year in Review We published a year in review of the data management sector, covering trends in object storage, cloud storage, cloud data lakes and more. We also shared our top news of 2021 including our fall Deep Analytics Actions product update.   _______________________ This Week, We Published Our Results from 2021 The results included 115% growth in annual revenues and 200% growth in new customers. Read the press release. _______________________ New Support for Cloud File Storage In December, we announced support for new AWS file storage and analytics services, including Amazon FSx for NetApp ONTAP, AWS Snowball and Amazon S3 Glacier Instant Retrieval. Komprise can also now move files data to Azure Files NFS. _______________________ The Crystal Ball of Data Management As is typical, December was a fun month for tech predictions and we contributed to several articles focusing on big data, unstructured data and data management. Read this eWeek article in which our VP of Marketing Darren Cunningham wrote about the changing roles of storage IT professionals and data management security. In The New Stack, our CEO Kumar Goswami discussed how unstructured data will be key to big data analytics in 2022, along with “right-data” analytics. Finally, in this VMBlog article, COO Krishna Subramanian discusses the growth of cloud file storage and how to benefit from data silos. _______________________ Komprise & AWS Komprise was recently recognized as a launch partner for the AWS Migration and Modernization Competency, under the Data Mobility specialization. _______________________ ### The Most Popular Data Management Blogs of 2021 Last year was a time when many enterprises started to get more strategic about data management and we saw that in our most popular blogs – which focused heavily on data tiering strategies, cloud storage and analytics. In 2022, we see continued interest in these tactics and topics, plus momentum behind cloud-based data lakes and analytics tools, a drive to make data more accessible and the need for data enrichment. Our CEO Kumar Goswami discusses these emerging unstructured data management trends in our last blog post of the year. _______________________ Here are the most-read blogs of 2021:   1. What is S3 intelligent tiering and how does it work? AWS S3 is cloud object storage which offers many advantages for enterprise data management, from cost management to scalability and security. This blog details the seven different storage classes on S3 and how AWS S3 intelligent tiering automatically moves objects between tiers within the service. Read about the pros and cons. _______________________ 2. What you need to know before jumping into the cloud tiering pool. The cloud tiering approach you pick will affect cost savings of migrating unstructured data to the cloud as well as the overall benefits your organization is able to achieve from a cloud data migration strategy. _______________________ 3. Are cloud storage gateways a good choice for cloud data migration? Cloud storage gateways offer a path to the cloud by moving all data to the cloud and then caching a subset of the cloud data locally. They are helpful in distinct use cases such as backups but for data migrations, they can cause problems and unnecessary overhead in costs and time. _______________________ 4. Pfizer's Cloud Data Gambit “It’s been a very challenging yet rewarding time to work here at Pfizer,” said Matt Braunstein, director of hosting data services. With data intelligence critical to keep pace with ever-changing needs in R&D—Covid-19 vaccines notwithstanding—Braunstein led the charge to modernize data management by using Komprise to launch a successful cold data strategy on AWS. _______________________ 5. 10 Principles of Komprise Technology. Simple. Open. Vendor-Agnostic. Analytics-driven. Transparent. These are the top five principles of the Komprise Intelligent Data Management architecture. Read about them and the remaining five! _______________________ 6. Pure Storage Partners with Komprise. Pure Storage is a leading enterprise storage company with several Flash array products. In early 2021, the partnership between Komprise and Pure evolved with Komprise Asynchronous Replication delivering reliable data replication for Pure FlashArray™ file customers. “Together with Komprise, innovating in robust file replication will extend our market leadership in unified enterprise storage,” said Shawn Hansen, FlashArray General Manager, Pure Storage. _______________________ 7. Komprise Expands Support of Cloud NAS Options. Modern data management requires ongoing flexibility and this post talks about how Komprise added support for Amazon EFS, Amazon FSx for Windows File Server, Azure Files and third-party cloud NAS solutions. In late 2021, we announced support for Azure Files NFS and Amazon FSx for NetApp ONTAP. _______________________ 8. Komprise Introduces Deep Analytics Actions. In October, Komprise introduced Deep Analytics Actions, a systematic way to find specific data across hybrid cloud storage silos and move just the right subset of data for new uses such as cloud analytics. This post covers the basics: Global File Index, Elastic Grid architecture, policy driven data movement and more. _______________________ 9. Storage Tiering, Data Archiving, Transparent Archiving: What’s the Difference. These data management tasks sound like the same thing but they’re not. Read more to understand how to select the right approach for cold data movement to maximize cost savings and minimize proprietary lock-in. _______________________ 10. Komprise Technical Professional Training & Certification. The Komprise Technical Professional (KTP) program is an interactive, hands-on training experience and certification on the Komprise solution, open to partners and customers alike. In 2021, we saw 440 graduates, 170 of those being customers. The blog explains the purpose and format of KTP. _______________________ ### Komprise 2021 Data Management Year in Review What the cloud started and the pandemic accelerated is now charging full speed ahead: the data-centric economy is here to stay. Looking back on 2021, it was an invigorating year for data startups, new cloud services and major storage companies alike: tech companies are rushing to bring a data-enabled nirvana to enterprise IT buyers. But wait: is this merely a capital endeavor? No. There are human, societal benefits from excelling at the responsible curation, management and analysis of the ineffable volumes of data being stored in IT systems around the globe. Think more life-saving medicines. Think cleaner lakes, rivers and oceans. Think more efficient, healthier food production. Think smarter, less wasteful supply chains. Think safer, friendlier city streets. It’s only with good data, innovative technology and empowered leaders that we can make progress in solving these critical issues. Below, we review some of the top stories in the data management industry from 2021, followed by the most notable Komprise headlines of the year. _______________________ Data Management Industry Highlights of 2021 Hybrid Cloud Moves The Next Platform reports on IDC data that shows spending on public cloud infrastructure deployed on premises such as AWS Outposts and Microsoft Azure Stack is climbing. “Between 2019 and 2025, IDC reckons that spending on cloud outposts will grow at a compound annual growth rate of 151.8 over those years to $14 billion."   Smaller Clouds Get Attention Wasabi announced that it raised $112 million in Series C financing, bringing its total equity financing raised to $219 million as well as expanding its storage services to Asia. Cloudflare introduced R2 (a play-off of AWS S3). Cloudflare’s attack on the object storage market will be to target the thermal exhaust port of AWS S3 by not charging egress fees on R2 storage.   Google Tackles Data Silos Google launched three new services for “an integrated data cloud.” The new services include Dataplex for intelligent data management, Analytics Hub, to enable data access and sharing and Datastream to enhance data replication across multicloud environments.     NAS Cloud Backups Gain Popularity It’s not a big surprise that enterprises are preferring the cloud to other offsite backup options given the favorable economics and faster restore times. “More cloud providers have pivoted in the direction of NAS compatibility in recent years. Some options include Backblaze B2, Google Drive, Dropbox, MEGA, OpenStack Swift and Amazon Glacier.” There are hidden costs for cloud backups, however. Our blog post on cloud tiering breaks it down. No-Code ML The world’s biggest cloud unveiled Amazon SageMaker Canvas at AWS re:Invent. “The promise is that it will allow anybody to build machine learning prediction models, using a point-and-click interface.” Fitting nicely into the citizen science trend, we expect more no-code and low-code AI and ML tools to enter the market in 2022.   Databricks’ Billion-Dollar Splash in Cloud Data Warehouses Data lakehouse provider Databricks raises $1.6 billion in Series H, putting the company at a $38 billion valuation. As one of the largest VC deals in 2021, Databricks is leading the charge to unleash enterprise data to the cloud for the next generation of data analytics innovation.   Flash-Based Object Storage Grows Blocks & Files cites an ESG report that the performance needs of new workloads in application development, AI, and analytics are driving adoption of flash-based object storage. The lower cost of object storage is also a driver. While 95 percent of organizations are using flash storage systems, only 23 per cent of those use all-flash object storage. Most (87 percent) of those not currently using it intend to evaluate the technology over the next year. Object storage is also becoming a sound strategy for ransomware defense. “Data stored securely on an immutable backup system makes it fixed and unchangeable, meaning that it cannot be deleted or modified. This is especially important when it comes to ransomware as data on an immutable backup is impervious to infections,” as reported in ITProPortal.   Silos Impeding Data Analytics Hybrid cloud infrastructure has become pervasive in enterprise IT, bringing those inevitable data silos as organizations store data in many different places across on premises, cloud and edge systems. Tech Republic cites a survey by NewVantage that revealed over half of companies (54.9%) felt that they were behind in areas of data and analytics with up to 43% of data being unutilized—due largely to those silos. One answer to the barriers of data silos in analytics is a data fabric, which Gartner defines as: “a design concept that serves as an integrated layer (fabric) of data and connecting processes...which leverages both human and machine capabilities to access data in place or support its consolidation where appropriate.”   _______________________ Komprise Intelligent Data Management: Year in Review Komprise Began the Year with Excellent Numbers from Previous 12 Months The company achieved record growth in 2020, despite the challenges of the pandemic. Key growth drivers included: unstructured data under management grew by over 300% and a record number of major enterprises signed up as new customers. Komprise and Pure Extended Their Partnership With the announcement that Komprise will provide Komprise Asynchronous Replication to deliver reliable data replication for Pure FlashArray™ file customers. Komprise Intelligent Data Management 4.0 Announced in June, delivered new multi-site management capabilities. Global enterprise IT organizations and service providers can now deliver storage-as-a-service with a consolidated view across all data centers and cloud locations while giving storage managers the ability to manage each site per local requirements and policies. Krishna Subramanian, Co-Founder, President and COO, Named a “2021 Top 100 Women of Influence” by Silicon Valley Business Journal She has built three successful venture-backed IT businesses and held senior leadership positions at major tech companies, including Sun Microsystems and Citrix. In her words: “Simplifying complex problems through technology motivates me. I co-founded Komprise to tackle the two biggest problems companies have with data – managing the explosive growth of unstructured data and unlocking the business value of data.” Komprise Doubles Revenues in First Half of 2021 Key growth drivers compared with first half of 2020 included: 97% revenue growth, 190% growth of new customers and 200% growth in average deal size. Komprise was recognized by CRN as one of the 20 Coolest Data Management companies of the Top 100 Storage list. Komprise Released the “Komprise 2021 State of Unstructured Data Management Report” Based on responses from 300 storage IT decision makers in the U.S. and U.K. Key findings as reported by VentureBeat: “Sixty-three percent of companies are already managing over 1PB of data with 30%+ IT budgets spent on data storage and backups — and most organizations expect these costs to go up in 2021. Komprise Achieved Amazon Web Services (AWS) Migration Competency Status This designation recognizes that Komprise, an existing AWS Partner Network (APN) Advanced Tier Partner, provides proven technology for rapid, large-scale cloud data migrations to help customers move successfully to AWS through all phases of complex migration projects, discovery, planning, migration and operations. We also announced expanded support for new Amazon Web Services (AWS) file services to accelerate petabyte-scale file data migrations to the cloud and enable the use of cloud-based analytics. Komprise Deep Analytics Komprise Deep Analytics Actions, announced in October, delivers granular, flexible search and indexes data in-place across file, object and cloud data storage to build a comprehensive Global File Index spanning petabytes of unstructured data. Said Marc Staimer, president of Dragon Slayer Consulting: “Komprise solves the very real, concrete time problem that comes from moving petabytes of file data into a cloud data warehouse. With their “Deep Analytics Actions,” you can simply find and move a small fraction of what’s required to get the real-time analytics needed, which speeds up that time-to-value.” Komprise Wins Two Industry Awards In November, Komprise was recognized by New World Report in the sixth-annual Software and Technology Awards as winner of the Enterprise Data Management category and Komprise also won Product of the Year by National Association of Broadcasters in the cloud computing and virtualization category. _______________________ We'd like to thank all of our readers, customers, partners and employees for their support in 2021 and a joyous holiday season. We're already gearing up for a data-heavy 2022. You can check out some of our early predictions in TechHQ and InformationAge. Happy Holidays! _______________________ ### Komprise and Amazon FSx for NetApp ONTAP Your path to the cloud for file data just got easier: Komprise is pleased to announce full support for Amazon FSx for NetApp ONTAP. Migrating to the cloud with Amazon FSx for NetApp ONTAP and Komprise gets you out of the storage management business so you can ensure your data is in the right place at the right time to lower costs and increase value from your data. It’s time for a modern, analytics-driven approach to data management and mobility. From Managing Storage to Managing Data Amazon FSx for NetApp ONTAP is fully managed file services built on NetApp’s popular ONTAP, which makes it easy and cost-effective to launch, run, and scale feature-rich, high-performance file systems in the cloud. IT managers can leverage enterprise-grade file storage without the hassle of hardware provisioning, upgrades, and patching. This frees up time for storage professionals to start understanding data and making analytics-driven decisions to support users and departments with optimized data placement. Komprise analytics-driven data management and mobility empowers customers to understand their data across storage silos so they can make informed decisions. Before data analytics, all data was treated the same, leading to higher costs, poor resource utilization and rigid storage policies. The brains behind our Smart Data Migration approach is the Komprise Global File Index. This lets you see all your unstructured data across data centers and clouds to understand access patterns and performance requirements for specific data sets. With this knowledge IT can partner with line of business and data owners to create smart data migration, tiering and replication policies for data sets that make best sense. Migrate from any NAS Komprise now supports file data migration from any NAS device to Amazon FsX for NetApp ONTAP with a click of a button, speeding and simplifying data movement while also providing deep visibility into all data across storage environments so that IT can identify the optimal data sets to move to Amazon FSx. Komprise Elastic Data Migration lets you scale up resources to speed the movement of data and then scale down after the migration is complete. Komprise can simultaneously manage hundreds of separate data migration tasks and is the platform of choice for many enterprise professional services teams. For a deep dive into how migrations work with Komprise, check out our recent blog. Tier Cold Data to Low-Cost Object Storage with File Object Duality ONTAP FSx has built-in tiering that is purpose-built for snapshot or block data. To deliver a truly smart data migration strategy, Komprise intelligent tiering with Transparent Move Technology operates at the file level to let you use lower cost S3 storage, which is roughly one-fourth the cost of the default capacity tier for ONTAP FSx. Perhaps the most significant benefit of Komprise file-level tiering is file object duality. This means the data is readable both as a file and via the S3 object storage API. Cloud native S3 access means you can take full advantage of S3 features like object lock to defend against ransomware and you can mine your data using cloud analytics tools. By offloading data to S3 you save on storage fees and enable analytics and AI/ML workflows to uncover new value from cold data. Now that's a smart data migration! Replicate Data for DR in the Cloud for 50% Less If your current storage vendor’s recommended DR strategy is to simply purchase twice the hardware and double your admin efforts, it's time look at FSx ONTAP + Komprise. Komprise storage agnostic replication enables FSx ONTAP to be the disaster recovery target for any NAS. This new capability delivers a more efficient, lower risk way to procure disaster recovery in the cloud. Komprise’s policy-driven data management approach means that while you have doubled your protection and availability you haven’t doubled your administrative duties. Get the Most out of the Hybrid Cloud The old way of managing opaque storage silos was expensive, time consuming and locked away the value of data. Moving to the cloud without a new approach repeats the same mistakes. The combination of AWS ONTAP FSx and Komprise lets you manage data with agility and simplify NetApp cloud migration. By putting your data first you gain the visibility and intelligence to reduce costs, administration and get value from your data. Read the press release about how Komprise support for AWS Snowball and Amazon S3 Glacier Instant Retrieval. Learn more about Komprise for NetApp. Learn more about Komprise for AWS. ### Komprise Adds Support for New AWS File Storage and Analytics Services Komprise embraces new AWS innovations to accelerate file migrations to the cloud and maximize the use of cloud-native data analytics services.  Campbell, CA—December 7, 2021– Komprise, a leader in analytics-driven data management, today announced expanded support for new Amazon Web Services (AWS) file services to accelerate petabyte-scale file data migrations to the cloud, while enabling the use of native AWS data analytics and machine learning (ML) services to maximize data value. Komprise, an AWS Advanced Technology Partner which recently achieved AWS Migration Competency, is at the forefront of leveraging new AWS innovations: Data replication, migration, and file tiering with Amazon FSx for NetApp ONTAP Komprise customers can now easily leverage the new fully managed AWS service built on NetApp’s popular ONTAP filesystem. Komprise enables cloud replication and cloud data migration from any network-attached storage (NAS) to Amazon FSx for NetApp ONTAP. Furthermore, Komprise tiers at the file level, not block, to shrink not just storage but also backup and disaster recovery (DR) costs on FSx. Komprise Transparent Move Technology enables user to access the tiered files both from Amazon FSx and natively on Amazon Simple Storage Service (Amazon S3).  Simplify large data transfers via AWS Snowball Komprise brings an efficient, automated migration workflow to AWS Snowball for organizations that have petabytes of file or object data which must be divided among multiple Snowball units and shipped to an Amazon data center. Simply select your Amazon S3 destination, your source, and associate as many AWS Snowball devices as desired. Komprise does the rest. Read the blog post: Accelerating Petabyte-Scale Cloud Migrations with Komprise and AWS Snowball. Support for latest Amazon S3 tier, Amazon S3 Glacier Instant Retrieval Komprise will support the new Amazon S3 tier and class, Amazon S3 Glacier Instant Retrieval, early next year.  Deep Analytics Actions for data pipelines to AWS Artificial Intelligence (AI), ML, and data lakes Amazon offers a variety of AI and ML services for file data such as Amazon Comprehend for Natural Language Processing and Personal Identifiable Information (PII) Detection. But since files are scattered across multiple Amazon S3 buckets and file storage classes, searching and finding the right data to analyze can be a challenge. Komprise enables intelligent ingestion of files and object data into AWS AI/ML and data lake services by providing a Global File Index so you can search across all your Amazon S3 buckets and file storage. Komprise Deep Analytics Actions policies then systematically ingest just the right data into native AWS AI, ML, and data lake services. Read the blog post: Introducing Deep Analytics Actions Read the AWS blog: Using Amazon Macie with Komprise for Detecting Sensitive Content in On-Premises Data “IT leaders know that getting data to the cloud is just the first step,” said Kumar Goswami, CEO of Komprise. “It’s about how you leverage the flexibility and innovation of the cloud to deliver meaningful long-term benefits to the organization and its customers. Komprise Intelligent Data Management continues to invest in AWS so that our customers not only save on storage but can leverage cloud-native data services and tools to drive revenue-generating insights and better business outcomes.” Learn more about Komprise for AWS Read about Pfizer’s cloud data journey with AWS and Komprise   About Komprise Komprise is a multi-cloud data management-as-a-service that frees you to easily analyze, mobilize, and access the right file and object data across clouds without shackling your data to any vendor. With Komprise Intelligent Data Management, you can know first, move smart, and take control of massive unstructured data growth while cutting 70% of enterprise storage, backup and cloud costs. www.komprise.com  Media Contact: Kevin Wolf, TGPR www.tgprllc.com  kevin@tgprllc.com ### Komprise Plus Azure Files NFS for Cloud Flexibility In late 2021, Microsoft Azure announced GA support for Azure Files NFS. Azure Files offers fully managed file shares as a service in the cloud that are accessible via the industry standard Server Message Block (SMB) protocol and now adds support for Network File System (NFS) protocol. This will enable Linux as well as Windows files. Why are people excited about Azure Files NFS? There are three primary benefits: More availability zones, More tiers, and More performance options. Komprise is an Azure partner and our Intelligent Data Management solution helps customers maximize the impact of these new NFS options in the Microsoft Azure cloud. By giving you full visibility into your file data across any and all silos in your environment, you can create intelligence-driven, automated Azure data migration, replication, and data tiering policies to manage your Azure NFS file data with precision. Find the Right Data and Move Faster to Azure Files NFS Migrating to the cloud is an opportunity to optimize and ensure you are using the best storage resource for your data to save money while getting the right level of performance and data protection. Optimization starts by understanding your data with analytics-driven data management. Powered by Deep Analytics, the Komprise Global File Index gives you a single view across all your unstructured file and object data silos so you can find and move specific data sets to Azure Files NFS. Our powerful scale-out Elastic Data Migration lets you move entire shares or directories with confidence. Komprise also gives you the ability to leverage Deep Analytics Actions for data movement. You can create queries to find specific data sets over multiple locations, clouds and shares and then move them to Azure Files NFS. For example, you may wish to find just video files over multiple sites or shares and then consolidate them to an Azure Files NFS share. Read the press release: Komprise Partners with Microsoft to Support Azure File Migration Program. Data Lifecycle Optimization The value of data and how we use data is not static. A typical access pattern for data is that is accessed and used heavily for the first month and then less frequently as the data ages or becomes cold. Azure provides multiple tiers of performance with Azure Files NFS and Azure Blob. To take full advantage, you need a way to gain visibility into your data, to move it to the right tier and to maintain user and application access. Komprise lets you see access patterns and create automated tiering policies to optimize data placement from Azure NFS Files to Azure Blob while maintaining transparent access over the lifecycle of the data. Get More Value From Your Data Another advantage of moving your data to Azure is the ever-growing suite of analytics, artificial intelligence (AI) / machine learning (ML) and other intelligence services you can leverage. Komprise keeps your data in native format at every step of the data lifecycle journey. As Komprise moves from Azure Files NFS to Azure Blob the data is directly accessible, allowing you to leverage cloud-native analytics and AI/ML workflows. Nonstop Cloud Innovation Requires Data Agility With new cloud technologies and offerings developing at a staggering pace, having your data locked into a single resource potentially locks you out of opportunity. The combination of Komprise with Azure provides the agility to move, replicate and tier data as needed. By understanding their data, choosing the right resources, and then creating data management policies, customers can ensure they are optimizing and driving value from their data at every step of their cloud journey.                 Learn more about Komprise for Microsoft Azure. Learn more about Komprise Elastic Data Migration for Azure. ### Closer Look: Komprise Unstructured Data Migrations Intelligent data management is changing the way IT organizations view their data, data retention, ownership costs and positioning data for value and migrations. Unstructured data migrations have been a regular ongoing exercise as data growth requires larger systems and hardware continually ages out. But, they no longer need to be a complex burden. Instead, an unstructured data migration is an opportunity to adopt modern architectures with systematic, continuous data movement that breaks the cycle of storage-focused hardware refreshes. Customers like Pfizer are saving 75% on storage by using Komprise to understand their unstructured data and then optimize data placement over cloud and on-premises resources. In this blog, we will discuss best practices for analytics-first smart data migrations and explain how Komprise enables easy, more intelligent data management, breaking the cycle of legacy storage hardware migrations. When it comes to a Smart Data Migration, the Komprise mantra of “Know First, Move Smart, and Take Control” is being implemented by real-world customers with measurable benefits. Here are 5 industry unstructured data migration use cases. This post will review the benefits of an analytics-first approach and how you can move smart and take control with Komprise. Unstructured Data Migration: Know First (Gather Intel and Plan) A successful data migration should be 90% preparation and 10% perspiration. Proper planning, understanding data’s value, growth, activity and stakeholder concerns are key to an easy, successful, smart data migration. Smart Data Migration Tip: Communication with users, data owners and applications teams is vital to a successful migration. The migration goal and processes must be understood by all stakeholders. This leads to better decisions, faster migrations and fewer surprises. Professional services teams from leading enterprise storage companies use Komprise metrics to spot shares with rapid growth, identify issues and plan and execute migrations. The journey from legacy storage management to intelligent data management begins with understanding your data and then working with data owners and stakeholders to align goals. Komprise provides the intelligence on your unstructured data to make informed plans for data management and the movement of data. Simply point Komprise at your NFS, SMB and S3 sources and within 15 minutes, Komprise displays valuable analytics on all the data including how much is hot data, how much is cold data, how data is growing, and comparative data ownership costs. Armed with this knowledge, storage teams can work with data owners to choose the optimal storage for their data. In this example, our legacy storage array hosts multiple shares and we can see a large percentage of the data is cold. This rarely-accessed data is a perfect candidate for cloud or on-premises object storage. Smart Data Migration Tip: Using Komprise Intelligent Data Management and Transparent Move Technology™ ahead of a migration reduces data by 50 to 80 percent by tiering cold data first to cheaper, object storage. This significantly reduces the data movement and cutover times for a more efficient migration. By coordinating with data owners, you can move data as entire shares, specific directories, or based on parameters like file type. The ability to segment data enables precise choices and reduces the size of migration tasks. This is an opportunity to move the right data to the optimal storage with colder data stored for durability and lower cost while hot data can be moved to flash for high performance.   Unstructured Data Migration: Move Smart (and Quickly) Komprise offers several ways to migrate faster and smarter. Komprise Scale-out Performance There are several components that will affect the speed at which data can move: Source storage performance Destination storage performance Network bandwidth between source and destination The number of data movers Numbers of files, average file sizes and quantity of directories In large scale data migrations, the ability to parallelize the data across many nodes enables better utilization of network bandwidth and the capabilities of the source and destination storage. The Komprise Elastic Grid is designed to dynamically scale Observers and Windows Proxy Systems to optimize performance. Optimized File Transfers Standard file system clients were made to service end user and application requests—not perform massive data movements. Komprise optimizes file transfers in many ways. First, by using analytics, Komprise can handle large and small files differently, thereby minimizing network transfers over WAN. This reduces protocol chatter and leads to greater efficiency for large migrations. Komprise Architecture The Komprise Director is an intuitive user interface and management console: The Director gives a standard, comprehensive view of data across multiple systems and vendors. These analytics provide accessed last, file types, file sizes, who owns the data, and more. This visibility lets customers reposition data to where it can create value or reduce ownership costs. The Director is also a central migration management console where hundreds of migrations can be coordinated and administered. The Komprise data movers are known as Observers, for NFS, and Windows Proxies, for SMB data. [Read more about the Komprise Elastic Grid architecture.] These simple virtual appliances form an elastic scale-out grid where you can add more nodes to increase throughput performance. The Komprise Grid is easy to scale up for a large initial data migration task and then later scale down to support ongoing data management or smaller migrations. Move with Confidence After identifying systems, reviewing data, consulting stakeholders, and building out the Komprise Grid, the next step is to confirm the plan and begin moving data. Smart Data Migration Tip: Every file data migration plan should have these components: A tested cutover-plan. A clear set of steps must be in place to direct users and applications during the migration cut-over and seamlessly direct them to the new location. A verified fail-back plan. Equally as important as the cutover plan is knowing how to safely back off an active migration or cutover should an unexpected event occur. With a well-formed and confirmed data migration plan in place it's time to start the migration. The Komprise Director guides administrators through migration setup, prompting for the source and destination with options to maintain access time and full permissions.   Unstructured Data Migration Steps Once the migration tasks have been created, the migration runs continuous and automatic iterations that copy changes from the live source to the destination. This process is non-disruptive to users and applications accessing and updating data on the source. With each iteration, Komprise copies data from source to destination, including any new or updated files, while validating the integrity of the data by checking file checksums on both source and destination. The migration includes permissions and ACLs (access control list) to ensure identical security on the destination and to maintain access time for each file. Komprise logs any files that can’t be copied due to permission, file locking, or other issues. While some issues such as file locking will typically be resolved in later iterations, other issues such as permissions will require administrator intervention. Komprise maintains an advanced audit log to identify and help in resolving issues. After multiple iterations, Komprise identifies an average change or churn rate. This change rate gives an estimate of the amount of data that will need to be copied in the final migration iteration, and helps you plan the cutover. To prepare for the final iteration, the source must be placed in a read-only mode to prevent any file modification. This is a critical component of the migration plan where communication with the data owners is vital. During the final iteration, Komprise validates that all data on destination is identical to the source. Once the final iteration is complete the storage administration team will mark the migration complete, ending the active migration. The final step of the migration is to direct all users to the new storage and allow the appropriate permissions for production use. Take Control with Continual Data Optimization The smart data migration began with an analysis of the data, identified the optimal resources, and then created a migration task to copy the data. Now we move from data migration to Intelligent Data Management. Komprise data management and data mobility policies continually analyze and transparently move data to the right place. What began as a migration task has evolved to continual optimization. This is where infrastructure teams maintain their collaboration with the data owners to drive better business outcomes. Enterprise customers use Komprise Deep Analytics to create a Global File Index and engage with their lines of business. Data owners can create queries to identify data sets so the storage team can take actions like positioning data for more performance and tiering cold data to save costs without affecting end users. Considering a Cloud NAS Migration? Komprise for Amazon FSx for NetApp OnTap Komprise for Azure Files NFS Eliminating the Roadblocks of Cloud Data Migrations for File and NAS Data Learn more about the benefits of a Smart Data Migration for your enterprise file and object data with Komprise. ### Komprise Named Winner in the 2021 Product of the Year Award by the National Association of Broadcasters Komprise achieved distinction in the Cloud Computing and Virtualization category. Campbell, CA—November 18, 2021– Komprise, the leader in analytics-driven data management, announces that it has been recognized as a winner in the third annual NAB Show Product of the Year Awards. The awards recognize significant and promising new products and technologies relevant to media and entertainment. The media and entertainment industry has undergone dramatic shifts since the onset of the pandemic with rampant growth in digital content as consumers went fully online for news and entertainment. Growth in streaming video on demand (SVOD) accelerated during the pandemic and is projected to reach $81 billion in sales by 2025 and outperform box office revenues by 2023, according to PwC Global Entertainment and Media Outlook 2020-2025. Cloud adoption for video/entertainment delivery and for cost-effective data storage is accelerating in pace with streaming. Komprise Intelligent Data Management helps M&E enterprises resolve the expense and pain of uncontrolled data growth while delivering an easy path to the cloud. The company’s media and entertainment customers include ViacomCBS, NBC Universal, PBS and Electronic Arts. Komprise analyzes unstructured data across storage and clouds and transparently tiers file data to lower-cost storage according to the customer’s policies, saving up to 80% on storage, backup and disaster recovery. Komprise’s patented Transparent Move Technology provides file-object duality so users access their files exactly as before while also enabling native cloud services on the tiered data. “We are delighted to be recognized by NAB for enabling a better cloud journey, lowering storage costs and modernizing data for our media customers facing incredible disruption,” says Krishna Subramanian, president and co-founder of Komprise.  “Big Data and analytics are changing the game in the media and entertainment sector,” says Manny Punzo, VP of Data Management Strategy at Technologent. “While structured data is important, unstructured data provides a wealth of knowledge that numbers can’t explain. Organizations must find ways to harness unstructured data to make important business decisions and Komprise provides the easy button to data management by being 100% storage vendor agnostic, with built-in analytics and data migration capabilities.” About NAB The National Association of Broadcasters is the premier advocacy association for America's broadcasters. NAB advances radio and television interests in legislative, regulatory and public affairs. Through advocacy, education and innovation, NAB enables broadcasters to best serve their communities, strengthen their businesses and seize new opportunities in the digital age. Learn more at www.nab.org. About Komprise Komprise is a multi-cloud data management-as-a-service that frees you to easily analyze, mobilize, and access the right file and object data across clouds without shackling your data to any vendor. With Komprise Intelligent Data Management, you can know first, move smart, and take control of massive unstructured data growth while cutting 70% of enterprise storage, backup, and cloud costs. www.komprise.com  Media Contact: Kevin Wolf, TGPR www.tgprllc.com  kevin@tgprllc.com ### Komprise November Update: Product, People & News November! It’s time to rake leaves, finalize Thanksgiving plans, and ponder what if anything you can do with all the leftover candy from Halloween. We’re also in the midst of conference season: Microsoft and NetApp just wrapped their annual shows and AWS re:Invent is right around the corner. Here’s our take on intriguing IT infrastructure and data trends reported in the trades, along with our own news.   Microsoft Ignite Validates Hybrid Cloud With fall conference season in full swing, Microsoft Ignite had a slew of announcements with a focus on hybrid cloud management and Azure Arc, as reported in TechCrunch. With many customers straddling cloud and on-premises infrastructure, Microsoft Azure sees value in helping orchestrate VMs and containers across clouds and data centers.   Small Players Make Headway in the Cloud Game When you think of the cloud, the Big 3 come to mind (AWS, Azure, GCP) but some smaller players have been proving there is still room in the cloud storage market with offerings for small businesses, as reported in Protocol. “Neither Backblaze nor Cloudflare are in a position to challenge industry leaders like AWS, Microsoft and Google any time soon for the breadth and depth of cloud infrastructure business. But they both realize that catering to smaller companies represents a potential new era in the history of the cloud.”   Redefining the Role of Data eWeek is challenging The Economist’s 2017 article where they compared data to oil. To be fair, The Economist said that data had replaced oil as the world’s most valuable commodity versus being the “new oil,” but eWeek’s comparison of data to uranium is poignant. They make the point that data, like uranium, can be used for good or it can be weaponized, and that it requires tremendous care to manage data properly for the long term and to create value.   Data Analytics: Not Just for Data Scientists Anymore CNBC reports that Databricks, the data analysis and AI software startup, is making its second acquisition for “low” or “no code” to enable the creation of data applications by non-computer scientists. “Bringing simple capabilities to Databricks is a critical step in empowering more people within an organization to easily analyze and explore large sets of data, regardless of expertise,” said Ali Ghodsi, co-founder and CEO of Databricks. This reflects a trend of extending capabilities that traditionally were limited to IT teams to line of business employees, much like Komprise Deep Analytics Actions.   Data Pros: Facing Burnout Another reason to make data analytics usable by a wider audience is the short supply of data engineers compounded by a high attrition rate. Datanami cites a jointly produced DataKitchen and data.world survey that found 97% of the 600 data engineers surveyed reported feeling “burned out,” with another 70% saying they are likely to quit in the next 12 months.   Komprise News & Updates Komprise Recognized as Best Enterprise Data Management Solution Komprise was named a winner in the 2021 Software and Technology Awards by New World Report. “We are seeing a marketplace shift from managing storage to managing data and our mission is to help customers make that transition with an independent, analytics-driven data management solution,” said Krishna Subramanian, COO and president of Komprise, in the press release.   Komprise Achieves Amazon Web Services (AWS) Migration Competency This designation recognizes that Komprise, an existing AWS Partner Network (APN) Advanced Tier Partner, delivers proven technology for rapid, large-scale cloud data migrations to help customers move successfully to AWS through all phases of complex migration projects, discovery, planning, migration and operations. The joint announcement details the Komprise solution that AWS customers trust to reliably and efficiently move data to the cloud.   Storage Pros Evolving to Data Roles Komprise’s long time storage industry expert Rob Gordon wrote for InformationWeek about the shift of traditional storage administrators to cloud data management pros. With many of the arcane tasks of storage now automated, most of the storage person’s time will now be spent identifying, segmenting and defining data types and managing that data granularly, according to business and user needs.   The announcement of Komprise Deep Analytics Actions last month has been creating some buzz in industry press.   Google your Cloud with Komprise How to describe Komprise Deep Analytics in one sentence: "Imagine a Google search you can act on," says Komprise co-founder and COO Krishna Subramanian in TechTarget. "This is helping IT and business users find specific data sets and use them in a business workflow that was manual in the past." The Google analogy illustrates the challenge of scattered data and a simple approach to empowering customers, including less traditional non-IT staff, to drive value from data.     Get the Big Picture by Controlling Scattered Data VentureBeat focuses on Komprise Deep Analytics Actions efficiencies of finding and moving only the data required for a specific analytic task versus the massive time and expense of bulk data moves.     Taking on Healthcare Challenges with a Data-First Approach With petabytes of data, heterogenous storage and data intensive medical applications all contributing to the quality of care and their life critical mission, St. Luke’s decided to implement a data-first strategy using Komprise. St. Luke’s expects to save as much as 80% on storage with this initiative. The healthcare organization will also leverage Deep Analytics capabilities in Komprise to understand the unique requirements of different clinical file types. Read the blog! ### Komprise makes it easier than ever to find and act on needles in haystacks of unstructured data The data management company has created a way for companies to search petabytes of data across cloud and on-premises storage in minutes and take targeted actions. Campbell, CA – October 12, 2021 – Komprise, the leader in analytics-driven data management as a service, today introduced Komprise Deep Analytics Actions, a systematic way to find specific data across hybrid cloud storage silos and move just the right subset of data to rapidly feed data pipelines. For example, researchers at a pharmaceutical company can query and extract the files related to a specific experiment generated by a set of researchers, even if these files might be a small fraction of the petabytes scattered across datacenters and clouds, and then import this virtual data set into a data lake or data warehouse for further analysis. Komprise Deep Analytics Actions is the next phase of evolution for Komprise Intelligent Data Management, tackling some of the most pressing problems with unstructured data today: it’s too large, encompassing billions of files and objects; too scattered across many storage silos with limited visibility across the silos; too hard to search for specific user needs such as analyzing car test data for a particular scenario, and too slow to move into environments where it can be used for analysis and manipulation or for cold data storage. Deep Analytics Actions: From Data Volumes to Data Value Komprise Deep Analytics delivers granular, flexible search and indexes data in-place across file, object and cloud data storage to build a comprehensive Global File Index spanning petabytes of unstructured data. New Komprise Deep Analytics Actions leverages these virtual datasets, which can be enriched with data tagging, for systematic, policy-driven data management actions, delivering the following benefits: Users only move the data they need, with the ability to create queries on countless file attributes and tags such as: data related to a specific tag or project name, projects that are no longer active, file age, user/group ID’s, path, file type (aka JPEG) and specific extensions, data with unknown owners. Eliminates the manual effort of finding custom data sets and moving them separately from different storage silos since Komprise can create a virtual data set based on the query and systematically and continuously move data from multiple file and object silos to the target location. Improves IT and business collaboration around data, as data owners/users can participate in data tiering decision-making by tagging files and creating their own queries from any combination of tags and metadata. “We see a lot of use cases for deep analytics actions at the University,” says Matt Madill, senior storage administrator at Duquesne University. “For instance, different research groups have unique requirements which users can support with tagging so that those data sets can not only be discovered easily but they can apply the appropriate data management policies to them for long-term storage. We’ll be able to give users the power to have better control of their data and let us know what to archive and when. Komprise is helping us make smarter decisions on our data and that is a competitive advantage.” “Unstructured data has been and continues to grow exponentially. Time to actionable insights is a major factor in realizing actual financial value, a.k.a. time-to-value,” says Marc Staimer, president of Dragon Slayer Consulting. “Komprise solves the very real, concrete time problem that comes from moving petabytes of file data into a cloud data warehouse. With their “Deep Analytics Actions,” you can simply find and move a small fraction of what’s required to get the real-time analytics needed, which speeds up that time-to-value.” “With Komprise Deep Analytics Actions, departmental users can maximize the business value of their unstructured data by leveraging their domain knowledge to cull and find the right data sets to operate on across all their silos,” said Kumar Goswami, co-founder and CEO of Komprise. Stay up to date on the latest Komprise Intelligent Data Management news on our What's New page. About Komprise Komprise is the industry’s only multi-cloud data management-as-a-service that frees you to easily analyze, mobilize, and access the right file and object data across clouds without shackling your data to any vendor. With Komprise Intelligent Data Management, you are able to know first, move smart, and take control of massive unstructured data growth while cutting 70% of enterprise storage, backup, and cloud costs. www.komprise.com Media Contact: Kevin Wolf, TGPR www.tgprllc.com kevin@tgprllc.com ### Komprise Intelligent Data Management Achieves AWS Migration Competency Status With Komprise, organizations like Pfizer and Northwestern University have cut storage costs 50%+ by migrating petabytes of file data to AWS. Campbell, CA – October 5, 2021– Komprise, an analytics-driven data management as a service platform, announced today that it has achieved Amazon Web Services (AWS) Migration Competency status. This designation recognizes that Komprise, an existing AWS Partner Network (APN) Advanced Tier Partner, provides proven technology for rapid, large-scale cloud data migrations to help customers move successfully to AWS through all phases of complex migration projects, discovery, planning, migration and operations.  This news follows a successful H1 for Komprise, in which the company achieved 97% revenue growth, 190% growth of new customers and 200% growth in average deal size. Earlier in 2021, Komprise announced expanded support for cloud (Network Attached Storage) NAS, including Amazon Elastic File System (Amazon EFS) and Amazon FSx for Windows File Server Achieving the AWS Migration Competency differentiates Komprise as an AWS Partner that provides specialized technical proficiency and proven customer success with a focus on migration planning and delivery based on analytics and automation as part of its Intelligent Data Management platform. To receive the designation, AWS Partners must possess deep AWS expertise and deliver solutions seamlessly on AWS.  “We’re excited to achieve AWS Migration Competency status and continue our work with AWS to help enterprise IT organizations undertake successful journeys to the cloud,” said Krishna Subramanian, President and Chief Operating Officer (COO) of Komprise, “Komprise gives organizations an easier, faster path to the cloud using our Transparent Move Technology which means that users and applications do not experience any changes to data access once data is migrated or tiered. Komprise delivers a way to cost-effectively move and manage unstructured data and leverage it in native format without lock-in for new uses so enterprises can take full advantage of powerful analytics, AI and data lake capabilities on AWS.” AWS is enabling scalable, flexible and cost-effective solutions from startups to global enterprises. To support the seamless integration and deployment of these solutions, AWS established the AWS Competency Program to help customers identify Consulting and Technology AWS Partners with deep industry experience and expertise. Komprise can efficiently migrate billions of files and objects from multiple Network File System (NFS), ServerMessage Block (SMB) or Amazon Simple Storage Service (Amazon S3) object source to any file or object storage in the cloud. Komprise TMT™  is core to its Elastic Data Migration solution, which automatically parallelizes to maximize performance and achieves significantly faster migrations compared with generic tools. With Komprise, customers can manage all migrations from a central console. Learn more about Komprise Elastic Data Migration Learn more about Komprise for AWS About Komprise Komprise is the industry’s only multi-cloud data management-as-a-service that frees you to easily analyze, mobilize and access the right file and object data across clouds without shackling your data to any vendor. With Komprise Intelligent Data Management, you are able to know first, move smart and take control of massive unstructured data growth while cutting 70% of enterprise storage, backup and cloud costs. www.komprise.com ### AWS and Komprise Bring Intelligent Data Management to Public Sector Customers AWS Summit attendees receive a limited-time offer to modernize and accelerate file data migrations to the cloud. Campbell, CA—September 28, 2021– Komprise, a leader in analytics-driven data management as a service, today announces a special offer with Amazon Web Services (AWS) to attendees of the AWS D.C. Summit, taking place September 28 and 29, in Washington, D.C. AWS and Komprise have helped many public sector customers modernize their data storage with data-led migrations by transparently moving petabytes of file data to the cloud while saving up to 70% of infrastructure costs. Now, AWS Summit attendees can get a risk-free assessment of their data savings from Komprise, which is exhibiting at the Summit, and be eligible to receive up to 25% off their estimated first year AWS spend if they convert within 90 days.   Given challenges presented by the global pandemic, chip shortages, and federal mandates to close data centers, public sector organizations are under continued pressure to modernize IT and data management and move to the cloud.  Komprise allows data-led migrations to AWS by: Analyzing across multi-vendor network-attached storage (NAS) and file storage to find the right data to move to AWS; Transparently tiering files so users continue to access moved data as before with no disruption; Allowing Amazon Simple Storage Service (Amazon S3)-native access to the migrated data so organizations can fully monetize data using AWS services for artificial intelligence (AI), machine learning (ML), cybersecurity, and other applications.  Northwestern University is using Komprise to understand its data and manage it more efficiently on second-tier storage and AWS, saving more than $300,000 a year. “Until Komprise, we didn’t know what the data was, who owned it, or when it was last used,” said IT manager Kenneth-David Turner. “We needed to stay ahead of capacity demands and be compliant.” “We are pleased to collaborate with Komprise to offer public sector organizations a simple way to go beyond migrating data to gaining business value in the cloud,” said Sandy Carter, Vice President of Worldwide Public Sector Partners and Programs at AWS.  Learn more about Komprise in AWS Marketplace here. AWS Marketplace is a digital catalog with thousands of software listings from independent software vendors that makes it easy to find, test, buy, and deploy software that runs on AWS. About Komprise Komprise is a  multi-cloud data management-as-a-service that frees you to easily analyze, mobilize, and access the right file and object data across clouds without shackling your data to any vendor. With Komprise Intelligent Data Management, you are able to know first, move smart, and take control of massive unstructured data growth while cutting 70% of enterprise storage, backup, and cloud costs. www.komprise.com   Media Contact: Kevin Wolf, TGPR www.tgprllc.com   kevin@tgprllc.com ### 5 Ways to Use Analytics for Cloud Data Migrations We recently worked with our friends at AWS to develop an eBook that was inspired by the webinar: How Pfizer used analytics to create a cold data strategy and accelerate cloud data migration. Unstructured data is growing exponentially. Many enterprises have over 1 PB of data, which represents roughly 3 billion files, typically residing in multi-vendor storage silos. Meanwhile, enterprise IT organizations lack visibility into their file data. Choosing the right files to move can be challenging as there can easily be billions of files. To be agile and competitive, IT teams must evolve from storage management to a more holistic data management strategy. This eBook focuses on the importance of moving cold data to cloud data storage to reduce costs and complexity and reviews 5 Ways to Use Analytics for Cloud Data Migrations. Here are some unstructured data migration tips: Understand your data patterns: Run data analytics to assess your data usage and growth. Then, use the assessment to decide what data to migrate to the cloud. Plan using a cost model: Use data management tools to interactively model different tiering policies and assess their cost savings, factoring in cloud egress costs. Use data to drive stakeholder buy-in: Use metrics to demonstrate how cold data is offloaded from expensive file storage, backups and replication, therefore saving 70% or more of its costs. Eliminate user disruption: Use transparent file data tiering that does not change the user experience. Create a systematic plan for ongoing data management: Analytics-driven data management should continuously move data to the cloud and manage data lifecycles in the cloud according to policy. The AWS eBook also features a brief review of the Pfizer case study. Komprise gave Pfizer a way to quickly analyze all its data and transparently tier cold files to AWS. The company created a global data index with tagging on AWS so Pfizer researchers can search for data relevant to a prior project and analyze that virtual data lake in the cloud. Download the Analytics for Cloud Data Migration eBook. Learn more about Komprise for AWS NAS. Interested in going to the cloud? Komprise is the easy, fast, no lock-in path for file and object data. ### Making the Most of Unstructured Data with Komprise A review by  Cloud storage, on-prem storage, hybrid cloud and cloud outposts: there are so many options today for enterprise data storage. But it all starts with your data. Where is it, what types of data and files and how much of it do you have, how is it stored, how much does it cost to store and back it up, and which is hot, warm or cold? By understanding all of this, you can do things with your data that you might never have considered in the past. Komprise is data management software for unstructured data which looks across all your storage to give you holistic insights for decision-making. We help enterprises “right-place” their data for cost and performance and we do this continuously, through analytics and intelligent automation. Our product roadmap is focused on helping customers monetize data better and securely manage their data across diverse environments. Lower your risks, lower your costs, and increase business value. But don’t take it from us. This review from Zach DeMeyer of Gestalt IT describes how we do it and how we can help IT teams gain better ROI from storage and data management.   The Problem of Unstructured Data We already know that data is everywhere and helping to drive key business decisions around the world. When you stop and think about the amounts of data we produce and consume on a yearly basis, however, the true scale of data operations today becomes somewhat unnerving. In May of 2020, IDC reported that 59 zettabytes of data would be consumed over the course of the year. For reference, one zettabyte is the equivalent of 250 billion DVDs worth of data. To make matters worse, the majority of that data is considered to be unstructured, that is, captured and stored against no particular data model. Well, just because this growing amount of data is unstructured doesn’t mean that it is useless. All data captured can provide value to businesses in helping shape their decision-making, even if it doesn’t fit a prescribed model. The problem often faced in this situation, though, is how can companies leverage their massive amounts of unstructured data in a way that also complies with their budget?   Rethinking Storage through Data Management For many, when faced with growing amounts of data, structured or otherwise, the first response may be to simply buy more storage. Although this approach helps companies accommodate for more data accumulation, it doesn’t address the root issue. It’s like if your apartment keeps getting cluttered with items — do you rent a bigger apartment, or should you find a way to manage and organize your belongings that frees up floor space and helps you better use them at the same time? If you ask Kumar Goswami and the rest of his team at Komprise, they’ll tell you to go with the latter. Goswami is Komprise’s CEO and co-founder, and a major proponent of treating all data differently. By understanding your data and how it can benefit you in the long run, he claims, you can ultimately make better decisions while saving money in the process. Komprise Intelligent Data Management Komprise’s Intelligent Data Management platform is built on the same foundation. The Komprise platform allows IT practitioners to take a top-down view of their data as a whole, using analytics to evaluate data at-will across multiple different clouds, NAS devices, and data centers.   Industry Unstructured Data Management Examples When talking about one case where a pharmaceutical company needed a method to archive and access all of their lingering data, Goswami gives a miraculous figure. Within 90 days, the cost benefits that the company reaped by leveraging Komprise outweighed the costs they spent by implementing it. With a return on investment like that, it’s no wonder that Komprise is radically transforming the enterprise data space. To learn more about this story, watch the recorded video where a Pfizer storage director discusses his cold data strategy with representatives from Komprise and AWS. Komprise Intelligent Data Management Case Studies   Zach’s Reaction Personally, I’m gobsmacked by the potential of unlocking so much value with one product that it literally pays for itself. Add on the future benefits that unstructured data accessibility can provide, and it seems like an intelligent data management system, such as Komprise’s, marks a bright future for cloud storage. Komprise recently participated in Storage Field Day 22. See what delegates had to say in this roundtable video from the event. ### Komprise September Update: Product, People & News September: It’s the end of summer and time to vacuum the last of the beach sand out the car, pack the kids off to school and of course, take a look back at key IT and data management industry stories. Storage, Data and IT Infrastructure Trends Customers Look to the Cloud to Mitigate Storage Pains Komprise released the “Komprise 2021 State of Unstructured Data Management Report” based on responses from 300 storage IT decision makers in the U.S. and U.K. Key findings as reported by VentureBeat: “Sixty-three percent of companies are already managing over 1PB of data with 30%+ IT budgets spent on data storage and backups — and most organizations expect these costs to go up in 2021.” Blocks & Files calls out that while organizations want to migrate to the cloud in response to growing data, a lack of visibility is hindering those plans. The performance needs of new workloads in application development, AI, and analytics are driving adoption of flash-based object storage. Object Storage in the Fast Lane Object storage has traditionally been viewed as “cheap and deep,” known for low cost and massive scale but not performance. Blocks & Files cites an ESG report that the performance needs of new workloads in application development, AI, and analytics are driving adoption of flash-based object storage. This is another indicator of how pervasive object storage adoption has become across the industry. Can SSD Displace the Kings of Capacity? Not so fast! According to Horizon Technology, despite price declines and increasing adoption, SSDs are not on track to unseat spinning disk as the dominant storage for enterprise. HDD shipped capacity increased by approximately 25% between 2018 and 2020, hitting 1 ZB (one billion terabytes) last year, the article states. “By 2024, HDD will remain the primary storage technology regardless of use case, with 54% of total stored data captured on spinning platters, per IDC’s projections.” Shadow IT: A Good Thing? Shadow IT has been a sore spot for companies, with employees purchasing and deploying new technology and services without going through IT. It’s no surprise that cloud has changed the way businesses consume IT. Protocol reports that shadow IT has become so normalized that vendors are changing their sales models to target these buyers. In the article, Netskope CIO Mike Anderson says: "We have to shift our mindset from a control organization to an empowerment and enablement situation. But we have to do it in a secured, governed way.” Feds in Cloud..Moving Right Along While Covid-19 has accelerated cloud adoption in many sectors, Meritalk says the U.S. government is seeing clear benefits from years of cloud adoption strategies, including the ability to rapidly respond to challenges, leverage cloud tools, and gain environmental benefits from data center consolidation. Says the EPA’s CIO Vaughn Noga: “To me, it’s a mission imperative to look at what we can identify and migrate to the cloud and increase our agility. The ability to scale up and scale down based on seasonal requirements is certainly huge.”   Komprise in the News Big Pharma = Big data Komprise co-founder and COO Krishna Subramanian wrote for Forbes on trends regarding data management in drug development. As pharmaceutical organizations look to accelerate discoveries and leverage existing data for future breakthroughs, they are adopting artificial intelligence and machine learning to be more efficient and accurate. “This raises the need to optimize data storage for cost and space efficiency while ensuring these petabytes and zettabytes of information are not just stored securely but are also easily accessible and shared in global collaborative initiatives,” Subramanian writes. Komprise Looks to the Future Well into a year where Komprise has seen record growth Komprise co-founder and COO Krishna Subramanian talked with TechTarget about her vision for the company.   Master Class on Data Management at Duquesne University Matt Madill of Duquesne University sat down with ITPro Today to discuss his strategy for unstructured data management. Leveraging Komprise to manage data across on-prem enterprise arrays and multiple clouds (AWS, Azure, and Wasabi), Duquesne is saving a bundle.   Data Management is Everyone’s Responsibility Komprise co-founder and COO Krishna Subramanian talked to Jaxenter about the importance of data management for business leaders to optimize costs, security and ensure a future proof strategy. “Educate your business stakeholders on the data lifecycle,” she says. “Data management is fluid – not a set and forget exercise. And this requires new thinking and awareness.”   Upcoming Events The California Virtual Digital Government Summit Join Komprise along with government IT leaders in this one-day, free event on September 14. Learn about the latest public sector practices for digital innovation, cloud and data management. Register here.   The 2021 Komprise State of Unstructured Data Management  Analysis, Insights, Recommendations Live Webinar: Sept. 15 2021 at 9am PT / 12pm ET The recently published Komprise State of Unstructured Data Management Report found a prevailing interest in analytics, followed closely by data lakes, to foster better ROI from data management. Join the Komprise team to review the highlights of the report. We’ll discuss the implications for storage leaders in the enterprise and share key insights and recommendations from the report: 65.5% spend more than 30% of their IT budgets on data storage. 1/3 acknowledge that +50% of data is cold while 20% don’t know. 42% are interested in tagging data for future use and enabling data lakes. Register Now ### Komprise Top 10 Blogs of 2021 Every few months we like to share the blog posts that are the most viewed on the Komprise blog. If there’s a theme here, it’s cloud data storage. IT leaders know that cloud storage is a smart move across many aspects: cost, resilient backups, flexibility, scalability, and a way to leverage many new cloud-based data analytics tools. Yet, there are a lot of variables that go into selecting the best cloud storage platform and the best way to migrate or tier data to the cloud and manage it once there. Many of our most popular blogs touch upon these topics. Thanks for reading. We hope you find this summary useful. And, if you have an idea for a topic or feedback on the blog, please let us know!   1. What you need to know before jumping into the cloud tiering pool. The cloud tiering approach you pick will not only have major implications on your short, medium, and long-term cost savings of migrating unstructured data to the cloud, it will also impact what overall benefits your organization is able to achieve from your cloud data migration strategy. Read the blog for the full skinny on your options.   2. What is S3 intelligent tiering and how does it work? AWS S3 is cloud object storage which offers many advantages for enterprise data management, from cost management to scalability and security. But the choices are compounding. There are now seven different storage classes on S3. AWS S3 intelligent tiering automatically moves objects between tiers within the service but there are some drawbacks. The blog lays it all out.   3. Are cloud storage gateways a good choice for cloud data migration? Cloud storage gateways offer a path to the cloud by moving all data to the cloud and then caching a subset of the cloud data locally. They are helpful in distinct use cases such as backups but aren’t the best choice for migrations. Read why.   4. Pure Storage Partners with Komprise. Pure Storage is a leading enterprise storage company with several Flash array products. The blog details our expanded partnership to provide Komprise Asynchronous Replication delivering reliable data replication for Pure FlashArray™ file customers.   5. Komprise Expands Support of Cloud NAS Options. Modern data management requires ongoing flexibility and this post talks about how Komprise added support for Amazon EFS, Amazon FSx for Windows File Server, Azure Files, and third-party cloud NAS solutions.   6. 10 Principles of Komprise Technology. Who doesn’t like a top-10 list? The first three for Komprise are: Simple, Open and Vendor-Agnostic. Find out the rest! Just for fun, check out some unique top 10 lists here across any subject imaginable.   7. Cloud Storage Problem? There are some valid risks with cloud storage—for instance, many cloud migrations fail or don’t deliver intended benefits--but there are ample ways to turn those challenges and opportunities. Read this blog to learn why it’s more important to first know your data; properly plan your move; and ensure you’re able to manage data efficiently and effectively across data centers and multi-cloud environments before you go to the cloud.   8. Unstructured Data Management Glossary of Terms. Data management spans a lot of areas—cloud and on-premise storage technologies, governance, analytics, migration, archiving and standards. To get it straight—what’s zombie data anyway—read the blog which introduces our glossary.   9. Komprise Technical Professional Training and Certification. All work and no learning nor exchange with peers is a recipe for burn-out. Komprise Technical Professional (KTP) program is an interactive, hands-on training experience and certification on the Komprise solution that includes lively discussions between students on best practices and learnings related to deployment, use cases and more. Read all about it!   10. How to Overcome the Top 3 Cloud Data Migration Challenges. Oh if it were only as easy as sharing a playlist on Apple Music. But cloud data migrations aren’t and there are many reasons behind that. Yet unlike some projects (such as, planning a family vacation that teenagers will actually enjoy), there is a silver lining to the hard work when you plan well and later reap the benefits of modernizing data management. ### Why Data Management Must Be Independent from Storage New: 5 Considerations When Choosing a Storage Tiering Solution Last week the Komprise team presented at Storage Field Day 22. We had a chance to present live in San Jose, CA, which allowed for an interactive whiteboard session. The delegates were all online so it was still virtual, but it was great to not be 100% on Zoom. As always, thanks to the team at Gestalt IT for hosting the event and to all the delegates for their engagement. In the room as @KumarKGoswami presents the @Komprise view of unstructured #datamanagement at #SFD22 pic.twitter.com/UNC15FtUsM — Darren Cunningham (@dcunni) August 5, 2021 In this post I’ll summarize the Komprise sessions and share the videos. Why Data Management Should be an Independent Layer In this session, Komprise CEO and Co-Founder Kumar Goswami reviews the unstructured data management challenges we see at Komprise and shares our recent momentum and customer stories (including how Pfizer saved 75% on storage costs by tiering cold data to AWS). Kumar goes on to make the case for data management being established as a separate, independent layer from data storage. He shares his 5 principles of analytics-centric data management. Tiering today's data gets seriously complex #SFD22 #Komprise pic.twitter.com/4uFdCXw9Ry — David Klee (@kleegeek) August 5, 2021 Here’s the Komprise unstructured data management video: Komprise Intelligent Data Management Demonstration In this session, Komprise SVP of Engineering and Cloud Operations Mohit Dhawan demonstrates: Visibility, assessment, and planning across multisite storage with Komprise. Deep Analytics and fine-grained policy management with a preview of new Deep Analytics Actions. Transparent Move Technology™️ (TMT) and the benefits of the simple Komprise end user experience. Nice demo of the #Komprise management interface and how data is moved #SFD22 pic.twitter.com/1sz0vDKfAS — David Klee (@kleegeek) August 5, 2021 Here’s the Komprise Intelligent Data Management video: Komprise Transparent Move Technology™️ Chalk Talk In this whiteboard session, Komprise CTO and Co-Founder Mike Peercy reviews the power of Komprise TMT and how Komprise transparently extends your NAS to any storage while: Keeping native access in the cloud Being open (non-proprietary) Not using agents or stubs Staying outside of the hot data path There’s even a caption contest for Mike’s session – feel free to chime in! Caption this!@Komprise #SFD22 pic.twitter.com/sCE61ixZX5 — Stephen Foskett (@SFoskett) August 5, 2021 Here’s the Komprise TMT chalk talk video:  ### Pfizer’s Cloud Data Gambit Pfizer Cold Data Storage Savings Pfizer needed to change the way it was managing petabytes of unstructured data to cut costs and reinvest in areas with patients at the center. Key takeaways from the webinar and case study: Pfizer is saving 75% on storage by using Komprise to analyze and continuously move cold data to Amazon S3 as it ages. Storage managers and researchers both are finding additional benefits from this new analytics-based data management strategy, including zero user disruption and a foundation for data lakes. If there’s one company that’s become a household name in the last 12 months, forget Netflix, Hulu and Instacart. Think Pfizer. To date, there have been more than 208 million doses delivered of Pfizer’s Covid-19 vaccine in the United States-- far bypassing the Moderna and J&J vaccines. The global pharmaceutical giant has been developing vaccines and therapies for decades, but 2020 most certainly threw down the gauntlet like no other initiative in the past. “It’s been a very challenging yet rewarding time to work here at Pfizer,” said Matt Braunstein, director of hosting data services, in a recent AWS webinar discussing Pfizer’s cloud data migration initiative using Komprise. Extending data life cost-effectively in the cloud Braunstein oversees storage, data protection and disaster recovery at Pfizer. He had been seeking a better way to manage the company’s 10PB of unstructured data: “We knew that 65% of our data hadn’t been accessed for at least two years from a predecessor program. With research and development for the Covid-19 vaccine, along with other vaccines, data growth has been exponential year-over-year.” Pfizer has offices on 6 out of 7 continents, works with several of the leading storage vendors and has an installed base of many different generations of data management products. The company’s active acquisition strategy means that it is regularly acquiring additional data storage technologies, increasing overall complexity. Pfizer needs to keep historical data for future R&D: recall that SARS data was useful to researchers in 2020 when developing vaccines and treatments for Covid-19. Yet keeping petabytes of data on top-grade, on-premises storage isn’t a sound financial decision if the data is not accessed regularly. Enter AWS S3. Anthony Fiore, senior migration solutions expert for AWS, offered the following points during the webinar as to why enterprises are moving data to Amazon S3 object storage. Security. “This is job zero at Amazon, and we have lots of tools and services here to help protect data and maintain high availability,” Fiore says. Pay as you go. Whether it’s a terabyte or a petabyte, customers only pay for what they need. Analytics and monetization. AWS has built a foundation for data lakes and data analysis, through BI tools like Amazon QuickSight and new services designed for data-intensive verticals such as Amazon HealthLake. How Komprise helped Pfizer create a cold data migration strategy on AWS Braunstein chose Komprise to execute Pfizer’s cloud tiering plan: “What sets Komprise apart compared with other tools is the end-to-end process of analyzing and moving data. You can use Komprise to scan all your data, analyze costs and create business rules and then Komprise will act automatically against those rules.” Braunstein said it was also important that Komprise could transparently enable users to access cold data from the cloud without users knowing the difference. Users can find files at the same location as they’ve been all along, as Komprise creates symbolic links and uses its patented Transparent Move Technology (TMT) to dynamically map cloud objects back as files without users or applications noticing any changes. Using Komprise, Pfizer migrated 2PB of data to Amazon S3 in 2020, saving 75% on cold data storage. Komprise migrates data automatically to Amazon S3 as it reaches 2 years in age, and Braunstein has heard more positive than negative feedback from Pfizer researchers. “Our R&D community is a heavy user of S3 and they definitely access the data there after it has been moved. It has been a surprise, but I’ve had people ask to move more of our data to the cloud.” Braunstein says he’d like to be more aggressive in moving data to cloud storage by potentially shortening the window for tiering cold data from 2 years to 1.5 or 1.75 years. A new way to look at data management Enterprise IT teams typically can’t see information about their data stored in one place, Fiore said. Detailed visibility, delivered by data management software like Komprise, is exciting to IT people: “We have customers with NAS shares which contain many silos of data in a single share and it’s hard to know how they can break it up by line of business or if they even care about this data. But once they see all the metadata, they get a better understanding of how everything works and then they can tag and search for it later.” Pfizer’s vision for data management is to host data based on its analytical profile, rather than throwing high availability storage at it. This is a cultural shift, Braunstein says, since researchers often worry about losing their data or visibility into it. Yet Komprise’s analytics approach has proven that Pfizer’s cloud data management strategy is safe and transparent to users—and supports the company’s higher-level mission. “We can now make razor sharp business decisions based on data so we can reinvest in areas that are more important to patients,” Braunstein says. “Our goal at Pfizer is to win the digital race in pharma and make breakthroughs that change people’s lives." The webinar is now available on-demand: Watch Now > Read the Pfizer case study. Read the eBook: 5 Ways to Use Analytics for Data Migrations. Learn more about Komprise for Healthcare and Life Sciences, Komprise for AWS, and let us know if you’d like to schedule a custom demonstration to get started with Komprise Intelligent Data Management. ### Data Management for Higher Education: Get to the Cloud Faster Over the last few years, Komprise has worked with some of the largest higher education institutions to help manage their growing volumes of unstructured data more effectively and save on their overall data storage costs, backup and cloud costs. As we prepare to return to school in the fall, we want to make it even easier to get started with Komprise Intelligent Data Management. Intelligent Data Management for Higher Education A common data management scenario we see is summarized well by an IT manager at Northwestern University: We didn’t know what the data was, who owned it, or when it was last used…now we set policies to move data off to cheaper media. We save ~$330,000 a year. Examples of Komprise Intelligent Data Management for Higher Education in action: Major West Coast university sees that over 60% of the data on their NAS has not been accessed in over a year and calculates that keeping a DR copy in the cloud versus on premises would be 70% less expensive. Read the case study on cutting Data Replication Costs> A central IT team at an Ivy League school was able to provide each department with analytics on how their data was being used, by whom, and what data was cold. They also provided a cost analysis showing how much the department would save by transparently tiering and archiving cold data with Komprise; each department could set their own specified policies and use them to move and transparently archive their data, without any changes to users or applications. Read the White Paper: Getting Departments to Care About Storage Savings >  A storage administrator at Duquesne University identified, tiered, and archived years of cold data to enable the move to an all-flash array. Data management policies were put in place to meet the unique needs of each department as the IT team accelerates their path to cloud services and infrastructure. There has been much written about the challenges facing higher education IT organizations over the past 18 months and how accelerated digital transformation is driving the implementation of new business models. When it comes to data storage, institutions typically have a long history of not deleting and properly managing unstructured data. There is a constant struggle to classify and categorize all the data that is being stored for departments, faculty and researchers, and too often they are storing and replicating everything on high performance, high-cost primary storage systems, instead of lower-cost alternatives for cold data that is infrequently (if ever) accessed. Storage Cost Savings Are Just the Beginning At Komprise we want to do everything we can to help higher education IT organizations store, backup, tier, archive, and replicate intelligently. We call it “right placing” your data to ensure the most efficient use of all your university’s resources. Komprise Intelligent Data Management ensures you know first, move smart, and stay in control of your data, giving faculty and students uninterrupted access to the data they need—anytime, anywhere. Why Komprise for Higher Education? Move data to lower-cost storage alternatives, without disruption. Tier and archive data off primary storage to save and cut backup times. Ensure users can access moved data just without disruption. Please contact us or schedule a demonstration for more information. ### Healthcare: Big Medical Image Files with Nowhere to Go? Cloud Tiering is Coming of Age for Clinical Files Healthcare organizations today are storing petabytes of medical imaging data—labs, X-rays, MRIs, CT scans and more—a number that is expanding with no end in sight. To make matters worse, due to regulations, healthcare providers typically must retain medical imaging files for several years; they may even have an enterprise-wide policy of not deleting data ever. Aside from compliance requirements, clinical researchers may need access to the data indefinitely while clinicians require collaboration and file sharing across a patient's continuum of care. This presents a conundrum from both an economic and IT management perspective. Internal storage for large image files is expensive—costing millions a year for some organizations on Porsche-grade NAS devices. The data must be secured, replicated and backed up. Meanwhile, in most cases, imaging data is rarely accessed after a few days. To get more flexibility and cost savings, healthcare organizations are increasing their investments in cloud data storage. In a recent webinar sponsored by the Society for Imaging Informatics in Medicine (SIIM), storage architects from two large healthcare systems discussed strategies for tiering and migrating images to the cloud. Such decisions can be rife with politics and long-standing institutional perspectives. Health systems are generally risk-averse—they are handling sensitive patient information after all—and tolerance for downtime is usually quite low. Read the Whitepaper: How to Manage Medical Imaging Data Growth Costs Cloud Tiering in Healthcare for Dear Life Healthcare professionals depend upon accurate, timely data to make the best decisions; the loss of important patient data can have dire consequences. Keeping these large files safe and readily available could be a matter of life or death for a patient with a serious illness. One of the storage managers interviewed in the SIIM webinar noted that a TCO study projected savings of 65% from moving pathology images that are 90 days or older from the on-premises HCI and NAS arrays to a third tier on Google Cloud Object Storage. That’s compelling evidence to consider a new unstructured data management strategy. In this particular case, the organization is scanning 1TB of pathology slides per day; they remain on the Tier 1 HCI storage for three days, after which they are moved to the Tier 2 NAS device. Using Komprise, the post-90 day old slides are automatically tiered to Google Cloud storage, and once there, move between two tiers based on age. “The Komprise transparent move technology (TMT) hides the location from technicians,” the storage manager said. “They don’t even notice it’s coming from the cloud.” Since these older images stored in the cloud are accessed so rarely, the cloud egress fees to bring them back to the on-premises digital pathology solution have been minimal. Komprise pulls the slides back to the Tier 2 storage for rehydration and afterward they are deleted since there’s a copy in the cloud.   Without Komprise: Medical imaging files are being stored on the expensive, high-performing NAS, including imaging files that have not been recently accessed and have gone cold (blue).   Using Komprise, medical imaging files that have gone cold (blue) are transparently moved from the NAS to the cloud. Clinicians, technicians, and other healthcare employees can still access these imaging files exactly as they did before.   Using Komprise for Medical Imaging Cloud Tiering and Cloud Data Management Medical imaging systems use high-performance NAS devices to store medical images. This ensures fast access to files for the medical staff. However, such high-end storage is expensive and the images are generally not used after patient diagnosis. Komprise provides a tightly integrated solution with NAS devices to automatically move older images (e.g. images over 90 days old) to the cloud based on policy for significantly cheaper storage and without affecting user experience. Komprise is in use by large hospitals throughout the nation. Here's how it Komprise Intelligent Data Management works: Transparent Tiering: Komprise TMT ensures that clinicians can find old images as they did before from the original file location. However, the old images now reside in the cloud as objects. This reduces storage costs by as much as 70% and extends the capacity and lifespan of the expensive NAS devices. When a user wants to access an archived image, Komprise streams it back from the cloud storage and caches it locally on the NAS for fast access. Cloud-Native Access: Komprise stores the images in the cloud in native form enabling research staff to access these images using new cloud-native services and tools for AI and ML processing. For example, Amazon HealthLake is a new data lake service incorporating machine learning models for analytics projects. Azure has several machine learning initiatives in healthcare including a partnership focused on decoding the immune system. By enabling direct cloud access to tiered images and data, healthcare organizations gain a larger portfolio of advanced tools and services to further their R&D efforts. Global Search: Furthermore, with Deep Analytics, Komprise creates a global file index of all the images on the NAS regardless of whether they were tiered to the cloud or not. This allows the IT and research staff to search and find specific images for IT administration or clinical research. If Komprise is used across a set of hospitals, you now have one global index of all images across the entire health system, supporting global data management requirements and initiatives such as compliance. Paving the Way for Medical Image Longevity As high-value unstructured data like medical images exceed the limits of on-premises storage, the options are becoming increasingly limited within static budgets. Healthcare organizations need to craft a long-term plan with simple execution for cloud data management. Consider this comment from the SIIM webinar speaker: “Because we’re keeping slides indefinitely, the amount of storage that would be required to house that much data indefinitely (600k slides annually, 1TB a day) would be prohibitively expensive. We had to come up with a new solution for Tier 3, which is the archived data.” Sound familiar? Learn more about Komprise Intelligent Data Management for health and life sciences. Read the blog post: Healthcare and Unstructured Data Management ### Komprise H1 Results: Revenues, Customer and Product Growth Protect data. Move to the cloud. Digitize your business. Cut costs. Be agile. These are just a few of the broad-sweeping mandates facing IT leaders everywhere as we move forward into this wobbly post-lockdown economy. IT execs depend on their infrastructure teams to build and optimize the foundation to keep employees productive and customers returning. Storage is an intrinsic part of this foundation. At Komprise, we are helping customers around the world manage data, not just storage, so that they can save money, protect the user experience and ultimately liberate and monetize the petabytes of unstructured data collected and stored across different technologies in the average enterprise organization. The year 2021 has been a busy and successful one for us so far, and we owe it to our partners and forward-looking customers to these core achievements mid-year. Read the full Komprise Doubles Revenue in First Half of 2021 press release. Compared with first half of 2020, Komprise realized: 97% revenue growth, 190% growth of new customers and 200% growth in average deal size. Komprise develops intelligent data management software for file and object data. Our analytics-first approach means you can understand your data – what file types are stored where and usage patterns – and then can create policies to automatically migrate or tier data to the optimal storage technology or cloud data storage. Komprise enables IT organization to save 70% on enterprise storage, backup, and cloud costs. Komprise 2021 highlights so far include: Intelligent Data Management Platform Innovation Komprise announced expanded support for cloud NAS. Customers can now migrate data to and from Amazon EFS, Amazon FSx for Windows File Server and Azure Files. Komprise was awarded a patent that extends the capabilities of the Transparent Move Technology (patented in 2019) to enable asynchronous restoration of files from delayed recall storage such as tape. This patent was a joint application with partner Spectra Logic, a leading provider of tape and secondary storage. Komprise announced new capabilities for global data management with multisite controls, giving IT directors a single consolidated view across multiple Komprise-managed sites while enabling local execution to meet site-specific policies and needs. Enterprise Customer and Channel Growth Komprise accelerated its pace of new customer acquisition by 190% in 2021 as enterprise IT organizations emerge from the global pandemic with new mandates and conservative budgets. Komprise added customers across several verticals, with highest adoption in the public sector, higher education, financial services and healthcare. Komprise had 233 new graduates from the Komprise Technical Professional training program in the first half of 2021, representing students from 29 unique partners and from 30 unique customers and prospects. Expanded Alliances and Industry Recognition Komprise inked a partnership with Fortune 500 IT distributor TechData, allowing the company to sell Komprise Intelligent Data Management to customers in Europe. Komprise and existing reseller partner Pure announced that Komprise Asynchronous Replication would deliver reliable data replication for Pure FlashArray™ file customers. Komprise and Nutanix announced a partnership where Komprise supports file data migrations and data tiering to and from Nutanix. Komprise President and COO Krishna Subramanian was honored as a Top Woman of Influence in Silicon Valley, by the Silicon Valley Business Journal. Komprise announces support for AWS for Health Initiative, a new program featuring services and solutions from AWS and AWS Partners, built specifically for healthcare, biopharma, and genomics customers. New Leadership: Komprise added two executives in EMEA in 2021: Martin Gibbons joined as Channel Director, EMEA, while Ben Conneely joined Komprise in January 2021 as Regional Sales Director for the UK, Ireland and Northern Europe and was promoted to VP of EMEA in April. “Customers are adopting Komprise because we not only find and move the right data to the cloud, but we tier data without users and applications noticing any change and without locking data in the cloud in a proprietary format.” -- Kumar Goswami, CEO of Komprise ### Komprise Announces Support for AWS for Health Initiative Campbell, CA--July 15, 2021– Komprise, developer of an analytics-driven data management as a service, announced support for the AWS for Health initiative from Amazon Web Services (AWS) to accelerate healthcare organizations’ journey to the cloud by enabling a secure, no lock-in, transparent migration and cloud tiering process that maximizes cost savings and minimizes user disruption. Cloud tiering is a technique that offloads less frequently used data, also known as cold data, from on-premises file storage to lower cost storage in the cloud. AWS for Health is an initiative featuring services and solutions from AWS and AWS Partners, built specifically for healthcare, biopharma, and genomics customers. The initiative makes it easier for health customers to select the right tools and partners for their highest-priority workloads across the health community. For customers looking to accelerate deployments with solution-specific support, AWS for Health also identifies dedicated AWS health industry specialists, AWS Professional Services teams, and leading AWS Partners in each solution area. To meet compliance and/or long-term research requirements, many healthcare organizations must keep files, such as Picture Archiving and Communication System (PACS) and medical images, genomics data and research data, in storage indefinitely. Yet the astronomical growth of this unstructured data – including all the backup copies required – is testing the limits of on-premises storage capabilities. Healthcare organizations such as providers, pharmaceutical and biotech companies, researchers, and government agencies need easy access to historical data for R&D purposes and to improve care plans for specific diagnoses and patient populations. Komprise Intelligent Data Management delivers quantifiable return on investment for healthcare organizations by analyzing which data can be tiered to warm or cold storage on Amazon Simple Storage Service (Amazon S3) and Amazon S3 Glacier, saving organizations up to 70% on storage and backup costs. Komprise Elastic Data Migration accelerates file data migrations to Amazon Elastic File System (Amazon EFS) and Amazon FSx for Windows File Server. Komprise file-based tiering ensures cloud-native access to data stored in Amazon S3 and Amazon S3 Glacier, enabling researchers to leverage AWS machine learning services, such as Amazon HealthLake, unlike proprietary tiering solutions. The Komprise Transparent Move Technology enables rapid data transfers as well as a non-disruptive user experience, allowing healthcare professionals to access moved files from the original location. Komprise Intelligent Data Management is a hybrid cloud data-management-as-a service that delivers visibility across a customer’s storage infrastructure, from on-premises to the cloud: Komprise uses analytics to discover data, shows cost savings by changing data plans, and maximizes savings by systematically moving data to lower-cost storage tiers as it ages. Komprise gives AWS customers the opportunity to migrate and archive data to the cloud in phases, right-placing data for optimal cost and required performance based on usage. Once Komprise moves data to AWS, it can automatically tier data across Amazon S3 storage classes as it ages out. Customers can get to their data in AWS whenever they wish, with the option to access it directly versus rehydrating files back to the primary storage. “A lot of times I come in and feel like it’s Christmas morning because we had planned 100TB to go to AWS and it’s 115TB because Komprise did their next scan and pulled some data I wasn’t counting on that aged out,” says Matthew Braunstein Director of Storage, Data Protection, Disaster Recovery & Application Integration Services with Pfizer. Join Komprise and AWS on July 22 at 10am PDT to learn how Komprise helped Pfizer stop 20 years of increasing storage costs and leverage the data tiered to AWS for research, all without changing how users and applications access their files: Webinar: How Pfizer Used Analytics to Create a Cold Data Strategy and Accelerate Cloud Data Migration Learn more about Komprise health and life sciences offerings and Komprise for AWS in AWS Marketplace.   AWS Marketplace is a digital catalog with thousands of software listings from independent software vendors that make it easy to find, test, buy, and deploy software that runs on AWS. About Komprise Komprise is the industry’s only multi-cloud data management-as-a-service that frees you to easily analyze, mobilize, and access the right file and object data across clouds without shackling your data to any vendor. With Komprise Intelligent Data Management, you are able to know first, move smart, and take control of massive unstructured data growth while cutting 70% of enterprise storage, backup, and cloud costs. www.komprise.com Media Contact: Kevin Wolf, TGPR www.tgprllc.com kevin@tgprllc.com ### Komprise Doubles Revenues in First Half of 2021 Strong revenue and customer growth prevail as IT organizations seek to right-place petabytes of unstructured data to cloud storage for savings and transparent data access.   Campbell, CA – July 13, 2021 – Komprise, the leader in analytics-driven data management as a service, today announced it achieved strong growth in the first half of 2021. Key growth drivers compared with first half of 2020 included, 97% revenue growth, 190% growth of new customers and 200% growth in average deal size. “Enterprise organizations are looking for an easier path to the cloud for file data while not compromising access,” said Kumar Goswami, CEO of Komprise. “Customers are adopting Komprise because we not only find and move the right data to the cloud, but we tier data without users and applications noticing any change and without locking data in the cloud in a proprietary format.” Komprise’s analytics-first approach enables enterprise customers to understand their data – what file types are stored where and usage patterns – so they can create policies to migrate or tier data to the optimal storage technology or cloud service. Users can map out different storage plans to analyze their cost savings and then use Komprise to execute and track migrations and/or continuous tiering of cold data to lower-cost storage. Komprise 2021 highlights so far include: Intelligent Data Management Platform Innovation Komprise announced expanded support for cloud NAS. Customers can now migrate data to and from Amazon EFS, Amazon FSx for Windows File Server and Azure Files. Komprise was awarded a patent that extends the capabilities of the Transparent Move Technology (patented in 2019) to enable asynchronous restoration of files from delayed recall storage such as tape. This patent was a joint application with partner Spectra Logic, a leading provider of tape and secondary storage. Komprise announced new capabilities for global data management with multisite controls, giving IT directors a single consolidated view across multiple Komprise-managed sites while enabling local execution to meet site-specific policies and needs. Enterprise Customer and Channel Growth Komprise accelerated its pace of new customer acquisition by 190% in 2021 as enterprise IT organizations emerge from the global pandemic with new mandates and conservative budgets. Komprise added customers across several verticals, with highest adoption in the public sector, higher education, financial services and healthcare. Komprise had 233 new graduates from the Komprise Technical Professional training program in the first half of 2021, representing students from 29 unique partners and from 30 unique customers and prospects. Expanded Alliances and Industry Recognition Komprise inked a partnership with Fortune 500 IT distributor TechData, allowing the company to sell Komprise Intelligent Data Management to customers in Europe. Komprise and existing reseller partner Pure announced that Komprise Asynchronous Replication would deliver reliable data replication for Pure FlashArray™ file customers. Komprise and Nutanix announced a partnership where Komprise supports file data migrations and data tiering to and from Nutanix. Komprise President and COO Krishna Subramanian was honored as a Top Woman of Influence in Silicon Valley, by the Silicon Valley Business Journal. New Leadership: Komprise added two executives in EMEA in 2021: Martin Gibbons joined as Channel Director, EMEA, while Ben Conneely joined Komprise in January 2021 as Regional Sales Director for the UK, Ireland and Northern Europe and was promoted to VP of EMEA in April. To see Komprise in action and start saving on your storage, backup and cloud spend, schedule a demo. About Komprise Komprise is the industry’s only multi-cloud data management-as-a-service that frees you to easily analyze, mobilize, and access the right file and object data across clouds without shackling your data to any vendor. With Komprise Intelligent Data Management, you are able to know first, move smart, and take control of massive unstructured data growth while cutting 70% of enterprise storage, backup, and cloud costs. www.komprise.com Media Contact: Kevin Wolf, TGPR www.tgprllc.com kevin@tgprllc.com ### Cloud Storage Gateways: Expensive New Silos, Ongoing Proprietary Costs In our recently published cloud tiering paper, we reviewed the options for storage-based tiering, gateways and file-based tiering. In this post, I’ll review the challenges with cloud storage gateways to ensure you choose the best, no lock-in path to the cloud for your file and object data. By doing so, you can avoid paying 300% higher ongoing costs in addition to unnecessary initial investments. Cloud Storage Gateways: A Brief History In the early days of the cloud, few applications natively spoke cloud storage or object APIs such as AWS S3. Cloud gateways arose to provide a file interface backed by the cloud. The promise was a device with fast disk or SSD to cache hot data while cold data was tiered to the cloud. Cloud gateways promised high performance and the unlimited scale and durability of the cloud with the added benefit of providing access via file interface at multiple locations. Over the years storage teams found that cloud gateways are useful for collaboration by teams at distinct locations with applications that needed file protocol access to the same data set with modest performance requirements. Cloud gateways could also be used to cache data to small office locations for local access where deploying a full enterprise NAS is an overkill. An Expensive, Inflexible Path to the Cloud… Cloud storage gateways commit all data to the cloud for an authoritative copy to be distributed to other locations and keep only “hot data” on premises in cache. This works well for applications that write files and modify infrequently but not for demanding workloads such as database or virtualization. For efficiency, data migrated to the cloud via gateways is stored in a proprietary “block” format. This means the data stored in the cloud is only readable by the cloud gateways, locking in customers and eliminating open access by cloud native tools such as compute, analytics, and AI/ML. Here’s a brief review of why cloud gateways are not an ideal solution for migrating NAS data to the cloud: Additional On-Premises Infrastructure: Cloud storage gateways are typically hardware-based since they serve hot data from the cache. As they act as the primary file server for data that users are actively accessing, this requires performant hardware at each site, increasing costs. Duplication of Data in the Cloud: Cloud storage gateways typically put all the data in the cloud and then cache some data locally. So, if you are using a cloud storage gateway for 100TB, then all 100TB of data is in the cloud and a subset of it (maybe 20TB or 30TB) is also cached locally. This means you may need 130TB of infrastructure to house 100TB of data. Depending on the size of the local cache, this duplication of data storage may be larger. Licensing Charges to Access Data in the Cloud: You cannot directly access your data in the cloud but must go through the gateway software in the cloud, which means you must pay gateway licensing costs as long as you need your data. Lose Benefits of Hybrid Cloud: Organizations want to move to the cloud to innovate and build new workflows using the constantly evolving set of AI and ML tools and services offered by the major cloud providers. But to do that, they need to natively access their data in the cloud. Since cloud gateways simply move legacy storage to the cloud and lock customer data in their proprietary format, they simply do not enable the benefits of hybrid cloud. So, How Should You Tier to the Cloud? Where cloud gateways require an entire new storage silo for cloud tiering, Komprise takes a different approach. Working with existing NAS and leveraging analytics to understand data usage and costs, Komprise can help IT teams create intelligent data management policies for cloud tiering, while maintaining open access to data. Data tiered to the cloud is maintained in native format; you don’t need to go through any vendor’s storage system to access data in the cloud and you maintain the flexibility to move to new storage platforms without rehydrating cloud data back to the gateway solution in your data center. Finally, by leveraging the existing NAS, there’s no additional hardware investment required. Komprise’s commitment to an open-standards format helps to future proof your data, preserving access for your applications today and opening a world of possibilities for future applications and cloud workflows. (See the Gartner 2021 Market Guide for Hybrid Cloud Storage, which includes Komprise.) Figure 1: Cloud storage gateways vs. Komprise for file-level cloud tiering. Learn more about the migration path for cloud. Reviewing the ROI Komprise is a significantly more cost-effective solution for cloud tiering versus gateways, saving organizations on average 70% annually on storage, backup and replication costs. Here’s the breakdown: Assuming $700/TB per year of cloud storage gateway licensing costs, cloud storage gateways have 287% higher annual costs than using a file-level data management solution with the cloud. This is a recurring cost that you pay for over the lifetime of your data. Komprise analytics enable you to see the costs and benefits of data management before you take any action. Leverage Komprise’s free trial to analyze your environment with your specific cost model. Figure 2: Cloud storage gateways have ~300% higher annual costs than file-tiering with Komprise. With cloud gateways, you need to guess which workloads can tolerate reduced performance. Komprise allows you to leverage analytics to determine which data sets need the top performance of their existing NAS infrastructure and then tier the rest to warm and cold storage. You can reduce storage spend even as data volumes continue to grow exponentially, shrink backup needs, and reduce friction for cloud migrations. To learn more about cloud tiering choices, read our recent white paper. ### Komprise Intelligent Data Management Summer 2021 Update Do more, see more, in less time. With hybrid cloud infrastructure and globally-distributed teams, complexity can run wild. Enterprise IT organizations need a single pane of glass into IT assets and spending to stay on top of this dynamically-changing environment. According to “Gartner Market Guide to Hybrid Cloud Storage, 2021”: “By 2025, 40% of I&O leaders will implement at least one of the hybrid cloud storage architectures, up from 15% in 2021.” Further, “By 2025, more than 40% of enterprise storage will be deployed at the edge, up from 15% today.” (Komprise was included as a Representative Vendor in the Data Management sector) To that end, the latest release of Komprise gives global enterprise IT organizations and service providers a consolidated view across all data centers and cloud locations with new multisite data management capabilities. Storage managers still have the ability to manage each site per local requirements and policies. Holistic visibility with local control is the best way to manage spending while meeting distinct departmental and geographic requirements for data storage. Here’s what you’ll find in our latest release: Multisite Support Komprise Multisite Benefits: Manage data at multiple sites from a single Komprise Director; Provides ability to set local (site-specific) policies (Plans, migrations, cloud tiering); Enables users to get consolidated overview of all sites on dashboard. Customers can now set up multiple sites, each with their own storage and data management policies and activities and manage them from a single, on-premises or cloud-based Komprise Director. Using a single dashboard saves time, avoiding the need to login to multiple Directors, and gives a consolidated view across all sites in the entire deployment. This will show you vital metrics on cloud migrations and cloud data tiering, such as the amount of data transferred across all sites, total migrations, how much data is on-premises versus the cloud, and the ability to manage configurations for all the sites in one place Each site has its own Komprise Observer virtual machines to connect to all the storage and clouds for that site and perform the associated data management tasks. This ensures that policies and execution are controlled locally within each site, according to distinct departmental needs and security requirements. We’ve also added additional information to the site-level dashboards: updates on active plans and details on migrations in progress. Migration/Replication Enhancements: Centralized Audit Logging Benefits You can select a single location for all migration and replication audit logs; This keeps replication destinations “clean” for just replicated data; Custom location can be an NFS or SMB share, or S3 bucket; Audit logs include both source and destination checksums and timestamps. During a data migration, log files live on the destination. After you’ve completed a number of migrations, there are lots of log files. For compliance and audit reasons, you may need to find logs for files within certain dates or created by certain owners, and that can be difficult. Now, Komprise users can customize the location of migration and replication log files for centralized audit logging. Deep Analytics Enhancements to Highlight Archived/Tiered Data Benefits Search and find data that Komprise has archived; Keeps Plan and Deep Analytics synchronized; Enables showback and chargeback. Customers can now easily see how much data has been archived by Komprise and where it lives, in the Deep Analytics graphs and reports. This gives you the ability to search for data by owner (such as with an ex-employee) or date, and segment data by department for chargeback reporting. As well, with the new multisite support, you now have a single searchable virtual data lake of all unstructured data that can be easily shared across the enterprise. Customers can develop custom queries to identify specific data sets such as data belonging to a project that might be strewn across data centers and clouds. Attend the webinar on June 24th to see a live demo of the Komprise Summer Release. ### Komprise Simplifies Global Data Management with Multisite Controls Latest Release Enables Enterprises and Service Providers to Manage Complex Hybrid Cloud Storage Architectures with Global Visibility and Localized Control Campbell, CA – June 17, 2021 – Komprise, the leader in analytics-driven data management as a service, today announced Komprise Intelligent Data Management 4.0. The new release enables global enterprise IT organizations and service providers to deliver storage-as-a-service with a consolidated view across all data centers and cloud locations while giving storage managers the ability to manage each site per local requirements and policies. Enterprises are increasingly deploying more complex hybrid cloud storage architectures which require greater global visibility and control to effectively manage costs, security and performance. According to “Gartner Market Guide to Hybrid Cloud Storage, 2021”: “By 2025, 40% of I&O leaders will implement at least one of the hybrid cloud storage architectures, up from 15% in 2021.” Further, “By 2025, more than 40% of enterprise storage will be deployed at the edge, up from 15% today.” “As infrastructure becomes more distributed, visibility can suffer,” said Kumar Goswami, co-founder and CEO of Komprise. “Aside from the cloud, many organizations are geographically dispersed or have security protocols and organizational boundaries that require them to localize where data movement occurs. Komprise brings granular flexibility and control with new multisite data management capabilities.” Komprise Intelligent Data Management is an analytics-driven data migration, data tiering and archiving solution that enables enterprise IT organizations and service providers to analyze, mobilize, and access the right file and object data across clouds without shackling data to any vendor.  The 4.0 release introduces: Centralized Management across Multiple Sites: You can now set up multiple sites, each with their own storage and with separate data management policies and activities. All sites are managed from a single, on-premises or cloud-based Komprise Director. This enables centralized management for cost and performance optimization through a single dashboard, providing a consolidated view across all sites in the entire deployment. Localized Policy Management and Execution: Each site has its own Komprise Observer virtual machines to connect to all the storage and clouds for that site and perform the associated data management tasks. This ensures that policies and execution are controlled locally within each site, according to distinct departmental needs and security requirements. Global Search and Deep Analytics Across Sites and Clouds: With a centralized dashboard, enterprises now have a single searchable virtual data lake of all unstructured data that can be easily shared across the enterprise. Customers can develop custom queries to identify specific data sets such as ex-employee data, or data belonging to a project that might be strewn across data centers and clouds. To learn more about Komprise Intelligent Data Management and schedule a demonstration, request a free demo. Visit the What’s New page for all the latest at Komprise. Gartner, ‘Market Guide for Hybrid Cloud Storage’, Julia Palmer, Raj Bala, May 03, 2021 About Komprise Komprise is the industry’s only multi-cloud data management-as-a-service that frees you to easily analyze, mobilize, and access the right file and object data across clouds without shackling your data to any vendor. With Komprise Intelligent Data Management, you are able to know first, move smart, and take control of massive unstructured data growth while cutting 70% of enterprise storage, backup, and cloud costs. www.komprise.com Media Contact: Tara Lefave Stred komprisepr@watersagency.com ### Cloud Tiering with Komprise: Three Customer Use Cases Benefits and Trade-Offs for Hybrid Cloud, Public Cloud, and Private Cloud Tiering and Archiving At least 70% of data on average is cold and the cost of cold data is not just the cost of storing it—even though that can get pricey on top-tier NAS devices. Two-thirds of the expense is the active management of data: the backups, the replication and the everyday management costs. At Komprise, we are helping organizations think differently about data because unstructured data growth is eventually going to kill organizations’ competitive advantage by consuming too much budget and too much manpower. Is there a simpler way? We think so. Show Me the Money! Organizations across industries are running Komprise Intelligent Data Management to cut 70% or more of data storage and backup costs. We do this by helping IT organizations understand where their data lives, who’s accessing it and how often. In this post I’ll review the most common use cases that we encounter with our customers across all sectors and include some interesting ways to think about the potential ROI for each. Here’s a summary: 1. Hybrid Cloud Tiering The majority of Komprise customers are on a cloud transformation journey. A simple way to adopt the cloud without disrupting users and applications is to tier cold data from on-premises NAS to a cost-efficient object class in the cloud such as Azure Blob or AWS S3. But how you tier from NAS to cloud can make a huge difference in costs and in your ability to access and use data in the cloud: Komprise provides file-level cloud tiering and archiving without any lock-in to deliver 75% lower cloud egress costs and 300% lower ongoing costs versus storage-tiering options (for example, NetApp FabricPool and Dell EMC Isilon CloudPools) and cloud storage gateways. More info here. Storage vendors use proprietary block tiering which is useful for tiering proprietary components such as snapshots but not for cold data because it has much higher cloud egress costs due to unnecessary pulling back of data. These costs can occur with both random reads (such as when a user opens a file), to sequential reads (such as indexing scans and antivirus scans). Proprietary storage tiering and cloud gateway solutions also result in 300% higher ongoing costs as you need to continuously license their software in the cloud just to access your own data. More info here. 2. All-Cloud Tiering Enterprise IT organizations are starting to put file data in the cloud on a cloud file storage option such as Amazon EFS, FSX, or Azure Files or NetApp CloudVolumes ONTAP. These cloud NAS solutions are a great way to move file workloads to the cloud as they require no rewriting of the applications. But, cloud NAS solutions can get just as expensive as on-premises NAS. Here’s why: A cloud file storage solution still needs to be highly performant and requires replication to another site for resiliency and backups and snapshots for data protection. Cloud file storage solutions sometimes offer built-in tiering to cheaper file storage classes or to Azure Blob and AWS S3 classes. While storage file system cloud tiering can be useful for tiering proprietary components such as snapshots, their block-based proprietary nature means you only save on some underlying storage costs for the primary copy but not on the cloud file system costs nor its replication and backups. Komprise delivers significant savings when tiering data from a cloud NAS to a cloud object class compared with cloud-tiering “Pool” solutions because Komprise tiers the entire file from the cloud file storage to the S3 or Blob classes and eliminates the full cost of the cold files from the cloud file system and its replication region. Learn more about it with our resource on what to know about cloud tiering. In a real customer analysis storing 1PB on NetApp CVO, after Komprise moved 900 TB of this data to cold storage (Azure Blob GRS), we showed a savings of more than 75% annually. 3. Private Cloud Tiering Some enterprise IT organizations still have all their infrastructure hosted within their data centers. They may have NAS storage, an object storage solution, and a cheaper third-level solution such as tape storage. A few things to keep in mind: Proprietary storage-based tiering solutions do not work with the heterogeneous mix of vendor solutions that most customers have. Komprise customers find that with open, standards-based file-level tiering even in a fully on-premises scenario, they can double their savings over Pools solutions. At a customer site, we found that on 10PB of file data, the savings with using a proprietary storage tiering solution to object storage over the status quo was 30% whereas the savings when using Komprise file tiering to the same object store was 65% and if a third tape tier was leveraged, then the savings with Komprise was more than 75%. Considerations for Enterprise Cloud Storage Planning By asking the following questions, you can get a better idea of your ongoing storage needs and apply the appropriate cloud tiering solution regardless of whether you plan to use a hybrid cloud, all cloud or private cloud model: How much total data do you have and what are your growth forecasts over the next 12-24 months? Most companies are facing double-digit data growth and need to find a way to cut ongoing costs. Do you take snapshots of your NAS? Taking 30-90 days of snapshots will result in at least a 30% increase in your overall data footprint. Is your NAS replicated? Most customers forget about this cost. For 1PB of data you could be managing 2.6PB with snapshots and replication. Are you planning to use cloud file storage? If so, consider not only the file system costs in the cloud but also the costs of its underlying infrastructure and the cost of the replication copy. Do you have an ILM plan? The right information lifecycle management plan should allow you to continually manage the data lifecycle as data ages and automates data migration, tiering, and archiving to colder storage as needed or by policies. Most storage vendors will only move your data once using their tiering technology, limiting the savings. Consider a Komprise Data Assessment to find out how much you can save with analytics-first unstructured data management from Komprise. Also visit Komprise Path to the Cloud to review your options and learn more about Komprise Intelligent Data Management. ### Moving to the Cloud, Government-Style Since the onset of Covid-19, there’s been a global acceleration to the public cloud like never before. This is also true for government, which has traditionally been more conservative in its adoption of modern IT technologies and strategies. According to a recent study by Maximus and Genesys, a majority (91-93%) of U.S. state, local, and federal governments have at least some systems and solutions in the cloud. AWS RedRiver Webinar Public and private sector organizations have similar motivations for moving to the cloud, but the challenges and strategies can diverge. For one, public sector organizations have strict compliance requirements regarding privacy and security. For two, legacy environments are typically more ingrained and complicated and thirdly, budgets are more stringent. Recently, Michael Del Castillo, Regional Sales Director with Komprise, teamed up with Sean Phuphanich, an AWS Senior Solutions Architect and Jeff Drewes, CTO of Consulting Services at Red River, to discuss public sector strategies for cloud data management. Below are highlights of the webinar; or you can watch the full webinar on Advancing Cloud Data Strategies. Progressing the CapEx to OpEx Transition The argument for reducing the hardware procurement lifecycle in exchange for on-demand services is still making its way through government agencies. The age-old model of planning IT infrastructure for years in advance and paying for it upfront is no longer viable given today’s dynamic technology environment and impatient user expectations; this model is also no longer financially astute. The cloud allows federal organizations to benefit from predicted spend, meet unexpected demand and optimize budgets yearly, if not continuously. “CapEx requires predicting workloads and requirements three to five years in advance, which results in underutilized hardware for large parts of the lifecycle,” remarked Phuphanich. “Before I joined AWS I was working at a company where we were saving 30% a year in the cloud by continually optimizing our services and using discounts. AWS Reserved instances can be a huge savings if you understand your workloads and you always have the option to burst if you need more capacity.” Data Governance In the public sector, data governance is an essential underpinning of cloud infrastructure. The webinar participants talked about striking the right balance – modernizing data governance so that organizations can reduce shadow IT and its associated risks and costs yet still provide flexibility and agility for employees to use cloud services. Instilling a strong data governance practice is paramount to ensure that data is in the appropriate locations based on its age and storage requirements and can be easily discovered and used for analytical purposes when desired. AWS serves 7500+ government agencies around the world and complies with FedRAMP requirements. Komprise helps support safe shadow IT through our Transparent Move Technology which securely migrates, copies and archive data to the cloud while retaining native access. Komprise’s unstructured data analytics engine quickly identifies ownership, access history, storage composition and other insights on file data to help IT managers determine where to best store data for cost and performance while also allowing individual departments to understand usage for chargeback analysis. You can model projected cost savings based on parameters like number of backup copies and internal and external storage costs, factoring in costs for Komprise. Central Visibility A barrier to IT directors in deploying hybrid cloud and multi-cloud infrastructure is that they can’t easily see their data and files and ensure that information assets are properly secured and managed. This lack of visibility also hampers cloud spend management. In AWS, says Phuphanich, IT directors can gain tight control over what can be deployed to which environment, settings that can be managed at the AWS services level. This ensures that IT policies transfer to the cloud while still giving developers freedom to have sandboxes and deploy where they want, within the pre-established parameters. Komprise enabled central visibility of the unstructured data, giving insight and control over the data and the associated spend. Here are a few tips shared by Red River’s Drewes on how to plan for a cloud migration: Conduct a thorough assessment of your environment: know first! Collect costs and performance benchmarks of existing data center environments. Run analysis of near-term and long-term compute and storage needs. Understand dependencies and interactions between core IT systems. Gather critical requirements, such as acceptable outage windows, size of the migration and encryption requirements. Don’t forget post-migration planning, factoring in what tools and services are needed for ongoing management and monitoring of the cloud environment. Komprise for AWS Data Management Use Cases: Cloud Data Migration Data Analytics Data Archiving and  Tiering, Data Replication Native Data Access Amazon EFS, FSX Amazon S3 and Glacier Classes AWS GovCloud (AMI) AWS Outposts Where Komprise Can Help: Komprise Intelligent Data Management can analyze unstructured data at more than 10 PB/day. Leveraging Komprise Deep Analytics, you can create detailed migration plans involving owners, groups, access time and other file metadata. Archiving and migration plans can be tested within the Komprise UI, showing up-to-date ROI calculations. Migrate data 27x faster using an iterative process and automated error handling. Automatic archiving for cool and cold data combined with AWS lifecycle management to AWS Glacier and Deep Glacier storage, based on last access time. Using Komprise for AWS migrations can help estimate the ROI of AWS in your environment, move data transparently to any storage class in AWS, and do ongoing lifecycle management of the moved data with simple automated policies. Read more about Komprise for AWS benefits and check out the white paper: Smartest Path for Public Sector File Data to the Cloud - Storage-Driven vs File-Driven, which is better and why. ### The Easy, Fast, No Lock-In Path to the Cloud We regularly hear from enterprise data storage teams about the need for an easier, faster path to the cloud for file data. A path that doesn’t lock you into one data storage or backup vendor’s technology. Recently, Komprise co-founder and CEO, Kumar Goswami, published this article on DevOps.com: Tiering Cold Data to the Cloud Without the Tears. He concludes: By running an analysis of data assets across your storage ecosystem and setting up policies for migration of cold data, you can ensure that data is always living in the best places from both a cost and business perspective. Here is what you need to know about the Path to the Cloud for file data.   Why is it so important to migrate file data to the cloud? File data can be petabytes of data and billions of files. Migrating this much unstructured data to the cloud takes time and can be disruptive. Planning and analytics are required. We've reviewed your options when it comes to moving file data to the cloud: Lift and Shift Cloud Data Tiering Not all file data migration options are the same Ok, so you’re planning a file data migration. Do you go with free tools, point data migration solutions, or Komprise Elastic Data Migration? Take a moment to better understand your cloud data migration choices. Or, maybe you're using cloud tiering as a starting point for what we call a Smart Data Migration - analytics first. You’ll want to read our latest white paper that is focused specifically on cloud tiering solutions: Storage-based, Cloud Storage Gateways, File-based – which is better and why? For an overview of moving file data to the cloud, check out the Path to the Cloud section of our website. Let us know if you have questions or would like to schedule an assessment to see how we can help you know first, move smart, and take control of unstructured data growth. ### Komprise for Nutanix File Data Migrations and Data Management Last week we announced support for Nutanix and were featured on their blog: Nutanix File Data Migrations and Data Management with Komprise. Here are a few of the highlights from the post, which was written by Paul Chen, Director of Product Management at Komprise. Paul also recorded this overview demonstration of Komprise for Nutanix: Key use cases with Komprise and Nutanix covered in the post: Analytics and Planning: In most enterprises, over 80% of data is cold and not accessed in over a year, yet it continues to consume expensive storage and backup resources. Komprise analyzes across your NAS and cloud data to identify the right data to move to Nutanix and the savings you can expect. File Data Migrations to Nutanix: Migrate file data up to 27 times faster with reliable MD5 checksums on every file. Manage hundreds of migrations with a single console. Transparent Data Archiving/Tiering from Nutanix: Cut ongoing storage and backup costs by finding cold, inactive data on file storage and transparently archiving and file tiering data to Nutanix object storage or the cloud. Archived files continue to be accessed exactly as before so users see no change before and after the file migration. Native Access to Tiered Data: Komprise moves data in its native format, so files and objects moved by Komprise can be directly accessed from the destination without needing Komprise or any third-party software. Benefits of Komprise Intelligent Data Management and Nutanix As your unstructured data continues to grow, managing data across storage and cloud environments is becoming a bigger challenge. Komprise and Nutanix simplify data management at scale by providing powerful data analytics across all your storage, by making it easy to migrate the right data to Nutanix, and by reducing ongoing costs of managing data on Nutanix. Next Steps To try Komprise in your environment and assess how much you can save with Nutanix and Komprise, email nutanix@komprise.com or visit our Komprise for Nutanix page.               Be sure to read the full post on the Nutanix blog: Nutanix File Data Migrations and Data Management with Komprise. ### Komprise Tech Tips: Copy, Replication and More When managing data in the modern, hybrid cloud enterprise, there are many different use cases at play simultaneously. Your organization is likely storing data in several places and your unstructured data is growing exponentially. To get the most cost benefit and performance out of your data you should be constantly evaluating where it lives. For instance: There may be times when you want to replicate data to a secondary environment, such as to your favorite cloud provider for research and analysis. You need reliable data replication to your disaster recovery sites. If transitioning to a different storage platform, you want to quickly and painlessly migrate the data without risk of data loss or negative user impact.    In our recent TechKrunch talk, Data Migration or Data Tiering/Archiving, Komprise product experts Randy Hopkins and Eric Platt shared a few best practices for cloud data management and unstructured data management. The chat included quick demos showing how easily Komprise can migrate object store data. You can watch the short on-demand webinar here.    Meantime, here are some highlights of the chat: Copy data: With Komprise, you can create a point-in time copy of data to another location; that can be a NAS system or any object system – whether that’s on-premise or in the cloud.  You also get the benefits of accessing the data in its native file format.  For instance, let’s say you are uploading research into AWS; you can run a query on it directly from the cloud.  Another common use case is to create a backup copy of your data into another environment, protecting against loss from cyber-attacks or other incidents. Migration flexibility: You can run migration jobs per your schedule, and that can be every day, every hour or even every minute depending upon your needs and policies in place. We have customers that use Komprise for asynchronous replication with no cutover, so that they are continuously replicating to enable an always updated redundant location. Of course, we also support migration cutover, in which the migration job has a specific endpoint. Note, Komprise also enables Komprise Dynamic Links, so you can migrate and archive data with no need for data recall. All of this is transparent to end users so that there is no impact to their data access or performance. Pure Storage replication: Komprise enables asynchronous replication of Pure Storage  We use Pure’s snapshot technology to replicate the data from point A to B.  You can see various metrics on the replication such as the speed of migration. You can also set up failover and failback to and from the replication site.  Read more about the Komprise partnership with Pure Storage. There’s more to the story on how Komprise is addressing important data management challenges.  A significant one is cost: handling growing storage needs with static IT budgets. This is not a wonderful optic for IT leaders right now. Here at Komprise we talk a lot about “right data, right place, right time.” You want to give users the best performance for your high-demand hot data sets. Yet justifying the top-performing flash storage platform might be difficult when you are talking about 1 or 2 PB of data or more.  However, let’s say you can offload 75% of your data store to a cheaper, cold storage service in the cloud—aka cloud tiering. Then, you only need to purchase the fancy Porsche storage for 25% of your data – far less than a petabyte. Suddenly, this option becomes affordable. Using Komprise, you can run the unstructured data analytics first and see how much hot data you really have before you make a purchase. ### File-Based Cloud Tiering with Komprise Intelligent Data Management At Komprise, we believe that data management functionality is a layer independent of storage. By considering data to be separate from the storage in which it resides, it is possible to manage data holistically across vendors, be they on-premises storage arrays or cloud providers; and across technologies, be they files or objects. This approach has allowed Komprise to create a data management solution that is vendor agnostic and integrates tightly with on-premises and cloud storage to create a hybrid data management platform that works across both. This post reviews the benefits of open and data agnostic file-based cloud tiering with Komprise. An Open Approach to Cloud Tiering This open approach to data management ensures Komprise offers cloud tiering with 75% lower cloud egress costs, 300% lower TCO, and provides you with full access to your data in the cloud without lock-in. Komprise uses open standards to read and write data to ensure data is always stored in a format native to the storage service to ensure there is no data lock-in. This allows customers to manage their data independent of the storage devices or data management services they use. It future proofs the customer, allowing them to select and later change or update their storage devices. As an example, one of our customers tiered cold data to tape. Later, they shifted to an on-premises object store and recently moved to the cloud. Through it all, the one constant was Komprise. In my last post, I reviewed what you need to know before jumping into the cloud tiering pool – and why the approach you use can result in 75% higher cloud retrieval costs. In this post, I’ll dive deeper into file-based vs. block-level tiering to the cloud and the benefits of file-based cloud tiering from Komprise. Unlike traditional cloud tiering solutions that are storage-centric and use block tiering, Komprise tiers an entire file. Komprise replaces the file on the source storage array with a symbolic link. Symbolic link is a standard file system construct and is supported by NFS and SMB protocols. When a file is tiered to the cloud it is written in the format recognized by the cloud. For example, if the file is tiered to AWS S3, it is stored as an object and can be read by a standard S3 browser without any third-party software and that includes Komprise. If the fie is tiered to AWS’s EFS, it will be stored as an NFS file. If files are tiered to a SMB cloud storage such as AWS FSX, they will be stored as SMB files. Unlike traditional cloud tiering solutions that are storage-centric and use block tiering, Komprise tiers an entire file. The Benefits of Komprise TMT for Cloud File Tiering and Smart Data Migration Komprise uses a patented Transparent Move Technology™ (TMT) to transparently tier cold data. Komprise is the only vendor that provides: transparent tiering from the source storage array, with native access to the cold data on the target, without getting in front of hot data on the source. When a user tries to access a tiered file, the symbolic link directs the file system request to Komprise. Komprise fetches the file from the cloud and responds to the file system request. The tiered file is streamed back and cached by Komprise to minimize latency and eliminate further egress and API costs if the file is re-accessed. Komprise provides a custom rehydration policy that the user can configure to meet their needs. Data need not be re-hydrated on the first access. Komprise also provides a bulk recall feature if needed. Avoiding High Rehydration Costs Data need not be re-hydrated on the first access. Komprise also provides a bulk recall feature if needed. Komprise works seamlessly with storage arrays’ block tiering solutions, virus scanners, and backup software. Typically, storage array’s block tiering is used to tier snapshots and certain log files that are almost never accessed and is used to provide storage efficiency. Most backup software has a configuration setting to prevent following symbolic links. When the backup software sees a symbolic link, it simply backs it up. The tiered file remains in the cloud. In order to restore a tiered file, its link can be restored from the backup and followed to access the file.   Komprise is designed to transparently tier data to the cloud while providing the most cost savings and enabling maximum flexibility without any vendor or data lock-in. Block versus File Tiering: Why Komprise Transparent File Tiering It is instructive to compare the features of the two approaches side by side to understand the implications and advantages of each approach. The table also highlights the sorts of questions you should be asking when evaluating a cloud tiering solution.  Here is a summary: Core Features of Tiering KOMPRISE TMT™ FILE TIERING Storage Array's Block Tiering Transparent, continuous tiering YES YES Flexible tiering policies YES Komprise provides a range of ages as well as exclusions based on size, file type, directory. In the next major release, Komprise will allow users to granularly specify what to tier based on custom queries. NO Generally, can only specify an age. But that too can be limited where cold data is specified as anything 6 months or older. Tiering across multi-vendor storage arrays YES Komprise is vendor agnostic. Komprise can be deployed across most common storage arrays allowing one, consistent, global way to tier data. Komprise provides a single pane of glass across multi-vendor storage systems. NO Each storage array only supports tiering from its storage devices, and you must manage each cluster independently. There is no single pane of glass to manage tiering across the clusters. Prevent rehydrating tiered data on first access YES Komprise allows you to configure just when an access data is rehydrated. NO Tiered data that is accessed is immediately rehydrated. Requires that extra storage be kept reserved for such rehydration there by reducing cost advantages. Avoid performance impact on hot data YES Komprise is involved only when cold data is accessed. NO Since the core tiering engine is used to tier data, high latency to the target can impact performance. Furthermore, the block tiering approach requires a constant traffic to the cloud to defragment blocks stored in objects. This constant traffic drives up cloud costs and impacts performance. For this reason, storage arrays clearly indicate that if more than 300TB is to be tiered, local object storage should be used. Fast access to tiered data YES Komprise streams the data and does not wait for the entire file to be read. Komprise caches the data locally to ensure future requests are fast. YES Blocks are read back instead of the entire file. The blocks are stored back on the array to ensure future requests are fast. However, this approach request that some amount of storage be left unused to house accessed data. This reduces cost savings. Bulk recall of tiered data YES Komprise provides a bulk recall feature. In many cases it may be necessary to bring back a large set of data. NO Data is brought back as it is accessed. Native cloud access tiered data (NO DATA LOCK-IN) YES Komprise writes tiered data in the format used by the target. For instance, if tiered to S3, it will write the data in S3 format. The data can be accessed from the source or, in this case, the cloud using a standard S3 browser. This enables processing of tiered data without burdening the source storage array. NO The data is in proprietary form and the entire file may not be on the target. The tiered data can only be read from the source. Showback or chargeback YES Today, in the UI Komprise provides percentage of a share’s total storage that is local, and which has been tiered. In an upcoming release a full report will be provided for easier chargeback. NO Decommissioning without rehydration (NO VENDOR LOCK-IN) YES With Komprise you can migrate from one vendor’s storage array to another without re-hydrating the huge quantity of tiered data. The tiered data will be transparently accessible from the new storage array. NO It is very difficult to switch vendors once you start tiering with a storage array’s tiering solution. At Komprise, we believe it is critical to know first before you make important decisions or investments, especially with today’s large data sets. An incorrect decision can cost millions of dollars and lost time. Want to learn more? Read the blog post: What you need to know before jumping into the cloud pool Download the white paper: Cloud Tiering: Storage-Based vs. Gateways vs. File-Based Review your cloud tiering choices Learn more about Komprise tiering and archiving I want to make sure it’s clear why when it comes to cloud tiering and cloud data management you don’t compromise – you Komprise! ### What You Need to Know Before Jumping into the Cloud Tiering Pool Cloud tiering is now a critical capability in today’s increasingly hybrid, multi-cloud world of enterprise storage. Cloud tiering and archiving can offer significant cost savings, a path to the cloud, and a zero-disruption solution that leverages existing investments. But, not all cloud tiering and cloud archiving solutions are the same – you may end up paying 75%+ more in cloud egress and 300% more in ongoing storage licensing costs by picking the wrong strategy. The cloud tiering approach you pick will not only have major implications on your short, medium, and long-term cost savings of migrating unstructured data to the cloud, it will also impact what overall benefits your organization is able to achieve from your cloud data migration strategy. In this series of posts, we will review: Why tier cold data to the cloud and what to consider when tiering cold data to the cloud The benefits of file-based tiering to the cloud with Komprise But first, I want to talk about what you need to know before jumping into the cloud tiering pools. This first post covers the differences between tiering to the cloud through your storage vendor vs a data management file tiering and archiving solution like Komprise. Storage array vendors, while providing insight into the array’s operations and performance, have historically done little to provide insight into the data stored on them. Addressing the need for analytics-driven data management across storage silos has been where Komprise comes in. However, to meet customer demand, storage array vendors have re-packaged their tiering solutions used inside the array to externally tier data to the cloud. While these so-called “Cloud Pool” tiering solutions, for example NetApp FabricPool for NetApp cloud tiering and Dell EMC Isilon CloudPools for Isilon cloud tiering, may reduce the storage cost of today’s super-fast, expensive flash-based storage by blending in the lower cost benefits of cloud storage, it’s important to understand when they are a good choice and when they are not as part of your overall intelligent data management strategy. Cloud Tiering: Blocks vs. Files File storage-based cloud tiering provides limited data analytics and limited policies by which you can select data to tier. However, they provide continuous, transparent tiering which enables IT to systemically roll out cold data tiering to the cloud without disrupting users. File storage arrays use an efficient block-based storage system to store files. Each file is represented by a set of equally sized blocks. As a file grows, more blocks are provided to store the file’s content. To reduce the cost of the storage arrays, vendors provide multiple tiers of storage. The highest tier using flash storage is the fastest and the most expensive. Then come tiers that store data on SAS drives and finally SATA drives. Some storage array vendors use pure flash tiers with varying performance and costs associated with them. These storage arrays use a tiering system whereby the file metadata and the frequently accessed blocks (from any file) are stored in the highest tier and less accessed blocks are downgraded to lower, less expensive (and higher capacity) tiers. This automated storage tiering approach allows the vendor to reduce costs by using smaller, faster tiers while still providing good performance. With the demand to leverage the cloud, these array vendors are now using their tiering system, designed to work efficiently with internal storage tiers, to tier data to the cloud. The tiering system tiers cold blocks rather than files to the cloud. Multiple blocks are stored within a single object stored in the cloud. Metadata resides on the storage vendor filesystem and all data access needs to occur through the storage filesystem. While storage tiering solutions are good for tiering snapshots to the cloud, they result in unnecessary costs and lock-in when tiering and archiving files. Read: Block-Level vs File-Level Data Tiering - What's the Difference? Why does it Matter? Array Block-Level Tiering is a Mismatch for the Cloud This approach of NetApp cloud tiering or Isilon cloud tiering blocks rather than entire files has the following ramifications: Limited policies result in more data access from the cloud. Policies to specify the blocks to be tiered are limited. For instance, one popular storage array vendor can only maintain hot blocks that are less than 183 days old. Older blocks must be tiered to the cloud. This results in much higher access rate to the cloud, resulting in higher cloud API and egress costs. Many customers prefer to tier data older than one or two years to the cloud but cannot do so with the limited policies provided by storage arrays. Policies to exclude certain files, types of files or even directories are generally not possible with the limited policies provided by block-based storage array tiering. Defragmentation of blocks leads to higher cloud costs. Accessing blocks in the cloud leads to defragmentation of the object in which these blocks are stored in the cloud. Once some percent, say 20%, of the blocks in an object have been read, the entire object is brought back into the array and coalesced with other defragmented objects and then written back to the cloud. While this reduces the storage used in the cloud, this continuous defragmentation process results in a continuous egress and API costs. Sequential reads lead to higher cloud costs and lower performance. Sequential reads caused by applications such as virus scanners or 3rd party backup can increase the cost of cloud storage. The sequential read operation from these applications can be detected by the storage array to prevent the re-hydration of the blocks, however, all reads are still handled by the cloud resulting in higher API and egress costs as well as lower performance across high latency channels. Data tiered to the cloud cannot be accessed from the cloud without licensing a storage filesystem. Since blocks from many files, as opposed to entire files, are tiered to the cloud, the data can only be accessed from the storage array. What is stored in the cloud has no meaning to any application other than the storage array. This data lock-in eliminates the ability to access and process the cold data independently from the storage array. Tiering blocks impacts performance of the storage array. Block tiering to the cloud can reduce the performance of the storage array. The mechanism to maintain block tiering to the cloud causes continuous, on-going traffic between the array and the cloud across a high latency channel that ultimately impacts the performance of the overall array. For these reasons, the storage array vendors strongly recommend limiting the tiering to only 200 or 300 terabytes to the cloud. Given the vast quantities of data most enterprises are dealing with today, block tiering is not suited for general data tiering to a public cloud across high latency channels. Block tiering is better suited for private clouds, which unfortunately lack the benefits that are attracting more and more enterprises to adopt public clouds. Storage array vendors strongly recommend limiting the tiering to only 200 or 300 terabytes to the cloud. Data access results in re-hydration. Block tiering re-hydrates any data accessed from the cloud. This requires that there be space to accommodate some percent of cold data. This in turn reduces the potential cost savings. Block tiering does not reduce backup costs. Third party backup applications read and store the hot and cold blocks of each file on the storage array. As a result, the backup window and the backup storage footprint are not reduced. Tiering cold blocks does not provide sufficient storage savings. Block tiering locks you into your storage vendor. Since the cold data is tiered to the cloud in a proprietary format, when it is time to decommission your storage array and replace it with a new one you must stay with the same vendor. If you elect to change vendors, you will have to re-hydrate all of the data back to the original storage array and then migrate that data to the new storage array and then tier that data using some other tiering solution. You will have to do this iteratively many, many times since the cold data will be several multiples of the capacity of the storage array. In short, you will be locked-in to this vendor. Proprietary Lock-In and Cloud File Storage Licensing Costs. You cannot directly use native cloud services to access your data in the cloud – it has to be through the proprietary storage file system itself. This creates unnecessary licensing costs that customers must pay forever to access their data and creates undesirable lock-in as you cannot directly use native tools without relying on the filesystem for access. Be aware of proprietary storage vendor lock-in and cloud file storage licensing costs when it comes to cloud tiering. Here is a summary of the differences between storage-based cloud tiering vs. the open and storage-agnostic file-level cloud tiering and archiving approach from Komprise: Storage Cloud Tiering (e.g. NetApp FabricPool, EMC CloudPools) File-Level Cloud Tiering and Archiving with Komprise Approach Block-level File-level Leverages Existing Infrastructure YES YES Users Access Moved Data without Disruption YES YES Eliminates Lock-in NO YES Works Across Object/Clouds NO YES Works Across File storage vendors NO YES Flexible Policies NO YES Native Access in the Cloud NO YES No 3rd party filesystem cloud licensing costs NO – Requires ongoing cloud license of filesystem to access data YES Ideal Use cases Tier snapshots Tier and archive files Read: Block-Level vs File-Level Data Tiering - What's the Difference? Why does it Matter? But will I lose storage efficiencies such as dedupe by not using the storage tiering solution? You may wonder if you are losing some of the storage efficiencies such as dedupe by not using the storage vendor tiering solution to go to the cloud. The overhead of keeping blocks in the cloud due to egress costs, rehydration costs and defragmentation costs significantly overshadows any potential dedupe savings. Also, when data is moved at the block level to the cloud, you are really not saving on any third-party backups and other applications because block tiering is a proprietary solution – read this white paper for more background on block vs file tiering. So if you consider all the additional backup licensing costs, cloud egress costs, cloud retrieval costs plus the fact that you are now locked-in and have to pay filesystem costs forever in the cloud to access your data, then the small savings you may get from dedupe are significantly overshadowed by overall costs and the loss of flexibility. When should I use block tiering provided by my storage vendor? Tiering provided by a storage vendor such as NetApp FabricPool and Dell EMC CloudPools are ideally suited for tiering snapshots, certain log files and other data from Flash storage – data that is proprietary and deleted in short order. Such temporal data is typically not backed up or virus scanned, they are only accessed in the event of an error or disaster and yet are generally large in size resulting in notable storage efficiency when tiered. Tiering this specific type of data reduces storage costs without incurring most of the shortcomings above. Block tiering is ideally suited for tiering such temporal data. Block tiering techniques such as NetApp FabricPool and Dell EMC CloudPools are not well suited for tiering general user data for the reasons mentioned above. Pools solutions create 75% higher cloud egress and retrieval costs on file data, and their ongoing cloud licensing costs add 300%+ ongoing cloud costs. Pools solutions create 75% higher cloud egress and retrieval costs on file data, and their ongoing cloud licensing costs add 300%+ ongoing cloud costs. In my next post I’ll dive deeper into file-based vs. block-level tiering to the cloud and the benefits of file-based cloud tiering from Komprise. Be sure to download the white paper: Cloud Tiering - Storage-Based vs. Storage Gateways vs. File-Based - Which is Better and Why? ### Eliminating the Roadblocks of Cloud Data Migrations for File and NAS Data The Cloud NAS race is on. Network Attached Storage (NAS) refers to enterprise data storage that can be accessed from different devices over a network. NAS environments have gained prominence for file-based workloads because they provide a hierarchical structure of directories and folders, making it easier to organize and find files. Many enterprise applications today are file-based and use files stored in a NAS as their data repositories. This data can be everything from user-generated data to home directories and file shares, but increasingly file data comes from applications such as genomics, PACS imaging, media audio and video files, self-driving car data, IoT sensors and edge devices. File data can be seismic data, electronic design data, and IoT data. As a result, file data volumes are enormous. But, Gartner forecasts worldwide IT spending to grow by only 6.2% in 2021. Nevertheless, as I noted in my last post, at least 70% of enterprise data is cold data, sitting on expensive storage, consuming the same backup resources as hot data. Now that CEOs are intimately involved in the cloud journey, core enterprise applications are moving to the cloud faster than ever – and these enterprise workloads are primarily file-based. Cloud data migration without rewriting the application means file-based workloads must be able to run in the cloud cost effectively and without user, application, and customer disruption. Cloud NAS to the Rescue? Cloud NAS refers to a cloud-based storage solution to store and manage files. Cloud NAS, or cloud file storage, is gaining prominence as many vendors have introduced cloud NAS offerings, including AWS, Azure, NetApp, and Qumulo. A few things to know: Cloud NAS storage is accessed via the Server Message Block (SMB) and Network File System (NFS) protocols. On-premises NAS environments are also accessed via SMB and NFS. Cloud NAS is often designed for high-performance file workloads. Its high-performance Flash tier can be very expensive. Many cloud NAS offerings offer less-expensive file tiers. Putting data in these lower tiers requires the right approach to hybrid and multi-cloud data management. When considering cloud NAS file tiering ensure you have visibility across storage silos and ensure data does not get locked into a proprietary solution that will disrupt your users. The need for cloud native data access and data mobility should not be underestimated. (Read the white paper Why Standards-Based File Tiering Matters.) Is a Cloud Storage Gateway good for File Data Migration to a Cloud NAS? Short answer? No. A cloud storage gateway is an on-premises appliance, typically hardware based, that provides a file gateway to data in the cloud. Since cloud storage gateways put data in the cloud in a proprietary format, they are not a good choice for file data migrations to a cloud NAS. Read more about the pros and cons of using cloud storage gateways for data migrations.   Compare Cloud Data Migration and Unstructured Data Management Options and Choices.     The Roadblocks of Cloud Data Migration for Files The goal of a cloud data migration is to move large production data sets quickly, with data integrity intact, without errors, and without disruption to users. But, the path to the cloud for file data migrations is paved with unstructured data migration challenges. Here are some of the file data migration challenges we often see: Sunk Costs: Free tools are labor intensive. Tools like robocopy and rsync are error-prone, they do not handle failures well, and they require a lot of human effort and babysitting. There are some point data migration solutions and cloud storage gateways, but these do not typically scale. Cloud gateways hold the data in the cloud, ransom to a proprietary format, and do not put you on the right path to cloud data management. Data Integrity: There are always source and storage incompatibilities and challenges keeping access controls and metadata intact. Time Consuming: Whether it's WAN latencies, a mix of small and large files, or the fact there are billions of files in a large data migration project, these can all result in a massive time commitment if not properly managed. Downtime Impact: This is always a big one – the impact on users and application access to data. The length of the cutover is also always a roadblock. Complex Cloud Storage Factors: There’s a lot to consider, including file vs. object data migration, performance vs. pricing, and the need to ensure you have the right option at the right time. Insufficient Planning: It’s shocking how often enterprise IT organizations are flying blind, trying to plan and manage file data migrations without data analytics and insight. This is why, at Komprise, we say Know First and Move Smart. Ad-Hoc Approach: No continuity, no learnings from each migration, no real program management, instead a one-and-done cloud data migration mindset. Sound familiar? Closer Look: Komprise Elastic Data Migration for Unstructured Data   Why Cloud Data Migrations Fail We put together a cloud unstructured data migration infographic to summarize the common cloud data migration roadblocks.   Avoiding Unstructured Data Migration Pitfalls With an analytics-driven approach to cloud data migration and unstructured data management, you can avoid the common data migration challenges and: Know before you migrate – analytics drive the most cost-effective plans Preserve data integrity – maintain metadata, run MD5 checksums Save time and costs – multi-level parallelism provides elastic scaling Be worry-free – built for petabyte-scale that ensures reliability Migrate NFS 27X faster and Migrate SMB data 25X faster – forget slow, free tools that need babysitting Learn more about Komprise Elastic Data Migration and read our white paper on accelerating NAS and cloud data migrations.   Cloud NAS Migration? At Komprise, we working closely with our partners to ensure our customers have the best cloud NAS data migration experience, while ensuring data is delivered in native format for cloud native data access, which means no lock-in and maximum enterprise data storage savings. Here some examples: Komprise for Amazon FSx for NetApp OnTap Komprise for Azure Files NFS ### 5 Ways to Get to the Cloud Faster and Smarter with Intelligent Data Management Who isn’t looking for faster cloud data migrations? What about smarter cloud data tiering? At Komprise our mission is smarter, faster unstructured data management – manage data across any file and object storage without proprietary interfaces - no stubs, no agents, use open standards, deliver native data access everywhere. We want our customers to be able to analyze, mobilize, and manage unstructured data at any scale across their storage and clouds – without lock-in. So, why do enterprise IT organizations, who are facing the challenges of massive data volume growth and shrinking budgets, think their only option is to buy more storage when they reach capacity? Consider this: 70% of data in most enterprise organizations is cold data and has not been accessed in months, yet this data sits on expensive storage and consumes the same backup resources as hot data. 60% of the storage budget is not really spent on storage. It’s spent on secondary copies of data for data protection – backups, backup software licenses, replication, and disaster recovery. (IDC) But, isn’t cloud storage the answer? Consider this: 50% of the 175 zettabytes of data worldwide in 2025 will be stored in public cloud environments. (IDC) 80% of businesses will overspend their cloud infrastructure budgets, according to due to a lack of cloud cost optimization. (Gartner) So, costs continue to rise, backup times lengthen, disaster recovery remains unreliable, and it has become increasingly difficult to get maximum value from Flash and cloud storage solutions. Meanwhile, you’re trying to get file data workloads to the cloud faster to save money and ensure your IT infrastructure and operations are agile and efficient. 5 Ways to Get Enterprise File Data Workloads to the Cloud Smarter and Faster Here are 5 ways to get to the cloud smarter and faster as you embark on your journey to the cloud for your file and object data: Establish a Cold Data Management Strategy. Since the bulk of data is cold, finding and tiering cold data can save millions, since it offloads data from expensive storage and backups. (Read the Pfizer case study) Tier without Tears. Data tiering has been a solution for years, but it was limited to a storage vendor’s solutions and was highly proprietary. Tiering needs to be frictionless with no disruption to users and applications across multi-vendor storage, without proprietary lock-in. Even after data is moved, it needs to be accessed by users and applications exactly the same way as before the move. And, migrating the data to another platform or use by 3rd party applications should not create unnecessary rehydration of data which erodes the savings. (Read Cloud Data Tiering Choices white paper) Don’t be a Block Head. The way tiering is done can significantly change how your actual savings affect your options to access cold data. Cold data can be tiered at the block level or at the file level, and there are many differences between the two. Storage vendors are now using block-level tiering to move data out of the file server and into an object or cloud tier. All file access must be done through the original file server. The moved blocks cannot be directly accessed from their new location, such as the cloud, because they are meaningless without all the other data blocks and the file context and attributes (the file’s metadata). Storage tiering, also known as pools solutions, use block-based tiering so be sure you understand the limitations and lock-in. Block-based cloud tiering also creates significant egress and data transfer costs due to unnecessary rehydration. (Read the Block versus File Tiering white paper) Choose Open Standards. File level tiering fully preserves file access at each tier by keeping the metadata and file attributes along with the file no matter where it lives, even on object storage and cloud. (Read Why Standards-Based File-Level Tiering Matters) Quantify the Data Storage Cost Savings. They say measure what matters, right? So establish the right metrics and share the results (see an example of an IT health check in this Komprise customer video). Some benefits of file-based cloud tiering include: Maximizes space savings by eliminating the need to rehydrate data for common operations such as data access, backups, and migration which significantly shrinks your storage footprint. Provides up to 3x greater savings because it not only reduces cold files on the primary tier, it also shrinks backup footprint without rehydration, and it shrinks DR footprint without rehydration. Should be storage agnostic, which puts you in control of your data without lock-in to either the storage vendor or the data management solution itself. (Read Quantifying the Business Value of Komprise.) The Benefits of Open, Transparent File Tiering for Cloud Data Management Here is a table from our paper, “Why Standards-Based File Tiering Matters” that will help you understand the impact of open and transparent file-level tiering for Intelligent Data Management: In my next post I’ll review some recommendations for optimizing cloud data costs and accelerating cloud data migrations. In the meantime, be sure to read our white paper: Cloud Tiering: Storage-Based vs. Gateways vs. File-Based - Which is Better and Why? and learn more about the easy, fast, no lock-in path to the cloud for file and object data. ---------- ### TechKrunch: Transparent Tiering for Microsoft Azure Files and Azure Blob In the Komprise Spring 2021 release we expanded support of transparent data tiering and archiving to Azure Blob Storage to include native format for the archive blobs. The advantages of Komprise Azure tiering and delivering data in native format include: Users and applications can access the archived data directly from Azure, without requiring Komprise or the original NAS filesystem to mediate access. Optimized Azure API costs when writing and retrieving files, since files are stored as single objects instead of as multiple objects for each file chunk. Higher performance data transfers in native format over other formats (e.g., chunked, chunked and compressed), due to multi-part uploads and other optimizations. Transparent Tiering for Azure Files and Azure Blob We recently focused on this new Azure tiering functionality in a TechKrunch session with Randy Hopkins and Glenn Speer: Transparent Tiering for Microsoft Azure Files and Azure Blob. As Randy noted, we often get asked about our Azure tiering capabilities and integration with Microsoft Azure (see table below): We also get unstructured data management questions such as: Can Komprise help us build our cloud data management strategy? Can Komprise migrate data from Windows or Unix on-prem environments to what Microsoft calls fully managed serverless file shares? Can Komprise tier cold data from Azure Files and Azure NetApp Files into Azure Blob storage? The TechKrunch session started with a quick primer, reviewing the differences between Azure Files and Azure Blob storage. The good news is that Komprise Azure tiering and cloud data migration can leverage both. Next, Glenn demonstrated how to migrate data to Azure Files from on-prem legacy NAS and how to tier data to Azure Blob transparently. File Data Migrations from any NFS, SMB to Azure Files He started by showing how you can easily tier and migrate data to Azure Files with Komprise. First, you would set up Azure Files as a source in Komprise: Click “Add File Server” to see the many different options available: You can opt to “Discover shares” to have Komprise automatically discover all shares on the file server, or “Specify share” to manually configure a specific share. Shares can be either SMB or NFS. If you need to migrate data to the cloud, navigate to the Migrate tab and set up a migration: You can opt to migrate the entire share or a directory within it.  Then you’ll select the migration destination – in this case Azure Files – and then select whether to migrate to the destination share, or to a directory within it. Next, you can name the migration and configure settings (such as preserving access time on the destination and migrating SMB ACLS to the destination), and then you’ll be ready to review and then start your migration. Next, you can name the migration and configure settings (such as preserving access time on the destination and migrating SMB ACLS to the destination), and then you’ll be ready to review and then start your migration. The Azure file migration will run and try to copy what it can. If it misses a file because it was open or perhaps due to a transient network error, the file will be copied in the next iteration. You can run as many iterations as needed to migrate all the data into Azure File Services. Finally, you perform the cutover and complete the migration. It’s fast and efficient, and Komprise makes it really easy to migrate data to Azure Files. (You can see our performance results and why we say we migrate 27X faster in this white paper: Accelerate NAS and Cloud Data Migrations.) Glenn then explained that the migration capability of Komprise can also be used to replicate data and keep a secondary copy of the data in the cloud. You can read and write to this data just as if the data were on prem. If you don’t perform the migration cutover, Komprise will continue to run to keep the data in sync, similar to an asynchronous replication. Tiering Data from On-Premises NAS to Azure Blob The second part of the TechKrunch session focused on tiering data from your on-prem NAS, identifying cold data and moving that data to Azure Blob, while still keeping data accessible and readable within the cloud provider storage environment. The first step in the Azure tiering demo was to set up Azure Blob as a target: You can determine which access tier you want to archive data to – Hot, Cool, or Archive tier – and you can choose the format of the data on the target. In chunked format, Komprise breaks the data up into smaller blocks and sends them to the cloud provider. When a user accesses the archived file, Komprise reads the chunks, rebuilds the file, and returns it to the user. In native format, you can read the data without using Komprise: you can go directly to the location of the data and read the data with native tools. And the best part is that you do not have to rehydrate the data to an on-premises NAS or use a gateway to access it! And finally, when it comes to encryption, you have choices, but we generally recommend you use what’s available from the cloud provider. Now we go to the Plan tab to determine how you want Komprise to tier and archive data. First, you select the source shares from which Komprise will archive data: Next, determine which files you want to move based on their last accessed time – in this case, files not accessed in the past 1 year. (This ensures that only cold data will be archived.) Then, set Azure Blob as your target. In this demo, Glenn showed that data had already started being moved. Then Glenn showed what this looks like from the cloud storage perspective. He logged into the Azure portal, navigated to the Storage Explorer, found his Blob Containers, and drilled down to see the data moved into Azure Blob by Komprise: Since Komprise tiered and archived the data in native format, you can read the data directly within Azure (with the proper credentials) – you can access it, open it, download it, etc. As Glenn noted, this is an Azure tiering / archiving use case, but you can also can also make full copies of data in the cloud, so you can run attached compute instances within Azure and run analytics against the data – there are many use cases for moving the data in native mode to the cloud. The possibilities to use your archived data are endless. Azure Tiering Demo Question: When you tiered the cold data to Azure and left that link behind, is there any special software needed by end users to read those links? No. We use industry-standard symbolic links. Nothing is needed on the end-user side to access the data transparently. (Learn more about the patented Komprise Transparent Move Technology.) Thanks Randy and Glenn! You can watch the full session here: You can also learn more about Komprise Azure Tiering and Cloud Data Migration and Management on our Microsoft partner page. ### Komprise Konnects: Cloud Alliances and Career Advice with Caitlyn Possehl In our Komprise Konnects spotlight series, we’ve been sharing interviews with our partners talking about the challenges of unstructured data management and how they work with Komprise. For this post, I spent some time interviewing one of our own partner gurus, Caitlyn Possehl, Global Strategic Alliance Leader at Komprise, and took the opportunity to ask about her career path and advice for other women pursing a career in the tech industry. Caitlyn is based in Denver, Colorado. She joined Komprise in 2020, leading our focus on partnerships with the top cloud providers: AWS, Microsoft Azure, Google Cloud, and Wasabi. Tell us about your role at Komprise? I am on the Global Strategic Alliances team and I have the absolute pleasure of managing Komprise’s relationships and strategy with each of the cloud powerhouses of today: AWS, Microsoft Azure, Google Cloud, and Wasabi. What brought you into the world of technology? I entered the tech world in an interesting way. And I think it’s important for people to be flexible in how they enter an industry. I had no STEM degree, no data analytics background, no coding languages. But, I did have languages of a different kind behind me. I spent most of my high school and undergraduate years studying English, Spanish, and Portuguese. My peers and parents and professors all wondered about these selections. But, what will you do with these skills? How do they translate into a job? I felt the strong pressure of the American definition of stability, security, and success in the job title you possess or strived for. At its core my studies were how to read, write, and speak compellingly. What job opportunity wouldn’t require that? And I happened to be able to do so in three languages. Those communication skills got me my first job in sales. And that happened to be for a cyber security company, covering Latin America. What has been your most exciting project to date? Startups have been the darlings of the 2000s and the dream job for many millennials. But, I was intimidated by them! I’d found a comfortable groove in the stability and bureaucracy of large enterprises. Komprise was a big leap of faith for me. Coming from the land of technology giants, it’s been refreshing and challenged me in new ways. I have loved standing up our cloud alliances and strategies. On the very forefront of how our clients are managing their most valuable twenty-first century resource, data, has been an exciting challenge. How do you combat biases in the tech industry? I have been lucky to be surrounded by wonderful examples of women in leadership. The biggest weapon against biases I’ve found is naming them when they occur. So much of the bias women face in the technology industry is unconscious, and most times unintentional. Calling them out with kind firmness has allowed me to show some of my colleagues’ and partners’ behavior back to them in what I hope is an illuminating mirror. I have empathy for the fact that structures are changing, cultures are being turned on their heads. That takes some time for adjustment. But, it’s happening. It’s relevant. And, it’s for the better of us all, our technologies, our clients, our cultures. So, let’s all be a part of the story of turning this corner to a more diverse, representative ecosystem. What advice would you give women interested in pursuing a career in tech? Anything you wish you had known? My advice for women is always to be brave. Take the interview. Get creative. Pitch yourself. Believe in your strengths and communicate them clearly, without asking for forgiveness. So many of my female network shy away from publishing their accomplishments, talent, and worth. We have to be our own best advocates. That’s also what I’d tell my younger self. Be your own best advocate. Know how you are best managed, know the conditions under which you excel, know the structure in which you deliver the highest value. And, communicate those clearly to your management, leadership, colleagues, and partners. People don’t know what they don’t know. Don’t shy away from sharing how best to leverage yourself as a resource. You deserve to give your best! Help your organization, clients, and leadership to understand how to help you do so. What’s the future of tech look like as it relates to your expertise? The fourth industrial revolution we’re all experiencing is driving everything to technology. Our homes, cars, and companies are getting ‘smarter’. But, there aren’t any silver bullets on building perfect environments. It’s going to take the cooperation and melding of minds across organizations changing the face of technology. We are always going to be better together. In technology and outside of it. The partnerships we’re developing, managing, and inspiring will be the foundation of solutions we’ll see changing our world. Who is the person you most admire and why? The wave of female entrepreneurs, executives, and politicians I witnessed growing into an adult have begun to normalize the reality of women sitting in these important seats. Sara Blakely, Whitney Wolfe, Alexa von Tobel, Emily Weiss, these women are examples of ingenuity, creativity, and strength in a professional world that isn’t always friendly to those outside the mold. My own boss, Krishna Subramanian here at Komprise, is a female founder and c-level executive who I learn from each day. I’m excited for the future of our organizations and society as a whole as our leadership becomes more equitable in its reflection of the diversity in our communities. ### Intelligent Data Management at Cloud Field Day #CFD10 Last week, the Komprise team had the opportunity to present at Cloud Field Day 10, an event that focuses on the impact of the cloud on enterprise IT. There were three sections to our team's one-hour session: Intelligent Data Management in the cloud, Komprise architecture overview/demonstration for hybrid cloud, and Komprise architecture overview/demonstration for multi-cloud. First up was our CEO and Co-founder, Kumar Goswami. He reviewed our 2020 results and how Komprise Intelligent Data Management allows enterprise IT organizations to analyze, move, and manage file and object data at any scale while reducing enterprise storage, backup, and cloud costs. In this presentation, summarized three key cloud use cases for Komprise: Data Migration: Shift entire file data sets to the cloud Hybrid Data Management: Right data in the cloud, seamless access from existing on-premises NAS, zero disruption Cloud Data Management: Cut 70%+ ongoing cloud storage costs and create virtual data lakes In the next presentation, Mike Peercy, CTO and co-founder, spent a few minutes reviewing the Komprise architecture and Mohit Dhawan, SVP Engineering and Cloud Operations, demonstrated Komprise data migration and hybrid data management use cases. They highlighted some of the key differentiators of the Komprise analytics-first approach, including: No Storage Agents or Stubs No Interference to Hot Data Path No Scaling Limits No Changes to User or App Access No Silos In the final presentation, Mike reviewed the Komprise architecture for multi-cloud use cases and elastic provisioning of Komprise Operations Services and Mohit demonstrated Komprise cloud data migration and cloud data management use cases. Thanks to the team at Gestalt IT for hosting a very well run virtual event and to all of the delegates for their questions and feedback. You can check out some of the social buzz from the session on Twitter: ⚡️ “Komprise @ Cloud Field Day 10 #CFD10”https://t.co/ZomQACQ6nF — Komprise (@Komprise) March 12, 2021 ### Komprise Spring 2021: Expanded Support of Cloud NAS Options As always, our product team has been busy building out the Komprise platform for Intelligent Data Management. In the Fall of 2020, the team delivered a number of innovations and enhancements to simplify cloud data migrations and cloud data management for our customers and partners. Our Spring 2021 release builds on this theme with support for cloud NAS solutions that enables customers to use Komprise to manage cloud files as easily as their on-prem NAS files. We have also made it easy for customers who start with replication for Pure FlashArray Files to upgrade to our full data management solution. In this post, I’ll focus on updates that our customers and our alliances partners will love: Enabling Komprise intelligent data management of cloud NAS storage Supporting native format for Transparent Archiving to Azure Blob Storage Expanded support for NetApp data stores, including dual shares Seamless upgrade path from our replication solution for Pure Storage FlashArray Files to the full Komprise unstructured data management solution Enabling Komprise Intelligent Data Management of Cloud NAS Storage The Spring 2021 release allows us to work even more closely with our cloud provider partners, by integrating support for their NAS offerings into the Komprise platform. Komprise now supports these cloud NAS options: Support for Amazon EFS: Easily analyze and migrate data to/from Amazon EFS (NFS protocol)​ and migrate on-prem datacenters to EFS. Support for Amazon FSx for Windows File Server: Easily analyze and migrate data to/from Amazon FSx for Windows File Server (SMB protocol) and migrate on-prem data centers to FSx. Support for Azure Files: Easily analyze and migrate data from/to Azure Files (both NFS and SMB protocols) and migrate on-prem data centers to Azure Files. Komprise also supports third-party NAS solutions in the cloud, such as NetApp Cloud Volumes ONTAP and Azure NetApp Files. Customers can use our analysis to first identify what data needs to be migrated to cloud NAS options, then migrate the data reliably using Komprise Elastic Data Migration, and finally manage all their data in the cloud. Since Komprise keeps data in the cloud in native format, you can use your files and objects directly in the cloud using native cloud tools like an S3-browser and leverage Komprise Deep Analytics to search across all your data and build a Global File Index, which is virtual metadata lake. Learn more about our AWS NAS partnership. Learn more about Komprise for Microsoft Azure. Supporting Native Format for Tiering/Archiving to Azure Blob Storage Komprise now expands support of Transparent Archive from their on-prem NAS storage to Azure Blob Storage to include native format for the archive blobs. The advantages of native format include: No vendor lock-in: Users and applications can access the archived data directly from Azure, without requiring Komprise to mediate access. Cost optimization: Optimized Azure API costs when writing and retrieving files, since files are stored as single objects instead of as multiple objects for each file chunk. Better performance: Higher performance data transfers in native format over other formats (e.g., chunked, chunked and compressed), due to multi-part uploads and other optimizations. Expanded Support for NetApp Customers already use Komprise with their NetApp ONTAP environments to analyze, migrate, transparently archive, backup and replicate data.  We have now expanded our support of NetApp to NetApp mixed-mode (or, DUAL) shares – shares accessible over both NFS and SMB.  Komprise has supported transparent tiering/archiving for DUAL shares on EMC Isilon for quite some time. With the Spring 2021 release, we now support transparent tiering/archiving for NetApp DUAL shares. Some of the ways Komprise works with NetApp include: Migrating off 3rd Party NAS to NetApp AFF, FAS (NFS to NFS, SMB to SMB) Migrating data to NetApp in the cloud from any NAS – including NetApp Cloud Volumes ONTAP (CVO) on AWS, Azure and Google, and Azure NetApp Files (ANF) Transparently tiering/ archiving from any NAS to NetApp StorageGRID or a lower tier NetApp NAS such as NetApp E-Series Use as a data lake for AI/ML with search and tagging with native access to moved data Learn more about Komprise for NetApp. Seamless Upgrade for Customers Using Komprise Asynchronous Replication for Pure FlashArray Files With the Spring 2021 release, we’ve made it easy for Pure FlashArray Files Asynchronous Replication customers to upgrade to the full Komprise Intelligent Data Management platform. They can start with replication and expand to the full Komprise data management solution at any point with a single change to the license. The recently announced data replication capabilities for Pure FlashArray Files: Enable protection of Pure FlashArray Files Services arrays with periodic copies of source shares​ Ensure a point-in-time copy of the source​ Ensure a safe, consistent copy is always available on the destination​ Enable failover and failback for recovery operations​ Enjoy all the recent NFS and SMB performance improvements made for data migrations​ Learn more about Komprise for Pure Storage. To learn more about Komprise Intelligent Data Management Spring 2021, contact your account or customer success manager. ### Simplified Data Archiving with Komprise Komprise is a data management company that can address many of an organization’s issues related to rising storage costs and backups. The Komprise Intelligent Data Management platform makes recommendations around which data to archive and can simplify the archival process. ### Reduce storage management costs: Komprise and dynaMigs start partnership The companies dynaMigs and Komprise recently signed a partnership agreement for the DACH region. dynaMigs, a service provider for data migration, data management and process automation for medium-sized and large companies, becomes a reseller for the multi-cloud data management-as-a-service platform from Komprise. ### Data Migration with Komprise Data migrations are an inevitable part of IT. Systems come and go. Vendors come and go. But the data remains. Data persists whether or not it’s still in use. Depending on the type of data, there’s a really good chance the data is “cold” data – data that hasn’t been accessed in quite a while. Depending on your industry, you might even have regulations that prevent you from deleting data before a set number of years, if ever. ### Komprise partners with Pure Storage Komprise, a leader in data management, extends its partnership with Pure Storage. The FlashArray product line powered by Purity 6.1 operating environment with integration of Compuverde software for file services, now relies on replication capability from Komprise Elastic Data Migration (EDM). EDM is known for its migration, copy engine and tiering functions across NAS and S3 storages wherever they reside on-premises or in-the-cloud. ### AWS and Komprise: Intelligent Data Management for Healthcare and Life Sciences Komprise recently hosted a webinar featuring Anthony Fiore, Senior Partner Solutions Architect at Amazon Web Services (AWS), and Krishna Subramanian, co-founder and COO of Komprise. Krishna kicked things off talking about how we’re in data explosion mode and how enterprise IT organizations are looking to the cloud to address their growing volumes of unstructured data. In the first phase of cloud adoption, organizations created new applications in the cloud and new models of building and managing these cloud native apps emerged like DevOps. We’re now in the second phase of cloud adoption, where enterprise IT teams are looking to migrate and manage core file-based applications and workloads to the cloud. Anthony commented on how difficult it is to refactor all of these applications and meet the corporate mandate to get to the cloud and save on cost. He noted, It’s great that there’s a tool like Komprise out there that lets customers have their cake and eat it too when it comes to integrating a cloud strategy without having to refactor when it comes to NAS file storage of their applications. Krishna then reviewed the challenges we see in data-heavy Life Sciences, Genomics, and Healthcare organizations when it comes to moving core-file workloads to AWS. Common characteristics include: Generating TBs of data every day – ever increasing footprint Moving to a “cloud-first” strategy Datacenter space is limited Not all data is S3 – a lot of file data Data is valuable – but not all data is hot, a lot of data is inactive/cold Need to move to the cloud without disrupting researchers and applications Need to use data in AWS without requiring 3rd party tools Anthony shared a story about a healthcare customer he worked with who had cold data today, but knew that it might not be cold next week. They needed a solution that could grow with them as there are peaks and valleys of demand as the temperature of the data changes. Komprise is not just talking the point in time of hot or cold data and tiering it, but over time how Komprise can be used to manage on-going data mobility requirements. He also stressed, Another nice thing about Komprise that customers love is that it moves files or archives files in native format. The goal is to unlock the value of the data, not just a one-way cloud data migration. Before the demo, we had a good discussion about what has historically prevented enterprise IT organizations from taking a more data-centric approach to storage and why this is changing. We flashed the Gartner quote: The IT industry doesn’t have a storage problem; it has a data management problem. Why? Lack of Awareness: We’re used to running out of capacity and buying more storage. We’re used to backing up everything. We’re now in a position where this won’t work anymore. It’s too expensive. The backups are taking too long. Data volumes are growing fast and storage budgets are staying flat. Data Value wasn’t the Priority: There is now a recognition in the importance of analytics and ultimately the value of data. To know first and plan and say, “I don’t to back up everything on our most expensive backup solution because 80% of the data hasn’t changed.” Storage-Centric Mindset: Changing the mindset to recognizing that buying more capacity is not the answer. There is a better solution out there. Anthony summarized it this way: Having been on the customer side for most of my career, I’m well aware of the refresh cycles, and the process of budgeting and buying more storage…you’ve got to think about the overhead, you have factor in growth for projects that haven’t even been considered yet. It may have worked 10 years ago, it doesn’t work now as companies of all sizes are trying to optimize their spend and keep a tight lid on expenses and even capital purchases. They want to make sure they’re doing things in the most efficient way as possible. The first step is understanding what you’re buying. If you keep throwing money at storage on-prem and none of it has been accessed in over six months, but you still need to keep it around for compliance purposes. Komprise is a really attractive option for being able to give customers insight into the data and to being able to make decisions with tool based on that. So, here’s the goal: smarter, faster, cost-effective data management. Krishna shared some customer stories, highlighting that the benefit isn’t just cost savings and efficiency. The larger benefit is unlocking data value so you’re not just dumping data into the cloud. You’re using the cloud to get more value from your data. Anthony notes: It’s rare to see ROI this impactful, this quickly. It makes me smile! Then Krishna jumped into a Komprise demonstration: Know First. Move Smart. Take Control. There were some good questions and comments throughout the demonstration. When it comes to transparent data migration or archiving, Anthony noted: Komprise sits outside the hot data path. It’s not adding latency. It’s not impactful to the actual NAS system or the applications. It’s almost like a background process so it’s not going to cause your NAS systems to overheat, so to speak, from a performance perspective. Krishna highlighted that files that Komprise moves to the cloud are available natively. Komprise doesn’t lock the data in. The customer always is always in control of their data, not the storage or backup vendors. Anthony is sometimes asked, “who owns the S3 bucket?” The answer is the customer. The customer maintains full control at all times. Krishna pointed out that all of the data Komprise moves to the cloud retains all of the metadata so you can actually build virtual data lakes across all of your storage (cloud and on-prem). She showed an example of searching for all data tagged as “project X” and you quickly see the actual file data sitting in different shares – a single view of data across cloud and on-prem NAS – then use this data for other strategic initiatives. Here’s an example of Komprise Deep Analytics from the demo: In the final segment of the webinar, Anthony reviewed how AWS accelerates time to value with Healthcare and Life Sciences customers. He reviewed AWS security and compliance for healthcare, talked about AWS Global Infrastructure and expanding number of availability zones, and reviewed why S3 is the best place to store your data. Krishna wrapped up with a summary of why Komprise and an overview of the Komprise and AWS partnership: We squeezed in a few minutes of Q&A at the end and each of the presenters shared their final thoughts on what Anthony refers to as “the art of the possible.” He also advises: Don’t just use Komprise as another data migration or archival tool. You’re missing the other half of the story. It’s the analytics. Watch the full on-demand webinar on intelligent data management in health and life sciences by Komprise. ### Pure Storage and Komprise Extend Data Management Partnership to Enable File Replication Pure partners with Komprise to provide Asynchronous Replication to joint customers Komprise, a leader in analytics-driven data management-as-a-service, and Pure Storage, the IT pioneer that delivers storage as-a-service in a multi-cloud world, announced that Pure will partner with Komprise to provide Komprise Asynchronous Replication to deliver reliable data replication for Pure FlashArray™ file customers. As an existing partner, the expanded agreement adds data replication capabilities to the company’s existing reseller offerings. ### Pure Storage enters partnership with Komprise Pure Storage has announced it has entered into a strategic partnership with Komprise, a specialist in analytics-driven data management-as-a-service. Pure Storage, which delivers storage as-a-service in a multi-cloud world, says the partnership with Komprise will provide Komprise Asynchronous Replication to deliver reliable data replication for Pure FlashArray file customers. ### BRONZE: Komprise Intelligent Data Management for Multicloud Enterprise data storage 2020 Products of the Year finalists Komprise has been awarded Bronze in the Storage magazine and SearchStorage 2020 Products of the Year - Storage System and Application Software category awards. Learn more ### Storage Tiering, Data Archiving, and Transparent Archiving – What’s the Difference? Not too long ago Komprise published a white paper that summarizes the differences between Tiering, Data Archiving and Transparent Data Archiving. The paper also outlines the differences between data migration and data archiving, which are terms often used interchangeably, but are quite different. With cloud data migration a top priority in the era of digital transformation, it’s never been more important to move “cold data” to a cheaper capacity storage to reduce costs. This can be done via data tiering—a process of taking cold files and tiering them to a cheaper location. But, data tiering solutions often move data off the original location so users have to change behavior and look for data in the new place. Such disruption to your end users and applications does not have to be inevitable. Transparent tiering solutions enable cold data to be tiered to cloud storage without any change to user and application access, which is key to gaining user adoption. When your users are worried about not being able to access tiered data as before, they’re less likely to allow data archiving, which reduces data storage savings. Transparent data tiering is key to maximizing adoption and data storage cost savings I think it’s important to emphasize this point: the ability for both your users and your enterprise applications to still access files exactly where they were before being archived—without having to rehydrate the file—is possible. That is the power of what we call Transparent Move Technology™ (TMT). With Komprise, files get archived and they still appear in the original location, so users and applications continue to access the files exactly as before. Additionally, file-level tiering ensures that the entire file is archived as an object, so it can be accessed natively in the cloud by any standard S3 tools, without having to go back to the original file system or to the data management software itself. Komprise TMT enables fully transparent archiving using file-level tiering, so users see no disruption and data is accessed without lock-in. So how do you ask the right data tiering questions to make an educated choice, avoid surprises, and save your organization data storage, backup and cloud costs? Data tiering, takes many forms, many of which can cause different types of disruption. Some solutions claim the ability to transparently tier, but are unable to reduce the backup footprint and make it nearly impossible to switch primary vendors, thus eroding savings and imposing vendor lock-in. Our data archiving white paper includes a table that categorizes tiering into three categories: Traditional tiering, which creates significant disruption to user access, requires manual approvals and archives only in batches, eliminating a substantial amount of cold data to be archived and cost savings to be realized. Proprietary transparent tiering via storage tiering, which is the method behind Hierarchical Storage Management, is cumbersome and includes brittle and unreliable stubs and agents. Storage vendors provide archiving with tiering software, which eliminates stubs but can impact performance and imposes vendor lock-in. It also drastically reduces the cost savings afforded by transparent archiving because it fails to reduce the backup and DR footprint. Standards-based transparent tiering is the only true transparent archiving method that eliminates all disruption and delivers maximum archiving savings. Komprise Intelligent Data Management uses a patented Transparent Move Technology that offers standards-based transparent data tiering and provides maximum data storage savings. It tiers data without disruption to users or getting in front of hot data—all without using stubs or agents or creating vendor lock-in. Learn more about transparent tiering. Data Tiering and Archiving Choices Key Archiving Feature Traditional Data Tiering Proprietary Transparent Tiering via Storage Tiering Standards-based Transparent Tiering Maximizes storage & backup savings No Manual process results in archiving. No Only 20% of savings since backup and DR footprint and license costs aren’t reduced. YES Provides 100% savings since primary storage footprint is reduced as is backup and DR. Non disruptive to users and apps No Users must search for data in two places. No It’s transparent to users but not applications, such as backup and virus scanners. YES Users and apps are not disrupted. Backup footprint is reduced; virus scanners are not impacted. Vendor agnostic Yes Data is lifted and shifted to any secondary storage with no connection to the primary storage. As a result, migrating to another primary storage vendor has no implications. No Migrating to primary storage of another vendor is not simple. All tiered data must be rehydrated and then migrated to the new primary storage. YES You can readily migrate from one primary vendor to another without rehydrating all the archived data. Outside hot data path   Yes The file is fully moved.   No Tiering cold blocks to slow secondary storage (e.g. cloud storage) can adversely impact performance of these arrays. YES Sits behind hot data and metadata paths. Never in the hot data path.   Native access on secondary storage Yes So long as the file was stored in a format native to the secondary storage. No The blocks and files moved are in proprietary format and can only be accessed from the primary storage device. YES The full file is written in a format native to the secondary storage device. Example solutions Project-based archiving, ad-hoc archiving or backup-based archiving. Ideal for tiering snapshots that are not usually backed up and tiering warm data. Not designed for wholesale data archiving. Komprise Intelligent Data Management Provides both project-based and policy-based data archiving. We often hear, “why didn’t I know about Komprise sooner?” I hope this storage tiering, data archiving, and transparent archiving overview, table, and white paper are useful resources that help you identify the right solution for your use case. Be sure to check out our Data Management Glossary of Terms to learn more about Cloud Tiering and the advantages of moving infrequently used cold data to a cheaper cloud storage tier. Also, be sure to read the guide: Cloud Storage Tiering - Storage-Based vs. Storage Gateways vs. File Based and take the time to review Why Cloud Storage Gateways are not a good solution for File Data Migrations. ### News Bits: Trilio, Canonical, Microchip, Lightbits Labs, Alluxio, StorCentric, Komprise, & AWS In this week’s News Bits we look at a number of small announcements, small in terms of the content, not the impact they have. Trilio technology to be leveraged in Veritas For OpenStack. Canonical unveils Ubuntu Core V20. Microchip Switchtec PFX PCIe 5.0 family unveiled. Lightbits Labs announces new patents and revenue growth. Alluxio achieves 3.5x Year-Over-Year revenue growth. StorCentric sees a strong 2020. Komprise expands in Europe through Tech Data. Amazon EBS store local snapshots on AWS Outposts. ### Are Cloud Storage Gateways a Good Choice for Cloud Data Migration? Many enterprises are looking for easy ways to migrate large amounts of unstructured file and object data to the cloud, but understanding the available options and their trade-offs can be confusing. In this post, we will cover what Cloud Storage Gateways are, the use cases they address, and the pros and cons of using cloud storage gateways for cloud data migrations. What is a Cloud Storage Gateway? A Cloud Storage Gateway is infrastructure that is designed to front the cloud. As the name suggests, they are an on-premises “gateway” to data in the cloud and they fulfill this function by moving all data to the cloud and then caching a subset of the cloud data locally. How a Cloud Storage Gateway works: Local Infrastructure: Cloud Storage Gateways are typically hardware-based since they have to serve hot data from the cache. Many vendors also offer virtual appliance options for smaller deployments. 100% Data in the Cloud + Cache Overhead: Cloud Storage Gateways typically put all the data in the cloud and then cache some data locally. So, if you are using a Cloud Storage Gateway for 100TB, then all 100TB of data is in the cloud and a subset of it (maybe 20TB or 30TB) is also cached locally. This means you may need 130TB of infrastructure to house 100TB of data. Depending on the size of the local cache, this may be larger. New Storage Silo: A Cloud Storage Gateway is a new storage infrastructure that caches some data locally and keeps all of the data in the cloud. It replaces your existing Network Attached Storage (NAS), it does not work with it. Use Cases for Cloud Storage Gateways Cloud Storage Gateways are used when you need low latency access to data that is in the cloud and you do not want to use your existing NAS to deliver that low-latency access. Some common Cloud Storage Gateway use cases are: Backups to the Cloud: Use a Cloud Storage Gateway to backup data to the cloud and then locally restore backups. File Server for Cloud Data at Branches: If you need a local cache at a branch site for data in the cloud and you do not want to use a NAS, a Cloud Storage Gateway could be a local file server. Why Cloud Storage Gateways are Not a Good Choice for Cloud Data Migrations Data migrations to the cloud require fast, reliable data movement to the cloud. They do not require a local “gateway” to the moved data. Migration software is all that is required. Cloud data migrations are not a core use case for Cloud Storage Gateways. In fact, using a Cloud Storage Gateway to handle data migrations results in a mismatch and unnecessary overhead in costs and time. Local caching of moved data by Cloud Storage Gateways creates unnecessary overhead that is not acceptable for the cloud data migration use case. Cloud data migration requires fast data movement and a way to elastically spin up a lot of capacity initially to move the bulk of the data and then spin down this capacity to do incremental updates. A Cloud Storage Gateway is not designed for such elastic scaling and will either result in slow initial transfer or over-provisioning of resources. Also, insufficient planning is a primary reason why cloud data migrations fail. To plan data migrations, you need analytics of on-premises NAS and object data to understand what data you have and how it’s being used, so you can prioritize the migrations. Cloud Storage Gateways typically do not provide such analytics. Komprise Elastic Data Migration is designed to deliver fast, efficient, reliable cloud data migrations for both file and object data without any unnecessary “gateway” overhead or expensive infrastructure. Komprise scales to migrate petabytes of data and spin down once the data migration is complete. Komprise Elastic Data Migration optimizes migrations to the cloud and is shown to perform 27 times faster – download the white paper to learn more. For customers who do not want to migrate entirely off their NAS, Komprise offers a transparent way to archive cold data to the cloud while maintaining all data access from the original NAS – without creating another gateway silo. Read the TMT white paper to learn more. Here is a table that summarizes the common cloud data migration requirements and the differences between Komprise Elastic Data Migration and Cloud Storage Gateways: Core Feature for Cloud Data Migrations Cloud Storage Gateways Komprise Elastic Data Migration Migration Planning No. Lacks any data migration planning since this is not a core use case. Yes. Provides analytics of any NFS, SMB, and object stores to plan your data migration strategy. Fast Data Migrations No. Fixed infrastructure for caching means you cannot elastically add and remove capacity as needed for fast migrations. Yes. Elastically increase VMs to migrate data 27 times faster and spin down once the initial data migration is completed. Manage Migration Iterations No. Since Cloud Storage Gateways are not designed for data migration, they do not have any capabilities to manage data migration iterations. Yes. Intuitive dashboards and API to manage data migration iterations and monitor progress. Minimize Migration Infrastructure No. Cloud Storage Gateways require hardware appliances or dedicated virtual appliances since they act as primary storage. There is no easy way to increase and shrink capacity dynamically to support data migrations. Yes. Elastic architecture designed to dynamically grow and shrink virtual capacity as needed to handle data migration loads. Outside the Data Path No. Cloud Storage Gateways front all the data they move and this can result in a high amount of IO to them. Yes. Komprise migrates data outside the data path — you continue using the original NAS until you are ready to cutover. MD5 Checksum Reporting No. Since data migration is not a core use case, no reporting on data migrations are available. Yes. Komprise reports on data migrations, and reports results of MD5 checksums on every file.  This is useful for organizations that require an audit log for compliance reasons. Minimize Cutovers No. Since Cloud Storage Gateways are not designed for data migration, they do not provide mechanisms to cutover to the cloud or minimize disruption unless you use the Cloud Storage Gateway as the new NAS. Yes. In summary, cloud data migrations require an elastic data management solution that can provide data migration planning, fast data movement, and efficient cutovers with minimal downtime. Cloud Storage Gateways are not designed to enable cloud data migrations and are not tailored to handle this use case. Using Cloud Storage Gateways for data migrations results in unnecessary headaches, costs, time, and complexity. Be sure to also read the white paper: Cloud File Tiering: Storage-Based vs. Gateways vs. File Based ### Komprise Achieves Record Growth in 2020 as Multi-Cloud Data Management Becomes a Business Priority Unstructured data growth and cloud data migrations drive demand for analytics-first approach to enterprise storage, backup and cloud investments San Jose, CA – January 13, 2021 – Komprise, the leader in analytics-driven data management as a service, today announced it achieved record growth in 2020, despite the challenges of the pandemic. Key growth drivers included: unstructured data under management grew by over 300%, a record number of major enterprises signed up as new customers, and the company expanded key strategic partnerships in 2020. Ideally suited for IT organizations dealing with petabyte-scale volumes of unstructured data across on-premises and cloud infrastructures, Komprise puts customers - not storage, backup, or cloud vendors - in control of their data so they can easily analyze, move, manage and harness data anywhere. “2020 was a difficult year for everyone, which is why we are extremely grateful to our customers, partners and employees for helping us achieve another year of strong growth,” said Kumar Goswami, co-founder and CEO of Komprise. “As our customers transition from cost savings and efficiency to driving more value from their data, Komprise is ideally suited to be the independent platform of choice to easily analyze, move and manage file and object data at any scale.” Komprise 2020 highlights include: Intelligent Data Management Platform Innovation Introduced Elastic Data Migration, expanded cloud data management, and added ransomware protection, helping customers go from insights to outcomes without being tied to their storage, backup, or cloud provider. Added two new patents for Transparent Move Technology, which reliably moves and tiers cold data, transforming files to objects and back, to and from on-prem NAS, object stores, or the cloud—all without end-users noticing any difference. Driving Enterprise Customer and Channel Success Komprise accelerated its pace of new customer acquisition in 2020 as more enterprises were looking to cut costs and accelerate cloud transformations. Existing customers also expanded their Komprise deployments, leading to strong revenue momentum and over 300% growth in data under management in 2020. Enterprise adoption was strongest in data heavy industries like Genomics, Life Sciences, Healthcare, Higher Education and Government. Over 50 new channel partners were on-boarded and hundreds of partner employees went through technical training and certification through an expanded Komprise Konnect partner program. New Leadership to accelerate growth: Mike Munoz joined as CRO, Darren Cunningham joined as VP Marketing, and Clare Loveridge joined as VP of EMEA Sales. Expanded Alliances and Industry Recognition Certified Elastic File Migration on Pure FlashArray and FlashBlade, achieved co-sell ready status with Microsoft Azure, qualified as AWS Outposts Service Ready, and announced availability on the AWS GovCloud. New partnership established with TechData in Europe to allowing customers to procure a Komprise Intelligent Data Management through their distribution channels. Achieved the highest rating in the Gartner Peer Insights ‘Voice of the Customer’ for File Analysis Software and was included in the Gartner Market Guides for File Analysis Software and Hybrid Cloud Storage. Named an Outperformer by GigaOm in their Radar for Unstructured Data Management. To see Komprise in action and start saving on your storage, backup and cloud spend, schedule a demo. About Komprise Komprise is the industry’s only multi-cloud data management-as-a-service that frees you to easily analyze, mobilize, and access the right file and object data across clouds without shackling your data to any vendor. With Komprise Intelligent Data Management, you are able to know first, move smart, and take control of massive unstructured data growth while cutting 70% of enterprise storage, backup, and cloud costs. www.komprise.com Media Contact: Tara Lefave Stred komprisepr@watersagency.com ### The Cloud Gold Rush: The Implications of a 'Cloud-First' Strategy This post first was written for the Forbes Technology Council by Krishna Subramanian here. The cloud is increasingly seen as a key IT strategy for businesses, as over 90% of enterprises are shifting to a multi-cloud strategy, according to a study by The 451 Group. Not only are businesses adopting the cloud more, they are also increasing their use of cloud resources by shifting from a “cloud transformation” strategy to a “cloud-first” strategy. As the President & COO of a data management company that focuses on cloud storage, I see this shift creating a gold rush to support core enterprise workloads in the cloud. Cloud Transformation vs. Cloud-First What is the difference between cloud transformation and cloud-first? For the past few years, enterprises have been on a cloud transformation journey — a measured approach to grow cloud adoption for the appropriate use cases. Often, this meant that cloud-native applications — new applications written to take advantage of the cloud — were the ones that moved first. Two good examples of cloud-native applications that have seen tremendous growth are Software-as-a-Service (SaaS) and Developers and Operations (DevOps) teams using the cloud for testing and development. In fact, Forrester Research proclaimed 2017 as “the year of enterprise DevOps” as the DevOps market hit escape velocity. Businesses are now more familiar with cloud benefits from these early forays and are moving from a cautious cloud transformation approach to a more bullish cloud-first approach. The pandemic has also accelerated this shift, as data centers have been harder to access and the workforce has gone remote. A cloud-first strategy is fundamentally different from a gradual cloud transformation approach because it means enterprises will look to add any new infrastructure in the cloud first — even for existing enterprise workloads — before they add any datacenter capacity. This means traditional enterprise workloads that have been running data centers will now have to run in the cloud. This is a pretty significant shift. The Rise Of Cloud Network Attached Storage The data formats used by enterprise applications are different from what the cloud traditionally supported, and so enterprise applications had to be written as “cloud-native” to run properly in the cloud. This is a big reason why traditional enterprise applications have been slow to move to the cloud until now. A recent survey found that 77% of enterprises are looking for more data integration and workload mobility across clouds. Most enterprise applications deal with unstructured data. Today, according to CIO, over 90% of the world’s data is unstructured — genomics, self-driving cars, audio, video, seismic data and documents are some examples of unstructured data. Most unstructured data used by traditional enterprise applications is stored as files in storage known as Network Attached Storage (NAS). NAS environments are designed to deliver high performance, but they are also expensive. The cloud supported a cheaper way of storing unstructured data as objects, not files. This is usually significantly less than the cost of file storage, but traditional file-based enterprise applications cannot run on it. But that is now changing. All the major cloud providers and enterprise file storage vendors are currently rushing to deliver cloud NAS or file storage in the cloud. Similar to NAS in a data center, this cloud NAS is designed to support the performance needs of enterprise workloads, but it is also significantly more expensive. As an example, Amazon Web Services, the largest public cloud provider, has EFS file storage ten times more expensive than their S3 Object storage. With such a huge difference in costs, the new file storage in the cloud should only be used when needed. Read: Eliminating the Roadblocks of Cloud Data Migrations for File and Object Data. Better Cloud Management Emerges Managing cloud costs is already a challenge for enterprises. In fact, Gartner Research estimates that in 2020, 80% of enterprises will overshoot their cloud costs. A new category of cloud data management software is helping enterprises address this issue by using analytics to understand data usage patterns and automatically move data in the cloud across file and object storage to optimize both costs and performance. Data management can also help businesses unlock the value of their data by creating virtual data lakes with just the right data needed for new applications like artificial intelligence and big data. Good data management is about having the right data in the right place at the right time, especially because the majority of data becomes inactive or cold within a year of creation yet consumes expensive storage and backup resources. Data management ensures that actively used hot data gets the best performance, albeit at a higher cost, and that inactive cold data is still accessible, but costs much less to store. This involves using data analytics to understand how data is being used by the business and then moving data based on analytics. This is done through techniques such as data migration, data archiving and tiering to dynamically move less-used data to cheaper storage classes, and global search and tagging to still find data easily no matter where it lives. When looking for a data management solution for your company, there are three key things to consider. First, how do you expect your data strategy to evolve over the next five to ten years? If you want the flexibility to change your cloud or storage providers, then consider a data management solution that works across a variety of storage and cloud platforms, but if you don’t need this option, you can consider a vendor-proprietary solution. Second, think about your budgets and the skillsets of the people who will be managing data — many customers are choosing data-management-as-a-service solutions because they are easy to deploy and use. Third, consider your data control posture: Data is the new oil and it’s one of your strongest corporate assets. With that in mind, how important do you think it is to keep control of your data? If it’s truly important, you want to understand if the data management vendor creates any lock-in. With public cloud spending soaring to $331B by 2022 according to Gartner, the gold rush is on for enterprise workloads in the cloud. ### Multi-Cloud Data Management Predictions for 2021 Komprise President and COO, Krishna Subramanian, has shared her 2021 data management predictions in a number of publications to close out the year. ITProPortal In ITProPortal she had this to say: “In 2021, cloud storage costs begin to overtake compute costs. For the past three years, cloud cost optimization has been a key priority for businesses. In fact, Gartner predicted that 80 percent of businesses will outspend their cloud budgets in 2020. A bulk of these costs so far has been in the compute, since cloud object storage is relatively cost effective. But this is changing, since cloud file storage is typically ten times more expensive than S3, and file data is way more voluminous than block data – all of which underscores the importance of using cloud file storage just when you need it. In 2021, enterprise IT organizations will begin adopting cloud data management solutions to understand how cloud data is growing and manage its lifecycle efficiently across the various cloud file and object storage options.” Disaster Recovery Journal In a predictions post on the DR Journal she noted: “In 2021, cloud replication will replace data center replication. The cloud is no longer just an inexpensive storage option for enterprise data. Enterprise IT organizations are realizing the importance of resiliency – to spin up access in the cloud if their datacenters become unavailable or to protect from cyberattacks with an air-gapped copy in the cloud In 2021, many companies will stop mirroring their data across datacenters and instead put a second copy of their data in the cloud. This cloud replication ensures that data is recoverable if a site goes down, a company gets hit with ransomware, or if users need to spin up some capacity in the cloud and want to access some of the data there.” VMblog And, on VMblog, she shared 3 predictions in her post: Enterprise File Workloads Shift to Cloud Data Management in 2021 They are: Cloud data migrations become intelligent Cloud storage management transitions to enterprise IT from DevOps Data management-as-a-service gains prominence Be sure to read the full post here. Forbes Technology Council Krishna is now a member of the Forbes Technology Council. Her first post is The Cloud Gold Rush: The Implications of a “Cloud First” Strategy where she summarizes 3 things to consider when looking for an unstructured data management solution: Understand how you expect your data strategy to evolve over the next five to ten years. If you want the flexibility to change your cloud or storage providers, then consider a data management solution that works across a variety of storage and cloud platforms but if you don’t need this option, you can consider a vendor-proprietary solution. Think about your budgets and the skillsets of the people who will be managing data — many customers are choosing data-management-as-a-service solutions because they are easy to deploy and use. Consider your data control posture: Data is the new oil and it’s one of your strongest corporate assets. With that in mind, how important do you think it is to keep control of your data? If it’s truly important, you want to understand if the data management vendor creates any lock-in. TruthinIT And, one more... Krishna was recently interviewed on the Small World Big Data that was featured on TruthinIT: How to Accelerate Cloud Data Migrations and Cut Cloud Costs: A Discussion with Komprise. Here’s the video: Great insights, Krishna! 2020, That's a Wrap As we close out the rest of this crazy year, I would like to also take this opportunity to wish you and yours all the best this holiday season and a healthy 2021 from all of the Komprise team. Stay safe, stay healthy, and be wise... Komprise. ### News Bits: Cisco, Atempo, Arcserve, XenData, AWS, Firebolt, Komprise, HPE, Trilio, & More This week’s News Bits we look at a number of small announcements, small in terms of the content, not the impact they have. Cisco Acquires Slido and IMImobile. Atempo updates Tina and Miria. Arcserve X Series released. XenData launches two LTO appliances. AWS announced new storage instances at re:Invent. Firebolt launches with $37 million in funding. Komprise now available on AWS GovCloud. HPE GreenLake offers HPC as a Service. Trilio raises $15 million. BackupAssist Classic v11 released. Portainer Business released. ### Komprise now enables data management for public sector in AWS Marketplace for AWS GovCloud Komprise expands AWS collaboration with a focus on accelerating cloud migrations while reducing costs. Komprise, a leader in analytics-driven data management as a service and an Amazon Web Services (AWS) Advanced Tier Partner, today announced its availability on AWS GovCloud (US), along with additional security capabilities such as ransomware protection on AWS through object locking. Now public sector entities at the federal, state and local levels (as well as other organizations that run sensitive workloads in the cloud) can easily take advantage of Komprise Intelligent Data Management to accelerate cloud data migrations, cut storage costs, and meet FedRAMP, ITAR, or CJIS compliance requirements. ### Komprise Now Offers Usage-Based Analytics and Cloud Migration for Microsoft Azure Komprise recently released an update to the cloud capabilities of our Intelligent Data Management platform. Just as Komprise provides accurate data access-based analytics for Amazon S3, this new release extends this to users of Microsoft Azure. Komprise Cloud Data Management customers can now get strategic data analytics of Microsoft Azure Blob access tiers based on data access/read activity and enterprise caliber data migration to and from Azure for those requiring multi-cloud data management. This enables customers to get valuable insights into how objects are being read and accessed and what it is costing them both on AWS and on Microsoft Azure from a single solution. This level of visibility and insight is not readily available from any of the public clouds or other unstructured data management and analytics solutions because by default the clouds track data modification times, not data access times. Usage-based analytics on data in Microsoft Azure Komprise provides enterprises with a single pane of glass to manage all of their data across clouds and on-premises using analytics-driven data management. Users can gain insights into actual usage and costs, plan capacity with growth projections, build data lakes across clouds, search enterprise unstructured data based on custom queries and organize data using tags, all with zero management overhead. With the latest Cloud Data Management release, Komprise provides complete data usage analytics for Microsoft Azure Blob access tiers with a patent-pending mechanism that includes data access/read patterns. Data is read far more than it is written. Yet native and 3rd party cloud management tools only collect analytics based on data creation/modification times. Neglecting the most common activity, accessing/reading data, results in incomplete analytics that may lead to poor management decisions. For instance, data written a year ago but accessed daily will appear to be cold. Moving such “cold data” to a less performant but cheaper tier will result in higher access costs and disrupt users and applications due to a much-degraded performance. Komprise provides analytics based on complete and actual data usage, enabling users to confidently develop cost savings strategies. This extends the capability Komprise already has in analyzing any file storage on Microsoft Azure based on access patterns to also now include Microsoft Azure Blob access tiers. Migration of data to and from Microsoft Azure According to an IBM Institute for Business Value study, 94% of enterprises are using a multi-cloud strategy to reduce risk, improve resiliency and reduce storage costs by choosing the most optimal cloud provider for their data sets. Komprise makes this possible with super-fast, reliable, and scalable Elastic Data Migration for data in the cloud and on-premises. Komprise now offers cloud data migration for Microsoft Azure, enabling users to move data into Azure from other cloud and on-premises object stores as well as to move data within Azure and from Azure to other cloud and on-premises object stores. Users simply specify their source and destination to start a data migration job and Komprise will move objects iteratively, automating the entire migration while reporting on the progress and retrying all transient issues along the way. Users can continue to make changes on the live source throughout the migration and Komprise will copy all changes incrementally. With this update, customers can now migrate both file data to Microsoft Azure Files and object data to Microsoft Azure Blob using Komprise. Komprise Cloud Data Management provides a single pane of glass to accurately analyze and then migrate, replicate and mange both file and object data on Azure. Storage Type Data Analytics based on actual data usage Data Migration Microsoft Azure Blob YES YES Microsoft Azure Files YES YES 3rd Party File Storage on Azure (e.g. Azure NetApp Files) YES YES To try Komprise cloud data management for your organization, schedule a demonstration. Be sure to also read our Q3 platform updates and what’s new in our November Komprise Intelligent Data Management release. ### Komprise Expands Intelligent Data Management Partner Program to Accelerate Cloud Data Management Komprise, a leader in analytics-driven data management as a service (DMaaS), announced new technical training certifications for cloud data management, financial incentives, and partner portal tools as part of the expanded Komprise Konnect Partner Program. Since the launch of the program, over 50 new partners have been on-boarded and hundreds of partner employees have gone through technical training and certification. ### AWS Partners Are Enthusiastic About Validating Their Solutions with AWS Outposts A year ago at AWS re:Invent 2019, we launched AWS Outposts, a fully managed service that offers the same Amazon Web Services (AWS) infrastructure, APIs, tools, and services to virtually any data center, co-location space, or on-premises facility for a truly consistent hybrid experience. ### Smarter, Faster Unstructured Data Management at re:Invent AWS re:Invent is always one of the most important cloud conferences of the year. Even though we missed the face-to-face networking and fun in Vegas due to Covid, we expect to see just as much innovation from AWS. At Komprise, our partnership with AWS has never been stronger. An AWS Advanced Tier Partner, last month we announced that Komprise Intelligent Data Management is AWS Outposts Ready, simplifying migration, file management, and object workloads on AWS Outposts. Customers can benefit from a comprehensive data management solution for any application in their own environment, on AWS Outposts, or in AWS Regions, for a truly consistent hybrid experience. Joshua Burgin, General Manager for AWS Outposts Be sure to check out the Solution Brief: Komprise Analytics-Driven Data Management for AWS Outposts. Earlier in the year, Mohit Dhawan, VP Engineering at Komprise, and Anthony Fiore, Storage Partner Solutions Architect at AWS delivered a great webinar: Efficiently Managing Your Hybrid Cloud Data Strategy. The theme of the webinar will undoubtably be a theme running throughout re:Invent 2020 – data growth is exploding, budgets are not. How do you optimize costs while driving strategic business initiatives in the cloud? And when it comes to managing your massive growth of unstructured data: How do you keep up with NAS expansion and shrinking backup windows? How do you understand all of your data across silos, vendors, clouds and make better storage and backup decisions? How do you avoid disrupting end-users or applications? How do you keep safe data copies for better cyber security? How do you manage data across clouds? In the webinar, Mohit walked through specific AWS use cases for Komprise, from storage analytics and optimization to native data access. Anthony noted: When I talk to AWS customers about the Komprise solution, they’re always very excited. They always ask, ‘why didn’t I know about this before?' We want to fix that. We’ve even put together a 14 day trial for AWS customers. Here’s a summary of Komprise for AWS, as reviewed in the partner webinar: Optimize Storage: Analyze your data first and then use your analysis to transparently archive data within Amazon S3, S3 IA, Glacier and Deep Glacier in order to reduce your storage costs. Komprise delivers a transparent archiving solution that goes beyond intelligent tiering and moves data seamlessly across both file and object storage classes so you get the right data in the right place at the right time. Migrate Faster: Whether it’s to file services like EFS or Amazon FSx for Windows, you need to be able to migrate your data faster across storage and cloud environments. That’s Komprise Elastic Data Migration. The Komprise architecture is designed for modern scale of data with minimum overhead. Be Resilient: You may need to have a copy of your data in S3 Standard, S3 IA or S3 Glacier for long-term storage. It’s important that you plan for resiliency. Prepare for Big Data and AI: Establish a virtual data lake across your storage environments, not just a single vendor, so you can easily find what you’re looking for when you need it. Access Data Natively: If you have data sitting in Amazon S3 or sitting on NAS or in your data center, it’s important that you can always access data natively using the tools users are familiar with. Migrate, Replicate data from other clouds to AWS: Komprise now offers Object data migration and replication as well as analytics, so you can analyze any Object or S3 formatted data stores, and migrate, copy and replicate data to AWS. One of the key points we like to ensure all of our customers understand is: Don’t treat all data the same. This table illustrates the cost savings after archiving cold data to AWS with Komprise: That’s a 75% savings on cold data! A recent GigaOm report on Komprise observed that, “The solution can seamlessly optimize the storage infrastructure while improving the ability to reuse data and increase its value over time.” Two recent customer examples really highlight the power and potential of Komprise for AWS customers: A Genetic Testing Company cut DR costs by 50% and a Media and Entertainment Company cut NAS costs by 70%. You can find more details on Komprise for AWS on our website. In the meantime, we’ll continue moving NAS data to AWS and saving our customers impactful money on their storage budgets. Gain visibility into your entire NAS or cloud-based unstructured data Move your data to AWS faster and smarter Drive efficiencies in your storage strategy that save you at least 40-50% on your annual storage budget. Look for some exciting news from us during the conference. In the meantime, be sure to let us know if you’d like to schedule a demonstration with our team. ### Komprise and NetApp: More Cloud. Less Cost. At NetApp Insight Komprise focused on the value Intelligent Data Management delivers to NetApp customers. Watch the full Komprise for NetApp webinar to learn more. We kicked off the session off with a summary of how Komprise Intelligent Data Management helps NetApp customers: Analyze across multi-vendor storage to identify data storage cost savings with NetApp Migrate off 3rd Party NAS to NetApp AFF, FAS (NFS/SMB -> NFS / SMB) Migrate data to NetApp in the cloud from any NAS – Komprise now supports NetApp Cloud Volumes ONTAP (CVO) on AWS, Azure and Google, and NetApp Cloud Volumes Service (CVS) as well as Azure NetApp Files (ANF) Transparently Tier and Archive from any NAS to NetApp StorageGRID Use NetApp StorageGRID as a data lake for AI/ML with search and tagging with native access to moved data You can read an overview on our NetApp partner page. Also check out the blog post: What You Need to Know Before Jumping into the Cloud Tiering Pool and our Glossary of Terms overview of Cloud Tiering. Dominque Garcia, who manages our NetApp alliance, reviewed some great customer stories, including: Pacific Biosciences: “Projecting a three-year 28X data increase, we could knock out walls and build more data centers, but for what? We needed a platform on which to grow our business.” Read the full case study on How Komprise helped Pacific Biosciences reduce storage costs by 60%. A major Ivy League school: “Now we can move data to the best location to save departments money.” Read the full case study on How Yale Reduced Storage Costs with Komprise. An engineering & semiconductor multinational corporation: “With tens of petabytes of data, we are constantly moving data from one platform to another. The insight that Komprise analytics provide and the speed of its data migration enable us to routinely move petabytes in weeks.” Each customer story had a slightly different use cases. In the Genomics example, Komprise helped consolidate Isilon and their NetApp namespace so they could easily migrate from Isilon to NetApp. In the higher education example, Komprise helped establish a “showback” model for central IT and transparent archiving helped grow the NetApp StorageGRID footprint intelligently. And, in the engineering and semiconductor example, Komprise migrated data to NetApp All Flash FAS (AFF). In the discussion about NetApp Insight 2020, we talked about NetApp’s big push to the cloud and announcements they made around NetApp Cloud Volumes OnTAP (CVO), Cloud Volume Services (CVS), as well as Azure NetApp Files (ANF). We also covered our recent announcement, which addresses the growing need to address file data management in the cloud with new capabilities for Microsoft Azure Files and ANF. These updates are designed to allow you to migrate to NetApp CVO and ANF 27 times faster. Komprise Data Management for NetApp At this point in the webinar, our technical guru, Steve Moore, set the stage for a Komprise demonstration. He outlined the primary questions we get from enterprise storage and backup customers: “Can you help us save money?” “Can you help us with our cloud strategy?” “What data should be move to the Cloud?” “How can we accelerate data mobility between on-prem and cloud?” “Can you help us manage the sprawl and growing costs of both?” Steve first reviewed the importance of having the right analytics and visibility, so you can quickly get to know your on-prem data: Same goes for your new multi-cloud reality – you need to know first in order to make informed storage, archive, and backup decisions. He then explained the Komprise scale-out architecture and how we work with enterprise customers to not just become data driven, but well data managed, regardless of the use case. Watch the Komprise for Netapp webinar on our BrightTalk channel. Eric Platt also recently recorded an 8-minute overview and demonstration of Komprise Elastic Data Migration to NetApp. Checkout the video below: ### What is S3 Intelligent Tiering and How Does it Work? For over a decade, Amazon Simple Storage Service (S3) has offered different levels of data storage classes to efficiently assist users in need of cloud-based storage infrastructure. Several additional storage classes have been added to increase the variety of use cases that S3 can support. Today, there are seven major storage classes available in AWS S3, which are optimized based on the frequency of access, importance of data, and archiving needs for the storage solution. Amazon made recent changes to their popular Intelligent Tiering class so it's worth revisiting AWS tiering. Classes of AWS S3 Storage These are the main storage classes available through S3: Standard (S3) – Used for frequently accessed data (hot data) Amazon S3 Intelligent Tiering – Used for data with unknown or changing access patterns or uncertain need of access. Standard-Infrequent Access (S3 Standard-IA) – Used for infrequently accessed, long-lived data that needs to be retained but is not being actively used. One Zone Infrequent Access (S3 One Zone-IA) – Used for infrequently accessed data that’s long-lived but not critical enough to be covered by storage redundancies across multiple locations. Glacier – Used to archive infrequently accessed, long-lived data (cold data) Glacier has a latency of a few hours to retrieve data. Glacier has a latency of a few hours to retrieve data. Glacier Deep Archive – Used for data that is hardly ever or never accessed and for digital preservation purposes for regulatory compliance. Outposts – Used for data on-premises that has local data residency requirements or requires being close to on-premises applications for high performance reasons. Simple lifecycle policies based on objects’ dates of creation can be implemented to move objects automatically to cheaper S3 storage classes to optimize costs. These policies can be used with the above storage classes. However, when the pattern of data access is less predictable or data is widely accessed by many applications, a more intelligent tiering model is often more cost-efficient. How AWS S3 Intelligent Tiering Works For a monitoring fee, Amazon’s S3 Intelligent Tiering automatically moves objects between tiers within the service. When objects have not been accessed for a certain period of time, they are moved into the infrequent access tier. If they are accessed at a later point in time, they are then automatically moved back into the frequent access tier. Users can further choose to automatically send data to archive tiers that offer asynchronous access. This type of unstructured data management strategy can help organizations save on storage costs mainly in environments where the frequency of data access is uncertain. But it may not always be the best choice of storage class if there is high confidence in access frequency eg via analytics. Also, S3 Intelligent Tiering is a storage class and you cannot have different treatment for different tiers in the storage class. S3 Intelligent Tiering acts as a black-box – you move objects into it and cannot transparently access different tiers or set different versioning policies for the different tiers. You have to manipulate the entire S3 Intelligent Tier as a single bucket. For example, if you want to transition an object that has versioning enabled, then you have to transition all the versions. Also, when objects move to the archive tiers, the latency of access is much higher than the access tiers. Not all applications may be able to deal with the high latency. When configured to automatically send data to archive storage classes, S3 Intelligent Tiering may require changes to existing workflows if access to archived data is required since it does not automatically restore data tiered to archives. Additionally, once sufficient time has passed such that the probability of access on archived data is low, data needs be transitioned to Glacier and Glacier Deep Archive storage classes using lifecycle policies. This will avoid paying recurring S3 Intelligent Tiering monitoring costs. In contrast, you can intelligently tier data based on accurate access patterns for your custom data sets across all S3 storage classes including Glacier and Glacier Deep Archive with Komprise Intelligent Data Management for AWS tiering. You can also set different versioning and other policies for each tier or storage class with Komprise. AWS S3 Intelligent Tiering Pricing The cost of Intelligent Tiering is based on how much of each type of storage is being used, how many requests are being made, and how many objects are being monitored. Amazon charges $0.0025 per 1,000 objects monitored. There is no retrieval charge incurred when objects are moved from tier to tier. Updates to S3 Intelligent Tiering In September 2021, AWS made two changes: Objects 128K or smaller no longer count toward the monitoring fee. These smaller objects are not eligible to be tiered and are always charged at the frequent access rate. Additionally, S3 intelligent tiering will no longer accrue pro-rated charges for objects deleted, transitioned, or overwritten within 30 days. Advantages of AWS S3 Intelligent Tiering Objects can be assigned a tier upon upload; No retrieval fees; No tiering fees; Objects are moved automatically to cheaper, appropriate tiers based on monitored access patterns; No operational overhead; No impact on performance; Designed for 99.999999999% durability and 99.9% availability over annual average. Disadvantages of AWS S3 Intelligent Tiering If access patterns are predictable, then lifecycle rules may be more cost-effective than Intelligent Tiering; It is not straightforward to identify objects that have been in the archive tiers for a long time so that these can be transitioned to Glacier and Glacier Deep Archive storage classes to avoid the S3 Intelligent Tiering monitoring fees; It is limited only to the S3, infrequent and archive tiers whereas some users may need to move data across EFS, FSX, S3 and Glacier storage classes for maximum efficiency; Policies to tier to archive tiers cannot be greater than two years; Objects smaller than 128KB are never moved from the frequent access tier; You cannot configure different policies for different groups or custom data sets, as it is an automated management solution that applies to entire buckets, prefixes or tagged data sets. Data tiering configurations need to be managed and configured for each bucket level instead of an account or global level for multiple buckets; You cannot set different versioning and backup policies for different tiers of S3 Intelligent Tiering; the policy applies to the entire bucket. Alternatives to AWS S3 Intelligent Tiering As an AWS Advanced Tier partner, Komprise offers intelligent data management tools that can provide significant savings on AWS storage costs with strategies built from analytics-driven input. While AWS S3 Intelligent Tiering is optimized for unknowns, Komprise analyzes your data so you can make the optimal placement. Additionally, Komprise will retrieve objects from archive classes such as Glacier without the need for administrators to manually issue restore commands. See how much you could save with the right data management platform providing in-depth insight into AWS storage efficiency. Get in touch with an expert at Komprise today for more information. Learn more about Komprise for AWS tiering, AWS data migrations, AWS unstructured data management, and our AWS partnership here. Download the white paper: Smart File Data Migration for AWS. ### Komprise 2021 Predictions: Enterprise File Workloads Shift to Cloud Data Management in 2021 The year 2020 has been a learning experience for all of us. The pandemic underscored the importance of being agile, responsive, and resilient. As the year comes to a close, 2021 is going to be a year of transition to a new normal. Businesses are adapting to the new realities of economic uncertainty, remote work, limited access to datacenters, and shifting priorities. Last year, we predicted a move from datacenters to the edge and cloud - not only did this happen, but this shift is accelerating with the pandemic. As we look toward 2021, we predict that data management will continue to be a priority in the cloud as organizations look for ways to accelerate cloud data migrations and manage data more efficiently. ### Managing File and Object Data on AWS Outposts with Komprise We are excited to announce that Komprise Intelligent Data Management is now qualified as Amazon Web Services (AWS) Outposts Ready, as part of the AWS Services Ready program. Customers can now simplify migrating and managing file and object workloads on AWS Outposts. AWS provides flexibility, agility and a wide array of services that can be spun up on demand. But not all workloads can be deployed in AWS – sometimes data privacy regulations, latency requirements, or edge processing restrictions may prevent certain data and workloads from running in the public cloud. AWS is addressing these requirements with AWS Outposts, which brings the features of AWS to an on-premises data center. Now Komprise enables customers to bring the right file and object data into AWS Outposts and manage the lifecycle of that data to optimize customer spend on AWS Outposts with the same ease-of-use and power that Komprise is well-known for with AWS customers. Customers are looking for better ways to store and manage their data across the enterprise as part of a comprehensive digitization initiative. With Komprise Intelligent Data Management for AWS Outposts, customers can benefit from a comprehensive data management solution for any application in their own environment, on AWS Outposts, or in AWS Regions, for a truly consistent hybrid experience. Joshua Burgin, General Manager, AWS Outposts, Amazon Web Services, Inc. Store and Process Data Locally on AWS Outposts AWS Outposts enables customers to run Amazon EC2, Amazon EBS, Amazon S3 as well as AWS database and analytics services on premises. Data is stored in AWS Outposts, which addresses data residency and compliance regulations and Outposts provides a consistent experience in places where an AWS region may not yet exist. Consistent Hybrid Experience A key benefit of AWS Outposts is that it offers the same APIs and is managed the same way as the cloud. AWS Outposts is a fully managed service so you don’t have the headaches of running a private cloud in your datacenter. Komprise Delivers Data Management of File and Object data with AWS Outposts Once you know what workloads you want to run on AWS Outposts, you need a simple way to get the right data into AWS Outposts. Several third-party Network Attached Storage (NAS) vendors provide SMB and NFS file services on AWS Outposts. AWS also offers Amazon S3 in AWS Outposts for object workloads. Komprise makes it easy to identify any NFS, SMB data that you want to run on AWS Outposts from across your datacenter, migrate the data to a NAS running on AWS Outposts, and then manage data lifecycle transparently end to end, from the NAS to Amazon S3 in region. This way, hot data that requires performance stays on AWS Outposts, while customers cut inactive cold data costs by leveraging AWS  S3 storage tiers in the cloud. Files archived by Komprise are still accessed exactly as before, and customers can also access the files directly on the AWS cloud. Plan Your AWS Outposts Data Strategy Finding the right data to migrate is always tricky. Komprise makes it easy by analyzing data across all your file and object stores – whether you have data accessible via NFS, SMB or S3. Komprise shows you how that data is growing, who is using it, how it's being used, and where it sits. This way, you can pick the right data workloads to migrate into AWS Outposts. Migrate Data Quickly and Reliably into AWS Outposts Once you know what to migrate, Komprise makes it easy to migrate file data into AWS Outposts from any NAS. Simply setup the migrations in Komprise, and it runs the migration with full data integrity, full preservation of access controls, and MD5 checksums on every file. Komprise scales with your data. It is designed to migrate billions of files, petabytes of data, thousands of shares, efficiently and reliably. And, Komprise Elastic Data Migration automatically parallelizes its execution to fit your workload so you get fast performance. Benchmark results show Komprise runs over twenty-seven times faster than other solutions. Cut AWS Outposts costs with Komprise Transparent Archiving Cut the costs of running file workloads on AWS Outposts by transparently archiving data as soon as it becomes cold.  Set policies in Komprise so that as data becomes cold, Komprise archives the files into Amazon S3 in region. This way, you can dedicate AWS Outposts resources for hot data that requires local data processing performance, and significantly cut costs on cold data since cold files are moved into the appropriate tier of Amazon S3, which has no upfront infrastructure costs and low ongoing costs. Komprise Transparent Move Technology (TMT) ensures that users and applications continue to access the cold files exactly as before, even though the data now resides as objects on Amazon S3 in region. By continuously archiving cold data from file stores into Amazon S3, Komprise enables customers to cut a significant portion of infrastructure, file storage and backup costs on AWS Outposts. The best part is that users and applications continue to access the archived files exactly as before from the file service – so there is no disruption or change in behavior. Extend Seamlessly to AWS Komprise makes it easy to leverage the hybrid cloud – when you are done with local processing and want to migrate, archive or replicate AWS Outpost data into AWS, Komprise manages the data movement. You can simply move data from AWS Outposts to your nearest AWS Region with Komprise. Contact us if you want to leverage Komprise in your AWS Outposts environment. Try Komprise on AWS with a free trial today. ### Cloud Storage Problem? It’s Time for Intelligent Data Management as a Service Not too long ago, Komprise COO Krishna Subramanian was featured in a webinar with The Register: Quit Your Addiction to Storage – Do You Own Your Data or Does Your Data Own You? The discussion, presentation, and demonstration do a nice job of summarizing the magnitude of the unstructured data growth problem and the power and potential of Intelligent Data Management. The discussion starts with an acknowledgement that enterprise data storage costs are out of control. Too often IT organizations are caught in an endless cycle of buying more storage. Unstructured data is typically strewn across disparate silos and the folks tasked with doing something with that data don’t actually know what’s important and what is not. By nature, we’re all data hoarders. The result is that all the data ends up being managed the same. This means: Cold data sits on expensive storage Everything gets replicated Everything gets backed up and backup windows are getting longer Data storage and backup costs are spiraling out of control Sound familiar? Data hoarding has become the norm in most industries today, where regulations mandate longer and stricter retention policies. So, how does IT get control of this situation? Is it possible to manage growing unstructured data volumes without disrupting the end-user experience? Can unstructured data management be simple, seamless, and transparent to your end users, who may still need data access, but don’t need to know what data is actively being managed? The good news is that the answer is Yes! That’s Komprise: Know First. Move Smart. Take Control. The bad news is that most enterprise IT professionals think they have a storage problem, when in fact, they really have an unstructured data management problem. A few points to consider from this IDC report: How to Manage Your Data Growth Smarter with Data Literacy: 60% of the storage budget is not really spent on storage. It’s spent on secondary copies of data for data protection – backups, backup software licenses, replication, and disaster recovery. 1/3 of IT organizations are spending most of their IT storage on secondary data. Clearly, a lack of active management of all of your enterprise unstructured data is where the problem really is today. So, what can be done? The IDC report recommends to remember three key things: Focus less on finding alternatives to store data better/faster and focus more on finding intelligent alternatives to data management. Use modern, next-generation cloud data management technologies that are lightweight and non-intrusive, and that demonstrate powerful return on investment. Aim to deliver continuous insights as a service to business and achieve speed of intelligence for a competitive edge. But what about the cloud, you ask? First of all, too many IT organizations are thinking of the cloud as a cheap locker for enterprise data. They start by backing it up, which results in pay-by-the-drink pricing for a very high percentage of cold data. As your unstructured data volumes grow, your frequency of buying more cloud storage grows too, resulting in an endless cycle of lock-in and out of control cloud spend. It’s old thinking being applied to a new set of challenges that your new hybrid, multi-cloud world represents. Secondly, most cloud migrations fail. In this two-part webinar series, we summarize 7 reasons why cloud migrations fail. They are: Complex Ad-Hoc Approach (one-off vs. on-going data management) Insufficient Planning Multiple Cloud Storage Options Downtime Impact Time Consuming Data Integrity Sunk Costs The bottom line is that when you’re considering moving to the cloud, it’s going to be even more important to first know your data; properly plan your move; and ensure you’re able to manage data efficiently and effectively across data centers and both cloud and multi-cloud environments. The good news is that’s Komprise Intelligent Data Management as a Service (DMaaS). As we like to say: Don’t compromise. Komprise. Know First. Move Smart. Save More. ### Komprise Expands Intelligent Data Management Partner Program To Accelerate Cloud Data Management DATA MANAGEMENTNEWS Komprise rolls out new cloud training certifications, Fast Finish incentives and new partner portal tools Komprise, the leader in analytics-driven data management as a service (DMaaS), announced new technical training certifications for cloud data management, financial incentives, and partner portal tools as part of the expanded Komprise Konnect Partner Program. Since the launch of the program, over 50 new partners have been on-boarded and hundreds of partner employees have gone through technical training and certification. ### The Komprise Konnect Partner Program in the Spotlight At Komprise we want our customers and our partners to be successful. No compromises. That’s why I’m so excited about today’s announcement: Komprise Expands Intelligent Data Management Partner Program to Accelerate Cloud Data Management. Since the launch of the program, over 50 new partners have been on-boarded, hundreds of partner employees have gone through technical training and certification, and we’ve helped our customers save millions of dollars by analyzing on-premises and multi-cloud environments before costly backup and storage purchases. As more and more enterprise data moves to the cloud, we work closely with our partners to ensure our customers are able to effectively manage cloud cost and complexity. Here’s an overview of the program and what’s new. What is the Komprise Konnect Program? Read more about the Komprise Konnect program. We want to ensure that our product trials and proof-of-value tools are able to be delivered effectively by our partners so they can demonstrate the savings Komprise can deliver on their customers’ NAS footprint and journey to the cloud. We want our partners to be able to enable their customers to adopt secondary object/cloud storage without any disruption to current NAS users or applications. We also want to reduce storage and operational costs by archiving, replicating, and protecting data on any object/cloud/scale-out storage. Finally, we want our partners to be able to take advantage of our partner marketing tools, sales training, and incentives. What’s new? Today we announced new Komprise technical training certifications for cloud data migrations and cloud data management. We also announced Fast Finish financial incentives that reward partners with increased sales margins when they help customers get to the cloud faster and smarter. We added new instructor-led training courses in Europe and Asia Pacific, new online training modules and we introduced new partner portal tools, including our popular TCO calculator. This tool helps customers get a detailed breakdown of the cost savings they can expect with Komprise Intelligent Data Management. What are our partners saying about the program? We love it when we hear from customers and partners. Our recent Gartner Peer Insights reviews are a great example of customer feedback. In support of our Komprise Konnect announcement today, we heard the following from our partners: At PKA Technologies, first and foremost we are a customer first driven organization. Striving to achieve world class customer support in all areas of touch, pre-sales, implementation and post sales is what drives us and why we wake up in the morning. Finding a partner like Komprise, who embraces these same traits, and value proposition was very important to us. Working with the Komprise team of professionals we are mutually driven to achieve success for our customers, migrating their data to the cloud, while maximizing their hybrid, multi-cloud investment. I look forward to a long and successful partnership. Paul Cohen, Vice President of Sales, PKA Technologies Being an end-to-end solution provider, Mainline works with our clients to make it easy to architect, install, configure and upgrade all types of enterprise storage solutions. For our customers, Komprise has become an integral tool in helping them improve efficiency and effectiveness while reducing costs and risk during their storage transformation journey. Randy Moeller, Vice President of Sales, Mainline Information Systems The Sayers partnership with Komprise gives our clients a comprehensive data management solution that offers everything from assessment to implementation. Through the NAS Assessment we’ve developed with Komprise, we provide joint clients valuable insights to better plan their data management before they spend more money. Joel Grace, Senior Vice President of Infrastructure, Sayers What is Komprise saying about our focus on partner success? Here is what our Chief Revenue Officer, had to say: With Komprise, our channel partners can quickly demonstrate cost savings and business value to customers and become a more effective advisor to their clients. We are focused on ensuring our partners can have the right insights and influence as they guide cloud capacity planning, cloud data management and overall purchasing strategies for their customers. Mike Munoz, CRO Komprise Thanks to our partners for their continued support. We’re just getting started! Be sure to check out today’s announcement on how Komprise has extended its partner program. For more information or to sign up to be a Komprise partner, visit the Komprise Konnect page. ### TechKrunch: How to Access Archived Data in the Cloud Welcome to the latest review of our TechKrunch sessions, which are also known as the Randy and Glenn Show. I love the casual conversation, the morning coffee. Is there a better way to start your day than hanging out with these guys and digging into how to intelligently migrate, archive, manage, and optimize your enterprise unstructured data in the cloud? Here are reviews of two recent sessions: TechKrunch: Using Komprise Data Analytics and Data Modeling TechKrunch: How to Find Hot Data for Migrations This week’s focus is on how to access archived data in the cloud. Customers are always impressed with how we archive data in the cloud – they often ask us if they can access their data in its native format using other tools? The good news is that with Komprise, it’s easy. So, let’s get to the Komprise Intelligent Data Management demo First, you create a target. In the Advanced section, select Native format. Unlike other tools that migrate or archive data in a proprietary format that cannot be read outside of the solution itself, Komprise uses open standards so your data is always available in the native format. So, now that you’ve run your analytics and you want to get your cold data to the S3 target, you go to Plan and start to build out your cloud strategy to archive that data with symbolic links and leave those behind. As you edit your Plan, select which type of data you want to tier off. If users haven’t accessed their data in two years, how much data would that be? You quickly see that it’s 10% of your data. Drop it to one year and you see that 19% of your data hasn’t been accessed. Now we’ll archive that data to S3. In addition to archiving data in the native format, you can also make a replicated copy in the native format. This would copy the data but not change the primary storage itself, giving you a readable copy of your data outside of Komprise for reporting, data protection, or some other reason. Activate the plan and your symbolic links get left behind. Archived data goes to AWS S3 and copied data goes to IBM Cloud Object Storage.  Once the data has been moved, Komprise uses symbolic links (read about the difference between Stubs vs. Symbolic links), which looks like a shortcut to the end user. So now you know how easy it is to archive data to the cloud transparently with Komprise, let’s dive into how to access the data. Of course, the best way to access the data will be through the Komprise interface. But what about accessing the data using other tools? In this demonstration, Glenn uses S3 Browser and he points to an AWS lab. It’s easy to get to your file server data. Just login to Komprise type /diag into your Director url. This will give you several different diagnostics that you can use for troubleshooting. In this case Glenn is looking for the ID Map to see which file servers correspond to which IDs, so you know where you’re actually pointing. Back on the file server, we strip out metadata when we write out to S3 so we can put it back in when we do an actual read. In the data folder, I see my data with all of the properties. I can download to my local machine. I’m completely outside of Komprise. I can mount a drive and point to an S3 bucket, which means I can mount a drive with all of the trace files and log files from another server and do some data mining on it. So, the beauty of what we demonstrated here is that with Komprise you aren’t locked in. Your data is always available to you. Of course, you can also write to NAS. Read more about the benefits of native data access. Pretty cool. Here’s the 15-minute session. Check it out and let us know if you have questions or feedback.   Next topic, you ask? It’s going to be about how to use the Komprise Confine functionality. Thanks for sharing, guys. Be wise, Komprise! ### 2020 GigaOM Radar for Unstructured Data Management: Komprise an Outperformer GigaOM recently recognized the power and proven cloud cost savings of Intelligent Data Management from Komprise, naming the company an “Outperformer” in their Radar for Unstructured Data Management. Last month I summarized how Komprise customers are saving big on their journey to the cloud. This month I want to summarize the GigaOm report. Chris Mellor, from Blocks & Files, was the first to write about the report and published the graphic of all of the vendors in his review: GigaOm: Cohesity, Komprise and Commvault lead unstructured data management pack. Six top-level metrics were established for evaluating Unstructured Data Management: Architecture Scalability Flexibility Performance Manageability & Ease of Use TCO The report notes: Komprise excelled almost across the board in these considerations, helping propel Komprise to a leader position in the Radar report. You can see the results below (and this was before some of our recent updates). Here are some of the conclusions from the 2020 GigaOM report: Komprise offers a simple, subscription-based licensing model with impressive ease of adoption and use and with immediate and tangible benefits in the form of rapid ROI. Komprise takes an analytics-based approach to this challenge, enabling users to understand the entire data estate and organize it more efficiently across various on-premises and cloud storage tiers. The solution can seamlessly optimize the storage infrastructure while improving the ability to reuse data and increase its value over time. Organizations working with Komprise have reported improvement in both TCO and ROI after adoption. In general, the more storage capacity under management, the bigger the savings. And TCO is further enhanced over the medium and long term through better capacity planning and storage infrastructure optimization—both outgrowths of improved visibility into stored data. Earlier in the year, the GigaOm analyst who wrote the report, Enrico Signoretti, sat down with Krishna Subramanian, president and COO of Komprise to talk about the world of unstructured data growth. One of her key points in the discussion was: Data management is about using the right mix of storage and backup and it’s about putting the right data in the right place at the right time. That’s why you need data management software that is independent of the storage, backup, and cloud provider but helps you use all of these environments with the proper mix. Enrico Signoretti also recently published a great article on The Future of Unstructured Data Management And Why It’s Important on the GigaOm blog. He notes: At the end of the day, by adopting a SaaS-based global data management solution, the user can quickly optimize costs and improve overall infrastructure TCO. This is only the low hanging fruit though. In fact, the business owners will have powerful tools to increment the value of data stored in their systems while responding adequately to several threats posed by poor data management. ### TechKrunch: How to Find Hot Data for Migrations At Komprise we talk a lot about Cold Data Storage. But increasingly IT organizations come to us and say, “our unstructured data is doubling every two or three years (if not sooner). We need faster performance of our applications that are sitting on our NAS. How do I get my hot data to an all-flash array?” The focus of this TechKrunch session is finding hot data for migrations. But, first a bit of context. Before we get to a demo in this session, Glenn sets the stage. One of the first problems we typically identify with our customers is that ~75% of their data hasn’t been accessed in over a year. At Komprise we’re really good at intelligent archiving - finding your hot and cold data and moving to more appropriate tiers of storage. We also do migrations. What’s so powerful about our Intelligent Data Management platform is that we combine the ability to analyze, archive and migrate. Ultimately this means that you can be smarter about what you actually move. The goal of this session is to show how Komprise customers are able to: ANALYZE to identify HOT and COLD data on legacy storage. Glenn uses the analogy of buying a smaller house and not moving all of your old furniture to your new home. With Komprise you can identify the data that’s actually in use before you move it to your new array. ARCHIVE COLD data to the cloud BEFORE moving to new storage. If 70-80% of your data is cold, you may still need it to be accessible. Set up a policy to send it to the cloud. This is where you experience the power of Komprise Transparent Move Technology.  We use industry-standard symbolic links to replace the data that we archived so data is still accessible on the source (by users and applications). RIGHT SIZE new all-flash arrays before moving your remaining data. We build out a storage trend plan over 3 years. So even if you’re using Komprise for migration and analysis, we’ll show you how much space you need. The blue line is what’s so powerful – the potential of the new plan we recommend. MIGRATE HOT data and links to new arrays fast and easy. When you migrate the data, with Komprise you’re only migrating the hot and warm data to your new array. You also move your links over, which means your data will also be accessible through the dynamic links. There’s no need to recall the data or re-archive on the new system. The links are active on the new array. Some of the benefits of this approach are: Faster migrations Reduced costs by reducing the size of the storage requirements on the new target. We’ve seen greater than 250% savings. Sound good? Now, to the demo. The first thing you do is connect to your source storage.   Once you connect (or Konnect), Komprise starts crawling through and getting data about data. Within the first hour or so you start to a profile of your data (see the TechKrunch: Using Komprise Data Analytics and Data modeling). The blue shows cold data – no reads, no writes. But let’s dig into the red data. 25% of your 4PB data is active. In Randy’s demo scenario, he’s created a fictitious medical university with Student Data, Research Data, and PACs images. He creates a new Data Policy, where that data in this group that hasn’t been accessed in 3 months is scaled off and moved to a different tier in the cloud. That’s about 15% of the data. (How to access archived data in the cloud will be covered in the November 4th TechKrunch session.) Once you’re done cleaning off the cold data and are done editing the plan, go ahead and Activate it and you’re left with your Hot Data. Now that you’ve sized your all-flash array, say for 25% of the total capacity, you don’t have to move all of your data. You can also look at specific shares in order to tier off the cold data and migrate the hot data that remains. Start a Migration task. Select the share and where it’s located. Then walk through the steps to identify source and target and the Komprise Elastic Data Migration moves the data in real time. More on Migrations in another TechKrunch. As always, there are good questions at the end. I suggest you take 20 minutes and enjoy the show. We hope these are useful and want your feedback. ### Komprise Intelligent Data Management Innovation Update At Komprise, our mission is to provide customers a consistent, simple, and efficient way to manage file and object data across clouds. Our customers are increasingly moving to the cloud – and this year, we have released a slew of new features and performance capabilities to make the move to cloud fast, simple, and seamless. We want to ensure our customers and partners are aware of what’s new and taking maximum advantage of the Komprise platform for Intelligent Data Management. In this post we’ll review the highlights from the most recent releases: October 2020 We just announced customers can now migrate file data to Azure Files and Azure NetApp Files and manage the lifecycle of files in Azure NetApp Files volumes.  As enterprises shift to a “cloud-first” strategy, moving traditional file workloads to the cloud is a priority. Komprise is adding support for performant cloud file systems including Azure Files and Azure NetApp Files so you can now identify the right data to move to the cloud, choose whether you want to migrate, archive or replicate the data, and still search and find data across everywhere. We also enhanced our cloud data management for Microsoft Azure. Enterprises can now analyze data on Azure, whether its on Azure Blob or Azure Files, to find out how much data is cold vs hot based on actual usage and not just based on when the data was created. This enables developing efficient cost optimization strategies that help avoid tiering off old data to cheaper storage classes and eliminate the risk of increased costs from accessing hot data in cold storage. Lastly, we extended our Elastic Data Migration capabilities to on-premises object store providers. Komprise now enables users to migrate object data across on-premises and cloud providers over the S3 protocol. Enterprises can freely move data across cloud and on-premises avoiding vendor lock-in and replicate data to cheaper providers to reduce storage costs. To learn more about Komprise’s fast, reliable, and cost-efficient Elastic Data Migration works, check out our white paper on How to Accelerate NAS and Cloud Data Migrations. September 2020 Our September 2020 release included many important updates for enterprise customers – including impressive performance gains of our Elastic Data Migration which is already 27 times faster than tools like Rsync, expansion of our support for Google Cloud, AWS and Microsoft Azure, and better dashboards and reporting. We also expanded the flexibility of data migrations – some customers wanted to make cloud datasets available to a broad range of their users for Big Data applications. So, we now have an option of skipping ACLs during migration. Platform: Performance, Scale, Ease of Use September 2020 introduced a new Elastic Data Migration user interface, which provides new levels of scalability and performance to the platform. Other updates include: Performance: When it comes to performance, customer experience may vary due to a variety of factors, including but not limited to: network environment, file server load and performance, VM operating systems, actual data set transferred. That said, this release saw over 700% improved Plan operations performance for small files and 20% improvement for migrations. Source Support for Microsoft Azure NetApp Files as source Target Support: Native format support for Google Cloud UI: In addition to the new Migrations UI, application headers and toasts were updated across all pages Komprise Elastic Data Migration Architecture Ease of Deployment We’re always looking for ways to make Komprise easier to set up and use. Here are some of the deployment updates in the September 2020 release: New Documentation: For setting up disaster recovery (DR) for a Komprise deployment Web Proxy: The ability to change the Proxy logging folder name and username/password support for Google Cloud AWS: Ability to deploy Director, Observers, and Deep Analytics in customer’s AWS Directors: Ability to set up multiple on-prem Directors with a single on-prem DA cluster TLS: Ability to disable TLS 1.0 and 1.1 Migrations We want our customers to be able migrate without migraines. With that in mind, this release included a number of data migration updates, including the option to skip nfs3 permissions (uid, gid, mode) in a migration job; the ability to allow skipping SMB ACLs during migration; improved "% Processed" algorithm to also count directories, symlinks, Komprise symlinks, and Komprise file links; and the ability to use fast-remove on Pure Storage migrations for directory deletes. Also, when Elastic Data Migration is used for data replication, a new property can now be set to ensure that files modified during iteration are flagged as an error Intelligent Data Management for Microsoft Azure In addition, we also launched intelligent data management for object data in Microsoft Azure. Enterprises can now gain instant visibility into how much data exists across their Microsoft Azure accounts and subscriptions, how much is cold vs hot, what are the characteristics of this data, who is generating it and how fast it is growing across. Moreover, with Komprise Deep Analytics. users can create virtual data lakes of all data in Microsoft Azure and use it to search, tag and organize data regardless of where it resides. Users can quickly find a needle across their cloud haystacks and feed AI and ML applications to extract value from their data. Summer 2020 Highlights We had a number of updates over the summer. Our August 2020 release focused on Additional source and target support, including Qumulo File Fabric and Pure Flash Array File Services as sources. Support for SNMP enabling users to monitor their Komprise deployments using the standard SNMP protocol. Major enhancements for UI scalability (handling a large numbers of shares). Launch of the Komprise free trial to analyze usage, costs and growth of object data in Amazon and Wasabi for free. Support for enabling access logging on Amazon S3 buckets from the UI in bulk when analyzing object data in Amazon. The July 2020 release saw an up to 500% performance increase on Plan move and copy over NFSv3. We also launched archival within Amazon S3 to lower cost storage classes including archival to Glacier and Glacier Deep Archive. Komprise cloud archiving is unique, as it is based on access time and not modify time, and it can save users as much as 50% on storage costs. In June, we focused on Elastic Data Migration performance. Including kcp and Elastic Shares we delivered an up to 500% migration performance increase on NAS migrations over NFSv3! To learn more, be sure to read the white paper: Accelerate NAS and Cloud Data Migrations. In addition to UI and analytics updates, the June release included reduced memory usage in our Observer, faster Observer start-up, and less VM memory requirements. Learn more about Komprise architecture. As always, customers should review the release notes for all of the details or connect with your account manager to do a deeper dive into what’s new. I want our customers to know that we’re always looking for feedback and always happy when you take a few minutes to post a Gartner Peer Insights Review. I’d also like to thank our product team for staying focused during such a challenge time in the world. ### Komprise: Neue Datenverwaltung für Azure- & Azure-Netapp-Files Der Datenmanagement-Spezialist Komprise steigt zum Co-Sell-Ready-Partner für Microsoft Azure auf und verpasst seiner Intelligent Data Management-Software ein Update: Anwender können nun File-Daten in Azure Files und Azure NetApp Files migrieren sowie den Dateilebenszyklus in Azure NetApp Files Volumes verwalten. ### Migrate to NetApp CVO and ANF 27 times faster with Komprise Enterprises are moving to a cloud-first strategy, which means their core file-based applications now need to run in the cloud. NetApp is addressing this need by enabling customers to run the performant NetApp file system in the cloud with CVO (Cloud Volumes ONTAP) and ANF (Azure NetApp Files). Forbes predicts 83% of enterprise workloads will be in the cloud by 2020, and more interestingly, only 27% of workloads will be on-premises by 2020. This would spell a 10% drop in absolute terms in just one year – as the same number for 2019 is at 37%. The Difference Between CVO and ANF CVO is a virtual instance of NetApp ONTAP running in a public cloud like AWS or Google Cloud – it is as if you are converting a physical to a virtual NetApp instance, and then you have to deploy the instance, manage the volume, pay for the cloud compute, etc. The benefits of CVO are that all the capabilities of ONTAP are available. ANF on the other hand is a managed service where NetApp hardware is sitting in Azure – but not all the ONTAP capabilities like SnapMirror and FlexClones are available. CVO and Azure NetApp Files Cost Savings CVO and ANF both have OpEx costs based on the level of storage and compute you use – you may see costs of $1,000/TB/month depending on the performance level you choose. NetApp has built-in storage efficiencies such as dedupe and tiering that are factored into these costs – but significant additional savings are possible with intelligent data management and transparent archiving. Komprise provides analytics-driven data management capabilities so you can understand your on-premises data, migrate and replicate just the right data to CVO and ANF, and cut 70% of ongoing costs with in-place transparent archiving in the cloud. Replicate and Migrate Just the Right Data to CVO and ANF with Komprise Analyze and Plan Before Incurring Cloud Costs Komprise enables you to analyze your multi-vendor NAS footprint across any NFS and SMB storage – across your data centers and clouds. You can analyze a mixed environment that has some NetApp, EMC Isilon, Windows File Servers, and any other NFS or SMB storage. Understand your data and its usage, and plan exactly what data you want to migrate to the cloud – both to NetApp CVO on AWS, Azure, and Google or ANF. Once you know what you want to move, you can choose to either migrate the data or replicate the data via Komprise into CVO or ANF. With the right approach to cloud migration analysis and planning, Komprise customers are able to cut down migration costs and time, and ensure that critical data they need in the cloud is moved over first. Migrate to CVO and ANF at 27x Faster Once you know what to migrate, Komprise Elastic Data Migration efficiently manages the cloud data migration. Simply setup the migration from any NFS or SMB to NetApp CVO and ANF – all you need to do is select any NFS or SMB source, select whether you want to migrate the entire share or specific directories, and your CVO or ANF target. That’s it – Komprise then migrates all the files to the cloud with fast performance using its patented elastic architecture and with reliability. Read our white paper on how Komprise Elastic Data Migration achieves more than 27x better performance compared to tools like rsync. Komprise and Azure NetApp Files Architecture Manage Data Lifecycle Since CVO and ANF both have an OPEX model, it is crucial to actively manage the environment and tier at the file level when you no longer need the performance of cloud file storage. But how you tier/archive data impacts how much you can save. NetApp offers block-level tiering with fabric pooling inside ONTAP where cold blocks are moved to a lower tier like S3 and when they are used again, they get moved back up to the hot tier. While this approach is useful to tier snapshots from the file storage tier down to object, it is not optimal to archive cold data since it is a proprietary approach that does not preserve backup and DR savings. Read the post: What you need to know before jumping into the cloud tiering pool Instead, by using file-level tiering, you can maximize your cloud storage and backup savings. Komprise archives the entire cold file based on your policy to a lower tier of your choice and performs ongoing lifecycle management of the data. The benefits of this approach include: Maximize not only storage but also backup savings: Since the entire file is archived, backup software simply backs up the link and not the full file. This maximizes your savings beyond just storage. Eliminate unnecessary retrievals and rehydration: Komprise uses standard protocol constructs to enable in-place access of archived files, and since these are standard solutions, any 3rd party access to the data does not require cold files to be brought back or rehydrated to the higher tier. No silo’ed management of different cloud storage and CVO/ANF: CVO and ANF tiering has to be configured per instance and this can become difficult as your volume of data grows. Komprise provides you a comprehensive way to set policies and manage data across ANF, CVO, and cloud native solutions like EFS, FSX, S3, Azure Files, and third party NAS solutions in the cloud and on-premises. Maximize ongoing savings throughout the data lifecycle: Komprise provides a full data lifecycle management solution so you can define how data continues to be archived throughout its lifecycle (E.g. after 3 months of being inactive move from CVO to S3 IA, then after 6 months move from S3 IA to Glacier, after 1 year from Glacier to Deep Glacier, etc). This ensures you continue to save on cold data throughout its lifecycle. Native data access from S3 and object tiers: Komprise moves the files in native format so you can not only access the moved data as files, but also directly as objects – this enables you to create cloud native applications without being tied to a file format. Non-proprietary solution with no lock-in:Komprise uses open standards to read and write data, ensuring data is always stored in a format native to the storage service, thereby ensuring there is no data lock-in. This allows you to manage your data independent of the storage devices or data management services they use. In our recent announcement, Karl Rautenstrauch, Principal Program Manager for Azure Storage at Microsoft put it this way: Data portability and lifecycle management is increasingly important to customers, and solutions like Microsoft Azure with Komprise offer the options they demand.   Next Steps: Learn more about Komprise for NetApp Watch a demo of Komprise Elastic Data Migration to NetApp Azure Files Learn more about Cloud Tiering and File-Based Cloud Tiering with Komprise ### Komprise Addresses Demand For File Data Management In The Cloud With New Capabilities For Microsoft Azure Files And Azure NetApp Files Komprise Is Also Selected as a Co-Sell Ready Partner for Microsoft Azure Komprise, the leader in analytics-driven data management, announced new capabilities for its Komprise Intelligent Data Management solution in Microsoft Azure. ### Komprise Customers Saving Big on their Journey to the Cloud Computer Weekly recently published a great story about Komprise customer, Carhartt: Carhartt shifts old data to the cloud with Komprise. The case study reviews how the US-based workwear manufacturer cut storage costs by 60%, and within Azure from $1 to $0.25 per gigabyte by deploying the Komprise intelligent data management solution. Carhartt shifted old files from their datacenter to Microsoft Azure Blob and Cold storage, taking advantage of the new Komprise capability that allows customers to easily migrate file data to Azure Files and Azure NetApp Files as well as manage the lifecycle of files in Azure NetApp Files. In the article, Earl Williams, systems engineer at Carhartt, highlights the benefits of Komprise simplicity and ease: It doesn’t take the user five minutes to open a file, and remote regions can access content on the Azure cloud. Komprise has a proven track record of customer success. Here are some common comments we hear when Komprise is first demonstrated to a data storage team: “Why didn’t I hear about this sooner?” Or, “There’s nothing in the market like this today.” Or even, “You shouldn’t migrate to the cloud without Komprise!” Gartner Peer Insight Reviews One of the first places to look when hearing about Komprise is Gartner Peer Insights. In the File Analysis Software category, the company was ranked highest overall by customers this year and highest in terms of services and support. Read our summary on the #1 file analysis software vendor. A few headlines include: Thanks to the Komprise customers who have taken time to share their feedback! If you’re a customer and want to post a review, please check us out on Gartner Peer Insights. Customer Case Studies and Videos There are some great published stories on the Komprise website, but we’re always looking for more. Here are 3 things each of these stories has in common: Cloud. Cost Savings. Simple. Easy. Fast. In two recent videos focused on higher education, it’s clear that Komprise is not just solving legacy data storage challenges. Komprise is delivering the visibility you need through actionable analytics that allow you to manage data across on-premises and cloud infrastructure and ultimately make better strategic (and financial) decisions. Duquesne University Leverages Komprise to Archive Cold Data “What drew us to Komprise was the ability to look at the data and take action on it.” Komprise For Higher Ed: Interview with Steve DeGroat, Yale University “This is visibility that didn’t exist before.” Regardless of the industry or geography, Komprise has consistently been able to cut overall data storage costs in half and reduce enterprise cold data costs for our customers by 70% or more. If you’re new to Komprise, take a few minutes to learn about the power of our Intelligent Data Management platform or better yet, schedule a demo with our team to learn more. Check out more Komprise customer stories. If you’d like to be featured on the blog or work with us to share results in a case study or webinar, please connect with your Komprise account manager. When it comes to migrating to the cloud and managing your growing volumes of unstructured data, this is not the time for compromise. This is the time for Komprise! ### Komprise identifies cold Azure data and sends it to Blobs Starting today, Komprise users can migrate file data to Azure Files and Azure NetApp Files. Cold file data is movable to cheaper Azure Blob storage classes transparently, reducing cloud network attached storage (NAS) costs by up to 70 per cent, Komprise claims. ### Komprise addresses demand for file data management in the cloud with new capabilities for Microsoft Azure Files and Azure NetApp Files Komprise is also selected as a co-sell ready partner for Microsoft Azure Campbell, CA - October 6, 2020—Komprise, the leader in analytics-driven data management, today announced new capabilities for its Komprise Intelligent Data Management solution in Microsoft Azure. Customers can now migrate file data to Azure Files and Azure NetApp Files and manage the lifecycle of files in Azure NetApp Files volumes. By using Komprise Intelligent Data Management, customers can migrate file workloads to the cloud more than 27 times faster than with other solutions. They can also reduce cloud network attached storage (NAS) by 70 percent by transparently archiving cold data from Azure NetApp Files to various Azure Blob storage classes. Komprise’s Transparent Move Technology™ (TMT) enables archived data to be viewed as files, native objects, or both. These new capabilities now allow Komprise to deliver the same on-premises NAS data management features to cloud-enabled NAS. “Customers are increasingly looking to run traditional file workloads in the cloud, especially with the rapid pace of digital transformation happening across businesses right now,” said Krishna Subramanian, COO at Komprise. “Our mission is to ensure that every company can reduce costs and gain performance on its data, no matter where it lives, through analytics-driven data management.” Karl Rautenstrauch, Principal Program Manager, Azure Storage at Microsoft Corp. said “Data portability and lifecycle management is increasingly important to customers, and solutions like Microsoft Azure with Komprise offer the options they demand." “At Carhartt, we are transforming to a cloud-first strategy using Microsoft Azure, and we wanted to reduce storage and backup costs for our Digital Asset Management data,” said Earl Williams, System Engineer at Carhartt. “By using Komprise, we were able to identify that 60 percent of our data was cold, and we have now been able to transparently archive it to lower-cost Azure Blob storage.” Komprise is co-sell ready with Microsoft Komprise has achieved co-sell ready status through the Microsoft One Commercial Partner program. Komprise is also available in the Azure Marketplace and is collaborating with Microsoft partners and the Microsoft sales organization to deliver unstructured data management on Azure. Companies that achieve co-sell ready status are provided comprehensive sales and marketing support by aligning with the global Microsoft salesforce to help drive new business and expand market reach. Attend a webinar on November 5, 2020 at 8:00 am PT to learn more. About Komprise Komprise empowers businesses to take control of their data and save costs with no interference to applications, users, or hot data. Komprise Intelligent Data Management provides a foundation for analytics-driven data management, which is key to putting data in the right place at the right time across all storage. Learn more at www.komprise.com. ### 7 Data Archiving Pitfalls That Reduce Your Savings Data archiving is a process that saves companies significant amounts on their storage costs. How effective your data archiving strategy turns out to be mainly depends on how you choose to archive and which company you choose to handle it. Improving Your Data Storage Solution with Data Archiving Data archiving (also called data tiering) is a process that saves enterprise organizations significant amounts on their data storage costs. How effective your data archiving strategy turns out to be mainly depends on how you choose to archive and which company you choose to handle it. At Komprise, we’ve spent years helping businesses from around the world understand, archive, and optimize their data storage systems, so we’ve seen many different ways in which traditional archiving and storage tiering have fallen short over the years. Here are 7 data archiving pitfalls to look out for as they can have a major impact on your data storage cost savings: IT involvement in file retrieval – In traditional archiving systems where cold data is physically moved off of primary storage devices, the user must often go through the hassle of filing an IT ticket to get access to those archived files. This is a frustrating time-sink for both parties and should never be tolerated in an archiving solution. Inefficient manual archival workflows – The way in which cold data is identified and approved for archiving in many traditional archiving plans can be a headache. Both sides have to approve of the file/directory/volume in question and the process must be repeated often. Requires entire projects to be archived – One of the biggest disadvantages with older archiving systems many companies offer is the limitation in which files can be archived. Most traditional archiving only allows for entire projects, volumes, or directories to be archived rather than storing specific files, which are used less frequently. Stubs add latency and risk – Hierarchal Storage Management (HSM) plans often use proprietary interfaces, such as stubs, which can slow down the rate of retrieval from the data archive and increases the risk that the data will be lost to corruption. Erodes significant backup savings – Unfortunately, many storage tiering plans have their archiving architecture set up in a way that lessens the benefits that archiving offers. In some cases, capacity storage used in tiering solutions are significantly slower at retrieving files when compared to primary storage and may even lengthen the backup window. Lock-in – The inability to switch vendors is one of the biggest red flags when looking for a data archiving solution, but many companies don’t reveal this limitation until after you’ve signed up. No native access on target – Since storage tiering moves entire blocks of data to the secondary storage, these files are often unable to be directly accessed on secondary storage if they need to be. Want to learn more about how your data storage savings can be affected by the way archiving is done? Download our brief by completing the short form below, or contact us to get in touch with a data management expert at Komprise. Thanks for reaching out. We'll be in touch ### Improving Your Data Storage Solution with Data Archiving 2020 has seen the greatest demand in data storage of any year so far. Individuals, companies, and governments are amassing a tremendous amount of data requiring an increasing volume of data storage every year, but a majority of these customers are overpaying for their data storage solutions without ever hearing of data tiering and archiving. Data archiving and data tiering are services that allows you to offload infrequently used data from primary storage and backups into a more cost-effective tier or archive for longer-term storage. Benefits of Data Archiving Archiving data is an essential part of any business that wants to run their storage solutions as efficiently as possible. NAS archiving provides a variety of advantages to businesses that work with increasingly large data sets to help them comply with regulations, save storage space, and cut storage costs. Maximize Savings on Storage Tiering Costs A well-developed data tiering and archiving strategy can drastically reduce the costs in tiered storage systems by optimizing the storage of cold data out of expensive Tier 0 and Tier 1 drives. Moving sections of less-used cold data out of your primary storage, backups, and DR systems can drastically reduce your storage footprint while cutting storage costs by up to 70%. Data Archiving is Non-Disruptive to Users and Apps Archiving cold data does not disrupt the function of the storage array being analyzed and archived. Users and apps can continue to function seamlessly during and after the data has been archived. The best data archiving tools avoid disruption by providing an interface that allows the file-based data to be archived as objects. Komprise’s Transparent Move Technology™ (TMT) creates an efficient bridge between these paradigms that allows storage function to continue uninhibited. Secure Data for Regulatory Compliance Sensitive data can be secured within the archive where it cannot be modified. Maintaining the archived data in this manner is required to comply with data storage regulations in a number of industries. Outside Hot-Data Path Tiering and archiving does not interfere with the pathways of hot data or meta data and does not degrade storage performance. The data archiving process alleviates strain on primary, DR, and backup storage systems, which can improve application response times and enhance hot-data storage performance. No Vendor Lock-In with Cloud Data Tiering and Archiving Cloud tiering and archiving does not lock you in to having to use the same vendor for your primary NAS storage. With access to standard protocols, including S3, SMB, and NFS, Komprise data archiving tools avoid locking you into having to use any particular storage device or vendor. Get the freedom to design a custom archiving solution built to meet the specific performance, capacity, budget, and scalability needs of your data storage system without having to deal with “pools” from storage tiering companies that create vendor lock-in. Native Access from Secondary Storage Archived data can be accessed directly from secondary storage solutions allowing access for ongoing and future use. In this way, the data is easily accessible so it can adhere to both the daily needs of the business and the constantly changing compliance requirements. Read the blog post: What you need to know before you jump into the cloud tiering pool Optimize Your Enterprise Storage with Cloud Tiering and Data Archiving Solutions from Komprise Komprise uses an analytics-driven approach to data management that will help you identify, organize, and archive data transparently without any disruption to your file access. Get to the cloud faster, optimize your hot data performance, reduce backup gap time, and significantly lessen your storage and backup costs today. Get in touch with a data storage expert at Komprise to get started. ### Komprise Announces Cloud Capability Komprise recently made some announcements around extending its product to cloud. I had the opportunity to speak to Krishna Subramanian (President and COO) about the news and I thought I’d share some of my thoughts here. ### Extended Capabilities of Komprise Intelligent Data Management to Include Cloud Data Customers are looking for better ways to manage their cloud data as it grows, such as ‘bucket sprawl, visibility into their cloud costs, and a simple way to manage data both on premises and in the cloud. The company provides enterprises with actionable analytics to understand their cloud data costs and also optimize them with data lifecycle management. ### Komprise Intelligent Data Management Drives Down Costs of Public Cloud by Over 40% Komprise, an Advanced Technology Partner in the Amazon Web Services (AWS) Partner Network (APN) and an analytics-driven data management company for on-premises and cloud environments, has announced the extended capabilities of Komprise Intelligent Data Management to include cloud data. Customers are looking for better ways to manage their cloud data as it grows, such as “bucket sprawl,” visibility into their cloud costs, and a simple way to manage data both on premises and in the cloud. ### Komprise Intelligent Data Management Drives Down Costs of Public Cloud by over 40% CAMPBELL, California, June 18, 2020 /PRNewswire/ -- Komprise, an Advanced Technology Partner in the Amazon Web Services (AWS) Partner Network (APN) and an analytics-driven data management company for on-premises and cloud environments, today announced the extended capabilities of Komprise Intelligent Data Management to include cloud data. ### Slimmer bewegen in een multicloudwereld De uitdagingen van het beheer van clouddata: beperkte zichtbaarheid en complexe prijsstelling Even though managing cloud costs is now a top priority, 80% of businesses will overspend their cloud infrastructure budgets. Gartner says it’s from a lack of cloud cost optimization, and it’s not hard to see why. Today we announced Komprise Intelligent Data Management for Multicloud that helps control costs and provide insight into your cloud usage. Read the press release here. Challenges of Managing Cloud Data Managing cloud data costs is tough: lots of manual effort, multiple tools, and constant monitoring. As a result, companies are using less than 20% of the cloud cost-saving options available to them. “Bucket sprawl” makes matters worse, as users quickly and easily create accounts and buckets and fill them with data—some of which is never accessed again. Cloud administrators are trying to optimize cloud data and have to battle with poor visibility and complexity. It’s hard to get a holistic picture of the data you have across accounts, buckets and sometimes even across multiple clouds. How do you know how much of that data is being used, how fast it’s growing and what your costs are going to be in a few months? Estimating these numbers can be daunting - with all the various storage costs, access costs, transition costs and so on. It’s just too difficult, as shown in this cloud savings infographic. Multicloud Data Management and Cloud Cost Optimization What’s needed is radical simplification to take the pain out of managing cloud data and saving costs. And Komprise delivered. Komprise extended capabilities of Komprise Intelligent Data Management to include cloud data to answer those needs. It gives customers a better way to manage their cloud data as it grows, (combat “bucket sprawl”), gives visibility into their cloud costs, and provides a simple way to manage data both on premises and in the cloud. Komprise now provides enterprises with actionable analytics to not only understand their cloud data costs but also optimize them with data lifecycle management. Komprise Intelligent Data Management for Multicloud removes the complexity of managing cloud data within a cloud and across clouds with a single set of tools, policies, and data management functions for today’s multicloud world. It takes your data size and data access patterns into account to intelligently and optimally store and manage your data. Check out the demo video here. Consider all you can see, know, and do with your cloud data with Komprise: Analyze your data across clouds, accounts, and buckets based on last access time. Try out “what-if” archiving scenarios to determine cost savings. Tier and archive data based on highly accurate last-access time. Migrate data within and across clouds with high fidelity. Use custom queries to search and tag your data and feed AI and ML applications and extract the most from your hot and cold data. I want to highlight that we tier and archive data based on access time as opposed to modify time. The former is a much more accurate prediction of whether data will be accessed or not, and allows us to automatically tier and archive more data and to lower tiers without having to pay inadvertent access costs. Using this, we can dramatically reduce the cost of cloud storage by 50% or more. Our white paper gives more details. Komprise just took the hassle out of cloud data management. Use it to migrate data to the cloud and between clouds and then efficiently manage where it’s stored to dramatically reduce costs and run customer queries across all of your buckets to find the needle across your haystacks and feed your AI/ML applications. To get a free trial of Komprise with your cloud data, sign up here. ### Making Smarter Moves in a Multicloud World The Challenges of Managing Cloud Data: Low Visibility and Complex Pricing Even though managing cloud costs is now a top priority, 80% of businesses will overspend their cloud infrastructure budgets. Gartner says it’s from a lack of cloud cost optimization, and it’s not hard to see why. Today we announced Komprise Intelligent Data Management for Multicloud that helps control costs and provide insight into your cloud usage. Read the press release here. Challenges of Managing Cloud Data Managing cloud data costs is tough: lots of manual effort, multiple tools, and constant monitoring. As a result, companies are using less than 20% of the cloud cost-saving options available to them. “Bucket sprawl” makes matters worse, as users quickly and easily create accounts and buckets and fill them with data—some of which is never accessed again. Cloud administrators are trying to optimize cloud data and have to battle with poor visibility and complexity. It’s hard to get a holistic picture of the data you have across accounts, buckets and sometimes even across multiple clouds. How do you know how much of that data is being used, how fast it’s growing and what your costs are going to be in a few months? Estimating these numbers can be daunting - with all the various storage costs, access costs, transition costs and so on. It’s just too difficult, as shown in this cloud savings infographic. Multicloud Data Management and Cloud Cost Optimization What’s needed is radical simplification to take the pain out of managing cloud data and saving costs. And Komprise delivered. Komprise extended capabilities of Komprise Intelligent Data Management to include cloud data to answer those needs. It gives customers a better way to manage their cloud data as it grows, (combat “bucket sprawl”), gives visibility into their cloud costs, and provides a simple way to manage data both on premises and in the cloud. Komprise now provides enterprises with actionable analytics to not only understand their cloud data costs but also optimize them with data lifecycle management. Komprise Intelligent Data Management for Multicloud removes the complexity of managing cloud data within a cloud and across clouds with a single set of tools, policies, and data management functions for today’s multicloud world. It takes your data size and data access patterns into account to intelligently and optimally store and manage your data. Check out the demo video here. Muliticloud Visibility with Cloud Data Management Consider all you can see, know, and do with your cloud data with Komprise: Analyze your data across clouds, accounts, and buckets based on last access time. Try out “what-if” archiving scenarios to determine cost savings. Tier and archive data based on highly accurate last-access time. Migrate data within and across clouds with high fidelity. Use custom queries to search and tag your data and feed AI and ML applications and extract the most from your hot and cold data. I want to highlight that we tier and archive data based on access time as opposed to modify time. The former is a much more accurate prediction of whether data will be accessed or not, and allows us to automatically tier and archive more data and to lower tiers without having to pay inadvertent access costs. Using this, we can dramatically reduce the cost of cloud storage by 50% or more. Our white paper gives more details. Komprise just took the hassle out of cloud data management. Use it to migrate data to the cloud and between clouds and then efficiently manage where it’s stored to dramatically reduce costs and run customer queries across all of your buckets to find the needle across your haystacks and feed your AI/ML applications. Connect with the Komprise Account Team today. ### Komprise Elastic Data Migration resolves bottlenecks when adopting a multi-cloud strategy Komprise, a leader in analytics-driven data management, has announced the availability of its Elastic Data Migration solution. Custom designed to address the critical migration issues IT faces today – speed, reliability, accuracy and cost – this super fast migration solution uses a highly parallelised, multi-processing, multi-threaded approach. With these latest enhancements, customers can migrate data across heterogeneous storage and cloud environments more than six times faster than the status quo – at less than half the cost. Businesses will benefit most when migrating data between their Network Attached Storage (NAS) and the cloud, or from cloud to cloud, because it’s optimised to address latency across wireless area networks (WAN). ### Komprise unveiled Elastic Data Migration Komprise, a leader in data management and well know for its Intelligent Data Management (IDM) product, just announced a new iteration of its NAS migration solution. The company has unveiled Elastic Data Migration (EDM) as a separate product to address the need of fast NAS transfer from on-premises to the cloud. For users who already picked IDM, EDM is included and we see the acronym logic... ### Resolving multi-cloud bottlenecks Custom designed to address the critical migration issues IT faces today—speed, reliability, accuracy, and cost—this super-fast migration solution uses a highly parallelized, multi-processing, multi-threaded approach. With these latest enhancements, customers can migrate data across heterogeneous storage and cloud environments more than six times faster than the status quo—at less than half the cost. Businesses will benefit most when migrating data between their Network Attached Storage (NAS) and the cloud, or from cloud to cloud, because it’s optimised to address latency across wireless area networks (WAN). ### Komprise Elastic Data Migration Hits GA Komprise announced the availability of Komprise Elastic Data Migration. As the name implies, the new solution aims to resolve bottlenecks as companies adopt multi-cloud strategies. The new solution addresses the main migration issues: speed, reliability, accuracy, and cost to help users migrate data across heterogeneous storage and cloud environments. Komprise goes on to claim this can be done up to six times faster and at less than half the cost, compared to other solutions. ### Komprise Unveils Elastic Data Migration To Move Data Between On-Premises, Cloud Elastic Data Migration's real power is in being included at no charge with Komprise's Intelligent Data Management offering, making it easy for solution providers to analyze the data before doing the migration, says Krishna Subramanian, president and COO of Komprise. ### Komprise rewrites data migration engine to ramp up cross-data centre capability Data migration is a secondary feature of Komprise, which primarily moves data for archiving, but its new Elastic Data Migration capability provides a highly parallelized capability that is ideal for WAN migrations, and should increase that use case. ### Komprise Elastic Data Migration lost knelpunten op bij het implementeren van een multi-cloudstrategie Campbell, CA - March 31, 2020-- Komprise, der Marktführer bei Analytics-driven Data Management, kündigte die Verfügbarkeit ihrer Elastic Data Migration Lösung an. Diese wurde speziell entwickelt für die Bewältigung kritischer Migrationsprobleme, denen die IT heute gegenübersteht – Geschwindigkeit, Zuverlässigkeit, Genauigkeit und Kosten – diese superschnelle Migrationslösung nutzt einen hoch parallelisierten, Multiprocessing und Multithreading Ansatz. Mit den neuesten Verbesserungen können Kunden Daten über heterogene Storage und Cloud Umgebungen mehr als sechsmal schneller als sonst üblich migrieren zu weniger als der Hälfte der bisher gewohnten Kosten. Unternehmen profitieren am meisten wenn sie Daten zwischen ihrem Network Attached Storage (NAS) und der Cloud, oder von Cloud zu Cloud migrieren, weil die Lösung auf die Kontrolle der Latenzzeiten über Wide Area Networks (WAN) optimiert ist. Ein typischer Datenmigrationsprozess kann sehr mühselig, fehleranfällig und zeitaufwändig sein, insbesondere für unstrukturierte Daten. Unstrukturierte Daten in die Cloud zu migrieren beinhaltet zusätzliche Herausforderungen wie die Nutzung des Internets oder WANs, die häufig geringere Geschwindigkeit und Zuverlässigkeit haben. Dies erfordert WAN-optimierte Datenmigration, die hohe Latenzzeiten und Netzausfälle verträgt. Bisher war dies eine Barriere für Unternehmen mit einer Multi-Cloud Strategie, insbesondere bei unzureichender Netzwerkqualität. Die Komprise Elastische Datenmigration Lösung erlaubt es Unternehmen, hunderte von Migrationen auszuführen, zu überwachen und zu managen und dabei noch den zeitlichen Aufwand dafür zu minimieren, wodurch Ressourcen für andere Aufgaben frei werden. Die Elastische Datenmigration Lösung von Komprise beseitigt die Latenzhürde für Unternehmen, die bereit sind, eine Multi-Cloud-Strategie einzuführen. “Komprise Elastische Datenmigration minimiert Migrations-Downtime durch Nutzung von Analytics für die Identifikation der richtigen zu bewegenden Files, wodurch die Effizienz der Datenmigration maximiert wird,“ sagt Kumar Goswami, CEO at Komprise. „Gartner stellte 2019 fest, dass schlechtes Datenmanagement zu schwindelerregenden Speicherkosten führt. Die IT hat kein Speicherproblem sondern ein Datenmanagementproblem. Elastische Datenmigration ist die Antwort, verfügbar als eigenständiges Produkt oder als eine Komponente der Komprise Intelligent Data Management Plattform, die alle Ihre Datenmanagementbedürfnisse erfüllt. Komprise Elastische Datenmigration übertrifft bestehende Enterprise Migrationstools zu weniger als den halben Kosten. Die jüngsten Verbesserungen beinhalten: Parallelität auf allen Ebenen: Maximiert die Nutzung der verfügbaren Resourcen durch die Nutzung von Parallelität auf mehreren Ebenen – Shares und Volumes, Directories, Files und Threads. Komprise Elastische Datenmigration adaptiert und managed seine Parallelität automatisch, um sich den verfügbaren Ressourcen anzupassen. Das Protokoll ist auf minimierten Overhead optimiert: dadurch wird die Round-trip Zeit über das Protokoll während einer Migration minimiert, um unnötigen Datentransfer zu vermeiden. Statt auf generische Protokoll Clients zu vertrauen, ist Komprise so eingestellt, dass der Overhead für jedes Protokoll minimiert wird, was besonders hilfreich ist, wenn Daten über langsamere Netze wie WANs bewegt werden. Hohe Datenintegrität mit MD5 Check jedes Files/Objekts: dies stellt sicher, dass Ihre Daten migriert werden mit allen Rechten, Metadaten und ACLs unverändert über unterschiedliche Storage Umgebungen, die Rechte und Metadaten verschieden unterstützen. Komprise bearbeitet und berichtet über die Integrität jedes Files oder Objekts mithilfe von MD5 Checksums. Intuitives grafisches Benutzerinterface und API-getrieben: häufig führen Unternehmen mehrere Migrationen parallel aus. Komprise stellt ein intuitives UI zur Verfügung, das es Ihnen ermöglicht, hunderte von Datenmigrationen gleichzeitig auszuführen, zu verfolgen und zu managen. Komprise stellt auch einen API Zugriff bereit für die zeitliche Planung und Durchführung von Datenmigrationen per Programm. Zuverlässige, sorgenfreie Migrationen: automatische Wiederholungen bei Netzfehlern und Vermeidung von Spekulationen und intensiver Handarbeit traditioneller Lösungen. Komprise wird auch künftig seine Produkte einschließlich Elastische Datenmigration exklusiv über seine Channel Partner vertreiben. “Komprise Elastische Datenmigration bietet ein neues Niveau von Leistung und Einfachheit, welche unseren gemeinsamen Kunden helfen wird, Storagemodernisierung zu beschleunigen und gleichzeitig Speicher- und Managementkosten zu senken“, sagt Ferrol Macon, Vice President, Architecture and Strategy, Veristor, ein führender Anbieter von transformativen Business Technology Lösungen und autorisierter Komprise Solutionpartner. ADDITIONAL RESOURCES Elastic Data Migration White Paper — sehen Sie Performance Test Results Elastic Data Migration Product Page Über Komprise Komprise, der führende Anbieter von Analytics Driven Data Management, ermöglicht es Unternehmen, das heutige massive Datenwachstum zu kontrollieren ohne Interferenzen mit Anwendungen, Anwendern oder heißen Daten. Die Mission von Komprise ist, Data Management radikal zu vereinfachen mit einer Lösung für Analyse, Migration und Archivierung der gesamten Storage Infrastruktur einschließlich NAS und Cloud. Komprise wird von Unternehmen für das Management von hunderten Petabytes an Daten eingesetzt. Für weitere Informationen besuchen Sie Komprise. Media Contact: Monica Giannella McNay pr@komprise.com ### Komprise Elastic Data Migration resolves bottlenecks when adopting a multicloud strategy Campbell, CA - March 31, 2020-- Komprise, the leader in analytics-driven data management, today announced the availability of its Elastic Data Migration solution. Custom designed to address the critical migration issues IT faces today—speed, reliability, accuracy, and cost—this super-fast migration solution uses a highly parallelized, multi-processing, multi-threaded approach. With these latest enhancements, customers can migrate data across heterogeneous storage and cloud environments more than six times faster than the status quo—at less than half the cost. Businesses will benefit most when migrating data between their Network Attached Storage (NAS) and the cloud, or from cloud to cloud, because it’s optimized to address latency across wireless area networks (WAN). A typical data migration process can be laborious, error-prone, and time-consuming, especially for unstructured data. Migrating unstructured data to the cloud has additional complexities from using the internet or WANs, which often have lower speed and reliability. This makes WAN-optimized data migration that can efficiently handle high latencies and outages a must. Until now, this has been a barrier for companies pursuing a multi-cloud strategy, especially where network quality is an issue. Komprise Elastic Data Migration enables enterprises to run, monitor, and manage hundreds of migrations simultaneously, while minimizing the time spent on these to free up resources elsewhere. Komprise’s Elastic Data Migration solution removes the latency hurdle for enterprises ready to adopt a multi-cloud strategy. “Komprise Elastic Data Migration minimizes migration downtime by using analytics to identify the right files to move, maximizing data migration efficiency,” says Kumar Goswami, CEO at Komprise. “Gartner noted in 2019 that bad data management leads to spiralling storage costs. IT doesn’t have a storage problem, it has a data management problem. Elastic Data Migration is the answer, available either as a stand-alone solution or a component of Komprise Intelligent Data Management platform, which addressees all your data management needs.” Komprise Elastic Data Migration outperforms existing enterprise migration tools at under half the cost. The latest enhancements include: Parallelism at every level: Maximizes the use of available resources by using parallelism at multiple levels—shares and volumes, directories, files, and threads—to maximize performance. Komprise Elastic Data Migration automatically adapts and manages its parallelism to adjust to the available resources. Protocol optimized to minimize overhead: Minimizes the round-trip time over the protocol during a migration to eliminate unnecessary chatter. Rather than relying on generic protocol clients, Komprise is fine-tuned to minimize overhead for each protocol, especially beneficial when moving data over slower networks, such as WANs. High-fidelity with MD5 check of each file/object: Ensures your data is migrated with all its permissions, metadata, and ACLs intact across different storage environments that may support permissions and metadata differently. Komprise performs and reports on data integrity of each file or object through MD5 Checksums. Intuitive graphical user interface and API-driven: Businesses often run multiple migrations in parallel. Komprise provides an intuitive UI that enables you to run, monitor, and manage hundreds of data migrations simultaneously. Komprise also provides API access to schedule and manage data migrations programmatically. Reliable, worry-free migrations: Automatically retries due to network failures and eliminates the guesswork and intensive manual effort of traditional solutions Komprise will continue to sell its offerings including Elastic Data Migration exclusively through their channel partners. “Komprise Elastic Data Migration delivers new levels of performance and simplicity which will help our mutual customers accelerate storage modernization while lowering data storage and management costs,” said Ferrol Macon, Vice President, Architecture and Strategy, Veristor, a leading provider of transformative business technology solutions and authorized Komprise solution partner. “Komprise also delivers analytics-driven data management which is at the core of one of our Strategic Data Storage Assessment services. It enables us to quickly provide our customers a comprehensive understanding of their unstructured data across their various platforms and make informed decisions around data management, storage and classification. Armed with that information Elastic Data Migration enables them to quickly and efficiently take action.” ADDITIONAL RESOURCES Elastic Data Migration White Paper — see performance test results Elastic Data Migration Product Page About Komprise Komprise, the industry leader in analytics-driven data management, empowers businesses to efficiently manage today’s massive scale of data growth with no interference to applications, users, or hot data. Komprise’s mission is to radically simplify data management with one solution to analyze, migrate, and archive across your storage infrastructure, including NAS and cloud. Komprise is used by enterprises to manage hundreds of petabytes of data. For more information, visit Komprise. Media Contact: Monica Giannella McNay pr@komprise.com ### AWS re:Invent: Komprise Unveils Cloud Data Growth Analytics for AWS At AWS re:Invent 2019, Komprise, Inc. announced Cloud Data Growth Analytics, which is designed to allow enterprises to understand their cloud usage across their organization so they can optimize their cloud storage. ### Komprise provides insights into cloud usage to optimise costs for AWS Komprise has announced at AWS re:Invent 2019 Komprise Cloud Data Growth Analytics, which is designed to allow Enterprises to easily understand their cloud usage across their organisation so they can optimise their cloud storage. ### Komprise attacks AWS cloud file storage fog with cut-through software service Komprise has extended its file storage usage and cost tracker to the cloud. It is showcasing Cloud Data Growth Analytics (CDGA) this week at AWS re:Invent in Las Vegas. Costs and availability are unclear at time of publication. ### Komprise bietet Einblicke in die Cloud-Nutzung, um die Kosten mit neuen Cloud Data Growth Analytics für Amazon Web Services zu optimieren Las Vegas, NV - 3. Dezember 2019 -- Komprise gab heute auf der AWS re: Invent 2019 Komprise Cloud Data Growth Analytics bekannt, mit der Unternehmen ihre Cloud-Nutzung im gesamten Unternehmen leicht nachvollziehen und ihren Cloud-Speicher optimieren können. “Wir haben Komprise entwickelt, weil die Transparenz des Datenwachstums für ein besseres Datenmanagement unerlässlich war. Kunden haben uns gebeten, unsere Data Analytics-Funktionen auf die Cloud auszudehnen, da die Sichtbarkeit der Cloud für sie noch schwieriger war” sagt Kumar K. Goswami, Komprise CEO. Komprise Intelligent Data Management erweitert seine Möglichkeiten zur Analyse und Transparenz des gesamten Speichers um Amazon Web Services (AWS). Komprise Intelligent Data Management erkennt kalte Daten und verschiebt sie transparent in kostengünstigere Speicher. Die Bereitstellung der gleichen Sichtbarkeit für kalte Daten in der Cloud war aus zwei Gründen eine Herausforderung: 1) Die Cloud verfolgt die Nutzung basierend auf der letzten Änderung und nicht basierend auf dem zuletzt aufgerufenen Zugriff. 2) Eimerausbreitung in der Cloud von verschiedenen Benutzern, die unterschiedliche Konten erstellen. Komprise Cloud Data Growth Analytics bietet Kunden eine einheitliche Ansicht über ihre verschiedenen Cloud-Konten, Buckets und Speicherklassen hinweg, um den Cloud-Speicher zu optimieren: Zeigt an, wer Daten erstellt, wie sie wachsen, wie sie verwendet werden und wie viel sie kosten Identifiziert kalte Daten basierend auf dem Zeitpunkt ihrer letzten Verwendung im Vergleich zur letzten Änderung, um unnötige Datenübertragungskosten und Latenzen zu vermeiden Analysiert und verwaltet Daten in einem einzigen Bereich, unabhängig davon, wo sie sich befinden - in der Cloud oder vor Ort Plant verschiedene Datenverwaltungsstrategien und visualisiert deren Kostenauswirkungen Ermöglicht Amazon S3-Migrationen (Amazon Simple Storage Service) mithilfe von Komprise Data Migration für Amazon S3, um Daten einfach über Clouds und Amazon S3-basierten Objektspeicher zu verschieben Komprise zeigt eine Live-Demonstration von Cloud Data Growth Analytics am Stand 2933 auf der AWS re: Invent 2019. ZUSÄTZLICHE RESSOURCEN Videoübersicht über Komprise Cloud Data Growth Analytics Über Komprise Mit Komprise können Unternehmen die Kontrolle über ihre Daten übernehmen, ohne Anwendungen, Benutzer oder Hot Data zu beeinträchtigen. Komprise Intelligent Data Management ist die Grundlage für ein analytikgesteuertes Datenmanagement, mit dem Daten in allen Speichern zur richtigen Zeit am richtigen Ort platziert werden können. Analysieren, verschieben und finden Sie Ihre Daten ganz einfach mit Komprise. Medienkontakt: Monica Giannella McNay pr@komprise.com ### Komprise provides insights into cloud usage to optimize costs with new Cloud Data Growth Analytics for Amazon Web Services Las Vegas, NV - December 3, 2019 -- Today, Komprise announced at AWS re:Invent 2019 Komprise Cloud Data Growth Analytics, which is designed to allow Enterprises to easily understand their cloud usage across their organization so they can optimize their cloud storage. “We created Komprise because visibility into data growth was imperative to better data management. Customer have been asking us to expand our Data Analytics capabilities to the cloud since cloud visibility has been even harder for them,” says Kumar K. Goswami, Komprise CEO. Komprise Intelligent Data Management is extending its ability to analyze and provide visibility across storage to now include Amazon Web Services (AWS). Komprise Intelligent Data Management identifies cold data and transparently moves it to less expensive storage. Providing the same visibility into cold data in the cloud has been challenging for two reasons: 1) cloud tracks usage based on last modified and not based on last accessed 2) bucket sprawls in the cloud from different users creating different accounts. Komprise Cloud Data Growth Analytics provides customer a single view across their various cloud accounts, buckets and storage classes to optimize cloud storage: Shows who is creating data, how it is growing, how it is being used, and how much it costs Identifies cold data based on when it was last used vs. last modified to eliminate unnecessary data transfer costs and latencies Analyzes and manages data on a single pane no matter where it lives – cloud or on-premises Plans different data management strategies and visualize their cost impact Enables Amazon Simple Storage Service (Amazon S3) migrations using Komprise Data Migration for Amazon S3 to easily move data across clouds and Amazon S3-based object storage Komprise will be showing a live demonstration of Cloud Data Growth Analytics at Booth 2933 at AWS re:Invent 2019. ADDITIONAL RESOURCES Video Overview of Komprise Cloud Data Growth Analytics About Komprise Komprise empowers businesses to take control of their data with no interference to applications, users, or hot data. Komprise Intelligent Data Management is the foundation for analytics-driven data management which is key to putting data in the right place at the right time across all storage. Analyze, move, and easily find your data with Komprise. Media Contact: Monica Giannella McNay pr@komprise.com ### Expansion of Data in the Cloud is Relentless – How this Impacts You Cloud storage is growing at a staggering rate! Unstructured Data is growing to 175ZB by 2025 (from 40ZB in 2019), according to IDC. Even more noteworthy is the fact that 50% of this data will be stored in public clouds. The rate of movement to the cloud is accelerating at an exponential rate! What are the implications of this movement we’re experiencing? One of the reasons behind this growth is due to applications running in the cloud which then generate data. Another reason is that more data is being replicated, backed up, and archived to the cloud and this in turn enables new applications that re-use that data. As you’ve all heard, data is the “new oil” that enables a company to better understand their customers and their processes, thus enabling change that fuels their growth. Furthermore, the public cloud is a perfect infrastructure for developing one global data lake for an organization. The cloud isn’t just about creating one centralized storage. Rather, this centrality with its ubiquitous access enables a company to more readily distribute and collaborate across an organization. The best thing is that the cloud is easy. It’s easy to fire up new accounts, buckets, and applications that create, store, and use the data. Though, with this ease comes costs. Unlike on-premises where certain organizations are tasked with purchasing and managing storage with the ease of the cloud, this function can be relegated and distributed to a wider set of people. This results in unthwarted “bucket sprawl” and shocking monthly fees, since everything is “pay as you go”. Gartner states that the high cost of storage is due to poor data management. This will become even more true for cloud storage. To control costs without stymying productivity or agility, you need an easily available “birds eye view” of what’s going on across your cloud deployment, accounts, and buckets. You also need the ability to slice and dice that data to understand what’s growing, how much, and who is creating it. This is the first step towards better and much needed cloud data management. Come by and visit Komprise at the AWS Re-invent booth #2933 to see how we can help you better manage your cloud storage. ### Komprise breidt ondersteuning van NetApp-platforms uit Komprise heeft aangekondigd dat het de ondersteuning van NetApp-platforms heeft uitgebreid met twee nieuwe mogelijkheden. Komprise Deep Analytics ondersteunt nu NetApp Cloud Volumes ONTAP® als bron, waarmee de bestaande ondersteuning van on-premises NetApp-omgevingen wordt uitgebreid. Komprise Data Migration bevat nu een bètaversie van Amazon S3-gebaseerde datamigratie naar de NetApp StorageGRID®, om de migratie van data tussen cloudplatforms te vereenvoudigen. ### Komprise expands its support of NetApp platforms Komprise has announced that it has expanded its support of NetApp platforms with two new capabilities. Komprise Deep Analytics now supports NetApp Cloud Volumes ONTAP® as a source, extending existing support of on-premises NetApp environments, and Komprise Data Migration now includes a Beta release of Amazon S3-based data migration to the NetApp StorageGRID®, to simplify the migration of data across cloud platforms... ### Komprise breidt de ondersteuning voor NetApp-platforms uit Tijdens NetApp Insight 2019 heeft Komprise de ondersteuning van NetApp-platformen uitgebreid met twee nieuwe mogelijkheden. Komprise Deep Analytics ondersteunt nu NetApp CloudVolumes ONTAP® als bron, waarmee de bestaande ondersteuning van on-premises NetApp-omgevingen wordt uitgebreid. Komprise Data Migration bevat nu een bètaversie van Amazon S3-gebaseerde datamigratie naar de NetApp StorageGRID® om de migratie van data tussen cloudplatformen te vereenvoudigen... ### Komprise expands support for NetApp platforms At NetApp Insight 2019, Komprise has expanded its support of NetApp platforms with two new capabilities. Komprise Deep Analytics now supports NetApp CloudVolumes ONTAP® as a source, extending existing support of on-premises NetApp environments, and Komprise Data Migration now includes a Beta release of Amazon S3-based data migration to the NetApp StorageGRID®, to simplify the migration of data across cloud platforms... ### Komprise verdiept de integratie in zowel cloud- als on-prem NetApp-omgevingen De cloudintegratie is een bètaversie die eenvoudige migratie tussen S3 en NetApp StorageGrid mogelijk maakt, terwijl de on-premise-versie de nieuwe Deep Analytics van Komprise uitbreidt naar NetApp Cloud Volumes ONTAP... ### Komprise ermöglicht Such- und virtuelle Datenseen für NetApp ONTAP und Cloud Volumes ONTAP sowie S3-Migrationen zu NetApp StorageGRID STAND # 701 Las Vegas, NV - 29. Oktober 2019 -- Komprise, der Branchenführer für intelligentes Datenmanagement, hat heute auf der NetApp Insight 2019 die Unterstützung von NetApp-Plattformen um zwei neue Funktionen erweitert. Komprise Deep Analytics unterstützt jetzt NetApp Cloud Volumes ONTAP® als Quelle und erweitert die vorhandene Unterstützung für lokale NetApp-Umgebungen. Komprise Data Migration enthält jetzt eine Beta-Version der Amazon S3-basierten Datenmigration zu NetApp StorageGRID®, um die Migration zu vereinfachen von Daten über Cloud-Plattformen. “Unsere Mission ist es, sicherzustellen, dass jedes Unternehmen, unabhängig davon, wie oder wo es seine Daten speichert, Leistungsverbesserungs- und Kostensenkungsvorteile erzielen kann, wenn es versteht, wie Daten verwendet werden, und sie an einem geeigneten Ort speichert”, kommentierte Krishna Subramanian, COO bei Komprise . “Durch die Zuordnung, Analyse und automatische Verschiebung der Datennutzung auf Dateiebene senken unsere Kunden die Datenspeicherkosten um bis zu 70% und reduzieren den Zeit- und Ressourcenaufwand für die Identifizierung von Daten für Analyseprojekte. Durch die Ausweitung unserer Partnerschaft mit NetApp können jetzt alle NetApp-Kunden, ob Cloud- oder lokale Benutzer, von der Expertise von Komprise profitieren und bestehende Kunden können mehr von der Plattform profitieren.” Komprise Deep Analytics, das erstmals im September 2019 veröffentlicht wurde, bietet einen zentralen Ort für die Suche in Speichersilos und erstellt einen virtuellen Datensee mit nur den Dateien, die den spezifischen Suchkriterien eines Kunden entsprechen. Deep Analytics erweitert die Unterstützung von NetApp ONTAP-Quellen jetzt auch auf NetApp Cloud Volumes ONTAP. Kunden haben jetzt einen einzigen Ort zum Anzeigen, Suchen und Kennzeichnen von Daten, unabhängig davon, wo sie sich befinden, und exportieren diesen virtuellen Datensee in eine Analyseanwendung oder ein Ziel ihrer Wahl, z. B. AWS Lambda. Der resultierende Datensatz kann als diskrete Einheit verarbeitet werden. Alle Berechtigungen, Zugriffskontrollen, Sicherheits- und Metadaten bleiben erhalten, wenn sich dieser Datensee bewegt. Datenmigrationen können kostspielig, fehleranfällig, zeitaufwändig und mühsam sein. Durch die Komprise-Datenmigration entfallen die Kosten, der manuelle Aufwand und die Komplexität von Migrationen zwischen NFS und SMB. Heute veröffentlicht Komprise eine Beta von Data Migration S3, um die Migration von Objektdaten über Clouds hinweg zu vereinfachen. Wählen Sie eine beliebige S3-Quelle und ein S3-Ziel wie NetApp StorageGRID aus. Den Rest erledigt Komprise. Führen Sie problemlos mehrere Migrationen parallel aus und verwalten Sie sie mit einer einzigen Glasscheibe. Komprise verwaltet die Migration effizient, drosselt nach Bedarf und versucht es erneut bei Netzwerk- und Speicherfehlern, sodass Sie dies nicht tun müssen. Komprise wird diese neuen Funktionen auf der NetApp INSIGHT Las Vegas, Stand Nummer 701, demonstrieren. ZUSÄTZLICHE RESSOURCEN ● Videoübersicht über Deep Analytics ● Video von Komprise + NetApp Cloud Volumes Über Komprise Komprise, der Branchenführer für intelligentes Datenmanagement über Clouds hinweg, ermöglicht es Unternehmen, das heutige massive Datenwachstum effizient zu verwalten und gleichzeitig seinen Wert freizusetzen. Komprise vereinfacht das Datenmanagement durch intelligente Automatisierung radikal. Zu den Komprise-Partnern gehören HPE, IBM, NetApp, EMC, Pure Storage, Google, AWS und Azure. Weitere Informationen finden Sie unter Komprise.com. Medienkontakt: Monica Giannella McNay pr@komprise.com ### Komprise enables search and Virtual Data Lakes BOOTH #701 Las Vegas, NV - October 29, 2019 -- Today at NetApp Insight 2019, Komprise, the industry-leader in intelligent data management, expanded its support of NetApp platforms with two new capabilities. Komprise Deep Analytics now supports NetApp Cloud Volumes ONTAP® as a source, extending existing support of on-premise NetApp environments, and Komprise Data Migration now includes a Beta release of Amazon S3-based data migration to the NetApp StorageGRID®, to simplify the migration of data across cloud platforms. “Our mission is to ensure that every company, regardless of how or where they store their data, can gain performance improvement and cost reduction benefits from understanding how data is used and storing it in the appropriate place,” commented Krishna Subramanian, COO at Komprise. “By mapping, analyzing and automatically moving data usage at the file level, our customers are decreasing data storage costs by up to 70% and reducing the time and resource needed to identify data for analytics projects. Extending our partnership with NetApp means that now all NetApp customers, whether cloud or on-premises users, can benefit from Komprise’s expertise, and existing customers can gain more from the platform.” Komprise Deep Analytics, first released in September 2019, provides a single place to search across storage silos and creates a virtual data lake of just the files that fit a customer’s specific search criteria. Deep Analytics extends its support of NetApp ONTAP sources to now also include NetApp Cloud Volumes ONTAP. Customers now have a single place to view, find, and tag data regardless of where it lives, and export this virtual data lake to any analytics application or destination of their choice, such as AWS Lambda. The resulting data set can be operated on as a discrete entity – all the permissions, access control, security and metadata are kept intact as this data lake moves. Data migrations can be costly, error-prone, time consuming and laborious. Komprise Data Migration eliminates the cost, manual effort, and complexity of migrations across NFS and SMB. Today, Komprise is releasing a Beta of Data Migration S3 to simplify migrating object data across clouds. Pick any S3 source, and pick a S3 target such as NetApp StorageGRID, and Komprise does the rest. Easily run multiple migrations in parallel and manage with a single pane of glass. Komprise efficiently manages the migration, throttles as needed, and retries on network and storage failures so you don’t have to. Komprise will be demonstrating these new capabilities at NetApp INSIGHT Las Vegas, Booth Number 701. ADDITIONAL RESOURCES ● Video Overview of Deep Analytics ● Video of Komprise + NetApp Cloud Volumes About Komprise Komprise, the industry-leader in intelligent data management across clouds, empowers businesses to efficiently manage today’s massive scale of data growth while unlocking its value. Komprise radically simplifies data management through intelligent automation. Komprise partners include HPE, IBM, NetApp, EMC, Pure Storage, Google, AWS, and Azure. For more information, visit Komprise. Media Contact: Monica Giannella McNay pr@komprise.com ### Northwestern halbiert die Lagerkosten mit Komprise Mit Deep Analytics von Komprise können Universitäten Forschungsdaten klassifizieren und auf billigere Festplatten und Clouds migrieren, da die Speicherkosten von 1,1 Mio. USD auf 680.000 USD pro Jahr gesenkt werden. ### Komprise Deep Analytics automates unstructured data finding across on-premises and cloud storage Komprise is making searching for and analysing unstructured multi-vendor data a whole lot easier US-based data management provider Komprise has announced the general availability of Deep Analytics, a new tool to help organisations find and analyse unstructured data across multiple on-premises and cloud storage platforms. ### Optimising Unstructured Data with Krishna Subramanian This week, Martin and Chris talk to Krishna Subramanian, President and COO at Komprise. We mentioned the Komprise technology back on episode #75 (It’s ILM All Over Again) as part of a discussion on managing the movement of files to and from archive storage... ### Komprise Enterprise Cloud Data Management With Support for User and Policy Driven Transparent Archiving As data growth continues to explode, businesses are looking for simple, efficient ways to manage the exponential costs of storing this data. The company enables customers to slash storage and backup costs by finding cold data across their NAS, and based on policies typically set by IT, offloading this data from expensive storage and backups into cost-efficient secondary storage... ### Komprise Modernizes Enterprise Cloud Data Management with Support for Both User and Policy Driven Transparent Archiving Komprise, the industry-leader in intelligent data management, today announced two major product updates: Support for both user and policy driven archiving, and the launch of its standalone NAS migration solution... ### Komprise dives into deep analytics search Komprise is developing a deep analytics function underpinned by an index of all the files in a global namespace that whirs away in the background while it operates as a file management facility for accessing migrated files through dynamic links... ### Boone County Archives to Azure with Komprise Data Management Komprise's data management software helps Indiana's Boone County government archive unstructured data in Azure cloud storage and reduce on-site storage. Like many local governments, Indiana's Boone County accumulated lots of records, court documents, audio recordings, video and other data over the years that employees rarely, if ever, need to access. ### Komprise Intelligent Data Management Empowers AI in the Cloud Komprise will demonstrate how Data Access Anywhere allows for new uses such as Artificial Intelligence for data archived to the cloud at AWS re:Invent 2018   Las Vegas, NV – November 27, 2018 -- Komprise, an industry-leader in intelligent data management, is demonstrating today at AWS re:Invent 2018 how its Data Access Anywhere capabilities fuel new uses such as Artificial Intelligence (AI) applications in the cloud. As data strategy evolves across on-premises and cloud, Komprise evolves with it. Komprise 2.9 enables multi-tier data management with full data access from any storage without rehydration to a proprietary format. Traditional data management and tiering solutions focus on cost and move blocks of data to the cloud while the metadata stays on the proprietary primary tier. This lessens the ability to directly access the data in the cloud without rehydration. Unlike these block-based solutions, Komprise moves data progressively down classes of storage with full file integrity so customers can directly access data and fully take advantage of native capabilities on Amazon Web Services (AWS) like Amazon Elastic MapReduce (Amazon EMR) or Amazon Transcribe. Komprise is an Advanced Technology Partner in the AWS Partner Network (APN). Komprise will demo how customers can migrate data to AWS and dynamically promote data from Amazon Simple Storage Service (Amazon S3) IA to Amazon Elastic File System (Amazon EFS) for AI and Big Data services at AWS re:Invent 2018, Booth #1909.   About Komprise Komprise, an industry-leader in intelligent data management across clouds, empowers businesses to efficiently manage today’s massive scale of data growth while unlocking its value. The Komprise mission is to radically simplify data management through intelligent automation. Komprise partners include IBM, Western Digital, NetApp, EMC, and several others. Komprise is used by enterprises to intelligently manage data at scale. For more information, visit Komprise. ##   Media Contact: Monica Giannella McNay pr@komprise.com ### Komprise Now Available in the Microsoft Azure Marketplace Microsoft Azure customers worldwide now gain access to Komprise Intelligent Data Management to take advantage of the scalability, reliability and agility of Azure to manage data growth and drive business strategy Campbell, CA – September 18, 2018 - Komprise, an industry leader in intelligent data management, today announced the availability of Komprise , an online store providing applications and services for use on Azure.  Komprise customers can now take advantage of the productive and trusted Azure cloud platform, with streamlined deployment and management.  Komprise has also received the Co-Sell Ready designation through the Microsoft One Commercial Partner Program, after meeting the competency, capability and performance standards. “We are pleased that enterprises can now use Komprise directly from the Microsoft Azure Marketplace, making it even faster and easier for businesses to start benefiting from the scale and cost-efficiency of Azure,” said Kumar K. Goswami, CEO Komprise. Sajan Parihar, Director, Microsoft Azure Platform at Microsoft Corp said, “We’re excited to welcome Komprise to the Microsoft Azure Marketplace, which gives our partners great exposure to cloud customers around the globe. Azure Marketplace offers world-class quality experiences from global trusted partners with solutions tested to work seamlessly with Azure.” The Azure Marketplace is an online market for buying and selling cloud solutions certified to run on Azure. The Azure Marketplace helps connect companies seeking innovative, cloud-based solutions with partners who have developed solutions that are ready to use. “Komprise enables us to move large amounts of data to Microsoft Azure without any downtime or disruption to our users,” said Sean Horan, Sr. Network Systems Specialist for Boone County. The Komprise software enables businesses to efficiently manage data growth across storage silos.  Komprise identifies hot and cold data, transparently moves data across multi-vendor storage, and delivers ongoing policy-based archiving and data migration. Data Growth Analytics: Identifies hot and cold data across multi-vendor storage and enables efficient capacity planning of storage and backup. Transparent Move Technology™: Moves data without disruption across on premises and cloud so users continue to access the moved files transparently from the original source and there is no performance degradation to hot data. Data Management Anywhere: Unified policy-based data management, analytics and data access across your storage infrastructure, on premises and cloud, regardless of vendor. About Komprise Komprise, the industry-leader in intelligent data management across clouds, empowers businesses to efficiently manage today’s massive scale of data growth while unlocking its value. The Komprise mission is to radically simplify data management through intelligent automation. Komprise is used by enterprises to intelligently manage data at scale. Komprise has been named to Gartner’s Cool Vendors in Storage Technologies 2017. For more information, visit Komprise. All other product and service names mentioned are the trademarks of their respective companies. ## For more information, press only:   Media Contact: Monica Giannella Read pr@komprise.com ### Komprise and Ochser Consulting Group (OCG) Form Strategic Partnership Scottsdale, AZ and Campbell, Calif. – August 28th, 2018 – Ochser Consulting Group (OCG), A Solution Provider focused on delivering Data Footprint Storage Hardware, Software and Services, and Komprise, the industry leader in intelligent data management across clouds, today announced a strategic partnership to help customer’s understand their Data storage footprint. “Users have been adding Storage at an alarming rate for years with little understanding of where their data resides, when it was last accessed and how much space dormant data is utilizing on Tier 1 and other primary storage, including cloud”, states David Ochser, President of Ochser Consulting Group, “Customers have been addressing their storage requirements by adding Terabyte’s of storage without really understanding their Data Footprint which is why OCG is excited to be partnering with Komprise. With Komprise software we’re able to show our customer’s first hand their Data Footprint and help them architect a strategy to better utilize their existing storage infrastructure, potentially reducing the need for additional storage purchases saving them capital expenditures." “We are excited to partner with OCG. Their knowledge and years of experience in the Storage industry will serve them well understanding how to talk to customers about their Data Footprint in a platform agnostic way”, states Zach Edwards, Director of Channels and Alliances at Komprise. “OCG’s model of designing a business and leading with Komprise versus a Hardware Platform is the kind of Partner we are looking for.” About Ochser Consulting Group Located in Scottsdale Arizona with offices in Denver Colorado and Austin Texas, Ochser Consulting Group (OCG) was formed with the mission of helping customers understand where their data lives. Helping customers understand their Data storage footprint enables them to make educated decisions around their storage utilization and ongoing consumpion. OCG’s belief that data is not created equal so why would you store your data on the same expensive storage platform. Media Contact: David Ochser +1 480-225-9966 david@ochserconsulting.com www.ochserconsulting.com About Komprise Komprise, the industry-leader in intelligent data management across clouds, empowers businesses to efficiently manage today’s massive scale of data growth while unlocking its value. The Komprise mission is to radically simplify data management through intelligent automation. Komprise is used by enterprises to intelligently manage data at scale. Komprise has won numerous industry awards including Gartner’s Cool Vendors in Storage Technologies 2017. For more information, go to www.komprise.com. Media Contact: Monica Giannella Reed pr@komprise.com ### Komprise Appoints Andy Hill As VP EMEA Sales CAMPBELL, Calif., July 2, 2018 /PRNewswire/ -- Komprise, the industry leader in intelligent data management across clouds, today announced that to address the rapidly growing global customer and partner momentum, Komprise is expanding regional presence and is appointing Andy Hill as VP EMEA Sales, who brings deep experience in scaling EMEA sales and channels. Andy Hill brings over 20 years of experience in leading EMEA sales, channels and business development.  Most recently Andy was VP Sales, EMEA for Nexsan where he was responsible for planning, strategy and execution of their EMEA sales, partner and channel strategies.  Prior to Nexsan, Andy held senior management positions at a variety of storage and data management companies including Pivot3, Sungard and Veritas. "I joined Komprise because its EMEA customers and partners rave about how Komprise has simplified the complex task of getting visibility and managing data across storage silos," said Andy Hill, VP EMEA Sales at Komprise. "I am excited to be a part of the team and expand the EMEA business." "We are pleased to have a seasoned and accomplished veteran like Andy lead our EMEA expansion," said Tony Craythorne, Senior VP of Worldwide Sales, Komprise.  "We are looking forward to building on our initial success in EMEA and rapidly growing our leadership position in the market." Growing Customer and Market Momentum Komprise continues to scale its momentum globally with both customers and partners.  In the first few months of 2018, Komprise has: 700% Year-over-Year Growth in Q1. Komprise added customers in key verticals including Research, Universities, County and State Agencies, Genomics, Insurance, and Engineering, many of which were multi-petabyte environments. "As a media business, our video content just keeps growing. Komprise enables us to stretch our budgets and leverage a mix of expensive and low-cost storage by transparently archiving inactive content. The best part is that our media artists continue to access the data as before, with no process change," said Mr Dietmar Schuldt, IT Manager at Studio Hamburg GmbH, a leading media producer in Germany. Announced Strategic Reseller relationship with IBM. See video of Eric Herzog, CMO of IBM Storage and Krishna Subramanian, COO Komprise Announced Komprise is now also available in the AWS Marketplace, Google, and most recently the Azure Marketplace. See how Boone County Cuts 88% Backup costs with Komprise and Microsoft Azure Expanded Komprise Intelligent Data Management to include NAS migration across both NFS and SMB environments, and added Multi-currency support. Customers can now analyze NAS data, identify ROI of data management, transparently archive data across file and cloud/object, replicate data at lower cost, and migrate data across storage with Komprise. About Komprise Komprise, the industry-leader in intelligent data management across clouds, empowers businesses to efficiently manage today's massive scale of data growth while unlocking its value. The Komprise mission is to radically simplify data management through intelligent automation.  Komprise is used by enterprises to intelligently manage data at scale.  Komprise has won numerous industry awards including Gartner's Cool Vendors in Storage Technologies 2017. For more information, visit Komprise. Media Contact: Monica Giannella Read pr@komprise.com ### How to Overcome the Top 3 Cloud Data Migration Challenges Data is at the heart of successful cloud deployments. Once you have decided to migrate workloads to the cloud, the trickiest part is managing the cloud data migration. Migrating unstructured data to the cloud can be difficult for many reasons: a) unstructured data is often millions to billions of files strewn throughout your organization - migrating this volume of data without some automation can be very challenging, and b) since unstructured data is typically stored as files within an enterprise, with metadata, hierarchies and access control, moving this to a flat object base can destroy the additional information needed to view this data as files in the cloud. Managing cloud data migrations, particularly of unstructured data, can be a frustrating, labor-intensive, error-prone process.  Manually copying data through tools like rsync requires a lot of planning, resources and manual intervention. Even after you successfully copy everything into the cloud, you don't have a good way to access the data since the file-access attributes may not have been preserved. Here are the key challenges to cloud data migration - join us for a webinar with AWS on June 14, 2018 where we discuss these and how you can overcome them with Komprise and AWS. How do you manage cloud data migrations without downtime? Data is the lifeblood of your organization. Unstructured data is predominantly stored and accessed as files by users and applications.  Migrating this data to the cloud can take weeks to months - during this time, you cannot disrupt your current users and applications. How can you migrate unstructured data without any downtime, so users and applications can continue to access the data as files from their on-prem systems while the data is migrating to the cloud? How can you automate cloud data migrations to eliminate manual effort? Migrating data through unmanaged tools such as rsync is very laborious and error-prone.  Any glitches in the network or temporary unavailability of your storage can abort the entire operation.  Also, if any failures do occur, you have to start the process all over again. Chunking up the data and moving it in parts is also left to you.  File permissions and access control are often not preserved during the copy, which renders the data less usable in the cloud.  How can you automate cloud data migration so you do not have to manually manage these issues? How can you ensure all the permissions, ACLs, metadata are copied correctly during a cloud data migration so you can access the data in the cloud as files? When moving data from file systems to the cloud, it is important to ensure all the ACLs and file metadata are preserved - so that you can access the data in the cloud as files with exactly the same permissions as before.  Essentially, you need a solution to migrate data that creates a file-system view of this data in the cloud. This is important so you can traverse the data as files in the cloud, and copy relevant data to environments like AWS EFS to do further compute in the cloud.  Your S3 data then becomes a data lake in the cloud. Fortunately, you can overcome these challenges with some planning and automation that preserves file-based access both from on-premise and the cloud. To learn more about the cloud data migration challenges and how Komprise enables you to overcome them, attend our webinar with AWS on June 14, 2018 ### Komprise signs first non-cloud strategic reseller relationship, with IBM Komprise has announced a new strategic reseller agreement through which IBM will resell Komprise Intelligent Data Management software on their price lists. This includes both perpetual and subscription licenses, and inclusion in both the Ready for IBM Storage and Ready for IBM Cloud validated solutions directory. Komprise has also expanded their distribution network, signing on Lifeboat for both the U.S. and Canada earlier this month. ### Komprise Announces Strategic Reseller Agreement With IBM IBM and Komprise Enable Businesses to Extend NAS Capacity Seamlessly and Streamline Costs CAMPBELL, Calif., May 30, 2018 /PRNewswire/ -- Komprise, the industry leader in intelligent data management across clouds, today announced a strategic reseller agreement with IBM. Under the agreement, IBM will resell Komprise Intelligent Data Management software alongside the IBM storage portfolio. Komprise will be available under both perpetual and subscription licenses through IBM. Komprise is also part of the Ready for IBM Storage and Ready for IBM Cloud validated solutions directory. IBM recently highlighted how customers can leverage Komprise with IBM Cloud Object Storage in its blog post on May 24, 2018. Customers leveraging Komprise Intelligent Data Management with IBM Cloud Object Storage can benefit from: A validated ROI-driven data management solution that leverages the simplicity of Komprise with the scale-out efficiency of IBM Cloud Object Storage A seamless solution that stitches existing NAS with IBM Cloud Object Storage combining the best of what a NAS and an Object Storage can provide while cutting costs by 70% and without any changes to users or applications Data agility across NAS, object storage and cloud without losing file-based access, permissions and security and all without lock-in "Today's scale of data requires a new approach to data management that is scalable, easy to administer, efficient, and does not disrupt or create silos," said Krishna Subramanian, COO of Komprise. "Our partnership with IBM enables enterprises to manage data while leveraging their existing storage investments and building a path to the future simply and efficiently." Additional Resources: Komprise IBM Joint Solution Brief Video: Two Steps to Cut Your NAS Costs: IBM & Komprise Videos: IBM and Komprise Use Cases with Eric Herzog, CMO, IBM Storage and Krishna Subramanian, COO Komprise IBM Blog on Komprise About Komprise Komprise, the industry-leader in intelligent data management across clouds, empowers businesses to efficiently manage today's massive scale of data growth while unlocking its value. The Komprise mission is to radically simplify data management through intelligent automation. Komprise is used by enterprises to intelligently manage data at scale. For more information, visit Komprise. IBM, IBM Cloud and IBM Watson as well as their respective logos are trademarks or registered trademarks of IBM. See https://www.ibm.com/legal/us/en/copytrade.shtml for additional trademark information and notices. All other product and service names mentioned are the trademarks of their respective companies. Media Contact: Monica Giannella Read pr@komprise.com ### Komprise is Going Global in Version 2.8 We are proud to announce Komprise Intelligent Data Management 2.8, which now delivers extended NAS Migration capability across both NFS and SMB/CIFS and Multi-Currency Support—along with other new features. The expanded migration capability will be available at no additional cost to customers, thus extending the use cases Komprise manages to encompass the full lifecycle of data, include live archiving, low cost replication/DR, transparent unified file/object access, and data migration. In addition, in response to global demand, we are expanding Komprise to include Multi-Currency Support so our customers can get ROI analysis and savings projections in their currency of choice. “IT is drowning in data and looking for a headache-free solutions that enables them to leverage the right mix of primary and secondary storage without lock-in. Komprise 2.8 offers features like expanded data migration capabilities and Multi-Currency Support to meet the demand from our rapidly expanding global customer base." Kumar Goswami, CEO Komprise will be showcasing this new release at both Dell Technologies World 2018, Las Vegas, Booth 1243 and at IBM Tech U, Orlando this week where we will be performing live demos and architectural presentations. ### Earth Day: Three Green Reasons for Cold Data Storage & Archiving This Earth Week, I thought would take a second to share my thoughts on the sustainability of our data center storage choices and how efficient use of secondary storage not only makes good business sense but also helps save our planet. Data Storage Power Consumption at Untenable Levels As we all know, data growth is exploding and predicted to reach 44 Zettabytes by 2020 (according to IDC) and continue to accelerate from there. Over 80% of this data is unstructured, and most of our storage systems were not designed for this diversity of data, of varying quality and value. As a result, most businesses end up storing, replicating and protecting all their data on expensive primary storage. Not only is this unsustainable from acost perspective, but it is also unsustainable from an environmental perspective. Since the majority of the data we store is rarely accessed or cold within weeks of creation, by separating out hot data and cold data during the data lifecycle and managing them differently, we can not only increase our cost efficiency but also our environmental efficiency. Transparently identifying and archiving cold data to highly efficient secondary cold data storage solutions such as object storage, tape and cloud can have a significant improvement in our energy usage. Having just celebrated another World Earth Day, it is a good time for us to think of three good reasons why the better use of transparent data archiving, cold data storage and the burgeoning variety of secondary storage choices helps both our bottom line and our environment. Reason 1: More choices to progressively tier cold data We are seeing tremendous innovation in the options for cold data storage, both on-prem and cloud, from Object storage solutions such as IBM Cloud Object Storage and Cloudian, to cloud storage solutions such as Google Nearline and Coldline, AWS and Glacier, and Microsoft Azure, to long-term tape options such as Spectra Logic. These secondary storage solutions are not only more efficient in storing data, but they also offer varying degrees of energy efficiency, as they are designed for cold data that does not need fast retrieval times, and hence they can be powered off or offline as needed to conserve power usage. Tape, for instance, is 20 times more efficient in terms of power usage than disk but has longer retrieval times. Object storage is typically slightly slower than primary storage, but not as energy efficient as tape. By using data management solutions that transparently tier colder data down secondary storage tiers (e.g. put data not used in over 6 months to AWS S3 and if it has not been used in another year, move it to Glacier), you can obtain the required SLA on data at the right price and energy consumption. Reason 2: Intelligent Data Management Solutions to Identify and Transparently Archive Cold Data A key reason why businesses end up keeping all data on the primary storage is that it has historically been really hard to identify cold data across a customer’s NAS environment and move it to secondary storage such as cloud or object or tape, without a lot of tedious manual effort by IT and without user disruption. This is no longer the case – you can now get analytics on all your NAS data, set policies for when you want data tiered to lower cost cold data storage, and archive it progressively to colder storage options, all without users having to look elsewhere for data or change paradigms between file and object. Reason 3: Cut Power Consumption Growth by over 50% A recent study found that taking this “business as usual” approach would lead to a 79% growth in energy use for data storage to handle tenfold data growth, even assuming the most optimistic use of efficient storage and cloud storage. Given that we are already on an unsustainable path of power consumption, this near doubling of power usage is clearly undesirable. This same study found that by adopting intelligent data management policies to aggressively identify and archive cold data to offline cold storage, data center energy for data storage would only grow by 24%. So, having just celebrated another Earth Day, let us take a moment to see how we can help extend the life of our beautiful planet by managing our cold data more efficiently. Bonus Reason: Save money while saving the planet Transparently moving data the secondary storage targets like those identified above is not only environmentally friendly, it is also budget friendly. Read our Quantifying the Value of Intelligent Data Management report to learn how organizations like yours are cutting their NAS costs by leveraging these more green storage solutions. Read Report ### How to quickly migrate to your new flash NAS and keep it humming You’ve just bought a spanking new, super-fast flash NAS You want to migrate the data off your old HDD NAS as soon as possible, as cost-effectively as possible and without tons of manual labor and oversight. You’ve used free tools like robocopy in the past but they are unreliable and require a lot of hand-holding and to speed up migration. You need to run multiple of them simultaneously. You will also need to ensure that all of the millions of files are transferred flawlessly. You have to oversee all of these parallel operations and manage all the failures manually. It’s not easy. The new Elastic Data Migration features of the Komprise Intelligent Data Management Platform may be just what you have been looking for. Komprise can migrate data from your current NAS to your new Flash NAS.  It will run multiple operations in parallel and provide you with one aggregate report per share telling you how it went, how much time it took and if there were failures and if so what files were involved and why. Here’s how it works: For each share you want to migrate, select a share on the new flash NAS and start the migration. Komprise will migrate the data from the old to the new share and then continuously repeat the process to re-copy any files that have since been modified. It will automatically use multiple threads to parallelize and speed up the effort. When very few files are left, a final copy iteration can be initiated to finish the migration. You’re done Keeping your flash NAS clean after the migration Once the migration is finished, you want to make sure the investment in your new Flash NAS is being used wisely and cost effectively. What you don’t want is to fill up your Flash NAS with cold data that’s not being accessed. Komprise can really help here to save your organization on data storage costs and make you a hero. The Komprise Intelligent Data management Platform analyzes your new Flash NAS and provides you with how much data you have, how fast it’s growing, and how much of that data is hot and cold. Not only is this great for capacity planning and budgeting, you can then set simple policies like “move all the data that has not been accessed in more than 6 months old” to ensure that the Flash NAS never fills up! The cold data can be moved to your old NAS or to another secondary storage target like a public or private cloud. When cold data is moved by Komprise, a dynamically managed symbolic link that looks and behaves like the original files is left in its place so users and applications can still access the cold data from the Flash NAS. Komprise Transparent Move Technology (TMT) ensures that users and applications are not disrupted and it allows IT to make these sorts of decisions without having to ask for permission. Like what you’ve read so far? Sign up for a demo and we’ll walk you through all of this live! Schedule a Demo ### Solving NAS Migration Pain Every few years an organization needs to purchase a new network attached storage (NAS) system. The old system either has reached its capacity limits or no longer meets the performance expectations of new users and applications. The options for a NAS replacement are almost limitless. Unfortunately, the choices for migrating data on the old NAS to the new NAS are not. IT planners typically have to choose from using free utilities that are error-prone and require manual intervention, or using specific migration tools that are complex, expensive and only good for that single task. ### KOMPRISE HOSTS MULTIPLE DEMONSTRATIONS AT NAB 2018 ON HOW MEDIA BUSINESSES MAXIMIZE MONETIZATION OF CONTENT WITH LIVE ARCHIVES Komprise demos available at multiple locations includingthe Komprise Booth, IBM Booth, Google Booth CAMPBELL, CA – April 3, 2018 – Komprise, the industry leader in intelligent data management across clouds, will be demonstrating how Media and Entertainment customers are able to continue monetizing content throughout its lifecycle even as they cut costs by archiving cold content on lower cost cloud/object storage. Komprise will be demonstrating its Intelligent Data Management solution in the following locations: Komprise Booth South Hall Lower Level 1421: Learn how you can intelligently manage data across all your storage (NAS, Cloud, Object, Tape) without any disruption IBM Booth South Hall Lower Level 5305: See how Komprise and IBM Cloud Object Storage enable Media and Entertainment businesses to cut costs without losing access to content Google Booth South Hall Upper 218: Watch how Komprise and Google Cloud Storage enable transparent archiving of content while enabling its long-term monetization Presentation at 4PM Wednesday in Cloudian Booth South Hall Lower 6321: Komprise will present and have a Q&A discussion on how media organizations are seamlessly cutting storage costs and maximizing monetization of content at the Cloudian booth. M&E businesses are rapidly adopting Komprise because they no longer have to choose between monetizing content and cutting costs through archiving – with Komprise, media businesses can identify cold assets and transparently archive it to any secondary storage of their choice, while the content is still fully accessible as before. No more challenges with media asset tags, or searching across inaccessible archives. “Komprise just finished another record quarter where we signed more media and entertainment customers across both Europe and US,” said Krishna Subramanian, COO of Komprise. “We are pleased to work with our storage partners and enable M&E businesses to handle the growing onslaught of data growth while maximizing monetization of post-production content.” Additional Resources: What’s Hot at NAB 2018 and How it Impacts Data Storage Sign up to book a meeting with Komprise at NAB, and enter a raffle for cool prizes! About Komprise Komprise, the industry-leader in intelligent data management across clouds, empowers businesses to efficiently manage today’s massive scale of data growth while unlocking its value. The Komprise mission is to radically simplify data management through intelligent automation. Komprise is used by enterprises to intelligently manage data at scale. Komprise has been named to Gartner’s Cool Vendors in Storage Technologies 2017. For more information, go to https://www.komprise.com. All other product and service names mentioned are the trademarks of their respective companies. ## Media Contact: Monica Giannella Read pr@komprise.com ### Tech Target : Komprise NAS migration leaves no data behind Komprise Intelligent Data Management customers can now migrate from one NAS system to another NAS system, leaving no data behind on the source filer... Summary Startup Komprise added NAS migration to its list of data management features in the new 2.7 release of its software. Komprise Intelligent Data Management customers can now migrate from one NAS system to another NAS system, leaving no data behind on the source filer. The new feature targets customers who want to replace or decommission one NAS system in favor of a new NAS system, according to Krishna Subramanian, chief operating office at Campbell, California-based Komprise... Read Full Article ### Komprise adds NAS migration to its Intelligent Data Management 2.7 release Komprise now offers effortless, affordable NAS migration as part of the Intelligent Data Management functionality suite at no additional cost CAMPBELL, CA – February 14, 2018 – Komprise, the industry leader in intelligent data management across clouds, today announced the general availability of Komprise Intelligent Data Management 2.7, which includes NAS Migration as a core capability. The new capability will be available at no additional cost to customers, thus extending the use cases Komprise manages to include live archiving, low cost replication/DR, transparent file access, and data migration. NAS Migrations are often a dreaded and laborious part of the storage management lifecycle. Free tools are riddled with risks, time/cost overruns and are extremely labor intensive and prone to errors. On the other hand, traditional migration tools are expensive point products that are complex and do not provide ongoing value - resulting in sunk costs. To match our growth, we have a major data center consolidation initiative underway, and reducing the risk and costs of NAS migrations is a key priority. Komprise makes NAS migrations effortless, reliable, and affordable. Best of all, with Komprise we know we have a long-term solution to manage our data long after the migration is completed,” Dimitar Boyn, Vice President of IT, Friend Finder Networks Komprise provides NAS migrations now as a standard capability in Komprise Intelligent Data Management at no additional cost – so customers can leverage the same simplicity and automation that Komprise already provides for live archiving and DR to now also migrate NAS data. Customer benefits: SET IT AND FORGET IT Simply set up NAS migration tasks in Komprise. Komprise automates and reports on the progress so you can focus on what matters instead of fire-fighting migration errors. AFFORDABLE Using free copy tools like robocopy to migrate data is labor intensive and error prone. NAS Migration products are costly. Komprise provides NAS Migration at a fraction of the cost of alternatives. Plus, you continue to leverage Komprise after the migration for ongoing analytics, cold data archiving, and DR management. EFFICIENT Komprise scales out on demand so you can throttle up or down the migration rates as needed without any dedicated infrastructure 70% SAVINGS Cut Storage, DR, and backup costs on every terabyte moved "We added NAS migration capabilities based on direct feedback from our customers who want to expand their use of Komprise for managing the full lifecycle of data," said Kumar K. Goswami, CEO Komprise. “Komprise makes it effortless and simple for IT to manage data across storage and clouds, with intelligence and analytics.” About Komprise Komprise, the industry-leader in intelligent data management across clouds, empowers businesses to efficiently manage today’s massive scale of data growth while unlocking its value. The Komprise mission is to radically simplify data management through intelligent automation. Komprise is used by enterprises to intelligently manage data at scale. Komprise has been named to Gartner’s Cool Vendors in Storage Technologies 2017. For more information, go to https://www.komprise.com. All other product and service names mentioned are the trademarks of their respective companies. ## Media Contact: Monica Giannella Read pr@komprise.com ### Komprise Reports Record Worldwide Customer Momentum; Appoints Tony Craythorne to Manage Massive Sales Growth Komprise’s game-changing intelligent data management technology affirms its leadership in cutting the costs and the effort of managing data across NAS and cloud storage CAMPBELL, CA – January 9, 2018 – Komprise, the industry leader in intelligent data management across clouds, today announced global record customer adoptions and sales growth, with a year-to-year 500% increase in the number of enterprise customers across all industries and regions, including dozens of Fortune 500 organizations. Komprise also announced today the appointment of Tony Craythorne as Senior VP of Worldwide Sales, bringing decades of deep sales and channel expertise to further accelerate Komprise’s global expansion. Tony brings over 25 years of sales experience, leading sales teams in the USA, Europe and Asia. Most recently he was Senior Vice President Worldwide Sales at NexSan Storage where he re-architected their go-to-market and channel strategy leading to successful consecutive quarters of growth for the company. Prior to NexSan, he held senior management positions at a number of storage companies including Brocade, Hitachi Data Systems, Nexgen (acquired by Pivot 3), Bell Micro and Connected Data. “In less than 3 years since inception, Komprise has raised the bar in data management by simplifying with analytics across storage the use cases of active archival, disaster recovery, and data migrations, and by empowering enterprises to manage their data in the cloud or on-premises at a lower cost,” said Tony Craythorne, Senior VP of WW Sales, Komprise. “Forging ahead in 2018, we are highly confident on our ability to quickly win new enterprise customers, while growing our repeat customer business — all of which will point to our next stage of growth and leadership.” Expanding Market Leadership Founded in 2015, Komprise has set new records for growth with its Komprise Intelligent Data Management solution. In less than three years, Komprise has accomplished the following: Grew employee base worldwide with new field offices across the globe. Hit 500% annual customer growth across segments – Financial Services, Engineering/Manufacturing, Genomics, Education, Public Sector. [Watch a customer video, and a case study] Partnerships with major storage and cloud industry players, and channel relationships 3 new key product releases, supporting different NFS, SMB/CIFS, Object and cloud platforms; Received 8 industry awards including: CRN Tech Innovator, Gartner Cool Vendor, CRN Emerging Vendors, CRN Women of the Channel, among others. Komprise Intelligent Data Management enables customers to streamline NAS storage costs and ongoing cloud operations by using analytics to intelligently automate archiving and replication/disaster recovery, as well as to provide transparent access of data across on-premises NAS storage and the cloud. By using analytics, Komprise’s solution identifies the data best suited for the cloud, then transparently archives and replicates the data. Customer-defined policies are automated by Komprise to move and manage data across on-premises NAS storage and the appropriate storage tiers, in such a way that users and applications continue to see the data as files on their NAS storage. About Komprise Komprise, the industry-leader in intelligent data management across clouds, empowers businesses to efficiently manage today’s massive scale of data growth while unlocking its value. The Komprise mission is to radically simplify data management through intelligent automation. Komprise is used by enterprises to intelligently manage data at scale. Komprise has been named to Gartner’s Cool Vendors in Storage Technologies 2017. For more information, visit Komprise. ## Media Contact: Monica Giannella Read pr@komprise.com ### Komprise deepens relationship with AWS by being named Advanced Tier Technology Partner Deepens the joint sales relationship between Komprise and AWS, provides access to co-marketing funds, and gives them a dedicated partner manager. Being named an Advanced Tier partner deepens the joint sales relationship between Komprise and AWS, provides access to co-marketing funds, and gives them a dedicated AWS partner manager: Read the Article ### Komprise Featured In Educause Startup Alley At Educause 2017, a premier research institution will discuss how Komprise enables Universities to manage data with intelligence and do more with less Startup Alley Philadelphia, PA – October 31, 2017 – Komprise, the industry leader in intelligent data management across clouds, announced today that it has been chosen to present its innovative data management solutions for higher education at the Startup Alley in Educause 2017. Educational institutions and universities are under tremendous pressure to do more with less, and this is especially difficult as the amount of data universities need to manage is growing rapidly in nearly every area, from research data to genomics data to media and sports videos to academic information. Komprise enables educational institutions to manage more data within tight storage budgets by showing analytics on how data is growing and being used, and intelligently automating transparent archival, replication and management of data to cut 70%+ of costs without users and applications facing any disruption. “Komprise has significantly improved understanding of data for both my team and clients of our storage service – being able to sit down with our departments and show them how their data is growing and being used allows a level of understanding that was never before available. They are then able to make decisions about how to better manage their own data, “ said Steve DeGroat, Manager of Enterprise Storage from a Top Research University. Steve will be at the Komprise booth in Startup Alley to share his data management experiences with other IT professionals and CIOs in higher education. The Komprise data-aware management software empowers businesses to efficiently manage today’s massive scale of data growth while unlocking data value. Komprise analyzes data across NFS and SMB/CIFS storage, and transparently archives cold data and replicates data to lower cost cloud or on-premise storage of the customer’s choice. Komprise requires no storage agents, no dedicated hardware or storage, no complex setup or proprietary integrations, delivers native access to data in the cloud, and is delivered as a hybrid cloud Software-as-a-Service (SaaS).  The patent-pending scale-out solution works across any NFS, SMB/CIFS, and REST/S3 storage. “Educational institutions face tremendous pressure to operate lean, and we are pleased to enable our Higher Ed customers to simply and efficiently cut 70%+ of storage costs by intelligently managing, archiving, and replicating data without any disruption or change to the users,” said Krishna Subramanian, COO Komprise. “Komprise is excited to present at Educause and meet with IT professionals from leading educational institutions.” To learn more about Komprise Intelligent Data Management and how it benefits Education customers, visit the data management in education page. About Komprise Komprise, the industry-leader in intelligent data management across clouds, empowers businesses to efficiently manage today’s massive scale of data growth while unlocking its value. The Komprise mission is to radically simplify data management through intelligent automation. Komprise partners include NetApp, EMC, Google Cloud Platform, Amazon Web Services, and Azure. Komprise is used by enterprises to intelligently manage data at scale. For more information, visit Komprise. ## Media Contact: Monica Giannella Read pr@komprise.com ### Komprise Intelligent Data Management Fall 2017 Release Streamlines Hybrid Cloud Data Lifecycle using Analytics The new Fall 2017 release extends Komprise leadership in analytics-driven data management software to now include ongoing data lifecycle management across cloud storage tiers to maximize cost savings while retaining data value Las Vegas, NV – October 2, 2017 — Komprise, the industry-leader in intelligent data management, announced today at NetApp Insight 2017 the general availability of the Fall 2017 release of its flagship software, Komprise Intelligent Data Management. This release further extends the leadership Komprise has established in using data analytics and data insights to efficiently archive, replicate and manage data across clouds by adding new ongoing data lifecycle management capabilities that completely free data from storage lifecycle and enable it to be moved optimally across classes of on premise and cloud storage without disrupting user or application access. As data footprint continues to grow exponentially while IT budgets remain flat, IT leaders require new ways to manage data efficiently without compromising user access or losing the value of data.  Komprise is the first and leading provider of analytics-driven data management software. Komprise analyzes and indexes metadata in a scale-out adaptive architecture to provide insights into data across any NAS, NFS, SMB/CIFS and Cloud/REST/S3 storage leveraging those insights to efficiently automate data archive, replication and management.  It helps businesses understand the value of their data, identify hot and cold data no matter where it sits and transparently automates active archive, replication and data management strategies to reduce over 70% of costs without losing access or data value. Komprise is intelligent data management software that scales out and works across storage without any agents, stubs, dedicated infrastructure, or changes to the hot data or control path; this  enables a sustainable approach to manage the growing onslaught of data with a radically simple to operate solution that require no changes to existing storage, processes, users or infrastructure. New Cross-Cloud Data Lifecycle Management and Analytics Capabilities Komprise has grown rapidly since the launch of its first release last year, and Komprise customers today manage petabytes of data on Komprise. Based on customer demand, Komprise is releasing a new version that build upon the core platform additional data lifecycle management and deep analytics capabilities to help businesses cut costs and optimize data retention: Ongoing Data Lifecycle Management: As data footprint continues to expand, both on premise and cloud storage providers are responding with hot, cool and cold classes of storage that offer vastly different levels of price and performance. Komprise enables businesses to leverage these different classes of storage across different vendors and clouds by providing new data lifecycle management capabilities that not only enable initial movement of data to a target but also ongoing lifecycle management across storage. For instance, customers can now move cold data from any on premise NAS storage to a cloud such as Amazon Web Services S3 or Google Nearline and after the data gets colder due to inactivity, move it to a less expensive tier such as AWS S3 IA or Glacier or Google Coldline.  Customers can define ongoing data lifecycle management policies in Komprise that dictate this movement.  All the moved data continues to be accessible exactly as before from the source regardless of the storage it sits on.   Data Confinement: While most data continues to remain valuable to an organization, sometimes data outlives its purpose or retention period and needs to either be no longer accessible to users or even entirely deleted.  As an example, many organizations are looking at ways to comply with the new GDPR regulations in Europe.   Historically, identifying such data and validating that it is indeed obsolete and no one is using it any longer has not been easy.  Komprise now provides a capability where businesses can identify such data and based on customer-defined policies, Komprise moves that data to a confined location outside the user and application namespace.  IT can validate that users and applications no longer require the data before taking further action on it such as deleting it.   Deep Analytics Beta: Komprise analyzes data across storage and indexes metadata no matter where the data sits.  Businesses can now leverage the full power of this information and perform deep analytics on metadata to get various insights into how data is being used.  For instance, businesses can identify “zombie data” – data that belongs to users no longer in the company but still consuming expensive resources, using Komprise deep analytics.  Komprise efficiently manages metadata without central databases or in-memory resources that create single points of failure and cause scalability issues.  Instead, the Komprise Grid is a patent-pending distributed fault-tolerant scale-out architecture that indexes and analyzes data across storage and efficiently provides analytics into metadata without any central bottlenecks or in-memory databases.   Support for NetApp ONTAP 9: Komprise now supports NetApp ONTAP 9 in addition to NetApp ONTAP 8, NetApp ONTAP 8/7 Mode, and NetApp ONTAP 7. Komprise Intelligent Data Management 2.6 is available now and it is priced to deliver instant savings on every terabyte you manage.  Komprise pricing is based on the amount of data managed, and for $0.007/GB/month, you get the ability to analyze your data, manage and move it by policy, and replicate/copy it.  For more information and a complimentary assessment of your data, visit Komprise. Quotes: “Our customers today use Komprise for a variety of data management needs – to plan capacity and understand data with unprecedented visibility across their storage, to archive and cut costs by transparently extending existing storage to the cloud, and to replicate data to lower cost storage reducing operational backup and DR costs.  As the usage of Komprise grows, customers are looking for additional capabilities that increase the variety of data management use cases we can address and Komprise Intelligent Data Management 2.6 is in response to these needs,” said Krishna Subramanian, COO Komprise. “Komprise provides valuable insights into data across our storage that we previously lacked – such as how data is being used by our different departments, what data is cold and can be managed more efficiently, and Komprise has enabled us to optimize our data across storage regardless of vendor or cloud. The new features in Komprise Data Management 2.6 extend the usability of Komprise as an all-in-one data management solution for our needs by providing deeper insight and ongoing lifecycle management capabilities independent of storage,” says David Kimball, Director of IT at LJA Engineering & Surveying, Inc.   About Komprise Komprise, the industry-leader in intelligent data management across clouds, empowers businesses to efficiently manage today’s massive scale of data growth while unlocking its value. The Komprise mission is to radically simplify data management through intelligent automation. Komprise partners include NetApp, EMC, Google Cloud Platform, Amazon Web Services, and Azure.  Komprise is used by enterprises to intelligently manage data at scale.   For more information, visit Komprise. ### Media Contact: Monica Giannella Read pr@komprise.com ### Komprise launches Komprise Konnect Partner Program Launches Komprise Konnect Partner Program, offering end-to-end data optimization at less cost Campbell, CA – August 28, 2017 – Komprise, the industry-leader in intelligent data management across clouds, today announced the Komprise Konnect Partner Program. The new program is designed to help maximize channel partners’ potential for new revenue streams, and take advantage of the massive growth of unstructured data with the demand for a proven data management solution that works both on-premise and on the cloud. Through Komprise’s innovative data analytics and data management software, global reseller partners can now offer a fully integrated, cost-efficient solution that delivers superior performance for all data management challenges including archiving, replication and cloud integration. “Channel partners are constantly being asked by their customers to help address their data management’s challenges with an efficient and scalable solution, while at the same time taking advantage of the investments they have already implemented,” says Zach Edwards, Director of Channels, Komprise. “Komprise gives our channel partners the expertise to easily deploy a solution that provides insight to their customer’s environment and show them how they can save 70% on capacity costs. With Komprise being a software solution that is fully agnostic, our partners can continue to embrace their legacy ecosystem partners, while at the same time continuing to build their new emerging partnerships with object store and cloud companies.” Connecting All the Dots -- The New Komprise Konnect Partner Program Offers: Product trials and Proof-of-Value tools to show customers in minutes the savings partners can deliver on their NAS footprint Ability to help customers adopt secondary object/cloud storage without any disruption to current NAS users or applications Lower storage and operational costs for customers by archiving, replicating, and protecting data on object/cloud/scale-out storage Partner marketing tools, sales training, and incentives The Komprise data-aware management software empowers businesses to efficiently manage today’s massive scale of data growth while unlocking data value. Komprise analyzes data across NFS and SMB/CIFS storage, and moves data transparently to lower cost NFS, SMB/CIFS or REST/S3 storage of the customer’s choice. Komprise is modern data management scale-out software that requires no agents, no dedicated hardware or storage, no complex setup or proprietary integrations, delivers native access to data in the cloud, and is delivered as a hybrid cloud Software-as-a-Service (SaaS).  The patent-pending scale-out solution works across any NFS, SMB/CIFS and REST/S3 storage. “Komprise is my favorite vendor for moving files and objects because they are doing it better and different than everyone else in the industry,” said Scott Gelb, Solution Architect at EVT. “The new Komprise Konnect Partner program will allow us to continue to deliver measurable value to our existing and new customers by helping them realize their maximum business value, with cloud-enabled technologies and flexible data-aware management.” “With data footprints continuing to grow at an exponential rate, we are constantly looking for new, innovative ways to add value to our customers," said Richard Wright, Area VP of Sales, Kovarus. "Whether it’s through easily providing tailored proof-of-value assessments or accelerating secondary storage adoption, the Komprise Konnect Partner Program provides tools and resources to ensure we are equipped to be our customer’s trusted advisor that will help manage their data more efficiently, while cutting cost and maximizing business value.” The Komprise Konnect Partner Program is available today. For more information or to sign up, page visit the Komprise Konnect page. About Komprise Komprise, the industry-leader in intelligent data management across clouds, empowers businesses to efficiently manage today’s massive scale of data growth while unlocking its value. The Komprise mission is to radically simplify data management through intelligent automation. Komprise partners include NetApp, EMC, Google Cloud Platform, and Azure.  Komprise is used by enterprises to intelligently manage data at scale. For more information, visit Komprise. ### Media Contact: Monica Giannella Read pr@komprise.com ### Europe Can Better Manage its Data Now... with Komprise Exciting times at Komprise! Over the past year, we have received a lot of interest from European resellers, such as BCLOUD in Italy and ITISO in Germany, for a unique data management solution. Now is the Time… Bringing over three decades of storage management expertise, Komprise is expanding operations to the European market. The Komprise Intelligent Data Management solution will help address the challenges of rapid data growth in the European regions. Providing organizations with the right tool to take control of their data growth—intelligent automation. The Komprise software adapts to every customer’s specific environment and scales across their storage silos. Our data management solution is radically simple to deploy, operate, and delivers immediate ROI. Read our press release on how Komprise is Expanding in Europe to learn more. ### Komprise Expands Momentum in Europe for Intelligent Data Management Komprise resellers across Europe see growing customer momentum for Komprise Data Management Campbell, CA – June 27, 2017.  Komprise, the industry-leader in intelligent data management across clouds, is building on its rapidly growing momentum in the United States by expanding to Europe through key channel partnerships.  Komprise resellers in Europe including data management specialists BCloud in Italy, Itiso in Germany and SymStor in the United Kingdom are enabling businesses to cut costs and maximize business value of their data and storage investments using Komprise. BCLOUD, SRL., a leading system integration service provider specializing in transforming IT operations with software-defined IT, today announced a new partnership with Komprise to radically simplify data management for Italian enterprises. itiso, a leading IT infrastructure solutions provider in Germany, is partnering with Komprise to maximize the business value of data management for customers within their own business processes. SymStor, one of the UK’s leading data management solutions providers, has partnered with Komprise to enable its customers to analyse and manage their data more effectively. SymStor are specialists in delivering innovative, service aligned, systems integration for the storage and protection of data in today’s cloud enabled era. QUOTES: “With data footprints continuing to grow at an exponential rate, businesses are more aware of the importance of managing data more efficiently to cut costs and maximize business value. Until Komprise, there has not been an easy way to accomplish this,” said Andrew Martin, CEO SymStor.  “We are excited to partner with Komprise to bring this industry leading technology to our customers.” “We have a long experience in Object Storage S3 and Komprise is a great solution to optimize the data growth needs, manage the data lifecycle and control the storage costs,” said Roberto Castelli, CEO and President of BCLOUD. “Our customers rely on our expertise around data analysis, virtualization, DR, and digital archiving, and we are excited to bring the innovative simplicity, scalability and cost-efficiency of Komprise to our customers,” said Ronald Martens, CEO itiso. “By combining our partners’ trusted market expertise with the proven data management solutions from Komprise, we are excited to enable our customers to address their data management challenges with unprecedented simplicity and efficiency,” says Krishna Subramanian, President and COO, Komprise.  “Together, we enable businesses to tackle data management challenges including capacity expansion, archiving, cloud integration, replication and analytics in a single solution that works across their storage.” About Komprise Komprise, the industry-leader in intelligent data management across clouds, empowers businesses to efficiently manage today’s massive scale of data growth while unlocking its value. The Komprise mission is to radically simplify data management through intelligent automation. Komprise partners include NetApp, EMC, Google Cloud Platform, and Azure.  Komprise is used by enterprises to intelligently manage data at scale.   For more information, visit Komprise.   ### Media Contact: Monica Giannella Read pr@komprise.com ### Cloudian, Komprise Enable Customers to Reclaim 60% of Tier 1 NAS Capacity The Cloudian/Komprise data management and storage solution helps customers tier data transparently to less expensive rsilient storage SAN FRANCISCO – May 24, 2017 - - Cloudian, Inc., a global leader in enterprise object storage systems, and Komprise, the industry-leader in intelligent data management, today announced a joint solution that enables customers to move dormant or infrequently used data to an on-premise active archive and reclaim more than half of their Tier 1 Network-Attached Storage (NAS) capacity. “Organizations have to contend with cold or infrequently accessed data that can occupy more than 60 percent of their most costly NAS resources,” said Jon Toor, Cloudian’s chief marketing officer. “The Cloudian/Komprise solution enables customers to move cold data to an onsite active archive to be transparently accessed when needed, with zero impact to the user experience.” The Cloudian/Komprise solution uses the Komprise data management software to identify inactive NAS data and then transparently move it to an on-premises Cloudian scale-out storage device. Customer-defined policies, such as data age, frequency of use, and file type determine which data should be tiered. All data remains within the data center. “We are pleased to partner with Cloudian and provide customers a simple, frictionless way to adopt cost-efficient secondary storage without disrupting current users or applications, “ said Krishna Subramanian, COO of Komprise. “Our software provides the visibility and detailed analytics that help customers make informed decisions about which data can be non-disruptively migrated off expensive NAS environments and moves data transparently. Once transferred, the data can still be searched, accessed, and modified as files. Users will never know the difference.” The solution is ideal for enterprise customers and service providers who have large amounts of file-based data and need a solution that can reduce storage costs. Cloudian provides high-availability, enterprise-class storage at 70 percent less than the costs of traditional NAS solutions. Additionally, data migrated to Cloudian no longer consumes backup license costs, reducing overall backup cost by as much as 60 percent. “Cloudian and Komprise have given us a new way to manage customer data in a manner that delivers significant cost savings with no noticeable impact on performance or the customer experience,” said Richard Tatham, GM of cloud services at Sithabile Technology Services in South Africa. “With more than 13 years of experience helping customers build and manage storage architectures that span from tape to high end storage devices, we see this solution as the best way for our customers to reduce their storage costs and maintain the performance they expect and need.” Cloudian and Komprise will hold a joint webinar on June 1, at 10AM PST (1PM EST) which will include a solution overview and the return-on-investment achieved by deferring costly NAS expansion projects.  To register for this live event, please visit https://cloudian.com/delay-next-nas-expansion. About Komprise Komprise, the industry-leader in intelligent data management across clouds, empowers businesses to efficiently manage today’s massive scale of data growth while unlocking its value. The Komprise mission is to radically simplify data management through intelligent automation. Komprise partners include NetApp, EMC, Google Cloud Platform, Amazon Web Services, and Azure.  Komprise is used by enterprises to intelligently manage data at scale.   For more information, visit Komprise.com. About Cloudian Based in Silicon Valley, Cloudian is the leader in scale-out object storage. Our flagship product, Cloudian HyperStore, enables service providers and enterprises to build reliable, affordable and scalable hybrid cloud storage solutions. Join us on LinkedIn, follow us on Twitter (@CloudianStorage) and Facebook, or visit us at www.cloudian.com. Cloudian Media Contacts: Touchdown PR cloudian@touchdownpr.com ### Komprise Recognized by Gartner as 2017 Cool Vendor in Storage Technologies Vendors selected for the “Cool Vendor” report are innovative, impactful and intriguing Campbell, CA – May 23, 2017 -- Komprise, the industry-leader in intelligent data management across clouds, announced today that it has been named in the May 2017 Cool Vendors in Storage Technologies, 2017 report by Gartner, Inc.  Komprise is disrupting the multi-billion dollar market for unstructured data management with an easy to adopt, easy to scale, storage agnostic solution that costs a fraction of the alternatives. Since announcing general availability of Komprise Data Management 1.0 last year, Komprise has quickly gained strong traction both with partners and customers. “Our incredible market momentum is a testament to the business value Komprise delivers to customers managing data across clouds,” said Krishna Subramanian, COO and cofounder, at Komprise. “We are excited to be included in Gartner’s Cool Vendors in Storage Technologies, 2017. We see this as a clear recognition of Komprise’s industry-first adaptive data management platform that  enables customers to analyze and manage data across storage to reduce 70% of costs and complexity.” Komprise Data Management Grid The Komprise Data Management Grid builds on the core Komprise analytics-driven management platform to add on-demand linear scaling without any central bottlenecks such as databases or controllers.  Unlike traditional solutions that are costly, complex, and limit scaling with centralized architectures, Komprise is the industry’s first and only adaptive scale-out data management solution that requires no hardware, deploys in 15 minutes, is storage agnostic without any storage agents, uses analytics to intelligently manage, archive, replicate and protect data across both on premise and cloud storage, and scales on-demand to handle massive scale. Scaling Komprise simply involves a single button-click to add more virtual appliances – and the Komprise grid automatically load balances, manages high availability through redundancy, and provides Disaster Recovery.  Komprise creates a sustainable approach to manage the growing onslaught of data with a radically simple to operate solution with no changes to existing storage, processes, users or infrastructure while enabling customers to save 70% of the costs.  For more information and a free trial, visit Komprise. Gartner Disclaimer: Gartner does not endorse any vendor, product or service depicted in our research publications, and does not advise technology users to select only those vendors with the highest ratings or other designation. Gartner research publications consist of the opinions of Gartner’s research organization and should not be construed as statements of fact. Gartner disclaims all warranties, expressed or implied, with respect to this research, including any warranties of merchantability or fitness for a particular purpose. About Komprise Komprise, the industry-leader in intelligent data management across clouds, empowers businesses to efficiently manage today’s massive scale of data growth while unlocking its value. The Komprise mission is to radically simplify data management through intelligent automation. Komprise partners include NetApp, EMC, Google Cloud Platform, and Azure.  Komprise is used by enterprises to intelligently manage data at scale.   For more information, visit Komprise. Media Contact: Monica Giannella Read pr@komprise.com ### Komprise COO Named as One of CRN’s 2017 Women of the Channel Campbell, CA - May 16, 2017 – Komprise, the industry-leader in intelligent data management across clouds, announced today that CRN®, a brand of The Channel Company, has named Krishna Subramanian, President and COO of Komprise, to its prestigious 2017 Women of the Channel list. The executives who comprise this annual list span the IT channel, representing vendors, distributors, solution providers and other organizations that figure prominently in the channel ecosystem. Each is recognized for her outstanding leadership, vision and unique role in driving channel growth and innovation. CRN editors select the Women of the Channel honorees based on their professional accomplishments, demonstrated expertise and ongoing dedication to the IT channel. Krishna Subramanian currently runs the sales and marketing efforts at Komprise, a 100% channel-driven enterprise. Prior to Komprise, Krishna held executive roles at both large companies and startups for over 25 years. She was VP of Marketing for the Cloud Platforms Group at Citrix where she was responsible for marketing and partner development for the Citrix Desktop and Cloud businesses. Krishna joined Citrix in May 2011 through the acquisition of Kaviza, where she served as chief operating officer, and head of marketing, sales and channels. Prior to Kaviza, Krishna led mergers and acquisitions for the Sun Microsystems cloud business that delivered over a half-billion dollars of incremental revenue. Before Sun, Krishna was the CEO and co-founder of Kovair, a venture-backed software-as-a-service CRM company. Subramanian holds a Master's degree in computer science from the University of Illinois, Urbana-Champaign. She brings over 20 years of industry experience in enterprise software, virtualization and cloud computing. “These extraordinary executives support every aspect of the channel ecosystem, from technical innovation to marketing to business development, working tirelessly to keep the channel moving into the future,” said Robert Faletra, CEO of The Channel Company “They are creating and elevating channel partner programs, developing fresh go-to-market strategies, strengthening the channel’s network of partnerships and building creative new IT solutions, among many other contributions. We congratulate all the 2017 Women of the Channel on their stellar accomplishments and look forward to their future success.” The 2017 Women of the Channel list will be featured in the June issue of CRN Magazine and online at www.CRN.com/wotc.  See the interview with Krishna here. Tweet This: @TheChannelCo names @Komprise's Krishna Subramanian to @CRN 2017 Women of the Channel list #WOTC17 www.CRN.com/wotc.   About Komprise Komprise, the industry-leader in intelligent data management across clouds, empowers businesses to efficiently manage today’s massive scale of data growth while unlocking its value. The Komprise mission is to radically simplify data management through intelligent automation. Komprise partners include NetApp, EMC, Google Cloud Platform, and Azure.  Komprise is used by enterprises to intelligently manage data at scale. For more information, visit Komprise.   About the Channel Company The Channel Company enables breakthrough IT channel performance with our dominant media, engaging events, expert consulting and education, and innovative marketing services and platforms. As the channel catalyst, we connect and empower technology suppliers, solution providers and end users. Backed by more than 30 years of unequaled channel experience, we draw from our deep knowledge to envision innovative new solutions for ever-evolving challenges in the technology marketplace. www.thechannelco.com   ©2017. The Channel Company, LLC. CRN is a registered trademark of The Channel Company, LLC. All rights reserved.   For Komprise PR: Monica Giannella Read monica@komprise.com ### Komprise Broadens Support For Dell EMC Platforms At Dell EMC World 2017, Komprise will showcase how enterprises now have a single solution to efficiently manage, move data to, and archive data from Dell EMC Unity, Dell EMC Isilon, and Dell EMC ECS. CAMPBELL, Calif., May 10, 2017 /PRNewswire/ -- BOOTH #1620 – Komprise, the industry-first intelligent data management solution provider, announced today that it is broadening its support of Dell EMC platforms to now include Dell EMC Unity along with the already-supported Dell EMC Isilon and Dell EMC Elastic Cloud Storage (ECS) platforms. Enterprises now have a simple, unified way to move data into the supported Dell EMC platforms from any NFS or SMB source, and to transparently archive and replicate data from Dell EMC Unity and Dell EMC Isilon to cloud targets such as Dell EMC ECS. Komprise will showcase a live demo at Dell EMC World 2017, Las Vegas, Booth #1620. "As enterprises continue their digital transformation, Dell EMC is working with its partner ecosystem to realize opportunities from by cloud-enabled technologies while maximizing flexibility," said Sam Grocott, Senior Vice President of Marketing, Storage and Data Protection, Dell EMC. "We welcome the expanded support of Dell EMC platforms by Komprise." The Komprise data-aware management software empowers businesses to efficiently manage today's massive scale of data growth while unlocking data value. Komprise analyzes data across NFS and SMB/CIFS storage, and moves data transparently across supported storage platforms. It requires no agents, no dedicated hardware or storage, no complex setup or proprietary integrations, delivers native access to data in the cloud, and is delivered as a hybrid cloud Software-as-a-Service (SaaS). The patent-pending scale-out solution works across any NFS, SMB/CIFS and REST/S3 storage. "As rampant data growth continues, enterprises are looking for cohesive ways to understand and manage data across their various storage platforms," said Krishna Subramanian, President and COO at Komprise. "Komprise is excited to broaden our support of the industry-leading Dell EMC platforms to provide customers with additional efficiency and flexibility." The Komprise Data Management solution with new support for Dell EMC Unity, in addition to Dell EMC Isilon and Dell EMC ECS, is available today. For more information, go to Komprise. About Komprise Komprise, the first intelligent data management service, empowers businesses to efficiently manage today's massive scale of data growth while unlocking its value. The Komprise mission is to radically simplify data management through intelligent automation. Komprise partners include NetApp, EMC, Google Cloud Platform, and Azure. Komprise is used by enterprises to intelligently manage data at scale. For more information, visit Komprise. Media Contact: Monica Giannella Read pr@komprise.com SOURCE Komprise Related Links www.komprise.com ### Komprise Raises $12M Series B, Announces General Availability of Komprise Data Management Grid Komprise Builds on Growing Customer and Partner Momentum with New Funding to Scale Expansion Campbell, CA – Tuesday February 14, 2017 -- Komprise, the industry-leader in intelligent data management across clouds, has raised $12M in Series B funding, led by Walden International,  with participation from existing investor Canaan Partners and notable luminaries including Bill Moore (co-founder ZFS, ex-EMC Fellow), and Sanjay Mehrotra (ex-CEO SanDisk).  Additionally, the Komprise Data Management Grid, which scales linearly with no central bottlenecks, is now generally available, enabling businesses to manage the massive scale of today’s data while cutting 70% of costs. Komprise will use the funds to scale operations globally and accelerate the company’s rapid momentum with customers and partners. Since announcing general availability of Komprise Data Management 1.0 last summer, Komprise has quickly gained strong traction both with partners and customers. Komprise has forged partnerships with major storage and cloud players including Amazon Web Services, Google Cloud Storage, Microsoft Azure, HPE, NetApp, EMC, Quantum, SpectraLogic and Scality. Komprise is now being used by leading enterprises across verticals including financial services, genomics, healthcare, manufacturing, engineering, media and entertainment, and government. Komprise is disrupting the multi-billion dollar market for unstructured data management with an easy to adopt, easy to scale, storage agnostic solution that costs a fraction of the alternatives.  According to the analyst firm IDC, by 2020 the digital universe will reach 44 zettabytes, 90% of which is unstructured data.  Existing data management approaches were not designed to handle this monumental explosion in the variety, volume, and velocity of data – 25% of IT budgets are already spent on storage and data management with no room to grow. Komprise enables businesses to squeeze data growth into flat budgets and cut 70% of costs, without disrupting users or the existing storage infrastructure. “We invest in proven teams with a clear solution that addresses a large well-defined market opportunity. Komprise addresses an urgent unmet market need by actually solving the data management problem that others have not – managing data growth intelligently with visibility across storage,” says Lip-Bu Tan,   Chairman of Walden International. “We are excited to help Komprise accelerate its growth as it has the potential to reshape how the world manages its data.” “Few startups have both leading enterprises as customers and major storage and cloud players as partners.  Komprise’ s incredible market momentum is a testament to the clear value it delivers to customers managing data across clouds.  We are excited to continue backing this innovative company and phenomenal team,” says Maha Ibrahim, General Partner at Canaan Partners.” Komprise Data Management Grid The Komprise Data Management Grid builds on the core Komprise analytics-driven management platform to add on-demand linear scaling without any central bottlenecks such as databases or controllers.  Unlike traditional solutions that are costly, complex, and limit scaling with centralized architectures, Komprise is the industry’s first and only adaptive scale-out data management solution that requires no hardware, deploys in 15 minutes, is storage agnostic without any storage agents, uses analytics to intelligently manage, archive, replicate and protect data across both on premise and cloud storage, and scales on-demand to handle massive scale. Scaling Komprise simply involves a single button-click to add more virtual appliances – and the Komprise grid automatically load balances, manages high availability through redundancy, and provides Disaster Recovery.  Komprise creates a sustainable approach to manage the growing onslaught of data with a radically simple to operate solution with no changes to existing storage, processes, users or infrastructure while enabling customers to save 70% of the costs.  For more information and a free trial, visit Komprise. “What impressed me with Komprise is that they have they built an analytics-driven, scale-out approach to automate tiering of data across storage systems.  Not only that, but it is dead simple to try out and deploy, showing up front how it will manage your data across both on premise and cloud storage, saving you management time and storage costs.  Something every CIO I’ve talked to has had as a top priority,” says Bill Moore, EMC Fellow and co-author of the ZFS filesystem. About Komprise Komprise, the industry-leader in intelligent data management across clouds, empowers businesses to efficiently manage today’s massive scale of data growth while unlocking its value. The Komprise mission is to radically simplify data management through intelligent automation. Komprise partners include NetApp, EMC, Google Cloud Platform, Amazon Web Services, and Azure.  Komprise is used by enterprises to intelligently manage data at scale. For more information, visit Komprise.   ##   Media Contact: Monica Giannella Read pr@www.komprise.com ### Cadence uses Komprise Intelligent Data Management to Extend Data to the Cloud and Cut Costs Komprise eliminates cost and complexity of legacy archive solutions with analytics-driven data management working across on-premise and cloud storage Campbell, Calif. – Wednesday February 7, 2017 – Komprise, the industry-first intelligent unstructured data management solution provider, announced today that Cadence Design Systems, Inc. is deploying the Komprise analytics-driven data management solution to gain visibility across its global storage infrastructure and transparently archive data to cut costs and increase efficiency. The Komprise data-aware management software empowers businesses to efficiently manage today’s massive scale of data growth while unlocking data value. The patent-pending scale-out solution requires no agents, no dedicated hardware or storage, no complex setup or proprietary integrations, delivers native access to data in the cloud, and is delivered as a hybrid cloud Software-as-a-Service (SaaS). “Komprise stood apart from other solutions we evaluated because it provides visibility across our storage infrastructure and manages data at scale at a fraction of the cost,” said Carl Siva, senior IT group director, Architecture and R&D Solutions at Cadence. “We had Komprise up and running in minutes, and we quickly gained insight into how much of our data was not being actively used so that we could archive it to the clouds of our choice—all without any disruption to users or applications. As our business grows, our data grows, and Komprise offers flexibility to meet the requirements of our business.” Cadence enables global electronic design innovation and plays an essential role in the creation of today’s integrated circuits and electronics. With the volume of business data continuing to grow, managing it efficiently is key for managing costs. With data volume in petabytes, and growing every day, Cadence wanted a scalable, distributed data management solution that would work across its existing storage infrastructure via the cloud. Legacy data management solutions were too costly, too complex, and hard to scale. By choosing Komprise, Cadence can cut costs, while creating a path to the cloud. “Cadence plays a key role in the electronics and semiconductor industry, providing the design automation software vital for innovation,” says Krishna Subramanian, COO Komprise. “By helping efficiently scale and automate data management, we can enable its IT team to manage data growth while keeping costs down to drive business results.” About Komprise Komprise, the first intelligent data management service, empowers businesses to efficiently manage today’s massive scale of data growth while unlocking its value. The Komprise mission is to radically simplify data management through intelligent automation. Komprise partners include NetApp, EMC, Google Cloud Platform, Amazon Web Services, and Azure. Komprise is used by enterprises to intelligently manage data at scale. For more information, go to www.komprise.com. ## Media Contact: pr@www.komprise.com ### Komprise Seamlessly Extends Enterprise Storage to new Google Cloud Storage Coldline Komprise Seamlessly Extends Enterprise Storage to new Google Cloud Storage Coldline Solution uses data insights to intelligently and transparently extend existing storage capacity San Jose, Calif. – October 20, 2016 -- Komprise,  the industry-first intelligent data management solution, now enables enterprises to analyze and identify inactive data across their existing storage and transparently move the data to Google Cloud Storage offerings including the new Coldline storage class to maximize cost-savings without disrupting user access. With no hardware to deploy, no storage agents, no static stubs, no changes to the hot data path, no complex configurations or proprietary interfaces, Komprise seamlessly works across storage environments, both on-premise and cloud. Customers use Komprise to understand data usage and growth across their existing storage infrastructure and transparently move inactive data to on-premise targets as well as to the cloud. Komprise has extended its validated support of Google Cloud Storage targets to include Coldline so IT administrators can leverage different tiers in the cloud while users and applications can continue to access the moved data without any changes.  Customers can also manage the lifecycle of data transparently across different tiers of Google Cloud Storage using Komprise. Komprise is offering a free assessment/trial for customers who want to understand the cost benefits of using Google Cloud Storage in their environment. Sign up at: https://www.komprise.com/google/ “As data growth continues to explode, and businesses have mandates to reduce their burgeoning storage costs they are looking for ways to leverage the cloud without disrupting existing users or applications. Komprise enables customers to transparently extend their footprint to leverage the Google Cloud Storage offerings including the newly launched Coldline,” said Krishna Subramanian, COO of Komprise. “Komprise gives customers the visibility they crave into their data footprint, and enables them to leverage the cloud seamlessly without any disruption to user or application access.” Read more about the integration here. About Komprise Komprise, the first intelligent data management service, empowers businesses to efficiently manage today’s massive scale of data growth while unlocking its value. The Komprise mission is to radically simplify data management through intelligent automation. The Komprise team has a successful track record with two prior businesses of eliminating storage/IT cost and complexity. Backed by Canaan Partners, Komprise was founded in 2014 and is headquartered in San Francisco. For more information, go to www.komprise.com. ## Media Contact: Monica Gianella pr@www.komprise.com 1.888.995.0290 ### Do You Know What Your Unstructured Data is Costing You? This article has been adapted from its original version on Dataversity. CXOs this year have witnessed a rollercoaster economy amid plenty of turbulent events – from ongoing inflation affecting consumer spending to large stock market swings, major overseas conflicts, and the uncertainties of an election year. Not surprisingly, the economic forecast remains murky at best. According to a CNBC CFO survey, CFOs seem to agree that inflation will remain elevated into 2026 and 54% say the economy is either in a recession or will enter one over the next year. Corporate leaders have been managing their workforces and business strategies during times of significant upheaval in recent years, starting with the COVID-19 pandemic. The problems that have been simmering beneath the surface for years – such as unsustainable costs in key consumer sectors like healthcare – are now becoming too hard to ignore. Yet here’s the glitch: Nobody wants to be left behind in the latest wave of technological disruption. Chief officers are looking at AI with a hard eye. They want to leverage AI magic soon for both operational efficiency and competitive advantage. They know that AI will eventually require notable investment across people and technology. If data is the fuel to power AI, IT leaders must ensure the organization’s data is in the best shape possible. This requires that IT teams clean up the data mess and get control of the unstructured data that is fueling AI and ML. A looming barrier is that today’s data estate incurs high costs to manage, impeding the budget for AI. The causes behind high unstructured data costs 1. Unstructured data, which makes up at least 90% of all data being generated in the world, is heavy and hard to manage. This data can be both big and small, including user documents, emails, text and chats, images, audio, video, sensor data, instrument data – anything not stored in rows and columns in a database. Nearly half of organizations are storing more than 5PB of unstructured data and nearly 30% have more than 10PB, according to the Komprise 2024 State of Unstructured Data Management report. The survey, in its fourth year running, has consistently uncovered that most organizations are spending 30% or more of their IT budget on data storage. In a large enterprise, this could be millions of dollars annually. And with data growing so quickly – from roughly 60 zettabytes (ZB) in 2020 to an estimated 180 ZB in 2025 – these costs will keep rising unheeded. 2. Data hoarding contributes to the problem. It’s more common to keep all the data year over year than to institute data deletion, or even data archiving policies. Enterprises often retain unstructured data for decades because it contains useful information such as customer insights, potential research intelligence, and machine learning training data for the future. Audits and compliance requirements also support the long-term or never-ending storage of data. Yet with today’s data growth rates, it’s no longer sustainable economically or from an energy standpoint to keep data forever. 3. Data storage is only 25-30% of the total cost. Most of the expense of storing unstructured data lives beyond primary storage. IT teams need to protect data with backups and replicate it for disaster recovery and that means creating multiple copies of data to store, manage and secure. Yet most data is not mission critical or active and can be archived at a lower cost and without making multiple copies. 4. One-size-fits-all storage: The previous point leads to this one; organizations too often store all or most of their data on expensive network attached storage (NAS) technologies when most of it doesn’t need that level of performance and availability. Years ago, before the Internet and mobile phones had taken off, this wasn’t a problem. Enterprises didn’t have enough data to worry about where it was stored. Today, though, there are many classes of data storage on-premises and in the cloud that help organizations store data in the right tier at the right time to save money. Placing “cold data” that hasn’t been accessed in a year or more to lower cost, secondary storage can save anywhere from 60 to 80% a year on annual data storage, backup and DR costs. 5. Lack of visibility: The ability to save on data storage and implement the cost-effective strategies outlined above is impossible without insights on the data such as its rate of growth, how much you have, storage costs, types and sizes of files, top owners, data usage trends and its value to the organization. This lack of visibility also brings data governance and compliance risk: You cannot protect your data if you don’t know what it is or where it lives. Getting this information is possible with an independent, unstructured data management solution that can gather metrics across all storage. With unstructured data analytics, from the data center to the cloud, you can create data management policies for different data sets. You can automatically move stale or cold data to lower-cost archival storage such as in the cloud, and the savings can be astounding. On a 4PB NAS environment with a 30% year-over-year growth rate, your enterprise could save over $2.6 million or more annually with the right cold data tiering and/or archiving strategy alone. Unstructured Data Cost Savings Decision Tree Here are three ways to model potential cost savings from unstructured data management: Cold data potential savings • How much data is rarely-used or cold? • How much you can save by archiving it, based on your own cost model? Orphaned data potential savings • How much data is orphaned such as from ex-employees? • How much is this costing us today? • How can we save either through archiving or staged deletion? • This also improves security posture and reduces risk. Duplicates potential savings • How much data might be potentially duplicates? • Should we have a process to work with data owners to reduce these? Unstructured data has been piling up unnoticed for years in data centers. Today, enterprises finally can classify, organize, and move it to affordable AI and ML tools where it can generate new value. But first, business leaders need to understand its costs and risks, and how to reduce both with the right data management strategy. ### Komprise Recognized as an IDC Innovator for Knowledge Management Technologies Komprise recognized for the enterprise need to index, manage and govern data for cost-optimization and AI initiatives. Campbell, CA – May 30, 2024 – Komprise, the leader in analytics-driven unstructured data management and mobility, today announced it has been named an IDC Innovator in the report: IDC Innovators: Knowledge Management Technologies. (Doc #US51480324, April 2024). The company was recognized for “Komprise Intelligent Data Management, a single platform to analyze, move and manage unstructured data.” You can read the excerpt here. The report states: “Over 90% of the data we produce is unstructured (source: IDC's Global Datasphere 2023) and it is a key asset of enterprise intelligence as well as a big part of storage costs. Komprise reduces the complexities with managing unstructured data growth with location-agnostic file analysis and indexing. That analysis is purpose-built to not "get in the way" (i.e., it will not disrupt data movement and operations). “Komprise's Intelligent Data Management helps the enterprise do two valuable things: unlock the value hidden in unstructured data and reduce storage costs. Proper metadata tagging and access ensures AI solutions can extract and present the right data, at the right time, and to the right person. Intelligent Data Management prepares unstructured data for AI consumers such as data lakes that feed into enterprise applications, including knowledge management platforms. The platform enables multiple use cases: migration, data tiering, replication, workflows, and AI preparedness.” The report noted the following differentiators of Komprise: Analytics UI, global search capabilities and Smart Data Workflows. In May, Komprise announced Smart Data Workflow Manager, a no-code AI data workflow builder that addresses use cases such as sensitive data identification, chatbot augmentation, image recognition and more. Komprise customers are enterprises in multiple sectors with petabyte-scale environments, including brand names such as Pfizer, Marriott, Kroger, NYU and Fossil. The SaaS company has achieved numerous honors this year, including a Gold Stevie Award in Data Tools & Platforms and TMC.net Cloud Computing Product of the Year. “Being named an IDC Innovator is a great honor and we believe our inclusion indicates how organizations are starting to treat data independently of storage to ascertain and nurture its true value across hybrid cloud infrastructure,” says Krishna Subramanian, COO and cofounder of Komprise. An IDC Innovators report presents a set of vendors – under $100M in annual revenue at the time of selection – chosen by an IDC analyst within a specific market that offer a new technology, a groundbreaking solution to an existing issue, and/or an innovative business model. It is not an exhaustive evaluation or a comparative ranking of all companies, but rather a document that highlights innovative companies in a specific market segment. IDC INNOVATOR and IDC INNOVATORS are trademarks of International Data Group, Inc. About Komprise Komprise is the leading provider of unstructured data management and mobility software that frees enterprises to easily analyze, mobilize, and monetize file and object data across hybrid cloud data storage without shackling data to any one vendor. With Komprise Intelligent Data Management, enterprise IT teams optimize enterprise storage, backup and cloud costs while making the right data available to analytics and AI tools. www.komprise.com ### Komprise Elastic Replication Cuts Disaster Recovery Costs for Unstructured Data by 70% Komprise Intelligent Data Management Winter 2024 release introduces snapshot-based file-to-file and file-to-object replication at the share level for affordable disaster recovery. Campbell, CA, February 6, 2024— Komprise, the leader in analytics-driven unstructured data management and mobility, today announced Komprise Intelligent Data Management Winter 2024, which introduces Komprise Elastic Replication to drastically cut the cost of replicating unstructured data. With more frequent and devastating natural disasters, cybersecurity and ransomware attacks, data protection and disaster recovery (DR) strategies are essential in the enterprise. IT outages are getting more and more costly. According to the New Relic 2023 Observability Forecast report, the median annual cost of an outage has now reached $7.75 million. Traditional approaches of network attached storage (NAS) mirroring work well for mission-critical file and object data, but in most organizations, it is too expensive for less critical unstructured data. This is because it requires identical infrastructure on the DR site and requires the replication of the entire physical volume. Additionally, NAS mirroring does not address ransomware due to its near instantaneous synchronization, which copies a ransomware infection immediately to the DR site before it can be detected. As of 2023, more than 72% of businesses worldwide were affected by ransomware attacks and the average cost of an attack was $1.85 million, according to Statista and GetAstra, respectively. A More Affordable and Secure DR Strategy for Unstructured Data Komprise Elastic Replication makes DR more affordable for growing volumes of unstructured data in the enterprise by right-sizing and right-placing DR copies and offering a more holistic approach to ransomware and data protection. Key benefits include: Right-sizing at the share-level: Choose which shares or even directories you want to replicate, without having to replicate the entire physical volume. You can also set the snapshot schedule to achieve your recovery point objective (RPO) for each data set. Right-placing to less expensive cloud/object or file destination: Replicate to a significantly less expensive cloud or object storage or to any file storage. Unlike NAS mirroring, Komprise does not require pre-provisioned identical infrastructure on the replication site, so you can replicate to the cloud and spin up resources as needed for cloud disaster recovery. Enabling file recovery with fidelity even from object storage: Komprise maintains file attributes while replicating data in native format to object or file storage destinations. This way, you can access the data directly from the destination or restore as files without losing fidelity. Improving cyber-resiliency against ransomware attacks: Komprise replicates asynchronously based on the schedule you set and it supports replication to an immutable destination with versioning and object-lock capabilities. This ability to asynchronously replicate to immutable storage enhances your defense against ransomware. Cutting DR costs: Komprise Elastic Replication right-sizes and right-places the replication saving 70%+ costs, as shown in this blog post. “Our customers are uneasy about not having disaster recovery plans for all unstructured data, but as unstructured data volumes continue to balloon, the one-size-fits-all mirroring approach is too expensive for most,” says Kumar Goswami, CEO of Komprise. “We are excited to help organizations customize disaster recovery so they can afford the protection they need for all of their data, within tight budgets.” Komprise Expands Pure Storage Support, Adds More Reporting Features As a modern SaaS platform, Komprise can innovate faster and be more responsive to enterprise customer needs. Additional updates in the Winter 2024 Intelligent Data Management release include: Customizable Report Templates: Komprise now allows users to save custom report configurations and maintain multiple versions of any type of report. For example, a Showback Report can be created for each department or business unit within an organization. Pure Storage: Building on the transparent data tiering between FlashBlade//S and FlashBlade//E, Komprise now supports FlashBlade object storage as an on-premises Plan Target. Read the white paper. Availability Komprise Elastic Replication is included in the Komprise Intelligent Data Management platform. To learn more about Komprise Elastic Replication visit www.komprise.com/whatsnew. About Komprise Komprise is a provider of unstructured data management and mobility software that frees enterprises to easily analyze, mobilize, and monetize file and object data across hybrid cloud data storage without shackling data to any one vendor. With Komprise Intelligent Data Management, enterprise IT teams optimize enterprise storage, backup and cloud costs while making the right data available to analytics and AI tools. www.komprise.com ### Komprise Winter Update Brings DR Savings & New Reporting Features Elastic Data Replication is snapshot-based file-to-file and file-to-object replication at the share level for affordable disaster recovery. Disasters – both natural and human created - are on the rise. Natural events like pandemics and climate events are becoming more frequent and more devastating. Cyberattacks and ransomware threats are increasingly sophisticated, hard to detect, and costly. The year 2023 had the most billion-dollar-plus climate disasters than any other year, costing the U.S. alone an estimated $92 billion, according to NOAA. Additionally, according to the New Relic 2023 Observability Forecast report, the median annual cost of an IT outage has now reached $7.75 million. Disaster recovery (DR) is no longer just for the most critical. data. IT organizations must find a way to keep an extra copy of all their large volumes of unstructured data for DR purposes. NAS mirroring is too expensive for growing unstructured data volumes Replicating huge, growing volumes of unstructured data in an identical mirror (known as NAS mirroring), however, is proving to be too expensive for most organizations. NAS mirroring is commonly used to ensure data can be accessible in the event of a disaster, but it requires identical storage infrastructure offsite. This doubles the cost of the infrastructure. Also, NAS mirroring is at the volume level, so you cannot choose which data to replicate and to where; all shares on the volume, including data and snapshots are replicated. Paying for an identical replica of infrastructure is often warranted for critical data, but it is hard to justify this expense for non-critical data. For this reason, non-critical data is often left unprotected. Organizations want to pick the right level of replication depending on the nature of unstructured data in each share, without having to make blanket decisions for entire volumes as required by NAS mirroring solutions. NAS mirroring is synchronous and does not fully protect from ransomware attacks Another challenge with NAS mirroring is that although it provides near instant recovery, it does not really protect from cyberattacks because changes are propagated to both sites at nearly the same time. While this ensures a near-instantaneous Recovery Point Objective (RPO), its drawback is that a ransomware infection in the primary site will also infect the secondary site. Komprise offers the ability to asynchronously replicate data at the share level to right-size DR for unstructured data. Komprise Elastic Data Replication With the release of Komprise Elastic Replication, Komprise now supports share-level asynchronous data replication capabilities. This is supported by snapshot-based file-to-file, file-to-object and object-to-object migrations. Komprise Elastic Replication allows IT users to configure replication policies granularly for specific shares or directories and not just at the volume level, delivering the following benefits: Customizable RPO: You can define the frequency of replication per share by picking the schedule by which Komprise takes snapshots and copies incremental changes to the secondary site. Therefore, you can pick the appropriate Recovery Point Object (RPO) for each share based on the criticality of that data, for both file-to-file and file-to-object replication. Non-identical infrastructure at secondary site: Komprise replicates data without requiring the secondary site to be identical to the primary. You can choose to replicate files from a NAS to any other file or object destination. Now, organizations can use lower-cost object storage at the destination such as the cloud for data that does not need to be recovered within days while using higher-cost file storage at the destination for data which requires a faster recovery. Asynchronous replication and support of object-locked destinations help in defense against ransomware: Komprise asynchronously replicates data based on a schedule you can set for each share. This provides a buffer for ransomware infections on the source share to propagate to the destination. Komprise also allows you to replicate file or object data to object-locked destinations. This offers additional protection as even if newer versions of copies are infected, you can recover from older versions. Policies configurable for each share: You no longer have to mirror an entire volume. Komprise enables you to pick which shares you want to replicate, to where, and on what schedule. You can configure different policies for different shares in the same volume. Full file fidelity: Komprise retains the file metadata even when replicating to an object share, delivering full file fidelity when restoring from the object share for NFS data and NTFS-level fidelity for SMB data. Komprise Share-based Replication saves 70%+ over NAS Mirror Assume you have a source volume with X amount of data and a typical snapshot overhead of 30%. With synchronous NAS mirroring, this would create 1.3X of footprint. Now, assuming the following profile of data/shares on the volume, you can see that Komprise Elastic Replication creates over 70% savings. Customers can still recover data as files, with a higher RPO for the shares that need it and lower RPO via object storage for the shares that are less critical. Right-sizing DR makes protecting unstructured data affordable and tenable The right DR strategy is becoming more critical as disasters are on the rise. The threat of cyberattacks requires asynchronous approaches to replication. NAS mirroring provides nearly instant recovery, which is excellent for critical data but an overkill with high expense for most unstructured data. Komprise now gives customers a simple way to configure their replication destination, schedule, and costs per share. This allows organizations to right-size DR and maintain the most cost-effective DR strategy as unstructured data continues to grow. Additional Winter 2024 Updates Other highlights of the Komprise Intelligent Data Management Winter 2024 update include: Customizable Report Templates: Komprise now allows users to save custom report configurations and maintain multiple versions of any type of report. For example, a Showback Report can be created for each department or business unit within an organization. Learn more about Komprise Reports. Pure Storage: Building on the transparent data tiering between FlashBlade//S and FlashBlade//E, Komprise now supports FlashBlade object storage as an on-premises Plan Target. Read the Pure transparent tiering white paper. VAST Data Platform (S3) supported as Plan target. ### Optimizing Pure Storage FlashBlade: Capacity, Performance and Cost Over the past few years, Pure Storage has done an amazing job of addressing the spectrum of unstructured data storage. And Gartner continues to recognize them as a leader: Pure Storage Named a Leader for Third Consecutive Year in the 2023 Gartner® Magic Quadrant™ for Distributed File Systems and Object Storage Pure Storage Named a Leader in the 2023 Gartner® Magic Quadrant™ for Primary Storage As a long-time unstructured data migration and data management partner, one question we often get asked by Pure Storage customers is, “How do we ensure maximum capacity and performance in the most cost-efficient way?” That’s where Komprise Intelligent Data Management comes in. Komprise is able to right-place data across the Pure Storage FlashArray File Services, FlashBlade//S and FlashBlade//E platforms to deliver optimal price / performance. In this new technical white paper authored by the experts from Pure Storage we show you how. Here are some excerpts from the white paper: Pure Storage FlashBlade Overview Pure Storage FlashBlade® is a consolidated storage platform for unstructured data, be it file or object, that is built for unlimited scale. FlashBlade//S is designed to deliver the efficiency, density, and top performance that modern unstructured data needs at scale. FlashBlade//E is designed to deliver the environmental, ease of use, and reliability benefits of all-flash storage for unstructured data workloads, but at a cost competitive with disk-based storage solutions. While performance-optimized FlashBlade//S and capacity-optimized FlashBlade//E can scale-out to petabytes of data, organizations can be most cost-effective by keeping data at the most relevant FlashBlade based on where data is in its lifecycle. Komprise Overview Komprise, a SaaS for unstructured data management and mobility, was designed with the understanding that data is always in motion and should not be treated the same. Komprise gives enterprises the ability to intelligently manage their data by identifying rarely accessed data from FlashBlade//S and transparently tiering it to FlashBlade//E without any changes to the user or application access. With Komprise Transparent Move Technology (TMT), users and applications access data in the same location as before with Komprise use of standard symbolic links (symlinks). The combination of Komprise Intelligent Data Management with the FlashBlade line of high-performance, resilient storage ensures the optimal cost/performance ROI in the industry. Benefits of Komprise Intelligent Tiering for Pure Storage customers: Different tiering policies for different types of data Eliminates rehydration Access data in native format Access data from any tier without going to the source The author validates the solution through a series of documented tests: Tiering NFS data on FB//S to NFS on FB//E Tiering NFS/SMB data on FB//S to Object on FB//E Accessing tiered data on NFS and Object on FB//E Recalling tiered data from FB//E to FB//S Download the white paper to further understand the need for and benefits of Pure Storage FlashBlade transparent data tiering, suggested architecture and solution validation. Learn more about Komprise for Pure Storage. ### Komprise Achieves Inc. 5000 Ranking Again in 2023 Unstructured data management SaaS provider doubles revenues for a third consecutive year, as enterprises seek solutions for storage cost savings and AI value. Campbell, CA, August 15, 2023 – Komprise, the leader in analytics-driven unstructured data management and mobility, announces that the company has been named for the second year in a row to the annual Inc. 5000 list, the most prestigious ranking of the fastest-growing private companies in America. Komprise was selected based on its revenue growth from 2019 to 2022. Komprise Intelligent Data Management helps enterprises address the exponential growth of unstructured data, which comprises at least 80% of all data created today. Komprise delivers advanced analytics on all data in storage to help IT teams save significantly on storage and backup costs and improve compliance and data visibility. Komprise delivers cloud cost optimization capabilities so that organizations can move to the cloud with the best ROI and without disrupting user access. Komprise also delivers a foundation for unstructured data governance, with deep search and analytics on data assets and data migration, data tiering and data services to help improve outcomes and reduce risks from AI initiatives. Komprise 2023 Highlights: Komprise started the year by announcing a $37 million infusion of growth capital and strong results from 2022 during which the company doubled subscription revenues for a third consecutive year. Komprise also achieved noteworthy customer loyalty and satisfaction with 120% net dollar retention (NDR) and generating 30% of revenues in 2022 from expansions. Also this year, Komprise announced further momentum with its alliance partners, introducing Komprise Intelligent Tiering for Azure, an exclusive offering for Azure customers. Komprise continued customer-led product innovation with a new standalone subscription for Komprise Analysis, new unstructured data reports for better departmental collaboration, and new features for unstructured data governance and access. The company’s work across petabyte-heavy industries such as healthcare, life sciences, public sector, media and entertainment and energy has garnered several new awards this year. “It’s fantastic affirmation to make the Inc. 5000 list for the second year running,” says Krishna Subramanian, COO and co-founder of Komprise. “This has been another difficult year for tech companies and we’re fortunate to have a proven solution for IT organizations to generate demonstrable ROI from new ways of managing and getting value from unstructured data. Our goal is to continue delivering quantifiable benefits to our customers while also considering emerging needs for unstructured data management—from governance to enabling more efficient AI workflows and cloud migrations.” For complete results of the Inc. 5000, including company profiles and an interactive database that can be sorted by industry, location, and other criteria, go to www.inc.com/inc5000. The top 500 companies are featured in the September issue of Inc. magazine, available on newsstands beginning Tuesday, August 23. “Running a business has only gotten harder since the end of the pandemic,” says Inc. editor-in-chief Scott Omelianuk. “To make the Inc. 5000—with the fast growth that requires—is truly an accomplishment. Inc. is thrilled to honor the companies that are building our future.” About Komprise Komprise is a provider of unstructured data management and mobility software that frees enterprises to easily analyze, mobilize, and monetize the right file and object data across clouds without shackling data to any vendor. With Komprise Intelligent Data Management, you can cut 70% of enterprise storage, backup and cloud costs while making data easily available to cloud-based data lakes, analytics and AI tools. www.komprise.com. Media Contact: Kevin Wolf, TGPR kevin@tgprllc.com ### New Reports on Unstructured Data & Storage Costs from Komprise Komprise is expanding the reports that we have available for customers to download and share. While customers have always been able to view these metrics in the product UI, sometimes it’s helpful to easily share reports with teams or individuals who do not have access to the Komprise Intelligent Data Management software solution. Now, IT managers don’t have to create these popular unstructured data reports – they’re available with the click of a button. We’re happy to share these new reports which are now available from the Reports Tab in Komprise. Some of these require a Deep Analytics subscription, as indicated below. In addition to these new reports, Komprise users can download and share reports for Data Analysis Summary for their data tiering strategy or Migrations to see a summary of all data migrations performed using Komprise. Learn more about Komprise Analysis. Watch the video series: Storage as a Service (STaaS) best practices. Showback Report The Showback Report displays the current data footprint for a department along with breakdown by top file types, shares and users. It shows storage costs, savings from ongoing data tiering to lower-cost storage and further savings opportunities. Benefits: Showback helps departments better understand and track their IT expenses and make informed decisions about resource allocation and utilization. Read the blog post: Bringing Data Storage Insights to Business Teams and Departments. Orphaned Data Report Requires Komprise Deep Analytics The Orphaned Data Report shows metrics on data from ex-employees—sometimes referred to as “zombie data” or “unowned data.” Most organizations have no idea how much orphaned data they have nor how much it’s costing them—and that’s both a cost liability as well as a potential compliance issue if your organization has policies on deleting ex-employee data. Don’t keep data you don’t need. Benefits Shows amount and cost of orphaned data; Recommends actionable steps to reduce these costs; Lists the top 10 shares with orphaned data by size.   Query Summary Report Requires Komprise Deep Analytics The purpose of the Query Summary Report is to deliver insight on any Deep Analytics query, showing number of files, size of data and space consumed by last access of those files. Benefits: Deep Analytics users can share their query results charts as a PDF so that stakeholders can understand uses and benefits of DA; IT can get deeper intelligence on top query interests across different use cases to inform analytics and query needs across the organization.     Potential Duplicates Report Requires Komprise Deep Analytics Deleting data that is not needed naturally lowers the storage footprint and energy usage. Often, especially in research organizations, data sets are replicated for different experiments and tests but never deleted. Excess duplicate data raises storage and backup costs needlessly, increases data sprawl and potentially grows compliance and security risk. Watch the demo. Benefits: See how much potential duplicate data you have and how it breaks down by size; Download a detailed CSV with details and locations of each copy; Understand the cost savings you can achieve by deleting duplicates.   Get More from Komprise with Unstructured Data Reports Accurate metrics on data growth, costs, hidden and orphaned data, file types, usage and capacity are critical components of unstructured data management solutions and the growing field of FinOps. Being able to share those metrics in easy to share and read reports with stakeholders in IT and across the business helps keep everyone on the same page, with shared goals. ### Navigating IT Spending Versus Cost Cutting in 2023 It’s March and across the U.S., wild weather continues—winter heat waves on the East Coast, record snowfall and cold in the West. What is an atmospheric river anyhow? People love to talk about the weather. Why? Because it’s common ground in a world where there’s frankly too much emphasis on our differences. People also find common ground on the topic of money: how to make it, how to save it, how to spend it. IT teams are no different in this regard and one major theme for 2023 so far has been how to avoid unnecessary expenditures. How to do more with less? The first item in this occasional blog on industry news speaks to the opaque nature of data storage spending. ---------- Seesaw Storage Vendor Revenues Chris Mellor, editor of Blocks & Files, wrote about the storage supplier market which has been partly affected by the global economic downturn. While Micron, Western Digital and Seagate have seen revenues spiraling downward, other data storage vendors are seeing healthier outlooks: “NetApp is slowly distancing itself from HPE in storage revenue terms, though both rise above the rest. Three suppliers are edging towards HPE – Pure, followed by Snowflake and Nutanix, which has been overtaken by Snowflake.” ---------- Cloudy Decisions on Cloud Similarly, there are opposing trends going on in cloud storage right now. Our CEO Kumar Goswami wrote about the Bulls and Bears on cloud adoption in his recent blog. The big cloud players have all seen softening revenues per CNBC as customers pull back on cloud spend after being burned on pricing. Yet in his second blog on how to optimize cloud spend, Kumar offers ideas on how to maintain an active cloud transformation journey while still being extremely cost-efficient. ---------- New Things to Ponder in IT Land It’s no fun to spend all day on the tactical. Below we’ve gathered a few new ideas to help your organization keep moving forward, even during times of high uncertainty. Top IT Job: Serial Specialist. IT leaders are weary of the ‘war for talent, says Deloitte’s chief futurist, Mike Bechtel, as interviewed in ERP Today. “The treasure hunt for the mythical 10x engineer is time consuming, and if you’re lucky enough to finally find her, you pay a queen’s ransom. Marry that up with our further research finding that the average lifespan of emerging tech is down to 2.5 years, and you have an unattractive proposition: You need a new superhero all over again.” So the new type of ideal tech worker is a “Serial Specialist” who can quickly get up to speed on a new technology and crush it and then in a few years move on to the next big thing. These are people who demonstrate a high degree of “aptitude, attitude, and curiosity,” Bechtel explains.   Multi-cloud isn’t easy: you need the metacloud. As David Linthicum defines it in InfoWorld: the metacloud is really a cross-cloud service layer that exists so we won’t have to define specific technologies for each public cloud that’s part of our multicloud. It solves a few problems, such as reducing complexity so that we’re not doing redundant things such as working with specific native security technology within each cloud provider.” The metacloud will be increasingly important yet tricky to mainstream since big vendors always want to protect their revenue streams. Says Deloitte’s Bechtel: “They’re understandably committed to incentivizing folks to maximize – or at least optimize – their time spent on their own platforms. That said, interoperability is always table-stakes, and the more that leading vendors provide in the way of shared interop, the less demand they’ll end up conceding to third parties.” Spend on stuff that saves money. Even if you aren’t facing IT budget cuts in your organization, if you can save money in a commoditized area like storage or networking, you can spend more somewhere else that can deliver real business value such as big data analytics or customer experience technology. Forbes surveyed some business leaders on new tech-oriented ideas and the concept of tech-driven efficiency topped the list. “There will be more artificial intelligence and machine learning developments related to automation and optimization, multicloud architectures, and FinOps, as well as more self-service digital products.” ---------- Komprise News: Introducing Komprise Analysis Komprise Analysis Subscription: In the vein of saving money, Komprise just announced a new Analysis-only subscription so that IT organizations can get insights on data such as data growth rates, top file types and sizes, storage costs and usage trends—without purchasing the full platform. Komprise Analysis also helps IT understand data better to improve compliance and support departmental objectives. Read the press release here. ---------- Analyst Steve McDowell covered the news on Forbes: “What I like most about the new Komprise Analysis offering is how it helps IT organizations control storage costs. The tool allows you to model the financial impact of different data management policies and cost models to help IT administrators make the right decisions about their data.” ---------- SearchStorage reporter Tim McCarthy also covered the news: “Komprise wants to take the management guesswork out of moving customer data without demanding a full commitment to its software suite.” Read the article here. Chris Mellor wrote in Blocks & Files: “The beauty of this is that you can pretty much immediately see if it’s going to be worthwhile buying Komprise’s software for hierarchical storage management functionality.” ---------- ### Komprise CEO talks up agnostic information lifecycle management Customers want data storage cost savings and ongoing cost optimization. Customers want flexibility of where their data lives and customers want to run workflows and monetize their data. This demand is driving the market, and it is excellent validation of the Komprise vision and value proposition. ### What Can Komprise Analysis Do For You? Gain insights into your unstructured data to optimize storage costs and position data for success. Komprise is an unstructured data management SaaS solution with an analytics-first approach. What makes Komprise unique is that minutes after identifying shares, users can see key metrics on shares, directories and files – across data storage silos, whether they’re on-premises or in the cloud. This matters because without data insights, you’re shooting in the dark or simply treating all data the same. That strategy (or lack thereof) leaves a lot of money and risks on the table. “You can’t do the right thing with data if you don’t know what you have,” notes Brett Sayles, a storage engineer with our customer, St. Luke’s Health. Komprise Analysis provides consistent unified insights into your unstructured data across many vendors’ storage and cloud platforms. Key metrics include data volume, data growth rates, where data is stored, top owners, top file types and time of last access. Komprise can create cost models using combinations of storage sources, targets and data tiering plans to identify ways to save money and achieve return on investment. Companies also use the metrics to create data lifecycle management policies for compliance and regulatory actions and reporting. Read more about unstructured data management policies. Getting started analyzing unstructured data is easy: Komprise will provide an admin console as a part of the engagement; Configure Komprise Observer virtual machines close to the storage you wish to analyze; Identify NAS (network attached storage) file systems or object storage for analysis. Below we share highlights of some of the metrics and charts you can see in the Komprise dashboard. For a deeper dive, download this white paper on Komprise Analysis. Know First: The Komprise Analysis Plan Page This view provides an overview of the status of the system, the number of files analyzed and their access time and more, which we call “the data donut”. The outer ring is made up of the data grouped by the last accessed time for selected shares. The different shades represent the different age date ranges. Red indicates hot data and dark blue indicates very cold data. The 3-Year Savings column, on the right, is an estimate of cost, capacity and backup capacity savings of moving infrequently accessed data to lower cost storage. These savings are based on the company's own actual storage costs. Users can drill down into Data, Usage, Space, and Metrics tabs across All Shares or reduced to Specific Groups or Shares. Customizing Date Ranges Komprise lets you organize the age presentation to fit the topic at hand. There are options for as little as a week to as much as 15 years. Common uses are to see yearly growth or select dates to match the company's backup or data retention policy.   File and Object Data Usage Analysis The Usage tab presents information by file type, file size, owners, groups, directories, and share-- viewable by Size (Capacity) or Files (Number of Files). These summary tables are great for initiating conversations with departments and teams about their storage needs and requirements. Another benefit is with data management planning. Understanding file data characteristics can help determine a tiering strategy. For example, start by tiering large files as they consume more expensive space. Organizations can avoid potential issues by knowing upfront the numbers of small files and how they impact data migration planning. Or, identify users with unusual data sets such as a lot of video files. Unstructured Data Cost Modeling (FinOps) Cost modeling in Komprise helps companies enter their actual storage costs to determine upfront new projected storage costs and benefits before spending money. Consider this question: How much does it cost to own your data? Look at the storage platform. Does the company pay per GB (Opex) or is it an owned technology (Capex)? For the latter, divide the current total amount of actual usable data by the cost to acquire the full system to attain cost/TB. For example, 1PB of physical storage may end up being just 500TB of actual usable capacity but only has 300TB of actual useable data on it. Use the 300TB because that is representative of today’s data ownership cost. Data ownership should also include the cost of data protection like backups and disaster recovery. Use the Cost Model tools to compare on-premise versus cloud models or factor in cloud tiering or adopting a new NAS platform. Customers want baselines to make intelligent decisions for unstructured data management today and tomorrow. The Komprise Global File Index offers enhanced queries, data tagging, custom reporting and selective data movement with Deep Analytics and Deep Analytics Actions. For example, leverage Deep Analytics to find orphan data (also known as zombie data) and use Deep Analytics Actions to copy or delete it. There’s a lot to learn about unstructured data using Komprise—and this is just a snapshot. See more charts and ideas in the comprehensive white paper on Komprise Analysis. You can also set a time with one of our unstructured data management specialists to discuss scheduling a custom Data Assessment. Watch our customer success webinar for a detailed demo of the ways to use Komprise Analysis in your daily file and object unstructured data management decisions.  ---------- ### Komprise Raises $37M to Fuel Growth and Advance Leadership in Unstructured Data Management Investors reward Komprise's momentum to deliver cost savings and business value by modernizing the way companies manage unstructured data. Campbell, CA, January 24, 2023— Komprise, the leader in analytics-driven unstructured data management and mobility, today announced new funding fueled by record-setting growth and customer expansions in 2022. The $37M of growth capital from Canaan Partners, Celesta Capital, Multiplier Capital and Top Tier Ventures will be used to scale operations and extend market leadership, bringing the total funding to $85M to date. “We invested in Komprise because of their impressive growth and path to profitability combined with the massive opportunity in edge data management and unstructured data for AI/ML in the cloud,” says Kevin Sheehan, Founder and Managing General Partner, Multiplier Capital. “We believe in the company’s market, vision, team and execution.” Komprise Year in Review The 2nd annual Komprise State of Unstructured Data Management Report found that more than half of enterprise organizations manage five petabytes or more of data and spend more than 30% of their IT budget on data storage, backups and disaster recovery. As a result, IT leaders are pressured to manage data more precisely and cost-effectively. The Komprise Intelligent Data Management SaaS solution delivers many benefits to enterprises facing these challenges, including visibility across all storage, multi-cloud mobility and increased data value. Highlights from 2022 include: Business Momentum & Recognition Komprise grew 306% from 2018 to 2021, with new customer logos in 2022 accounting for 30% of the total and 2022 Net Dollar Retention of 120%. Named one of the fastest-growing companies in the Bay Area and North America on the 2022 Deloitte Technology Fast 500™. Achieved 2022 Inc. 5000 ranking. Named Best Data Management Software of 2022 by Storage Newsletter. Silver Winner for Big Data Solutions—American Business Awards 2022. Recognized as the leader in the GigaOm Radar for Data Migration Tools. Included in numerous industry analyst reports, including the Gartner Market Guide for Hybrid Cloud Storage and Hype Cycle for Storage Data Protection Technologies, 2022 and the Coldago Map 2022 for Unstructured Data Management. Technology Innovation & Cloud Alliances Introduced Smart Data Workflows to automate unstructured data discovery and custom processing. Added self-service features so line-of-business data owners and data specialists can view data usage, run queries and more, fostering greater collaboration with IT. Launched Hypertransfer for Elastic Data Migration which moves SMB file data to the cloud 25 times faster than point cloud migration tools. Selected as a launch partner for the Microsoft Azure Migration Program “We’re thrilled that our investment partners see the opportunity we’re creating by developing a comprehensive platform for customers to modernize unstructured data management practices,” says Kumar K. Goswami, CEO and Co-founder of Komprise. “Komprise delivers immediate data storage cost savings and long-term business value with a foundation for unstructured data analytics and machine learning innovations.” About Komprise Komprise is a provider of unstructured data management and mobility software that frees enterprises to easily analyze, mobilize, and monetize the right file and object data across clouds without shackling data to any vendor. With Komprise Intelligent Data Management, you can cut 70% of enterprise storage, backup and cloud costs while making data easily available to cloud-based data lakes and analytics tools. Media Contact: Kevin Wolf kevin@tgprllc.com   - ----------------- ### Leading Idaho Health System Selects Komprise to Right-Place Data and Bolster Disaster Recovery St. Luke’s Health System is adopting Komprise to tier cold files from Flash and free up space for data snapshots with potential of 80% savings on overall storage and backup costs. Campbell, CA—October 21, 2021– Komprise, the leader in analytics-driven data management, announces that St. Luke’s Health System, a nonprofit health system based in Boise, Idaho, has selected Komprise to implement a tiered data management strategy. Recognized as a Top-15 health system for eight straight years by Fortune/IBM Watson Health, St. Luke’s is using Komprise to right-place unstructured data and save over 80% of storage and backup costs. St. Luke’s, like many healthcare organizations with petabytes of data residing on expensive NAS storage, is challenged by needing to maintain files for years yet storage costs are now comprising large percentages of IT budgets. “We have 20 years of files from various medical systems and we have been treating all data the same,” says Brett Sayles, storage engineer with St. Luke’s. “We are nearing capacity on our NetApp and Pure storage, and we know that a lot of these files can go to cheaper storage.” The capacity issues have also strained the organization’s abilities to retain data snapshots for disaster recovery purposes. St. Luke’s was only able to store three days of snapshots and with ransomware threats an urgent reality, this puts data at risk. St. Luke’s will use Komprise to archive data from Pure Storage Flash Array and NetApp to secondary storage on Qumulo spinning disk. Data will be archived according to policies set for specific applications and file types. Next, St. Luke’s will clean up IT department shares and delete files or archive them to Qumulo, using Komprise. Sayles says that Komprise’s Transparent Move Technology (TMT™) which uses industry-standard symlinks to move data so that users don’t experience any change of access while files are accessible from both the original and the secondary storage is a great advantage: “Komprise does what it says by being simple.”  St. Luke’s expects to save as much as 80% on storage with this initiative. The healthcare organization will also leverage Deep Analytics capabilities in Komprise to understand the unique requirements of different clinical file types so that IT can create the optimal storage environment for short and long-term access needs. “We love working with customers like St. Luke’s which have compelling needs to optimize data management on behalf of critical initiatives such as patient care,” says Kumar Goswami, CEO and Co-founder of Komprise. “St. Luke’s is on the forefront of a new era where organizations are treating data as a separate layer from storage so they can segment and manage it efficiently according to usage and value while meeting ongoing compliance requirements.” ### Komprise August Update: Product, People & News Summer is in full swing but innovation in data management doesn't take a holiday. Below we highlight some recent trends in the unstructured data management world as well as what’s going on at Komprise. Industry News Saving the Planet With heat domes and wildfires rampant, environmental sustainability is top of mind. Microsoft announced Microsoft Cloud for Sustainability to help companies measure, understand and take charge of their carbon emissions, set sustainability goals and take measurable action. DOD Just Says No to JEDI The Joint Enterprise Defense Infrastructure (JEDI) contract was intended to modernize the Pentagon’s IT operations for services rendered over as many as 10 years. Microsoft was awarded the cloud computing contract in 2019. AWS filed suit to protest the award and now after more than 20 months, the DOD shut it down as reported on CNBC. But it’s likely that both cloud giants will end up getting a piece of the big pie later in a new Pentagon multivendor contract called the Joint Warfighter Cloud Capability. “The agency said it plans to solicit proposals from both Amazon and Microsoft for the contract, adding that they are the only cloud service providers that can meet its needs.” AWS HealthLake Goes GA Previewed last year at AWS re: Invent, AWS HealthLake is now generally available. This service stores, manages and analyzes healthcare data. Writes Julien Simon on the AWS blog: “Traditionally, most health data has been locked in unstructured text such as clinical notes and stored in IT silos. Heterogeneous applications, infrastructure, and data formats have made it difficult for practitioners to access patient data, and extract insights from it. We built Amazon HealthLake to solve that problem.” In related AWS Health news, Komprise announced a partnership with AWS for cloud tiering data services in the healthcare sector. Microsoft and Google Cloud Growth Heats Up Protocol reports that Microsoft and Google are growing their cloud business at a 50% clip in the post-pandemic world. In fact, Azure growth accelerated compared to Microsoft's fourth fiscal quarter of 2020, from 47% last year to 51% this year. While both still trail AWS by significant margin, these results show strong competition, giving enterprise cloud buyers viable options. Coldago survey of IT digs into top priorities for 2021 Blocks and Files reports on survey results from research firm Coldago. Among the findings, top data management features to deploy this year include archive and backup to the cloud, with container storage in third place. Top technologies for this year are cloud storage, all-flash arrays (AFA) and all-flash NAS. Cloud Outposts: a Growing Trend? The Next Platform reports on IDC data that shows spending on public cloud infrastructure deployed on premises such as AWS Outposts and Microsoft Azure Stack is climbing. “Between 2019 and 2025, IDC reckons that spending on cloud outposts will grow at a compound annual growth rate of 151.8 over those years to $14 billion. Importantly, IDC says that cloud outposts will be consumed by enterprises in general but also by service providers who do not want to manage their own infrastructure design and installations, either in their own datacenters or in co-location facilities.” This shows an evolution of the major cloud player’s vision of the future: not everything will move to the public cloud and they see value in on-prem offerings. The Latest From Komprise Events: Pfizer’s Cold Data Strategy Last month, AWS featured Komprise in a webinar detailing how Pfizer saw its storage bills decline for the first time in 20 years. Pfizer leveraged Komprise to analyze petabytes of data and then “right-place” data in AWS S3.   TechKrunch: Migrating NFS & SMB Data with Komprise In this on-demand webinar recorded in July, our technical experts discussed the Komprise Multi-Level Parallelism & Protocol Optimization to show how you can achieve 7-25x performance with your migrations. Check out or download the Migrating NFS & SMB Data with Komprise webinar today. Learn about Hypertransfer for 25x faster SMB performance. Short and Useful Technical Webinars at Your Convenience! Visit our library of 15-minute on-demand data management webinars showing how to use Komprise for migrations, tiering, copy actions and more. Be sure to also subscribe to our YouTube channel. Komprise News: Momentum for Hybrid Cloud Data Management In July, Komprise announced H1 results: Key growth drivers compared with first half of 2020 included, 97% revenue growth, 190% growth of new customers and 200% growth in average deal size. AWS for Health & Komprise Also in July, Komprise announced support for the AWS for Health initiative from Amazon Web Services (AWS) to accelerate healthcare organizations’ journey to the cloud by enabling a secure, no lock-in, transparent cloud data migration and cloud tiering process that maximizes cost savings and minimizes user disruption. Komprise Press Coverage: Komprise has authored and been featured in a few articles lately: Venture Beat Why unstructured data is the future of data management Excerpt from this interview with Komprise President and COO Krishna Subramanian:  “Analysts are beginning to recognize data management software as a new category. Beyond the use cases above, consider all the new types of data analytics companies getting funded, such as SnowFlake, Databricks, and Apache Spark. So many companies are coming to light right now to solve data management and data analytics issues at scale.”   Blocks & Files Healthy Komprise doubles revenues and partners AWS in health sector Reporter Chris Mellor’s comment: “Komprise has partnerships with HPE, Pure Storage, and works with AWS, Azure and NetApp Cloud Volumes. It clearly has tech that works with other suppliers’ kit.”   Jaxenter Data storage cannot be a set and forget exercise Excerpt from this interview with Komprise CEO Kumar Goswami: “It’s time for IT execs to create a sustainable enterprise data management model appropriate for the digital age. By doing so, organizations can not only save significantly on storage and backup costs, but they will be able to better leverage ‘hidden’ and cold data for analytical purposes.”   Network World What is NAS (network-attached storage) and how does it work? Quote from Komprise COO/President Krishna Subramanian: "If you need the performance of cloud NAS for frequently accessed files, it is important to manage the data lifecycle," Subramanian says. "Cold files should be entirely moved off and transparently tiered at the file level from cloud NAS to less expensive yet resilient cloud object storage such as AWS S3 or Glacier or Azure Blob to maximize savings on storage, data recovery, and backups." ### Mainline’s Ken Scott Translates Technical Storage Conversations into Business Value In this Komprise Konnects partner spotlight I spent some time with Ken Scott, Storage Systems Engineer at Mainline Information Systems. Ken spent 23 years with IBM, 10 years running storage sales for a reseller in Texas and joined Mainline 4 years ago. He spends a lot of his time translating technical messages into sales messages to help his sales team better relate to clients. Ken has deep domain experience in the world of enterprise storage, so I thought he would be the perfect person to talk to about what’s happening in the industry. Tell me about Mainline Information Systems. Mainline is a full-service technology solutions firm with over 2000 clients across the US and Puerto Rico. We partner with premier technology companies to provide a wide range of services and solutions. When Mainline started out 32 years ago we were an IBM solution provider. Since then, we transformed into an agnostic IT consulting firm, partnered with all the majors and we have a robust ecosystem of partners. We hold strong relationships with our clients and they trust us to do the right things. We’re privately held, one owner, and even through COVID-19 we continued to grow. In fact, we just exceeded the $1B revenue mark. How do you approach the market today? I like to believe that there are no wrong answers – there are just varying degrees of right. In the technology world, a lot of people are hobbling along with their existing solutions. When it comes time to refresh, they find those technologies are outdated or don’t provide the capabilities designed for the challenges people are having today. This mantra of varying degrees of right really does apply. As long as you make a change and you put your heart into it, you’re going to get more out of it than you invest into it and you’ll be better off. The difference in solutions nowadays is that it can be really minor, but it still goes back to relationships. People buy from people they trust. I believe in this solution and I believe in you. This has been a successful approach at Mainline. Our skillset is applying the right technology for the client’s needs through an agnostic approach. What’s your approach in 2021? I see 2021 as really being a defining year. Given the challenge presented by the COVID-19 pandemic, I looked at 2020 like driving across an icy bridge – just keep the wheels straight; don’t accelerate; don’t hit the brakes, don’t change lanes; just move forward and stay safe. If you had a project underway, just get that project to the end state. Don’t start anything new. Don’t change anything. (Unless, of course, if the business had new requirements that demanded change.) When we get to the other side, we’ll take a breather, look back and assess what’s next. At the end of the year, the must-do projects were completed, but customers remained a bit leery about the marketplace and the change brought on by remote workers and they were not comfortable starting new projects. Now they’re looking at 2021 and figuring that they might not have a choice. They need to start these new projects and they have to make the best of whatever is available to them. This will bring interesting challenges. For Mainline it was a great year, partially due to projects already in the works, but also from new requirements necessitated from shifts in business models (i.e., work from home, new markets, security, etc.). I would suspect 2021 is going to be interesting because it’s been a year since we’ve been able to go out, work with a client face to face, and have that conversation across the conference room table or have a whiteboard session. These are the moments where we really connect with clients. What are some of the biggest changes you’ve seen in the storage market? The technology and the economics have changed exponentially. Case in point – the first storage system I ever sold was 45GB. It was a little deskside storage unit for $50K. In my first year at Mainline, I went to a sales conference and one of our suppliers gave me 2 free USB drives that combined were the size of the storage unit I sold. There has been a data explosion in the enterprise and for many businesses it’s a matter of keeping your head above water when it comes to storage. Throwing hardware at the problem has always been the easy solution, but is it really? You need to consider the cost of the actual box – what’s the CAPEX outlay? They don’t have a tendency to think about the cost of people assigned to manage it, or the impact to established business processes. It’s those ancillary costs that are going to become a greater and greater challenge. Many of my clients are still in the somewhat structured data space and feel they have a good understanding of that workload and which tools and skills they need. It’s not uncommon for them to have less of a grasp of their needs for managing the unstructured data side, because in part it was someone else’s problem to worry about, or the scale of that data – and the subsequent interest in managing it – seemed to come out of nowhere. Some clients don’t have a clear understanding of what they really have. The thought is, “If I don’t have a handful of NetApp systems, or a few large Isilon storage units, then I don’t have anything to worry about.” Ok then, how many Windows file shares do you have out there? 20? 30? If each of those has X amount of capacity, chances are you have more data in those shares than you do in structured data storage. Many organizations don’t have a way to measure it. If I don’t see it, it doesn’t really exist. There is an opportunity to come in and help these organizations manage their unstructured data more effectively. Gartner says we don’t have a data problem, we have a data management problem. I think this is absolutely true. Tell me about the Komprise partnership? We’ve partnered with Komprise for a couple of years. When I first saw a Komprise demo at an IBM event, I thought it was genius – that solution should sell itself. It reminded me of the IBM SAN Volume Controller, which IBM now calls the Spectrum Virtualize software family. It helped clients get more out of what they already had. That’s how I think about Komprise. Komprise isn’t a solution that tells you what a bad decision you’ve made when it comes to all of your unstructured storage. It allows us to say, “we’re here to tell you how to get the most out of it.” This is why I think the solution is such a unique offering in the market right now. It’s a very non-threatening message. “Your Isilon is at capacity? How about I help you manage it more effectively?” After all, it’s all about what data is where and who is managing it. That’s the opportunity. I think it’s key to help people make better use of what you already have. People don’t like to be told they’ve made a bad decision in what they’ve purchased. We want to help them learn more about what they’ve got and help them either archive or delete cold data and free up storage, so they don’t have to upgrade and spend unnecessarily every 6 months. That makes them a hero. I’ve been onboard since Day 1. Komprise is a really interesting solution that has a lot of promise. I’m just kicking myself that I’m not able to get out and whiteboard it and have more meaningful discussions face-to-face with clients. How is the Komprise value proposition being received? The people who are getting the message understand it right away. But they’re not the people hoarding the data. The people who are hoarding the data are the technical teams because they do not know what data needs to be available. They don’t know what data the business is going to request so they keep everything local and just buy more storage when they run out. Getting the data from another platform can be challenging without the right structure in place and they don’t want to have to be the ones who say what data is valuable and what data is not. People who have been around a while understand the need for hierarchical file storage management. Hoarding data is the problem – if we do have to hoard it, let’s at least hoard it in manageable places. Scaling up and scaling out is not as manageable as people think. When does the cloud come into your conversations? It varies. Some businesses will be quick to tell you they don’t have a cloud strategy, but every single employee has a Box account. There are many players in the cloud marketplace. Cloud storage has a lot of potential for the right thing, but you need to select the right data. Data needs to be segregated based on the need of that data. If you don’t need it, the cloud is a great for archiving. Cloud use cases are constantly changing, but you still need to think about egress charges. Put it away and think about bringing it down when you need it might be cheap today and it might be cheap tomorrow, but when you ultimately do need to go out there and bring back a large chunk of it due to a failed backup process or a security intrusion, you can find that you’ve spent more money in the long run than you would have if you had it managed on prem. The way Komprise provides native access is a nice touch: let people get to data the way they’re used to and let the technology manage it behind the scenes. How has the last year been for you? I definitely have cabin fever. I’m ready to get back to face-to-face with clients. We’re a remote company, but not having been to a customer’s office is starting to get to me. I’d love to have the opportunity to whiteboard. I miss walking through the data center and asking questions. What’s that? What’s it do? What data is on it? How old is it? This is how I learn about my customer’s business. I noticed you put in a new row of storage – what’s the application that’s driving that? Tell me more about that. This is why I think the storage market is so exciting. It’s changing so dramatically – there’s a little bit for everybody.   Thanks Ken!   About Mainline Information Systems, Inc. Mainline, headquartered in Tallahassee, FL, is an information technology solutions and IT consulting firm, who architects and delivers innovative IT solutions, including hybrid cloud and on premise data center offerings and cybersecurity, analytics, AI and managed services solutions. With national coverage, strategic partnerships with premier technology companies, and multi-vendor technology experts, Mainline Information Systems efficiently enables cost effective business outcomes. The company may be reached by phone at 850.219.5000 and on the Internet at www.mainline.com. Mainline Press Contact: Cristina Perez 850.219.5000 Cristina.Perez@mainline.com ### The power to know and make the right moves with your data: a 20/20 strategic vision As we enter a new decade, exponential data growth has put a new spin on the Roaring Twenties. The spiraling costs surrounding this explosive volume are a top concern among IT leaders, but while many see it as a storage issue, Gartner would disagree. “Bad data management leads to spiraling storage costs…The IT industry doesn’t have a storage problem; it has a data management problem.” —Gartner, 2019 And that’s where Komprise can shift the focus and change the storage game—by helping you know your data and act on it…in that order. Organizations typically store, replicate, and protect all of their data in exactly the same way—because they don’t know their data. Imagine how much money you could save if you could archive cold data off your primary storage and all the backup storage that contains it? Komprise gives you analytics into your data before you have to make expensive purchases, so you can make the moves that make the most sense for your organization. Other vendors give you analytics insight only after you’ve invested in their storage or backup appliance, strategically putting the storage cart before the horse. We don’t blame them for controlling the narrative. But at Komprise, we do things differently. We believe you are best positioned to make the right moves for your data. Our Dynamic Data Analytics and Transparent Move Technology™ allow you to do just that—understand then move your data easily without access hassle, vendor lock-in, or affecting performance of mission-critical activities. To us, it just makes sense:  it’s your data, you should have the power to know it, move it, and access it—wherever it lives.  That’s what Komprise Intelligent Data Management is all about. It’s also why we have a 91% success rate after a PoC.  Customers can’t believe what they’ve been missing when they discover the amount of self-defined cold data they have—and have been paying for. Your data and your savings should be in your control. It starts with 20/20 vision and the power to act on it, without impacting users. Curious about your true data picture and how much you could save? We’d love to show you how Komprise Intelligent Data Management can help.   Krishna Subramanian President and COO, Komprise ### It’s all about you: Keep your data in check with a tailored solution Data is growing at a staggering rate and managing it in a cost-effective and efficient way is therefore becoming increasingly difficult across every industry. Krishna Subramanian, COO at Komprise, discusses having a strategic approach to data management to create business benefits. ### Komprise Adds Virtual Data Lake Capability to Reduce Cost of Finding Data for Analysis Komprise, Inc. announced availability of its 2.11 release which includes a new Deep Analytics feature that addresses the biggest concern with big data analytics – searching across multiple storage platforms to identify the right data sets to analyze. ### Top 3 Data Management Trends from BioITWorld 2018 The rising cost of storage in genomic IT was a hot topic at BioITWorld 2018 where we presented a healthcare case study with Google and had several in-depth discussions on the importance of efficient data management at scale.  Here are our top 3 takeaways from this year's conference: 1) Storage is a major cost element in genomic IT Growth in genomics data generation and research storage is not slowing down anytime soon. Organizations are actively exploring ideas to manage massive data growth without breaking the bank. 2) Massive Scale Data Management Cannot be a Science Project A key theme we heard repeatedly was that organizations are tired of science projects - using open source tools and frameworks to cobble together home-grown solutions is very laborious, time-consuming, error-prone, and costly. Data management needs to become a turnkey commercially supported solution that works across storage vendors and platforms without lock-in. Several organizations spoke about the years they have spent trying to still get a workable solution from their home-grown efforts. Watch how Pacific BioSciences, a genomics leader, used Komprise to transform how they manage genomic test data and 700% YOY storage capacity growth.   Bio-IT Data Cannot be Locked Into Storage Silos The efficient management of data-at-scale requires data to be archived to lower cost capacity storage options such as object storage, tape and/or the cloud—but if users have to go to a new namespace to find this data, or if the data cannot really be used without going through the primary storage, then the value is of the data is very limited. BioIT organizations are looking for ways to archive and manage data in such a way that the data is transparently accessible no matter where it sits, and in ways that do not break native analytics capabilities of platforms such as the cloud.   (Source: Stephens ZD, Lee SY, Faghri F, Campbell RH, Zhai C, Efron MJ, et al. (2015) Big Data: Astronomical or Genomical?. PLoS Biol 13(7): e1002195. doi:10.1371/journal.pbio.10021) ### Besuchen Sie Dell Tech World, um die Lücke zwischen Enterprise NAS und Objektspeicher zu schließen Da die Unternehmensdaten weiterhin exponentiell wachsen, wird es für Unternehmen schwieriger, alle Daten auf dem NAS (Primary Network Attached Storage) zu speichern, insbesondere wenn man bedenkt, dass die meisten IT-Speicherbudgets für 2018 unverändert bleiben. Der größte Teil dieses Wachstums entfällt auf unstrukturierte Daten wie E-Mails, Dokumente, Videos, Fotos und Audiodateien. Die Nutzung eines sekundären Objektspeichers kann eine kostengünstige Möglichkeit zum Speichern von Daten darstellen. Zwei Hindernisse waren jedoch: a) Welche Daten kann ich in einen Objektspeicher verschieben und b) wie kann sichergestellt werden, dass Benutzer und Anwendungen keine haben Verhalten ändern, um auf die verschobenen Daten zuzugreifen. Komprise wird unter Dell Technologies World 2018 (Stand Nr. 1243) vertreten sein Enthüllung aufregender neuer Funktionen in Komprise und Demonstration, wie intelligentes Datenmanagement Hindernisse überwindet und die Lücke zwischen Enterprise NAS-Lösungen wie Dell EMC schließt Isilon oder Unity und Objektspeicherlösungen wie Elastic Cloud Storage (ECS). Wir hoffen, dass Sie sich mit uns auf der Dell Tech World treffen ( Vielleicht gewinnen Sie dafür einen Yeti-Kühler. Ich dachte, ich würde mir einen Moment Zeit nehmen, um schnell und auf hoher Ebene zu teilen, wie einfach es ist, Transparenz in Ihren Speichersilos zu erlangen, und eine Richtlinie festzulegen, um diese Daten in ECS zu verschieben . Innerhalb von etwa 15 Minuten nach der Einrichtung liefert Komprise Analysen darüber, wie alle NAS-Daten Ihres Unternehmens wachsen und verwendet werden, z. B. die Aufschlüsselung Ihrer Daten nach dem Zeitpunkt des letzten Zugriffs (siehe unten).    Mit den Erkenntnissen dieser Analyse ist es leicht zu verstehen, wie viele kalte Daten Sie haben, und Richtlinien im Komprise-Plan-Editor festzulegen, um den ROI beim Verschieben von Daten von Ihrem Unternehmens-NAS auf ein sekundäres Speicherziel wie Elastic Cloud Storage interaktiv anzuzeigen.  Sobald Sie eine Richtlinie festgelegt haben, aktivieren Sie sie einfach und Komprise beginnt mit dem Verschieben der Daten. Daten, die Komprise verschiebt, werden den Benutzern weiterhin so angezeigt, als wären sie auf dem primären NAS gespeichert. Wenn ein Benutzer oder eine Anwendung auf diese Daten zugreift, ruft Komprise die Daten automatisch ab, um Störungen zu vermeiden. Dies war nur ein Blick auf die Kraft von Komprise. Für einen genaueren Blick: Besuchen Sie unseren Stand (Nr. 1243) auf der Dell Technologies World Sehen Sie sich unsere Videoserie zur intelligenten Datenverwaltung an Lesen Sie das Datenblatt zur Komprise-Übersicht Lesen Sie unser Architekturdatenblatt Planen Sie eine 1on1-Demo ### Heading to Dell Tech World to Bridge the Enterprise NAS & Object Storage Gap As enterprise data continues to grow exponentially, it becomes harder for organizations to keep all of their data on primary network attached storage (NAS)—especially when you consider that most 2018 IT storage budgets are staying flat. The majority of this growth is in unstructured data such as e-mails, documents, videos, photos, audio files. Leveraging a secondary object storage can provide a cost-effective way to store data but two obstacles to doing so have been: a) what data can I move to an object storage, and b) how to ensure that users and applications don’t have to change behavior to access the moved data. Komprise will be at Dell Technologies World 2018 (booth #1243) unveiling exciting new capabilities in Komprise, and demonstrating how intelligent data management overcomes obstacles and bridges the gap between Enterprise NAS solutions like Dell EMC Isilon or Unity and object storage solutions like Elastic Cloud Storage (ECS). While we hope you meet with us at the Dell Tech World (you just might win a Yeti Cooler for doing so), I thought I would take a moment to quickly, and at a high level, share how simple it is to gain visibility across your storage silos and set a policy to move that data to ECS. Within about 15 minutes after setup, Komprise starts delivering analytics on how all of your enterprises NAS data is growing and being used such as the breakdown of your data by time of last access featured below.    With the insight provided by this analytics, it is easy to understand how much cold data you have and set policies within the Komprise plan editor to interactively see the ROI of moving data from your enterprise NAS to a secondary storage target like Elastic Cloud Storage.  Once you have set a policy simply activate it and Komprise starts moving the data. Data that Komprise moves still appears to users as if it is stored on the primary NAS. When a user or an application accesses this data, Komprise automatically recalls the data, preventing any disruption. This was just a glimpse of the power of Komprise.  For a more in-depth look, schedule a demo, or: Watch our Intelligent Data Management Video Series Read the Komprise Overview Data Sheet Read our Architecture Data Sheet Schedule a 1on1 Demo ### New Medicare Policy will Accelerate Rapid Genomics Data Growth On March 30th, the Center for Medicare and Medicaid Services announced that the federal health care program will cover the cost of genomic testing for cancer patients—enabling their cells to be sequenced to determine which treatments will be most effective. If private insurers follow suit, as has traditionally been the case, genomic testing has just become routine care for cancer patients and precision medicine has now become mainstream. But how will all this new genomics data be stored? IT administrators are faced with a new challenge—How to handle an explosion of genomics data caused by the mainstreaming of genomic testing and precision medicines without using up spend and resources that could be better used on finding better cures and outcomes for patients. The data storage requirements for genomics data are projected to be enormous—genomics data is estimated to reach up to 20 times the size of all content on YouTube by 2025! Here is the challenge massive data growth poses: The traditional approach of buying more high-performance primary storage to keep up with this data growth won't be tenable from a budget perspective. Nor is it necessary given that the majority of this data will become inactive, or cold, within months of creation and does not need to reside or be managed, replicated and backed up on the highest performing storage. Moving this data to a traditional offline archive on secondary storage also presents challenges as it requires IT to restore the files if they are required again, slowing the pace of research and even patient care. And, because an offline archive requires users to look elsewhere for data, it impacts productivity and creates user resistance. Also, as IT is not the creator of this data and also lacks visibility to which data is safe to archive and which is in use or needed in the future, determining what data is safe to move has traditionally been a near-impossible task for IT. To handle this coming explosion of data growth IT is going to have to leverage secondary storage without disrupting users. By utilizing Komprise Intelligent Data Management, they can do so in a way that is analytics-driven, so the right data is moved to secondary storage at the right time after it becomes inactive, and the access to this data is transparent, so there is no change to how users access the moved cold data. Komprise analyzes across all of an organizations storage silos and presents a single view of an organizations data. IT can use this data to set policies on how to migrate, transparently archive, and replicate for disaster recovery. Because the data is moved transparently users and applications still retain file-based access as if it is still right where they put it on the source. Genomics companies are utilizing Komprise to manage their data growth. Watch how Pacific BioSciences gave itself a storage report card and utilizes Komprise to manage a 7x YOY data growth on a flat budget. ### Media & Entertainment Companies Choose Komprise Media and entertainment companies are experiencing a rapid growth of data. With media assets providing a competitive advantage, the data must be maintained, managed, and easily accessible. Companies Cut Costs with Simple and Efficient Data Management Media and entertainment companies are experiencing a rapid growth of data. With media assets providing a competitive advantage, the data must be maintained, managed, and easily accessible. Key Challenges: Critical storage capacity planning and cloud decisions being made in the dark Data trapped in vendor storage silos and requires multiple management tools Corporate mandate to cut costs and create a path to the cloud Cold media assets are consuming expensive primary storage and backup resources Users do not want to change their behavior and remember asset tags to retrieve older media “The old way of having users remember asset tags to retrieve cold media archives is no longer tenable. Komprise enables us to have an “always-on” archive – data is fully accessible even when kept on cold storage” - VP Infrastructure IT, Media & Gaming ### Manage Research, Not Storage Universities are at the forefront of research and as technology advances, the pace of innovation is accelerating. This means that we are generating far more data than ever before. The majority of Education IT budgets are staying flat, or in some cases, even shrinking. How can you do more with less? How Research Universities are Enabling Departments to Keep More Data with Flat Budgets Universities are at the forefront of research and as technology advances, the pace of innovation is accelerating. This means that we are generating far more data than ever before. The majority of Education IT budgets are staying flat or in some cases, even shrinking, but this deluge of unstructured data needs to be stored, managed, protected, and harnessed... How can you do more with less? With storage already consuming anywhere from 25% to 50% of an average Educational Institution's IT budget, major universities are asking that exact question. “The addition of Komprise in our file sharing infrastructure has significantly improved our understanding of the data. The ability to see the types, size, ages, and ownership of the files has improved the data management for both my team and clients of our service. Being able to sit down with clients and show them the data allows a level of understanding that was never before available. They are then able to make decisions about how to better manage their own data.” Steve DeGroat, Manager Enterprise Storage, Top Research University Komprise Customer Stories Read about two premier research institutions, their use cases, and how they are intelligently managing data and leveraging the cloud, ultimately to cut costs and improve data access — with Komprise. Study 1: Major West Coast Research University Cuts Data Replication Costs Read Now   Study 2: Central IT Gets Departmental Support Read Now Read the blog post: Data Management for Higher Education Read the white paper. ### Oil & Gas: Modernize IT & Protect HPC Oil & Gas Companies are Turning to Komprise Oil and Gas companies use technology as a key differentiator to meet growing energy demands with lower operating costs. With the rapid growth of data, both seismic and general IT, companies are looking to modernize their data management practices: creating a sustainable path to the cloud, utilizing a business-centric-model, while still maintaining the data throughout its lifecycle. Key Challenges Legacy data: company grew through consolidation, so very hard to get a single view into how data is growing and why Corporate mandate: must modernize IT and streamline operations Disruption to users: Drive consumption of object storage without disrupting file-based access Data accessibility: Business users should not have to go elsewhere for their data – moved data should be accessible as files exactly as before. An oil and gas company turned to Komprise to lower the cost of managing seismic data, by identifying and transparently tiering and archiving cold data on object storage. Komprise identified the cold, inactive data that could be stored and managed on secondary storage, and transparently archived the data. Users were still able to see the moved data as files, exactly as before, using Komprise. Learn more about Komprise Transparent Move Technology. “Komprise enables us to manage storage as a Business, and not as an IT cost” -Sr VP of Information Technology, Oil and Gas ### Komprise Empowers Enterprises to Intelligently Manage The Span and Scale of Today’s Data Growth Komprise Empowers Enterprises to Intelligently Manage The Span and Scale of Today’s Data Growth Data storage veterans unleash elegantrevolutionary solution that uses data insights to intelligently and transparently extend existing storage capacity and cut 70 percent of costs San Francisco, Calif. – June 28, 2016 -- Komprise, the first company allowing businesses to automatically manage enterprise data, whether in the cloud or on-premise, announces its launch today. Companies can now view a larger span of historical data, as well as data across all systems, and move costly inactive data to secondary storage - saving them millions of dollars. With no hardware to deploy, no storage agents, no static stubs, no changes to the hot data path, no complex configurations or proprietary interfaces, Komprise seamlessly works across storage environments, both on-premise and cloud. The new solution is unique in that it not only understands what is happening across a customer’s storage, but it also adapts to the their specific needs of the environment and network. Additionally, Komprise consists of an architecture that scales on-demand so customers can grow the footprint as needed without over-provisioning. With affordable sub-cloud pricing, Komprise cuts over 70 percent of the storage cost related to buying, operating, managing and protecting storage. More so, the solution enables organizations to increase ROI by moving less frequently accessed data to secondary storage. The company already has partnerships with EMC, NetApp, Windows File Servers, Quantum, Google Cloud Platform, Amazon Web Services, and Microsoft Azure, and mid-to-large size customers including Pacific Biosciences, Medplast, and Houston Housing Authority. “Our data has been growing at an overwhelming pace, making it difficult and expensive to store and manage,” said Jay Smestad, Senior Director of IT at Pacific BioSciences. “We’d been searching for an affordable scale-out data management solution that works across our storage, and enables us to move data to where the best service and ROI for the data is, and not where the users put it, but existing solutions are expensive and often create disruption. With a tight budget, we turned to Komprise and have seen immense benefits from understanding and being able to dynamically place data. The interface is intuitive and straightforward, making it simple to manage with instantaneous insight.” “Imagine needing to store more photos and videos on your phone, but the memory is full and you cannot afford to purchase more--that’s the challenge many businesses face today with their enterprise storage,” said Krishna Subramanian, COO & co-founder at Komprise. “Now imagine your phone could transparently store those additional photos and videos, while cutting 70% of costs. And, you see all your photos as if they are right there on the phone, and if you open an older photo, the image is still instantly available via the cloud, though not stored directly on the phone. This is what Komprise does for data--transparent, seamless data storage and management across a customer’s storage infrastructure – on-premise or cloud.” “Komprise is taking a truly revolutionary approach to data management,” said Maha Ibrahim, General Partner of Canaan Partners. “They have created a “plug-in” concept that enables companies to gain a larger span and scale of data, and significantly cuts storage and management costs. If there is any team capable of helping enterprises transition to hybrid cloud, it’s this one.” This announcement comes on the heels of Komprise and Quantum’s recent partnership just last month. Using Komprise data management, Quantum now offers a joint solution to enable businesses to seamlessly extend storage with Quantum Lattus™ and Quantum Artico™ NAS Management. Komprise’s founding team has successfully built two prior startups to simplify complex IT infrastructures. Their last company, Kaviza, eliminated expensive storage for virtual desktops and drastically simplified deployments with a scale-out solution. The solution was successfully used by thousands of customers and was acquired by Citrix in 2011. About Komprise Komprise, the first intelligent data management service, empowers businesses to efficiently manage today’s massive scale of data growth while unlocking its value. The Komprise mission is to radically simplify data management through intelligent automation. Komprise's partners and customers include top companies such as Google, Quantum, Pacific BioSciences, and NetApp. The Komprise team has a successful track record with two prior businesses of eliminating storage/IT cost and complexity. Backed by Canaan Partners, Komprise was founded in 2014 and is headquartered in San Francisco. For more information, go to www.komprise.com. ## Media Contact: Lauren Barlow VSC for Komprise lauren@vscpr.com (415) 869-8629 ## Ransomware Protection and Data Security > By offloading cold data to immutable cloud storage, Komprise reduces the active ransomware attack surface by up to 80% while cutting costs. ### Scheduled Reporting with Komprise At Komprise we’re always looking for ways to expand the power of our analytics: How can we ensure our customers get greater visibility across data storage silos and not only make smarter investments when it comes to storage, backup, and ransomware protection, but also get more value from their unstructured data? In my last demonstration I reviewed the power of the Directory Explorer, which is available in both Data Stores and Deep Analytics. In the past year we’ve also added several updates to our customizable report templates, which today include: Showback Orphaned Data Potential Duplicates Migration Summary Users Customize and Schedule Reports in Komprise Now you can customize our report templates, email reports directly from Komprise, and set up a schedule for a report to run. With these features you can schedule customized reports to answer recurring business questions and to keep stakeholders informed of storage considerations and costs.For example, you can schedule Showback reports to coincide with your billing cycles, Migration Summary reports to periodically track migration progress, or Potential Duplicates reports to let the computations run in the background and receive the results at your convenience. Sending such reports to stakeholders means you can extend the value of Komprise across your organization with no need to train or manage additional users. In this demonstration, I walk through how to customize and schedule a report. As an example, I’ll schedule the Department Showback report to be sent to different departments on a quarterly basis.  Powered by Komprise Analysis and Deep Analytics Komprise Analysis is included in the Komprise Intelligent Data Management platform and as a standalone module. It delivers unified insights into unstructured data across all of an organization’s storage and cloud platforms. Key metrics include data volume, data growth rates, where data is stored, top owners, top file types and time of last access, along with FinOps cost modeling. Powered by the Global File Index,Komprise Deep Analytics is a core capability in Komprise Intelligent Data Management, offering customers a simple interface to create custom queries on their data to find the data they need for a project such as AI, or to create custom data management plans: As one customer remarked: “Deep Analytics allows us to do fine-grained searches and get surgical about what we can find and archive. Komprise empowers our end users to archive data the right way via tagging.” Learn more about the different use cases for storage teams with Deep Analytics. Learn more about Komprise Reports. ### 2025 CRN Cloud 100 For the fourth year running, Komprise has been selected to the CRN Cloud 100, which recognizes the leading channel-focused cloud companies across five key categories: cloud infrastructure, management, security, software, and storage. ### The File Data Problem for Ransomware Ransomware can enter your organization by infecting any data, not just your mission critical data. This poses a challenge for infrastructure and storage managers who typically focus the best data protection strategies on mission-critical data which is often block data. The large volume, variety and velocity of file data in the enterprise makes this unstructured data the hardest to defend against ransomware attacks and leaves the organization vulnerable. File data is most vulnerable to ransomware attacks File data is arguably the most difficult data to protect against ransomware attacks because it is touched by many different users, groups and applications. For instance, a research image created from one application may be incorporated into a document, emailed and shared with many others. This increases risk as all it takes is for one of these users or groups to make a mistake that leads to a ransomware infection. The large attack surface of file data is risky not only because an attack can enter the network through any one of the billions of files, but also because the attack could spread for months in the enterprise network without detection. Any ransomware defense strategy for file data must consider ways to reduce the active attack surface. But ransomware defense for file data is difficult and expensive because of the large attack surface Yet, defending file data against ransomware attacks is difficult and costly because file data can easily be billions of files and petabytes of data, which means the attack surface can be quite large. Even if you kept many backup copies of the file data, its volume makes it an easy target for ransomware actors to gain entry to the rest of the enterprise. Plus, all of your costs add up quickly when you copy petabytes of data.   If you have 1 PB of file data, you are really managing at least 3PBs with all the copies. Most (80%) of this data is cold and not actively used, yet by keeping it in active storage, it is still vulnerable to attacks and must be defended the same way as hot data.   Snapshots are also vulnerable to ransomware attacks Unfortunately, snapshots may not be an adequate defense to recover from a ransomware attack because snapshots can themselves become infected or corrupted. Storage vendors like NetApp now offer tamperproof snapshots that protect against deletion. This way, as long as you have snapshots taken earlier than the attack, there is some possibility of recovery. However, most solutions such as NetApp Tamperproof Snapshots do not allow storage-based tiering such as NetApp FabricPool or Dell CloudPools, as doing so could provide a backdoor access to the destination volume. Therefore, if you use their tamperproof technology, you’ll need to use a storage-agnostic solution like Komprise to tier data to private or public clouds. Shrink Ransomware Attack Surface with Hybrid Tiering at the File level Given these constraints, organizations must look for ways to shrink the file attack surface. Transparently offloading cold files through hybrid tiering cuts both costs and risks. Hybrid tiering offloads entire files from data storage, snapshot, backup and DR footprints and leaves behind dynamic links. This allows your users to continue seeing and accessing the tiered files without any change to application or user processes. Learn more about Komprise Transparent Move Technology (TMT)™. Unlike storage tiering which is typically offered by the storage vendor and moves blocks of files to the cloud, hybrid tiering operates at the file level which has several advantages: First, by offloading entire files, with hybrid tiering you remove the files from the ransomware attack surface. With storage-based tiering, the files remain on the active attack surface. Second, by using hybrid file tiering to an immutable location, you add another layer of defense from potential attacks through versioning. You have an older version of the tiered files to recover from in the event of an attack, because any modification to a file causes a new version to be saved. Third, Komprise hybrid tiering at the file level is transparent to the snapshot mechanisms, so your tamperproof snapshots continue to work. Fourth, hybrid tiering at the file level shrinks your storage, backup and DR footprints, thus reducing costs.   Reduce Vulnerability to Ransomware Attacks In summary, most file data isn’t mission critical--but it could be your weakest link for ransomware attacks. File tiering eliminates cold data from the ransomware attack surface while giving users and applications seamless access. This shrinks your attack surface by 70%+ and it reduces your ransomware defense costs while supporting technologies like tamperproof snapshots work seamlessly. To learn more, read the Komprise Data Tiering Guide. ### Top Unstructured Data Management Terms of 2024 The disciplines of unstructured data management, data storage, data security and AI are colliding. With so much changing all the time, how does one keep up with new practices and trends? Well, by visiting the Komprise Data Management Glossary, of course. We’ve compiled below a list of the most popular 10 terms for the year so far. Each has a short abstract but click on the link for the full description. AI Compute AI compute refers to the computational resources required for artificial intelligence systems to perform tasks, such as processing data, training machine learning models, and making predictions. These resources can be provided by various hardware and software platforms, including GPUs, TPUs, cloud computing, and edge computing devices. The amount of AI compute needed depends on the complexity of the AI system and the amount of data being processed. Data Hoarding Data hoarding is the common enterprise practice of retaining large amounts of data that is no longer needed or is rarely used, for extended periods of time. In many organizations, employees tend to save data out of habit, fear of losing it, or simply because they don’t know what to do with it. Enterprise IT teams may have retention and deletion policies but they can be hard to enforce. This can lead to overspending in data storage as well as compliance and security risks from large, unmanaged data estates. Data Management Policy A data management policy addresses the operating policy that focuses on the management and governance of data assets. This policy should be managed by a team within the organization that identifies how the policy is accessed and used, who enforces the policy, and how it is communicated to employees. Ultimately, a data management policy should guide your organization’s philosophy toward managing data as a valued enterprise asset. With automation, IT can “set and forget” the policy to ensure continuous adherence to policies.   Data Tiering Data Tiering is a technique of moving less frequently used data, also known as cold data, to cheaper levels of storage or tiers. The term “data tiering” arose from moving data around different tiers or classes of storage within a storage system, but has expanded now to mean tiering or archiving data from a storage system to other clouds and storage systems. The glossary page goes into further detail about different types of tiering and how to avoid common problems. Data Tagging Data tagging is the process of adding metadata to your file data in the form of key value pairs. These values give context to your data, so that others can easily find it and search and execute actions on it, such as move to confinement or a cloud-based data lake. Data tagging is valuable for research queries and analytics projects or to comply with regulations and policies. Learn more about automated data tagging with Komprise.   Metadata Metadata means “data about data” or data that describes other data. The prefix “meta” typically means “an underlying definition or description” in technology circles. Metadata makes finding and working with data easier, allowing the user to sort or locate specific documents. Some examples of basic metadata are author, date created, date modified, and file size. Metadata can be stored and managed in a database, however, without context, it may be impossible to identify metadata just by looking at it. Metadata is useful in managing unstructured data since it provides a common framework to identify and classify a variety of data including videos, audios, genomics data, seismic data, user data, documents, logs. Read our CEO’s two-part blog series on metadata management. Petabyte A petabyte (PB) is a unit of data storage that represents 1,000,000,000,000,000 bytes or 10^15 bytes. It is 1000x larger than a terabyte (TB) and one million times larger than a gigabyte (GB). Petabytes are commonly used to describe the capacity of large-scale data storage systems, run by data heavy industries such as those used in scientific research, big data analytics, and cloud computing. For example, a single petabyte could store over 200 million 5 MB photos, or about 13.3 years’ worth of HD video content. There are 1,000 petabytes (PB) in a zettabyte (ZB). Petabyte Comparison Examples A typical HD movie is about 4-5 GB in size. A petabyte could store around 200,000 HD movies. An average MP3 song is about 5 MB. A petabyte could hold approximately 210 million songs. A 1 terabyte hard drive can store around 250,000 photos. A petabyte could hold about 256 million photos. Rehydration Rehydration is the process to fully reconstitute files so the transferred data can be accessed and used. Block tiering rehydrates any data accessed from the cloud. This requires that there be space to accommodate some percent of cold data, which in turn reduces the potential cost savings. Since the cold data is tiered to the cloud in a proprietary format, when it is time to decommission your storage array and replace it with a new one you must stay with the same vendor. If you elect to change vendors, you will have to rehydrate all of the data back to the original storage array and then migrate that data to the new storage array and then tier that data using some other tiering solution. No rehydration is needed with Komprise, which uses file-based tiering. Secondary Storage Secondary storage devices operate alongside the computer’s primary storage, RAM, and cache memory and can hold from megabyte volumes of data to petabytes. These devices store almost all types of programs and applications. This can consist of items like the operating system, device drivers, applications, and user data. For example, internal secondary storage devices include the hard disk drive, the tape disk drive, and compact disk drive. Secondary storage is typically designed for long-term storage of less-active data and is often orders of magnitude cheaper than primary storage. Symbolic Link Symbolic Links, also known as symlinks and symbolic linking, are file-system objects that point toward another file or folder. These links act as shortcuts with advanced properties that allow access to files from locations other than their original place in the folder hierarchy by providing operating systems with instructions on where the “target” file can be found. Komprise uses the standard, built-in feature of Windows, Linux, and Mac symbolic links, which replace a file with a tiny pointer to another location. By using Dynamic Links inside the standard symbolic link, Komprise extends the file system to call these files from the cloud or other storage systems. Read more about Transparent Move Technology. Unstructured Data Management Unstructured data management is a category of software that has emerged to address the explosive growth of unstructured data in the enterprise and the modern reality of hybrid cloud storage. Data storage and data backup technology vendors are now recognizing the importance of unstructured data management as data outlives infrastructure and as data mobility is needed to leverage cloud data storage. Unstructured data management must be independent and agnostic from data storage, backup, and cloud infrastructure technology platforms. Check out the 2024 Komprise State of Unstructured Data Management to learn about the latest trends in this area. ### 7 Tips for Unstructured Data Security This blog was adapted from the original article on TDWI. Unstructured data security and protection requirements continue to expand due to ongoing ransomware threats, sophisticated cyberterrorism and cybercriminal organizations, and an increase in natural disasters. AI innovations in the last year have also introduced new threats and risks to corporate data. In this article, we'll cover what storage and IT managers should consider regarding  unstructured data security and governance. 1. Know your data. Gaps in visibility, hidden applications and obscure data silos in branch offices all contribute to higher risk. Consider that protected data will end up in places where it shouldn’t, such as on forgotten or underutilized file servers and shadow IT cloud services. Employees unwittingly copy sensitive data to incompliant locations more often than you’d think. You’ll need a way to see all your data in storage and search across it to find the files to segment for security and compliance needs. You can use the data management capabilities in your NAS/SAN/cloud storage products to search for file types such as HR and IP data, but you’ll need to integrate visibility across all storage vendors and clouds if you use more than one vendor’s solution. Knowing how much data is cold, what data is obsolete, and what data should be deleted are equally important for eliminating unwarranted exposure and risks. 2. Set cold data thresholds with security and business leads. IT infrastructure teams must collaborate with security and network teams to procure, install, and manage new storage and data management technology, but focusing on the data itself is imperative. The goal is to create requirements and guidelines for data management, security and governance and enhance those already in place. Cross-functional teams with departmental leaders can also create policies for data tiering and archiving, which reduces the footprint of data residing on primary storage where the 3x backup copy standard is in place. 3. Use AI/automation to tag and find sensitive data. A real struggle with massive volumes of unstructured data spread across enterprise data silos is that it can be painstaking  to find data sets that need a higher level of protection. Start by enriching file metadata with custom tags that indicate regulated or sensitive PII and IP information. Data classification is also useful in the case of a regulatory audit or even for use cases such as legal discovery. Adopt easy-to-use tools, such as Komprise sensitive data detection and mitigation.  After scanning, you can segregate and tag those data sets, move them to the most secure storage location, or delete them altogether if corporate rules require that. 4. Create policies for automated data movement across vendors. Such policies, for example, could dictate that files containing financial data move to encrypted cold storage after one year of age, customer files move to immutable cloud object storage for a period once an account is closed or inactive, or that ex-employee data be deleted after 30 days from an employee’s last day. Automated policy features in storage and data management technologies can make this easier to execute for small IT teams. The goal is to lower the risk of data being in the wrong place at the wrong time, thereby creating security loopholes that a bad actor can easily exploit. Getting rid of unnecessary data and/or moving it to archival storage is also a great way to save money on expensive primary storage. 5. Leverage monitoring and alerting features in IT systems. IT and data management applications today provide alerts and notifications that can help you proactively identify threats and improve unstructured data security. Use these tools to monitor storage and backup systems for any anomalies, such as excessive file retrievals from one user account, indicating a possible security incident. Monitoring features can show other details such as orphaned data or duplicate data that may increase liabilities. Or, you may want to see metrics indicating potential performance problems, such as a file server or NAS device reaching capacity. Ensure you have a process in place to review alerts and monitor data to escalate and fix issues. 6. Leverage affordable ransomware protection in the cloud. An immutable copy of data in a location separate from storage and backups provides a way to recover data in the event of a ransomware attack. However, keeping multiple copies of data can get prohibitively expensive. If the data is cold or inactive, you don’t necessarily need multiple copies of it. An effective strategy is to tier cold data from expensive storage and backups into a resilient destination such as Amazon S3 IA with Object Lock. By moving cold data to object-locked storage and eliminating it from active storage and backups, you can create a logically isolated recovery copy. Object-locked storage is an immutable medium to prevent deletion or alteration and it is also significantly cheaper than file storage. Cold data tiering is a fantastic way to reduce the attack surface in your data center by up to 80 percent. Learn more about ransomware and cyber-resiliency protection with Komprise. 7. Incorporate data auditing and tracking for generative AI. There is much to consider when it comes to safely and ethically adopting generative AI solutions in the workplace. Strategies may include developing employee guidelines for which data is sanctioned to send to generative AI tools and for what kinds of research and use cases. Conversely, IT must lock down sensitive data (such as software code, proprietary information, customer information, HR data) that individuals should not access for use in AI. Also, request documentation from vendors that incorporates AI in their products about how they are handling your data and how they can help mitigate any data risk from their tools. Maintain an audit trail of all corporate data used in AI applications and track who commissioned derivative works from generative AI tools. Doing so can protect your organization against any lawsuits for copyright infringement. Read this blog to learn more about data management and AI. ### Best Practices for Data Management and Storage in 2023 With each new year comes new threats, new technologies and changing business mandates. For managers and directors overseeing data storage and unstructured data management, there is always much to consider – from availability and security, cloud cost optimization, performance, and cloud migration strategies. These days, IT leaders must also factor in supporting AI initiatives, a top priority and challenge identified in our latest industry survey. In our Fall news blog, we review 2023 unstructured data management best practices to address these objectives along with recent news from Komprise. Storage Security “Data storage security is a 360-degree discipline that covers a lot of things, including protecting IT assets, setting appropriate security policies, vetting vendors and partners, and educating employees in secure practices,” writes Mary Shacklett in Enterprise Storage Forum. The tips include: enact company-wide data storage security policies; role-based access controls; data encryption and data loss prevention, maintain strong network security and endpoint security systems, copy data to redundant storage, and more. Storage Refresh This article in eWeek by Komprise COO Krishna Subramanian provides guidance on this evergreen topic by highlighting what not to do when performing a data storage refresh. “When IT managers discover that they need more storage, it’s easy to simply buy more than they need,” Subramanian writes. She advises getting metrics to understand data volumes, data growth rates, storage costs and how quickly data ages and becomes suitable for archives or a data lake. “These basic metrics can help guide more accurate decisions, especially when combined with a FinOps tool for cost modeling different options.” She also suggests matching distinct workload needs to vendor offerings: “You can likely build more cost-effective storage infrastructure if you select from the offerings of multiple vendors.” Data Backups Stephen Bigelow’s comprehensive guide for TechTarget on data backups delivers solid advice: “Because of ransomware, data centers must increase the frequency of backups -- once a night is no longer enough.” Among other tips, Bigelow addresses cloud backups: “Although the cloud backup provides an attractive upfront price point, long-term cloud costs can add up. Repeatedly paying for the same 100 TBs of data eventually becomes more expensive than owning 100 TB of storage.” At Komprise, we advise an intelligent tiering strategy to our customers. This entails analyzing data to understand data owners and usage patterns: delete zombie and orphan data and then tier cold data (often 80% of all data sitting on top-tier storage) to low-cost object storage where backups are not usually needed. This can save tremendously on backups, which typically comprise around 75% of the overall data storage bill. Data Governance Data governance is about setting and enforcing internal standards related to methods for gathering, storing, processing and deleting data when it reaches the end of its lifecycle, writes Drew Robb in ESF. Object storage is important, he adds, because it simplifies governance by providing a centralized and unified solution for data management activities and includes strong metadata capabilities. Understanding unstructured data: its volume, location, owners, growth rates, costs to manage and usage patterns is also key, Krishna Subramanian said in a quote. Look at what data can be deleted, tiered, archived and governed more and add automation to manage tasks efficiently and deliver an audit trail of what corporate data has been ingested by applications, she adds. Unstructured Data Management Optimizing unstructured data is a growing priority for enterprise IT organizations that need to both be cost efficient and deliver data to AI for new value and productivity benefits. Getting visibility into unstructured data across silos is the first step, per this TechBeacon article by our COO, Krishna. This is required for IT to make informed decisions about how best to manage it. Treat cloud data migration as an ongoing process and support it with policy-based automation wherever possible. Look for opportunities to deliver new value from data by indexing it so that data becomes easier to find, search, and use. Read the article for more tactics! What’s New at Komprise September was a busy month at Komprise, as we unveiled new research and a significant product update, after a long, hot summer. Komprise 2023 State of Unstructured Data Management Our third-annual survey covered the latest trends and priorities for data management and storage, across IT leaders in the US and UK. You can download the full report here. Top findings include: Preparing for AI is the leading data storage priority in 2023, followed by cloud cost optimization; Most organizations (90%) allow employee use of generative AI yet 66% of organizations cited top data governance concerns of preventing security and privacy violations, lack of data source transparency leading to unethical, biased or inaccurate outputs and corporate data leakage into the vendor’s AI model; The majority (40%) will pursue a multi-pronged approach to manage AI risk, encompassing storage, data management and security tools; Organizations managing more than 10PB of data grew from 27% to 32% this year, a 19% increase. James Maguire, Editor in Chief of eWeek, interviewed our COO Krishna Subramanian on the survey findings and what these trends signify for enterprise IT. https://www.youtube.com/watch?v=Dh6QezGweyE&t=2s Product News Komprise Intelligent Data Management 5.0 was released in September and the highlight is the new Storage Insights console which combines data and storage metrics in one customizable view. “As unstructured data becomes a central concern for IT administrators, the market needs tools to manage the sprawl while also addressing top-level governance and data protection directives,” said Steve McDowell, industry analyst with NAND Research, in an article for Forbes. “This is where Komprise is strong. This company continues to evolve, keeping a step ahead of the needs of the market it's serving. This is what makes it a leader.” Watch the demo:  New White Paper: Unstructured Data Management In the Age of Generative AI This paper delivers guidance for using enterprise data in AI platforms, considering needs for security, privacy, lineage, ownership and governance. Download it here to learn how to protect, segment, track and audit data in generative AI and the role of unstructured data management. For more unstructured data management best practices, subscribe to the blog! ### Lummus Technology Saves 80% on Storage and Backups with Komprise The global energy industry has been in a state of flux for the last few years, with no end in sight to the unpredictability. “The high energy prices of the past 18 months have cast a new light on the balance to be struck among energy security, energy access and energy sustainability,” according to Deloitte. As fossil fuels persist, despite volatility in supply and demand, renewables are on the rise: Renewables will become the largest source of global electricity generation by early 2025, surpassing coal, according to the International Energy Agency. Amid this backdrop, Lummus Technology is delivering energy process technologies for the spectrum of global energy products--spanning clean fuels, renewables, petrochemicals, polymers, gas processing and supply lifecycle services, catalysts, and proprietary equipment. The Lummus IT organization is modernizing to stay competitive and has been moving to an all-cloud strategy focused on Microsoft Azure since 2021. Yet the cost economics of the cloud aren’t quite working out to expectations. Its Azure NetApp Files service was too expensive for their use case, according to Lonnie Brown, IT systems administrator with Lummus. To develop a more cost-optimized cloud strategy, the company chose Komprise Intelligent Data Management to analyze its data first and then move it to the optimal storage, according to Brown. Komprise Migrates to New Azure Storage & Improves User Experience Cloud Data Migration: Lummus used Komprise to migrate 40TB of data from Azure NetApp to Windows File Server on Azure and Azure Files for archiving. Komprise is now managing a total of 108TB of Lummus data in Azure—60TB of which had been moved prior to Komprise coming on board. Brown says that he wants to begin cold data tiering with Komprise after evaluating cloud storage options. Deep Analytics: Komprise Deep Analytics, which leverages the Global File Index to allow users to conduct custom queries and tag data for enhanced segmentation, is simplifying data movement decisions. “I was sold on Komprise because of the Deep Analytics insight that helps us understand business usage—especially seeing how much cold data we have so that we could get the migration done correctly,” Brown says. “Now I don’t have to work with the departments to make these decisions; I can just put the data in cold storage. If users recall their files frequently, they will go back to the original location.”--Lonnie Brown, Lummus Data Storage Cost Savings Using Komprise to correctly identify cold data for archiving—which Lummus defines as not accessed after two years—is a major driver to significant annual savings. Migrating from NetApp Azure to Windows File Server on Azure and Azure Files is saving Lummus roughly 80% on storage and backups annually. Right-placing data for business needs: Like many global companies, data is distributed and accessed in many different places around the world. This can create latency issues if users are not located close to their data. The ability to use Komprise to drill down into data ownership, location and access metrics helps IT make the best decisions for the business. Data lifecycle management through analytics: With granular insight into its data assets, Lummus now has the knowledge to make smart business decisions such as consolidating shares to single disks to free up space. “And because nearly all of our storage is now on Azure, I can use Komprise to make sure that Azure cold storage is the cheapest for me because Komprise is storage-agnostic,” Brown says. Improved business collaboration: When business users come to IT with requests for more storage capacity, Brown can quickly view metrics on owners and usage to eliminate any conflicts when making decisions. “Working with departments is a lot easier now and we can make decisions much faster,” he says. “Komprise has been a wonderful solution for us to save a lot of money and analyze our data to optimize cloud data migrations and archiving,” says Brown. “As well, the Komprise customer success and support team has been super responsive to our changing needs during this journey.” Read more Komprise case studies here. ### Komprise Brings Data Storage Insights to Business Teams and Departments It used to be that storage engineers and architects primarily worried about installing and managing hardware and ensuring that data access was fast and reliable. In the last few years, there’s been so much more to worry about: spiraling costs, storage capacity running out, privacy and security concerns and adopting the right technologies that meet unique and changing departmental needs. In the most recent Komprise State of Unstructured Data Management survey, IT directors and executives indicated these top priorities: better supporting data initiatives and end user/department needs and improving self-service for users and departments. Deep Analytics Directory Explorer Fortunately, these findings are in line with our latest product update: Komprise Intelligent Data Management Fall 2022. The release introduces self-service features that bridge the visibility and trust gap that often develops between central and LOB IT teams when it comes to data storage and unstructured data management. The new Deep Analytics user profile gives authorized users read-only access to understand departmental data, such as size of file shares, amount of storage consumed, type and age of files, largest data consumers and more. This is important as it gives departmental IT teams, researchers and analysts a means to collaborate with central IT and storage administrators on data management, so the best decisions are made for cost savings, access to high-priority data and compliance. Unstructured data management use cases include: Showback: Authorized departmental users can monitor and understand their data usage (examples: how many and what type of files, where stored and biggest consumers) in an interactive dashboard. User-driven tagging: Users can enrich data with additional metadata tags to facilitate easier search and data management actions such as archival storage or compliance and legal hold. User-driven tiering and data mobility: Authorized users can identify data sets with certain characteristics (such as project or age) to move to cloud storage or other secondary storage to cut costs or support research initiatives. Data deletion: IT can set up data movement and deletion as a workflow in Komprise and the entire process is automated without requiring user or IT intervention. Compliance/Legal: During a merger or divestiture, department heads can help identify data sets that need to move to the new entity or which need to be retained in archival storage for auditing. “I've been looking for years for a way to know where my data is and what type of unstructured data it is, all with the goal of getting it moved to the correct tier of storage. Komprise does this for me and now I’m excited that I can offer a read-only role to end users. I can see how this would be handy for the business analytics crew and even for engineers/analysts during upgrades to see what data is out there and identify old data that could be purged.”--Brett Sayles, storage engineer at St. Luke’s Health Getting to the Answers Quickly with Komprise Deep Analytics Here are some example questions that you can answer about your unstructured data with Deep Analytics: Enterprise IT says our department is using 944 TB of data, but where is it and who’s using it? We can now see our top file servers from top file shares, and the top owners of data – a.k.a. the people storing the most files. We can also see what types of files they are, leading to interesting questions: Why is John storing 136 TB of video files? Pretty cool, huh? Suddenly, without meaning to, the Engineering IT manager has become an ally of enterprise IT: digging into the team’s data, finding anomalies and investigating who potentially owns inappropriate data. The manager could use Deep Analytics to find data that can be tiered to cheaper storage or even deleted based on their understanding of that data. This is something enterprise IT would not have been able to do, since they aren’t the owners of the data nor understand its relevance. Tagging files Deep Analytics users can tag specific data sets in Komprise for ease of access and use later. In the above example, the Engineering IT manager can tag all those suspicious video files. Enterprise IT already has a tag key called Classification, and the Engineering IT manager adds a new value, “Suspect”. By applying this tag to the 2.35 million files found, it’s easy to find them later and apply a data management action such as confine for possible deletion. If in later investigations, the Engineering IT manager finds other potentially suspicious files – for example, financial data files or contracts – those files can be tagged with the same tag. Easily tag files and use them with Deep Analytics Actions and Smart Data Workflows.   Enterprise IT can also use that new tag to easily find all the suspect data, in one query. In our demo example, the Engineering IT manager has become a business partner for enterprise IT, by helping find and tag data that just doesn’t belong. It doesn’t hinder Engineering’s efforts at all if those files are archived, or confined, or even deleted. Enterprise IT would not have been able to identify those files: it took the collaboration with the departmental IT managers to make that determination. You can watch the full demonstration below. Be sure to also learn about the power of Komprise Deep Analytics Actions and Smart Data Workflows and find new ways to unlock the potential of your unstructured data. Demo: Giving Data Insights to LOB Partners  Next Steps Learn more about Komprise Deep Analytics. Closer Look: Komprise Deep Analytics Powered by Elastic Search Investigate Smart Data Workflows Read: Komprise Deep Analytics Overview _______________________ ### Data Implications for Cloud Storage and Cloud Security Komprise April Update: Product, People and News We often use phrases like ”cloud journey” or “path to the cloud” as if the cloud is a destination -- and once we arrive the challenges and complexities we face with on-premises IT infrastructure will be a thing of the past. This month we explore articles illustrating that data management is an ongoing and evolving process that doesn’t stop where the cloud begins. Here’s our take on intriguing IT infrastructure and data trends reported in the trades, along with our own latest news. _______________________ Don’t Move Your On-Premises Data Problems to the Cloud There are a lot of amazing things the cloud can do for enterprise IT: instantaneous deployment, fully managed services and relentless innovation but it can’t fix poor data management practices. As David Linthicum in InfoWorld states: “News flash: The cloud fixes nothing. It’s simply another platform that will host your existing data problems.” The move to the cloud is an opportunity to assess current practices and avoid repeating mistakes with data in the cloud. _______________________ Which Flavor of Multicloud for You? DevOps and cloud computing influencer Lee Atchison discusses the continually evolving models of multicloud computing including polycloud and sky computing with in-depth descriptions of each. “Often, no formal decision is made and the choice to become multicloud happens haphazardly,” he writes. “This is especially common with companies that are new to the cloud or lack cloud sophistication. One group of engineers on one day decided to use an AWS service, such as Amazon S3. Another group on another day decided to use a GCP service. Independently, service teams may decide they need to deploy service capabilities within one or the other cloud provider’s services, and different groups may make different decisions. While using multiple cloud providers does have advantages, using them randomly or haphazardly like this seldom does. Yet, it’s a common pattern.” _______________________ Getting Granular on Cloud Storage Security Enterprise Storage Forum digs into some of the common issues and misconceptions around cloud storage security yet also provides some reassurances: “A major advantage of the cloud is that many security elements are already built into systems. This typically includes strong encryption at rest and in motion.” Among the best practices for cloud security are this one: “An organization can achieve high security cloud storage by mapping how data flows across systems, devices, applications, APIs and clouds.” The more intelligence you have on your data, how and where it is stored, the better chance you can adequately secure it for all its intended purposes. _______________________ Security Survey Indicates Gaps in Strategy and Visibility for Ransomware Defense A survey of more than 2,700 executives with influence over IT and data security found that one in five (21%) have experienced a ransomware attack in the last year, according to Security Boulevard. Yet the study revealed less than half of businesses (48%) have a formal ransomware plan. “For an organization to decide which levels of protection and controls to use, it must first be able to discover data wherever it resides and classify it,” said a source interviewed. “This means scanning all on-premises and cloud repositories for structured and unstructured data, which can be in many forms, including files, databases and big data. _______________________ What’s New at Komprise Komprise Architecture Closer Look We published a new page on our website illustrating how our platform can support many use cases including smart data migration, transfer data tiering and AI-ready data and workflows. The key architectural components include: a scalable, elastic grid architecture, patented Transparent Move Technology and a Global File Index. _______________________ VMBlog Q&A with Krishna Subramanian “Analyzing unstructured data is becoming pivotal because machine learning relies on unstructured data-and most sectors are keenly interested in the world of AI and ML,” says Komprise President Krishna Subramanian. “Enterprises need to rapidly parse through data — much of it unstructured — to find the information that will drive business decisions.” _______________________ Times of India Interview with Komprise Head of Engineering in India Our VP of India Engineering and Operations Prateek Kansal discusses the reskilling mandate for many storage IT pros. "As more infrastructure moves to the cloud and storage provisioning and management becomes automated, these individuals will need to study up on hybrid cloud architectures, cloud storage and how data moves across on-premises storage to different environments and back again along with the changing landscape of unified data management." _______________________ Komprise EMEA Channel Director in Ecommerce Age “IT directors need the flexibility to move their data to new devices and clouds as is desired and required to meet business requirements and achieve cost, performance and data protection advantages," said Martin Gibbons, head of channel in EMEA. “They want to do this without experiencing the penalties (in the form of license fees, integration hassles or cloud egress when data leaves a private network to reside on some other service etc.) associated with tiering, archiving or migrating data elsewhere.” _______________________ ### NetApp Insight 2021: AWS Automation, Ransomware Protection & File-Object Duality NetApp’s annual tech conference happened last week, and despite its digital nature, was chock-full of announcements from the company and overall buzz about hybrid cloud storage and data value. As an aside, NetApp customers are using Komprise to intelligently manage data and move to the cloud. Read more about our partnership here. Here are a few highlights that we tracked from NetApp Insight: Amazon FSx for NetApp ONTAP: This is a fully managed service that provides highly reliable, scalable cloud file storage built on NetApp's ONTAP hybrid cloud data management software. Why is this a big deal? This is not simply running an ONTAP VM on AWS file storage infrastructure. This is ONTAP as a fully integrated AWS service. Customers simply consume the service without any of the management tasks typically associated with running enterprise NAS. Sounds awesome—and there is no better way to move and get value from your data with FSx for NetAPP ONTAP than Komprise. Yes, that’s a mouthful, but let me break this down. Why Komprise for NetApp Data Management? Read the White Paper: Komprise Transparent Move Technology™ Komprise analyzes your data so you can determine optimal placement, whether in your on-premises storage or in the cloud. The analysis shows you how you can tier infrequently accessed data to lower cost AWS S3 storage while maintaining transparent access at source and destination. With your data footprint reduced to hot data only, you get several benefits: Komprise manages the migration for you and with cold data already tiered to AWS S3 there is less data to move, so migrations are faster. Cut costs with right-sized FSx for ONTAP instance as you are only storing hot data on FSx. Komprise Deep Analytics gives you a global file index so you can analyze data in place, across all your storage, before you move it. Komprise Transparent Move Technology lets you access your tiered FSx files via the S3 APIs to take advantage of cloud compute, Lambda functions and analytics. Use Komprise for continual replication from third-party NAS to FSx, enabling DR in the cloud. Enhanced Ransomware Protection: NetApp is also bolstering ransomware defense. As reported in Blocks & Files: “ONTAP, v9.10.1 as we understand it, will be able to protect against ransomware attacks autonomously, based on machine learning with integrated preemptive detection and accelerated data recovery. The system will monitor IO patterns and, if it detects unusual activity, will inform the cloud- and AI-based Active IQ array monitoring system. ONTAP will also make a snapshot of the relevant data as a preventive measure.” Read about ransomware protection with Komprise here. File-Object Duality: Komprise lets you read data stored in object storage via the original NAS protocol (this works for NetApp dual shares as well) without rehydrating to the original storage. If you are interested in how this can benefit you in a real-world scenario check out this Insight session: CSS-1210-2 - Yale New Haven Health: Computational Health Platform Evolution. NetApp CEO George Kurian’s keynote was lofty: Pointing to the preeminence of data-driven strategies and how IT leaders can support them: “From a business strategy standpoint, lean into digital and cloud. Pick a few big bets with hard impact business cases and move fast. Second, transform your IT portfolio and platform. Leverage cloud while simultaneously consolidating and simplifying data management. And third, be cloud-native. Develop cloud-native ways of working with cloud-native technologies.” This is the second year that NetApp Insight has been a virtual event and like so many other events that have gone digital, we miss the personal interactions. On the plus side, all the content is available on-demand and you can access it all at netapp.tv. Hoping 2022 brings us all back in person! ### Komprise October Update: Product, People & News It’s Fall: leaves are turning, temps are dropping and enterprise tech announcements are coming at a furious pace. In our monthly roundup of industry news, we focus on unstructured data value, object storage, ransomware and Komprise news. How to Get More Value from Unstructured Data There’s a lot of buzz around analytics for unstructured data and how it can unlock value and drive innovation. Tech Republic cites a survey by NewVantage that revealed over half of companies (54.9%) felt that they were behind in areas of data and analytics with up to 43% of data being unutilized. What’s holding these companies back from realizing the value of their data is the silos of storage trapping different data sets on different technologies and in different locations. For example, IoT data may live at the edge in factories while other data sets live at corporate data centers. This makes it difficult to analyze and draw correlations across data sets to generate new intelligence or solve problems. The article quotes Jeff Fochtman, senior VP of marketing at Seagate: “The need to access unstructured data near its source and move it to a variety of private and public cloud data centers…for different purposes is driving the shift from closed, proprietary and siloed IT architectures to open, hybrid models." Rebellion Against the Object Storage Empire Object Storage Is there enough room in the market for another object storage platform and do you need more Star Wars references? Cloudflare thinks the answer to both is yes. They have introduced R2 (a play-off of AWS S3 and of course everyone’s favorite droid). Cloudflare’s attack on the object storage market will be to target the thermal exhaust port of AWS S3 by not charging egress fees on R2 storage. Yes, Ransomware Criminals Want Your Tapes If you thought tapes were the bullet proof answer to ransomware, this eSecurity planet article is going to cost you some sleep. Ransomware has evolved from a the quick “smash and grab” attack to intrusions that can potentially own every aspect of your infrastructure—including your backup software and tape libraries. “It will just take them longer to encrypt the data on tape and hackers will have to write some new code of course, which they do all the time,” the author writes. Even if tape is not compromised the recovery can be so lengthy as to not be viable in a critical event. The final takeaway: don’t rely on any one single technology for protection against a ransomware attack. Kumar Goswami, CEO and co-founder of Komprise, offers some best practices in eWeek on how to protect your data and be ready to respond to an attack. CIOs Investing in IT for Growth A positive impact of Covid-19 was the massive investment which organizations were forced to make in IT. This ZDNet article quotes Gartner research that global IT spending will total $4.2 trillion in 2021, an increase of 8.6% from 2020. But CIOs and other tech leaders need to continue to up their game. “Rather than concentrating on infrastructure concerns, the role of CIOs in the post-Covid age will be to help the board take advantage of its new interest in IT and to identify how digital and data can help the organisation grow.” As for infrastructure leaders? They too will need to take on a higher-level perspective—away from implementing technology to enabling ROI and business value from better data management. Komprise in the News: Komprise is Klever I can’t improve on Justin Warren’s blog title so I am re-using it. His blog focuses on the elegant way Komprise tiers and provides transparent access to data by using standard features of modern operating systems – no agents, or proprietary stubs needed! Deep Dive on Komprise for AWS Amazon’s Anthony Fiore (Senior Migrations Solutions Architect) wrote a deep dive blog on how Komprise works with AWS to tier, migrate, and replicate unstructured data. He covers the components, architecture, features and benefits such as using immutable S3 object lock to protect against ransomware. This is an excellent blog to share with anyone curious about how Komprise works. Hybrid Cloud isn’t a Panacea Komprise President & COO Krishna Subramanian was quoted in this SearchStorage article about hybrid cloud tactics."[Enterprises] are not just using the cloud as a cheap storage locker. They are trying to transform their hybrid cloud architecture." Michael, Caitlyn, and Rob at AWS D.C. Summit AWS and Komprise Live and in Person! The Komprise team was live and in person at the AWS D.C. Summit on 9/28 and 29th. AWS and Komprise have a track record of helping U.S. public sector customers with their data management challenges. If you couldn’t attend, this AWS blog gives you the highlights. Hopefully this is just the start of being able to come together again. Upcoming TechKrunch! On October 13, our trusty engineers will gather again on a timely topic: how to use Komprise for moving and tiering files to a WORM-Compliant object store target as additional protection against ransomware. Register here for an educational and always-entertaining coffee break. Komprise-Qumulo Webinar On October 27, we’ll be teaming up with Qumulo to demonstrate how customers can reduce the challenges of migration from legacy NAS to Qumulo in the cloud. Register here. ### How to Protect File Data from Ransomware at 80% Lower Cost Ransomware attacks are unfortunately becoming so common that it’s no longer a matter of if a company will be attacked but rather when. Gartner predicts that by 2025, 75% of IT organizations will face one or more ransomware attacks. Two components of Ransomware Protection: Detection and Recovery Since roughly 80% of data today is unstructured file data, organizations cannot afford to leave file data unprotected from ransomware. Early detection of ransomware can deliver the best outcome, but as ransomware attacks are constantly evolving, detection is not always foolproof and can be hard to do. Organizations should also invest in ways to recover data if they do get attacked by ransomware. An immutable copy of data in a separate location separate from storage and backups provides a way to recover data in the event of a ransomware attack. But keeping multiple copies of data can get prohibitively expensive. To protect file data from ransomware, the solution must: Be cost-effective: An effective solution is to right size the protection based on whether the data is being actively modified and used or not, so that customers don’t have to pay the high cost of multiple copies of all file data. Protect if backups and snapshots are infected: Since ransomware can lurk undetected for weeks to months, snapshots and backups might already be infected by the time ransomware fully manifests. Ransomware attackers are also targeting backup software directly, so having your backup software create an isolated ransomware copy may not be effective. Provide simple recovery without significant upfront investment: In the event of a ransomware attack, you need to be able to recover your file data—but imagine if you could recover from a ransomware attack without having to pre-provision significant resources, by leveraging environments such as the cloud. Be verifiable: As our CEO Kumar Goswami stated in eWeek, the time to validate your plan is before an attack. You must be able to validate that your data is protected and available. How Komprise Provides Cost-Effective Protection and Recovery of File Data In a typical organization, 70-80% of file data is cold and has not been accessed in a year or more, yet it continues to consume expensive storage, backup and now ransomware recovery resources. Komprise makes ransomware protection affordable by transparently tiering cold data and archiving it from expensive storage and backups into a resilient object-locked destination such as Amazon S3 IA with Object Lock. By putting the cold data in an object-locked storage and eliminating it from active storage and backups, you can create a logically isolated recovery copy while drastically cutting storage and backup costs. Create Affordable Cloud Ransomware Recovery Copy that is Logically Air-Gapped: If you want to use Komprise for both hot and cold data, Komprise can create an affordable logically isolated recovery copy of all data in an object-locked destination such as Amazon S3 IA, so data is protected even if the backups and primary storage are attacked. Komprise creates a logically-isolated copy of your file data with the following properties: Physical Separation on Immutable Storage: The copy resides on a separate physical location or on a distinct network. For instance, when using Komprise with an Amazon S3 bucket with object lock as the destination, the Amazon S3 bucket is physically separated from your datacenters. This is also an immutable medium to prevent deletion or alteration. File-level Isolation: When Komprise moves a file to its destination, the entire file is isolated, as opposed to block-level tiering with backups or snapshots which only isolate parts of the file. This means that if a newer version of a file gets infected with ransomware, you can still recover older versions of the file. Komprise also enables you to detect file modifications on a schedule and copies the modified files as new versions into the Amazon S3 bucket. The schedule can be time-lagged (e.g., once a day or once a week or once a month), to provide greater time separation in the event of an undetected attack. Prevent Deletion: You can set the deletion time on the Amazon S3 bucket to a sufficiently long period, such as 12 months, to ensure the copy of the data cannot be deleted even if an attack were to happen on the bucket. Instant access and recoverability in the cloud without expensive upfront investments: Komprise copies your files as native objects in Amazon S3 along with all the file metadata intact. You can leverage all the AWS functions and applications on your data directly and view the copied data as files by mounting Komprise. This ensures you do not require expensive backup appliances and other upfront investments to access your copied data. Ransomware attacks are increasing and the payouts are getting bigger, so doing nothing is not an option. With backups also coming under ransomware attacks, an isolated recovery copy provides the best protection, but it can get very expensive for the large volume of file data. Komprise enables organizations to protect file data from ransomware at 80% lower cost through a combination of transparent cloud tiering and replication of all data to an object-locked location with file-level isolation. Komprise cuts active data management costs while enhancing an organization’s ransomware protection posture. ### Komprise COO: 2021 Growth and Future Software Plans Security, Regional Management and Data Monetization is the Focus of Komprise Intelligent Data Management This article original appeared on SearchStorage. In this Q&A, Krishna Subramanian, president and COO of Komprise, discusses development plans for the company into 2021 as well as the needs its customers have requested addressed. The past year has put a new focus on managing data in the cloud. How has this affected your customers? Krishna Subramanian: In general, we saw tremendous growth in our customers at this time because they realized they're not always going to the data center to see things. They need the visibility from someone like us, and they started moving more into the cloud. Data growth is just exploding, and customers are overwhelmed. They have so many choices in where to put their data, and it's growing. Our goal is to make our customers' lives simpler and to help them realize the value of their data. Because we keep the data in its native format, using us to move data to the cloud makes sense. Based on customer feedback, what are some features you've added? Subramanian: We added multisite capabilities because a lot of customers started using us in a department or a use case. Now they're expanding and want to use us globally across all their data centers. We added abilities to have multiple sites inside Komprise and roll them up into a dashboard and set local policies. The big theme for this year is helping customers protect the security of their data. We're seeing a lot of news today about ransomware, so we're providing capabilities to complement ransomware protection by putting a copy of the data on immutable cloud. The other thing we're adding is enhanced deep analytics capability. Giving the users a way to search their own data, to find the data they need -- we're adding more capabilities around that. How do we help our customers securely manage their data across diverse environments, and how do we help them monetize their data better -- those are the two angles on which we're doing a lot of product development. What differentiates this multisite control from past data management features? Subramanian: For us, it was always on our roadmap. We provide a single view across different environments. You could have different storage, different clouds. From day one, we've been providing that. The difference is customers sometimes want to carve that single view into smaller sites for security and other reasons. For example, maybe the European Union data should only be seen by people in the EU. Even though we can provide a global view across all data, they want only a global view in the EU region. They want policies in that region that are different than policies in North America. We can actually carve out that global view. We can say EU folks use the EU site and they can set their local policies, control their institution locally, and control security locally. Senior executives can still get a roll-up dashboard of key metrics of both of these regions. We're providing for greater ways to localize control, policies and security, while still giving global access. Customers are increasingly looking at things like security at a regional level. Where does Komprise see itself fitting into IT security against ransomware? Subramanian: We do have a unique perspective. We're not a security company, and we're not saying we're going to be a ransomware company, but data plays a very important role when it comes to security. Because we're analyzing data, we can find trends and anomalies very quickly. For example, if suddenly you see an unusual amount of reads in a directory or you see a large amount of writes, we alert IT. Sometimes that might be warranted, but sometimes it could be a malicious attack. Giving people some predictability to forewarn them is helpful. That's one place our analytics helps. The other thing is, you may never be able to prevent an attack and your data might be compromised, but if you had a copy of that data elsewhere and immutable, you have that added protection. Because we copy data, we enable customers to put data on Amazon S3 with an object lock. I wouldn't say we do all ransomware, but we help our customers defend through anomaly protection and immutable copies of data. ### Pure Storage Updates Purity Software for FlashBlade and FlashArray Pure Storage announced updates to its Purity software for FlashBlade and FlashArray that the company says will accelerate Windows applications, deliver ransomware protection across file, block and native cloud-based apps, and introduced a third generation FlashArray//C all-QLC platform. ### Pure Storage Beefs Up FlashBlade, FlashArray Lines In addition to enhancements to the Purity software behind Pure Storage’s FlashBlade and FlashArray aimed at increasing storage efficiency and protection against ransomware, the company unveiled an entry-level FlashArray//C all-flash storage array. ### Komprise Now Offers Ransomware Protection and is AWS Outpost Ready On 22 Nov 2020, Komprise released our latest Intelligent Data Management platform update. This release is focused on delivering several enhancements requested by customers to enable new use cases and simplify administration at scale as they expand usage of Komprise for their file and object data. Ransomware Protection with Support for Amazon S3 Object Lock Many of our customers use Komprise to archive cold data to Amazon S3 and want these files to be immutable for compliance and regulatory purposes. They may want protection against ransomware or malware incidents that can infect NAS shares. For both of these use cases, Komprise now supports Amazon S3 buckets configured with S3 Object Lock, which allows customers to store objects using a Write-Once-Read-Many (WORM) model. Once Komprise archives data into such a bucket, the data cannot be overwritten or deleted, providing file retention that meets compliance regulations and protects data from being encrypted by malware or ransomware. Archive, copy, or confine data from EMC Isilon dual protocol shares without requiring NFSv4 If you have dual protocol shares on EMC Isilon – shares that your users can access over both NFS and SMB, Komprise now supports the use of those dual shares in Plan operations (copy, archive, and confine) without requiring NFSv4 to be configured. This can simplify NFS share administration as well as avoid any impact on users and applications due to an NFS protocol change. And remember, when you copy or archive data from Isilon (or any NAS for that matter) to the cloud using Komprise, it is stored in the cloud’s native format allowing you to use the cloud’s native tools. This opens up a whole new dimension for our customers. For instance, if you archive cold data via Komprise to Amazon S3, you can use the data as the foundation for a data lake with the potential to query/explore data using services such as Amazon Athena, catalog data with AWS Glue,  process data with Amazon Elastic Map Reduce (EMR), and more. The possibilities are endless as you are able to unlock the value of your cold data in ways you may have never considered before. Support for AWS Outposts AWS Outposts is a fully managed service that offers the same AWS infrastructure, AWS services, APIs, and tools to virtually any datacenter, co-location space, or on-premises facility for a truly consistent hybrid experience. AWS Outposts is ideal for workloads that require low latency access to on-premises systems, local data processing, data residency, and migration of applications with local system interdependencies. For customers interested in using AWS Outposts, Komprise now supports Outposts, to find and migrate the right data into Outposts, to tier data within Outposts and into AWS, and manage all your hybrid cloud data from a single pane of glass. It’s exciting for Komprise to support Amazon as they expand their services and offerings to meet their enterprise customers’ needs! Learn more about how Komprise helps with AWS Outpost data management. There are many other updates included in the November 2020 release. Contact your Komprise account team if you’re interested in upgrading. Note that standard maintenance and support includes all updates and upgrades to your deployment. And as always: Know First, Move Smart, and Take Control with Komprise Intelligent Data Management. ### Lifeboat to Distribute Komprise Lifeboat enables resellers and customers to harness rampant unstructured data growth with Komprise Intelligent Data Management EATONTOWN, N.J.  May 09, 2018 Lifeboat Distribution, an international value-added distributor for virtualization, security, business continuity and emerging technologies, announced today a distribution agreement with Komprise, an industry-leader in intelligent data management that empowers businesses to efficiently manage today's massive scale of data growth across any NAS, Flash, Object and Cloud storage, while unlocking data value. Lifeboat will sell Komprise to help partners with their customers that continue to see complexity around managing environment that are both cloud and on-prem. "As more environments use on-prem and cloud for storage and applications, Komprise makes it easy for companies to efficiently plan storage capacity, identify and transparently archive cold data," added Dale Foster, Executive Vice President, Lifeboat Distribution. "They can also create a low-cost DR copy, migrate data and shrink backups to save costs. We see tremendous value in the solution Komprise offers to the ever changing environment of on-prem and cloud environments." "We were looking for a Value-Added Distributor with a focused approach aimed at introducing new emerging technologies, rather than traditional mainstream solutions," said Zach Edwards, Director of Channels. "Komprise is very excited to be working with Lifeboat Distribution and their dedicated sales team, marketing resources, and seasoned leadership team. With their strong focus on Komprise's offering, we know they will help us grow the market by introducing and enabling strategic Enterprise Resellers who are looking for an industry leading Intelligent Data Management Solution to help drive incremental value, revenue, high profits and market share". ABOUT LIFEBOAT DISTRIBUTION Lifeboat Distribution, a subsidiary of Wayside Technology Group, Inc. (NASDAQ: WSTG), is an international value added distributor for virtualization/cloud computing, security, application and network infrastructure, business continuity/disaster recovery, database infrastructure and management, application life cycle management, science/engineering, and other technically sophisticated products. The company helps vendors recruit and build multinational solution provider networks, power their networks, and drive incremental sales revenues that complement existing sales channels. Lifeboat Distribution services thousands of solution providers, VARs, systems integrators, corporate resellers, and consultants worldwide, helping them power a rich opportunity stream, and build profitable product and service businesses. For additional information visit http://www.lifeboatdistribution.com, or call 1.800.847.7078 (US), +1.732.389.0037 (International), +1.888.523.7777 (Canada), or +31.20.210.8005 (Europe). ABOUT KOMPRISE Komprise, the industry-leader in intelligent data management across clouds, empowers businesses to efficiently manage today’s massive scale of data growth while unlocking its value. The Komprise mission is to radically simplify data management through intelligent automation. Komprise is used by enterprises to intelligently manage data at scale. Komprise has won numerous awards including Gartner Cool Vendor in Storage Technologies, CRN Tech Innovators 2017, CIO Review 20 Most Promising Storage Solutions. For more information, visit Komprise. For Media & PR inquiries contact: Lifeboat Distribution Media Relations media@lifeboatdistribution.com Komprise Monica Giannella Read pr@komprise.com ### ### Stitching Your NAS to the Cloud If you are drowning in data and buying more and more Network Attached Storage (NAS) capacity to keep up, I’m sure you’ve heard the answer, use the cloud as your secondary storage. Depending on your needs and security requirements, it could be a public cloud like Amazon S3, Google Cloud Storage or Microsoft Azure or it could be a private cloud/object store such as IBM Cloud Object Storage, Cloudian, Spectra BlackPearl or NetApp StorageGRID. Moving data to secondary storage on the cloud enables you to scale on-demand, store a huge amount of data and can be far more cost-effective. With the public cloud, you can pay-as-you-go based on-demand and so with little upfront investment you can get started. But you have to weigh in issues such as security (you’re moving data outside of your data center), latency (it is farther way resulting in longer response times) and there are egress costs (charges for bringing data back from the cloud). With an on-premises object storage (e.g. a private cloud), the issues are just the opposite. You have an up-front investment but security, latency and egress cost issues are not relevant.   Before making the move to the cloud, it is important to consider several factors.   Impact of a different protocol and construct Cloud storage uses a different protocol and the construct are objects and not files. Now items you move to the cloud will need to be accessed differently and this can break applications and it can make it difficult for your users as they will need to use some other interface instead of their regular Windows Explorer or Mac Finder to access their data. You have to keep this in mind when deciding what to move to the cloud. Lack of permissions and access control Each file on your NAS has permissions that restrict who can view, change or execute the file. This is crucial to the security and privacy policies of your company. Yet, you lose this critical element when you move a file to the cloud. What happens if you want to recall it? How do you restore its access permissions? In today’s security-centric world, this is a very important consideration and a significant shortcoming with clouds. Slower performance and longer latency The cloud whether public or private will not be as fast as your local NAS. There are cloud gateways with caching that you can place in front of a cloud but that’s additional cost and does not provide the same performance of a NAS thus disrupting applications whose data is in the cloud. So you have to be careful and thoughtful about what data you should move to the cloud. Lack of continuity of the data Once the data has been moved, users and applications will not be able to access it from its original location. This is very disruptive and in most organizations, where IT does not create or use the vast majority of data, the decision as to what should be moved has to be relinquished to the end user. End users don’t like to move their data making the entire “let’s move data to a cheaper secondary storage” initiative less impactful with very little data moved or an initiative that never gets off the ground.   Komprise addresses each of these critical items by seamlessly stitching any cloud to your NAS storage system.   Frictionless stitching of cloud to NAS by bridging file and object seamlessly Komprise provides analytics that shows you what data to move based on their usage and it moves the data transparently so that it can still be accessed via standard file protocols and from their original location. Users and applications are not disrupted and they don’t need anything extra to access their data. Furthermore, they can still see that data on their NAS allowing them to still search and list moved files. Full preservation and enforcement of permissions and access control on moved data in the cloud Komprise ensures that the original permissions and access control are also moved and enforced when the user tries to access the data through the NAS or even through Komprise. If the data is rehydrated back to the NAS, it’s brought back with identical permissions, access control lists (ACL), and attributes — so nothing changes. Since users are not affected by moving the data, control and management of the data is back again in the hands of the IT staff.   No degradation of hot data, optimized access to cold data But what about determining what data to move and what if hot data is placed onto the slower cloud? Komprise addresses this too. Komprise analyzes all the data on your NAS and shows you the data that is being used and data that hasn’t been accessed in months to years. In most organizations, over 70% of the data has not been accessed in over a year. Imagine how much money you can save and how much room you can make on your existing storage if you can seamlessly move all of this cold data to the cloud! With Komprise you can set policies such as “move all data that’s over one year old to the cloud”. You can specify exclusions based on size, location, name and owner of the files. With this approach you are only moving cold data to the cloud and leaving all the hot data on the NAS where it continues to get the best performance. Still, you may argue that a lack of prior access does not mean a user won’t suddenly access a file tomorrow. While the probability is low, when a cold file in the cloud is accessed Komprise caches the file locally so that after the initial latency (which is dependent on network to the cloud and performance characteristics of your private or public cloud) it’s as if the file were on the local NAS. Komprise streams files so that large files don’t incur a huge latency. You can also specify recall policies so that if the file has become “hot” again (by being frequently accessed) it is rehydrated back onto the NAS. Komprise also provides a bulk recall feature so that if know that an old project is going to be revived you can recall all those files back to the NAS ahead of time. Full continuity of data – no changes to users or app When Komprise moves data to the cloud, it transparently archives the data so the moved data looks like it still resides on the source, and is fully accessible exactly as before, with no changes to users or applications. To summarize, Komprise seamlessly stitches a public or private cloud to your NAS infrastructure providing you the benefits of both storage devices without their faults. Via policies you can drain the NAS of old, cold data making room for new data without any disruption to your users. It saves cost by storing the bulk of your data in less expensive object stores while increasing the life, capacity and performance of your existing NAS storage which only handles your hot data. Seems logical doesn’t it? For more information please see the following or schedule a demonstration with one of our experts: • Architecture Whitepaper • What is Intelligent Data Management video series • Komprise Case Studies ## Industry Verticals > Komprise serves data-intensive enterprises across healthcare, life sciences, legal, higher education, government, energy, and media. ### SC24: Unstructured Data, AI and HPC trends in Healthcare & Life Sciences Big data analytics and AI are making an ever-greater impact in the healthcare industry. There are opportunities across the board, from speeding up genomics research and drug discovery to improving medical image analysis, clinical decision making, prevention and wellness programs, chronic disease management, clinician productivity and beyond. The interplay of unstructured data, AI and high-performance computing (HPC) is making all this possible. HPC, in focus at SC24 this week in Atlanta, provides the computing infrastructure required to efficiently analyze large and complex data sets. HPC made headlines in healthcare during the pandemic with the COVID-19 HPC Consortium, a private-public collaboration that supported dozens of research projects to accelerate early-stage drug development. The HPC market is expected to reach $49.9 billion in 2027, up from $36 billion in 2022, according to MarketsandMarkets. Healthcare, which creates an estimated 30% of the world’s data, will undoubtedly make up a healthy slice of this pie. Today, leading healthcare organizations like NYU Langone Health are developing ambitious AI projects running on HPC. “Our eventual goal is that all text generated by or for us will be touched by a large language model, whether in our education, clinical, research, or operations missions,” said Yin Aphinyanaphongs, MD, PhD, director of operational data science and machine learning at NYU Langone Health. “AI will transform how we care for patients, run hospitals, write research grants, and train medical students.” Underneath the covers at the institution is an HPC cluster called Ultraviolet, which supports a range of programs, including training sophisticated AI models. Komprise for HPC: Scalable, Indexed, Open, SaaS Komprise has been helping enterprise organizations manage petabyte-scale data environments for more than eight years. The core tenets of our architecture include: Built on a distributed, scalable, fault-tolerant architecture of stateless observers placed near the storage where they are most effective at analysis and mobilization. Global File Index and a centralized management console through which you can view and manage all unstructured data across storage silos. The platform is standards based (NFS, SMB, S3), with no agents or stubs and it is never in the hot data path. Delivered as a cloud service and is easy to set up and easy to use. Komprise in Healthcare and Life sciences Healthcare and life sciences organizations represent one of our company’s top sectors. Learn more here. One customer, a U.S.-based academic healthcare system with several hospitals and a medical school faces data challenges like many other healthcare groups today: rapid growth of large medical image files and research data, which is constraining IT resources to store, protect and manage data assets. The healthcare system adopted Komprise Intelligent Data Management for unstructured data tiering, migration and visibility across their data storage silos. Komprise indexes all the file and object data across storage to show insights which help IT teams and departmental research groups make better decisions. The Komprise dashboard shows metrics such as data growth rates, volume and most common file types, and then gives IT users options to model different plans for cost savings. Storage engineers can also use Komprise to classify data through metadata tagging and move it to new storage as needed. The storage team has moved more than 2PB of medical files over one year old to cloud object storage—freeing significant space on its NAS storage arrays and saving the organization 70% on its annual storage and backup budget. With these savings, the healthcare system is freeing up funds for AI projects that rely upon clinical documents, images and research files from HPC applications. The organization also plans to use Komprise Smart Data Workflows to automate the search, tagging, and curation of specific data sets needed by data scientists for research projects. Komprise at SC24 Komprise is a sponsor at the SC24 trade show and conference in Atlanta this week. Please stop by our booth, #414, to meet the team and learn more about how we’re helping HPC teams and researchers get more value from unstructured data, gain maximum storage efficiencies and achieve more protection from ransomware. Read the previous blog about how unstructured data management is relevant for petabyte-scale HPC IT environments. ### Making a Case for Legal Unstructured Data Management “Sagging productivity and declining realization have combined to put a pinch on law firm profitability growth such that even the high pace of rate growth has been largely unable to remedy the situation,” wrote the authors of the 2024 Report on the State of the U.S. Legal Market (Thomson Reuters and Georgetown Law). Like every other sector, the legal industry is undergoing transformation from global recession pressures, AI and high overhead. Firms are working to diversify and cope with competition from growing cadres of freelance lawyers. Fortunately, there is some relief on the horizon from AI and automation, which promise to take some heat off the time spent doing undifferentiating tasks such as billing, document review and research. Legal unstructured data management is another tactic, to right-place data and deliver cost savings on data storage and backups. The global legal technology market, projected to grow at a rate of 37% over the next several years, is a sign of this growing trend. IT and Data Challenges Law firms and legal departments are document heavy. IT organizations report an average 20% annual growth in storage for file-related data, resulting in the need to add expensive on-premises storage capacity every year or two. To make matters worse, most firms do not delete data. After all, cases can be reopened after decades. Therefore, managing costs is difficult. In some cases, law firms are moving data to the cloud – although cost savings aren’t guaranteed there either. Secondly, in recent years law firms have been lucrative targets for ransomware actors and other cyber criminals, given the amount of sensitive, personal data they retain. Meanwhile, law firms would like to take advantage of AI to be more efficient and reduce operational overhead. Innovation in the sector is attracting big dollars: startup Harvey announced a $100 million Series C round in July. Yet that opens another can of worms to avoid exposing sensitive client data into AI tools. How Unstructured Data Management Helps A strategic approach to managing file and object data can help reduce the high costs of data storage and protection while making data more useful for AI initiatives: 1. Cut costs with automated policies By understanding data usage patterns, you can determine which data sets are no longer actively needed and could be easily archived. Better yet, by tiering data to an online archive in the cloud, legal professionals can recall that data quickly if needed to support client work. Komprise Intelligent Data Management delivers the capability to establish internal policies for different use cases, such as to tier cold data that hasn’t been touched in more than a year to secondary storage or by situation, such as when a case has been closed. 2. Avoid user disruption Legal work can be fast-paced and urgent. IT leaders would like to avoid stressful disruptions where employees cannot find their documents after they have been moved. Because Komprise does not use stubs, agents, or other proprietary interfaces, applications and users always find their data in the same place as if it had never moved. This is valuable in an era when data is in motion more than ever before. Learn more about Komprise Transparent Move Technology (TMT). Users simply click on a Komprise Dynamic Link which seamlessly redirects to the new location. 3. Add another layer of ransomware protection By tiering cold data to immutable cloud object storage, Komprise delivers a proactive security posture preventing ransomware actors from accessing or making any changes to data and reducing the attack surface. Also, moving data out of on-premises data centers reduces the attack surface for hackers. Learn more about ransomware cost optimization. 4. Support a safe AI journey Komprise analysis of data across storage gives IT the insight to right-place data in the most cost-efficient storage for its current needs and easily move it to different tiers as it ages or as its value changes. This automated process can free up funds to support AI and achieve the benefit of cloud-native access to data after it has been moved --thanks to Komprise TMT. This is useful given the depth of innovation in cloud AI services, making it easier to leverage data already in the cloud. Finally, Komprise Smart Data Workflows allows IT to create automated, policy-driven processes to find, tag and move data to AI platforms and to enrich metadata tags using built-in integrations to machine learning tools such as Amazon Rekognition, Amazon Macie and Azure AI. Read how Katten Law embarked on a legal unstructured data management initiative using Komprise to tier cold data to the cloud for savings and ransomware protection. Read more Komprise Industry Posts. ### The Data Equation to Higher Ed’s Tectonic Shifts This blog is part of an industry series on unstructured data management. Today we cover unstructured data management trends in higher education. Read the previous posts on life sciences and healthcare, government and auto industries. Universities and colleges are facing an array of challenges that have been escalating since the 2020 pandemic: business and financial models are under pressure due to backlash about affordability and high student debt; demand for new types of non-traditional programs for revenue generation and workforce development; mental health programs for students are lacking, and more. Hanover Research discusses some of these issues in its 2023 Trends in Higher Education: “Many elements of the traditional higher education experience are undergoing major shifts as institutions seek to establish financial sustainability. College and university leaders are being called to examine new ways of promoting their brand while offering flexible, career-forward programming. They must also think creatively and strategically to find new sources of income to offset operating expenses and declining tuition revenue. And, as pandemic-related stressors continue to ripple through daily life, it’s imperative to offer robust, inclusive support services that can help students thrive and persist." Deloitte’s higher education outlook touched on the reality of ongoing crisis preparedness: “The ability of a campus to both prepare for and respond to a crisis is dependent on its physical infrastructure as well as human resources and leadership. Yet, with few notable exceptions, higher education institutions have failed to modernize these resources.” In these transitional times, higher education institutions increasingly rely upon data to understand shifting demand and consumption patterns, communicate with and influence students and community stakeholders, and develop new programs. Common higher ed unstructured data management challenges Universities and colleges store a broad collection of different unstructured data types including student records, health and financial data, HR data, research data, online lecture content (video and audio) and surveillance footage. Institutions have a long history of keeping all this unstructured data. Yet today, there’s a dire need to better manage data for cost efficiencies and to reuse it for analytics, research and marketing. Institutions may struggle to classify and categorize the petabytes of data they’re storing for all their departments, faculty and researchers. Decades of storing and replicating petabytes of data on high performance, high-cost primary storage systems, instead of lower-cost alternatives for data as it ages, has been detrimental to IT budgets. Data protection is another primary consideration for higher education. A major West Coast research university had accumulated petabytes of data on its Network Attached Storage (NAS). Protecting this data by keeping a data replication copy on a mirrored environment was too expensive. As a result, most of the NAS data was not replicated, risking data loss. Using a data management solution, the university realized that more than 60% of the data on the NAS had been untouched for over a year. The IT organization determined that keeping a copy in the cloud rather than on premises would be 70% less expensive, leading them to safely replicate data to Google Cloud. Unstructured data management for higher education Duquesne University Leverages Komprise to Archive Cold Data from Komprise on Vimeo. Komprise has been working with large public research universities since 2016. Our customers, including prestigious Ivy League schools, have decades of data across silos--of which most has not been archived or deleted. This comes at high costs and complexity! As the leading provider of unstructured data management and mobility, we want to fix this so universities can manage data more intelligently in the hybrid cloud. Using Komprise for an analysis-first approach to unstructured data management, our education customers have achieved remarkable savings while delivering enhanced data services for departments: Cut backup and storage costs by 70% by transparently tiering cold data to object storage, using our patented TMT; Reduce storage costs from $1.1M to $680K/year; Gained visibility into every department's data and tailored policies by department; Enabled researchers to precisely search and find their own data with automated tagging and the Komprise Global File Index; Moved data with zero access disruption to users; Provided an easy tool for researchers to search, find and help manage their own data; Adopted cloud storage for modernization and cost savings, per this Wasabi and Komprise case study for Ivy League. Departmental Showback reports are another great way for IT to save while giving data owners more control over their own data assets. There are tools to identify the capacity used on the storage devices, but they don’t work at the data level. Companies find that simply enforcing limits or threatening departments with quotas and budgets are not effective in controlling data sprawl. Komprise helps storage leaders align with departments on data management so that everyone wins, as detailed in this paper: Getting Departments to Care about Storage Savings. We have visibility on our data that we’ve never had before. Komprise lets us partner with the business to get the data into the best location for them. It shows what data hasn’t been touched in six months or year. It saves them money and saves us money.—Ivy League enterprise storage manager. Much of an institution’s budget is consumed by faculty and administration salaries and facility management. Less than 3% is typically allocated to technology investment and operations. It’s not a stretch to assume that nearly half of a university’s IT budget goes toward data storage and backups. Yet higher education depends upon smart data management to flex and grow with the pressures of funding, recruitment and meeting the needs of a diverse student population. Read more about Komprise Intelligent Data Management for higher education. Komprise For Higher Ed: Interview with Steve DeGroat, from Komprise on Vimeo. ### Auto Makers’ Data Management Needs Span Safety, Performance & AI This blog is part of an industry series on unstructured data management. Read the previous posts on life sciences, healthcare and government. Self-driving cars, electric cars, digital safety sensors and an increasingly digital driving experience are hallmarks of the new automotive market. Electric cars constituted 4% of all auto sales in 2020. That number had more than tripled to 14% by 2022, and EV sales were up 25% in the first quarter of 2023, according to the International Energy Agency. Technology futurist and strategist Bernard Marr recently wrote about automotive innovations in Forbes. He made note of novel anti-aquaplaning systems, and AI-powered simulations for Formula One teams that model billions of potential race parameters to determine what variables are most likely to lead to favorable outcomes. The variety and velocity of new data creation from sensor data, images and streaming content such as video is creating new needs and opportunities for unstructured data management in the auto industry. Electric and autonomous vehicles continuously collect and analyze sensor data to inform critical actions, such as alerting the driver when it’s time to recharge or refuel or to auto brake or steer to avoid a collision. This data has a longer shelf life than its immediate use during a drive; car makers need sensor data to resolve technical issues and improve their cars’ performance and safety. This requires powerful AI and analytics tools, edge and cloud storage, and the right data strategies. Cloud-based data lakes have made it easier and more affordable to query data and run machine learning models continuously on the data, compared with more traditional BI tools. Common data management challenges Autonomous cars are projected to generate as much as 40 TB of data an hour from sensors. These massive new data sets are gold mines for analyzing and optimizing vehicle performance and safety and delivering new features to make driving more fun and less stressful. Digital innovations and heightened customer expectations are creating urgency for car makers to manage their data differently than in the past. They need the ability to rapidly analyze and filter the right data at the edge to avoid crashing their data centers and cloud services with too much irrelevant data. How unstructured data management helps Unstructured data management solutions can help users tag, search and send the right data sets to AI tools and data lakes in the cloud, while avoiding waste and optimizing costs. Unstructured data management solutions can save money on storage through intelligent data tiering to object storage in the cloud. From there, researchers and engineers have cloud-native access to their data, which is necessary for cloud-based AI. Using an unstructured data management system, a car manufacturer could create a workflow like this: Find crash test data related to the abrupt stopping of a specific vehicle model; Use an AI tool to identify and tag data with “Reason = Abrupt Stop”; Move only the related data to a cloud data lake to reduce time and cost associated with moving and analyzing unrelated data; Move the unrelated data to an archival storage tier for cost savings (or delete it) once the analysis is complete. In the Komprise 2023 State of Unstructured Data Management report, IT and storage directors responded that preparing for AI is the leading data storage priority in 2023, followed by cloud cost optimization. The right unstructured data management solution and strategy can help accomplish both goals by by automating the curation of data to feed AI tools with data governance capabilities and by ensuring that data is always in the right place at the right time in its lifecycle, which can save organizations significantly by leveraging lower-cost storage when data is no longer active. Watch this short demo to see Komprise in action. https://vimeo.com/713436919 Watch the webinar: Building a Modern Strategy for the Automotive Industry with Komprise and AWS. Read the blog post: Modernizing the Automotive Industry with AWS. ### Healthcare and Unstructured Data Management This blog is part of an industry series on unstructured data management. Read the previous post on life sciences here. Personalized medicine, patient-centric care, telemedicine, digital imaging, digital pathology and AI-driven disease management are driving massive unstructured data growth in the healthcare industry. Healthcare is one of the largest industry creators of data.  Roughly 30% of the world’s data volume is generated by the healthcare industry, and this will grow to 36% by 2025, according to research compiled by RBC Capital Markets. Consider common medical files such as lab slides, X-rays, MRIs and CT scans. These everyday files take up petabytes of high-performing storage. Regulations often require their retention for several years. Clinicians may need to review some images again months later, so IT can’t hide them in a dusty basement tape archive. Dictation and nursing notes also contain patient data that’s valuable for data mining projects which organizations need to improve patient outcomes and develop personalized medicine programs. Common data management challenges: Managing data growth is a large initiative in healthcare. The global healthcare data storage market size is expected to grow from $4.17 billion in 2021 to $9.23 billion in 2026, according to The Business Research Company. Beyond data volumes, there are many different systems and clinical file types as technologies and protocols evolve. This complexity makes it laborious to search for specific files, meet compliance challenges and manage storage costs. Most healthcare providers are under tight budgetary constraints following the pandemic and ongoing industry pressures to lower the cost of care. The AI Opportunity The healthcare industry is on the brink of revolutionizing patient care. Two decades ago, electronic health records were still rare. Today, digitization has accelerated quickly with mobile apps, wearables, telemedicine and the integration of AI technologies into daily practice: AI is delivering more accurate, faster analysis of common scans such as mammograms, cardiograms and colonoscopies. AI is also behind intelligent alerting systems for community health, such as an environmental health crisis tracked to ER patients from the same location. AI and big data analytics are helping medical leaders create holistic care plans by analyzing demographic and social data for patients with a particular condition and delivering better preventive care by analyzing chronic disease data. Generative AI solutions are reducing the paperwork burden of clinicians and even improving communications between physicians and their patients. These programs depend upon timely access to the right data sets for real-time analysis. They need unstructured data management solutions that can deliver efficient ways to discover, tag, and move data to the right storage tiers as needed to support changing needs. AI’s positive impact on healthcare and the dangers of data bias and incomplete data. https://www.youtube.com/watch?v=uvqDTbusdUU How unstructured data management helps: Unstructured data management solutions help healthcare organizations lower the overall cost of data storage (including backups and disaster recovery) by 70% or more through intelligent analysis and placement of files. This frees up money for critical analytics programs required to maintain profits, high standards of care, grants and funding and patient satisfaction. The right unstructured data management solution can also bring deep analysis to data, allowing managers and researchers to understand data usage, easily locate and use or move data as needed and avoid compliance issues. Automated workflow capabilities create more efficient ways to find data, copy or move it to an AI tool for analysis, tag the results with metadata and then archive or delete the original data once the AI has finished. Case in point: St. Luke's Health Komprise customer St. Luke’s Health reduced capacity on its flash array by maintaining excellent performance for the data that people are using and moving data that hasn’t been touched in three years to lower-tier storage. As covered in this article: “The new technology also positions St. Luke’s well for healthcare and medical advances. A new type of digital pathology technology, for instance, generates extremely large file types. The data is initially very active until it’s read and a report is issued. After that, the large files probably won’t be needed too often. The IT team can now set up a policy to send those files to the archival storage tier after a specified period.” In our next post in this industry unstructured data management series, we will take a closer look at the public sector, which must be cost-effective with IT spend while delivering new digital initiatives to improve safety and services for citizens. Read the post: Why Unstructured Data Management Matters: An Industry View Read the post: Life Sciences and Unstructured Data Management ### Life Sciences & Unstructured Data Management This blog is part of an industry series on unstructured data management. Read the first post here. The life sciences industry has undergone significant industry transformation in recent years. The sector is an $8-10 billion global industry with leaders including Eli Lilly, Pfizer, Johnson & Johnson, Merck and Abbvie. Pharma and biotech companies have been at the center of global innovation, with rampant revenue growth and demand fueled by the Covid-19 pandemic. Investments in cloud, AI and digital technologies have intensified, delivering groundbreaking changes in how companies develop, test and deliver products to market. Common file types in life sciences include: clinical images, genome sequencing and other instrument data, as well as research documents. These data types don’t work well with traditional data analytics tools; life sciences companies are increasingly moving research data to the cloud to leverage affordable and scalable processing and analytics services for research data, offered by the large cloud providers (CSPs). Common data management challenges: Life sciences organizations can struggle with data silos hampering visibility and collaboration across teams; difficulties searching and securely accessing data exported into cloud-based data lakes and other platforms; continual change in regulations, affecting data practices and too much time–at least 50%– spent on data preparation and deployment, according to IDC. Last but not least, cost optimization is a prevailing sentiment which runs across all IT infrastructure. Since data storage comprises a hefty portion of IT budgets, managing data as pertains to storage, backup and DR costs is a growing priority. The AI Opportunity Artificial Intelligence played a significant role in the rapid development of Covid-19 vaccines. The potential for AI to positively impact all operations of life sciences is on the horizon. A few examples include: R&D: Running AI algorithms against large amounts of data to identify compounds that have the potential for development into new therapies will potentially cut years off time to market. AI’s also being used to optimize the design of new medical devices. Clinical trials: Large pharma companies are using machine learning and AI to identify ideal patient populations for their clinical trials and monitor patient outcomes during the trial. There are many other examples of the impact of AI on life sciences here. Faster, safer development of life sciences products depends upon getting the right unstructured data to the right tools at the right time. This could be instrument data from laboratory systems, genomics data, diagnostic and monitoring data from patient wearables, and patient demographics and outcomes data from clinical information systems. Organizations also need tools and processes to manage the data risks from AI technologies, which include data quality, data accuracy, data bias and protection of sensitive and regulated data sets. How Unstructured Data Management Helps An unstructured data management platform can index data allows users to apply metadata tags such as project, disease type, instrument type and demographics to the files. That way when IT moves files to the cloud, researchers can search on keywords and find what they need without manual digging. Automated workflow capabilities such as Komprise Smart Data Workflows (see diagram below) streamline the process of finding, copying, migrating and/ or tiering data to cloud data lakes and AI tools. After a project has finished, a researcher can and add tags to the resulting data sets to support new searches and projects. Unstructured data management solutions also help life sciences companies manage the vast expense of data storage, by identifying data that can move to cold data storage. Tools that can effectively migrate data to the cloud and to the right tier of storage based on the data set’s age and value will be imperative—especially with regulations requiring the retention of certain data types for many years. Case in Point Pfizer is saving 75% on storage using Komprise to analyze and continuously tier and migrate cold data to Amazon S3 as it ages. Pfizer storage managers and researchers are finding additional benefits from analytics-driven unstructured data management, including zero user disruption and a foundation for delivering self-service to line of business teams. The company is looking to use Komprise further by leveraging Deep Analytics and the Global File Index so that authorized research users can search for their own data and copy or move it to locations for analysis. “You can use Komprise to scan all your data, analyze costs and create business rules and then Komprise will act automatically against those rules,” said a Pfizer IT director. In our next post in this industry series, we will take a closer look at the healthcare industry, another sector with massive unstructured data challenges and which has been going through enormous transitions in recent years. ### Interview: PacBio's Data Management Journey Adam Knight has been at Pacific Biosciences, a company that develops genomics sequencers, since 2014. He started his career there in the HPC group and today is the director overseeing IT infrastructure, which consists of HPC compute, storage and networking. PacBio is one of the early customers of Komprise. It’s great to be still working with your company! Bring us up to speed on your use of Komprise and your overall data management objectives? AK: We're still on an aggressive growth trend with data and storage and facing the challenge of keeping as much data online as possible all the time. That’s where we leverage Komprise the most, as people aren't very good at bucketing or organizing that data in a way that makes it digestible for archiving. Komprise helps us by looking across our storage and surfacing metrics such as who owns the files and when the data was last accessed. Genomics is a data-intensive industry. What are some challenges that you face with storage and unstructured data management in this sector? AK: Having people properly organize their data is always a challenge. Who is generating it, how long does it need to be kept? How important is it? Is it going to the right place? If files can be identified ahead of time, then it's an order of magnitude easier than trying to identify them later. Other challenges are, do we need to keep data forever? The unclear lifecycle of our data is a challenge. Read the Case Study > > PacBio is using Komprise to tier data from your highest performance storage on VAST Data and NetApp to archival storage, which is currently Spectra Logic BlackPearl NAS. How do you create archive policies? AK: It depends on the type of data. For example, instrument-generated data is a giant classification of data here and we are trying to set a more consistent policy for that data type. Then we've got other types of data for secondary analysis and our manufacturing group is looking at data which may not be relevant for as long. We are trying to classify all departmental data so that we can set more granular policies for archiving and deleting. How about deletion policies? Does this relate to regulatory requirements for data retention? AK: We’re working closely with our internal customers to set policies for deletion and then using Komprise to target that data and delete it. For a scientist to run a sequence, that costs a certain amount from the sample prep and chemistry to the time it ran on the instrument, from the chips that consumed all of that, which helps determine the value of that data and when we can delete it. As well, researchers may need to keep data if a publication has written about it or that process is reevaluated a couple years later for manufacturing. It’s not a regulatory requirement but determining the value of the data from a future business perspective. What are your main use cases of Komprise today? AK: First, visualization. We now have a rapid way to visualize large amounts of data, which means that we can quickly determine the volume of data that is growing and the lifecycle of the data. That information includes how much data we have, how many files, when they were accessed and the types of files. That gives us good data to go to a group whether it's a department director or VP and say, hey, let's talk about your data. The second is movement of data from one cost tier to another including deletion. It's valuable to have Komprise do that in an automated fashion versus myself or someone in my group having to go do it manually. What’s next? AK: We want to be more granular with our unstructured data management so that we can save more. We’re excited to use Komprise Deep Analytics as we expand our use cases. I would like Komprise to operate on all of our data so that data lands on Tier 1 storage, a really high-performance tier, and then very quickly is tiered off to less expensive storage based on its importance and use. I also see the value in tagging data based on location, machine, size, research team and so on, which will make it easier to search for specific data sets and create plans around them. I’m sure we will take advantage of that in the future. ---------- ### Duquesne University Gains Data Intelligence with Komprise This blog was adapted from its original publication on ITProToday. Looking to better manage its consistently growing data, the university found a solution that not only saves space but also money by moving "stale" data off its storage array. Four years ago, the IT department at Duquesne University was at a crossroads. How could the university better manage its growing data so it wouldn’t have to invest in expensive new data storage and keep individual departments from having to overspend on data storage? The university currently stores about 250TB of data on NetApp, and it is growing at about 15% per year on average. “At the time, we were planning on buying an all-flash array. We usually add extra capacity as a buffer when we make a purchase, and we didn’t want to have to go back to our administration to request more capacity after Year 1,” explained Matt Madill, Duquesne’s storage systems administrator. We were looking for ways to get data off of the flash array that maybe didn’t need to live there to free up more space that we could actually use. What to Do with Stale Data in Higher Education Duquesne also lacked insight into the date it was storing. For instance, they didn’t know how much of it was stale, or cold. One example is department file shares. When administrators move to a different position in the university or leave it altogether, their data just hangs around, getting colder and colder. With these issues in mind, Madill came across data management company Komprise at a NetApp conference. Madill was sold on the concept immediately. Not only was the technology already integrated with NetApp—by then, the university had both a NetApp all-flash A300 array and an FAS 8020 array—but he liked the idea of being able to see all data, identify stale data, adjust policies and easily move data via Komprise to NetApp storage. After testing the solution, Madill discovered that as much as 80% of its data was stale, which the university defines as more than six months old. In addition, Komprise found numerous SIDs (security identifiers) from Active Directory without associated user names, which is a good indicator that the user was no longer in its directory. Analyze, Move, Manage Unstructured Data Madill’s team now points all file share data to the Komprise application, which analyzes it. Based on the results, the IT team can set up a plan to move specific data to a target location in the cloud based on specific criteria. From that point, Komprise will move files to a location in the cloud and leave a standard symbolic link in NetApp so end users will be able to retrieve the files as needed. When users need a file considered “stale,” they click on the link in NetApp and Komprise immediately retrieves the file from the cloud for them. This is all transparent to users. (Learn more about Transparent Move Technology) In addition to saving space, the solution also has saved Duquesne about $40,000 over three years, according to Komprise. Much of this savings is from departmental chargebacks. For example, when a department requests storage, it receives a file share on NetApp that is also connected to Komprise. If, for example, the library asks for 5TB to store oral archives, Komprise can ship the data from NetApp to the cloud immediately. That way, the data is available to access, but the library is paying cloud data storage pricing, not enterprise disk pricing. Over time, Madill expects to explore ways to allow more of the university’s applications to take advantage of Komprise. Recently, for example, he converted a classroom recording software application so that it could use Komprise. “We can take that same use case and apply it to different applications so we can run across a hybrid cloud environment. It could really help us save a lot of money,” he said. Watch a video of Matt Madill speaking about the Komprise deployment at Duquesne University. Learn more about Komprise for Higher Education. ### Future-Proof Your Unstructured Data Unstructured data is running our world in more than one way. Whether it is user-generated or file data from genomics, PACS imaging, seismic data, electronic design data, streaming or surveillance video systems, it has the potential to deliver valuable insights for business analysis and decision-making. If harnessed, this data can improve quality of life by helping us understand how things work. But its sheer volume is overwhelming IT organizations everywhere. It’s expensive, hard to manage and sometimes impossible to use. Krishna Subramanian, President and COO of Komprise, talks about these issues on a recent EM360 podcast. We captured the highlights below. You can listen to the entire conversation here. Let’s get clear on the unstructured data problem. What’s going on? KS: There is a lot of unstructured data today compared with 10 years ago. Every time we go to the doctor, use our phone, drive a car, it generates all this data. Around 90% of all data collected and stored is unstructured and that is a problem. Most of the systems in IT today were not designed to handle unstructured data. In a database, you might be updating one column or row and you can store that on expensive storage. But unstructured data is so huge and there are billions of files. If you apply the same techniques to manage it along with all the backups, it doesn’t scale on that expensive storage. So that’s why people have been talking about moving data to the cloud because it’s a cost-efficient solution. But how you migrate it will determine if the cloud will be cheaper or more expensive. Read the white paper: How to Accelerate NAS and Cloud Migrations. What are some top considerations when embarking on a cloud data migration project? KS: If you have 1PB of data, which is maybe 5 billion files, and you need to take all those files and move them to cloud storage it takes time. It may take a few weeks to do that. You don’t want users to not be able to access data for a period of time. So, how do you make a data migration non-intrusive and even when data moves to cloud, users don’t see a difference? If 70% of data hasn’t been touched in a year, why not move that first? It’s also important to not affect user experience if possible. Make sure that the data is transparent and that means providing the same link in the same location to access the data as the user has always had. Learn more: Smart Data Migration. Describe the two main approaches for migration: storage centric and data centric? KS: Storage-centric migration is a proprietary storage technique that moves data to the cloud and to get to your data you always have to go through the storage software, whereas data-centric migration (see storage-agnostic unstructured data management) is your data moved to the right place such as the cloud in a standard format so you can access the data always without needing any third-party software. The natural solution is to ask your storage vendor if they can do the cloud tiering. Cloud storage gateways were developed a few years ago as another option. They will take your data to the cloud and sync it for you. On the surface it seems like these solutions help with cost and user experience, but both are storage-centric. They are putting a storage software or hardware in your environment and bringing pieces of files to the cloud, at the block level, and keeping some pieces locally and it is all proprietary. But if you access that data too often you will pay to do that in the form of egress fees. And you have to pay licensing costs forever to the gateway vendor just to get to your own data in the cloud. Also, the cloud is not a cheap storage locker the way storage-centric solutions treat it. It’s an on-demand service, with a lot of compute capability, AI and big data tools. Read the white paper: Cloud Tiering – Storage-Based vs. Gateways vs Files And Komprise brings another option. Can you explain? KS: The way to use the cloud is to put data into the lowest cost storage possible but when I need it, I shouldn’t have to bring it back into my own data center to use it. I should be able to use the tools and technologies in the cloud to work with the data. The storage-centric solutions lock you in and don’t give you the ability to use the data in the cloud without going through them. This will actually create a more restrictive environment, which can result in 75% higher egress costs and 300% higher data management costs. The data-centric approach, which is what we do, is to take the entire file to the cloud as an object. You can see it from the original storage but you can use it directly from the cloud service without going through Komprise or the original storage. This can reduce cost of the cloud and your total TCO. Read: What you need to know before you jump into the cloud pool. Yet, isn’t the age-old storage problem here to stay? After all, data is going to continue to grow and take up more budget one way or another. KS: Data is taking up more budget, as it should, because organizations in all sectors are becoming data-driven operations. Yet you don’t want to waste money if you can avoid it. No matter what you’re doing with your data, every company should try and avoid vendor lock-in. The options to manage data are continually changing. Amazon began with three classes of storage and they now have 16. At some point you will have to bring all the data back and rehydrate it before moving to another solution and this can be very expensive. We can help you break lock-in and move file data to the cloud in its native format. You can switch vendors including Komprise, as you see fit. It’s all about future-proofing your unstructured data. Komprise: A Faster Path to the Cloud for Unstructured Data Listen to the Full Podcast > > ### Genomics Data Growth Beyond Astronomical The sheer volume of data being produced for genomics research is beyond astronomical—literally. The sheer volume of genomics data is doubling every 7 months. By 2025, the capacity needed for genomics data is estimated to reach 40 exabytes (40,000 petabytes) exceeding that for astronomical data by a factor of up to 40:1. Driving this growth are more advanced sequencers like those from Pacific Biosciences, as well as the mainstreaming of personalized medical treatments. Pacific BioSciences manages 700% YOY storage capacity growth Watch how Pacific BioSciences, a genomics leader, used Komprise to transform how they manage genomic test data and 700% YOY storage capacity growth.   Genomic Data Needs to Stay Accessible The greater the volume of genomics data available the more valuable the sum of that data becomes. Also, the retention and accessibility of this data is necessary to comply with mandates such as the Individual Right to Access in HIPPA or those specified in research grants and by individual studies. The challenge is how to store and manage this explosion of genomics data in an era of tight IT budgets, especially in academic and research organizations. The traditional approach of moving this data offline to a cost effective archival storage simply does not address the needs of genomics data and as a result the vast majority of this data is still residing on expensive, high performance, Network Attached Storage. Using Komprise to create and active archive for genomics data offers the cost effectiveness of moving cold data to archival storage solutions while retaining file based access to users and applications so that to end users the data appears as if it is on NAS providing the best of both worlds. Komprise will be at Bio-IT World the next week (May 15th - 17th) demonstrating how our customers are managing their explosive growth and giving architecture presentations at our booth #413. We will also be presenting sessions at the Google Booth #413 Wednesday 5/16 at 10:30am, 1:30pm and again at 4:00pm. If you are headed to Bio-IT World, let us know!   (Source: Stephens ZD, Lee SY, Faghri F, Campbell RH, Zhai C, Efron MJ, et al. (2015) Big Data: Astronomical or Genomical?. PLoS Biol 13(7): e1002195. doi:10.1371/journal.pbio.10021) ## Customer Case Studies > Validated outcomes from enterprise deployments across healthcare, legal, government, energy, and higher education. ### U.S. Academic Health System Saves Millions on Storage with Komprise Self-Service Tagging & Showback U.S. healthcare system deploys Komprise to achieve $4 million a year in savings and growing with 3x larger data volumes tiered after departmental involvement. U.S. healthcare system deploys Komprise to achieve $4 million a year in savings and growing with 3x larger data volumes tiered after departmental involvement. The IT organization at the large health system decided they needed an effective cost-cutting plan that reduced central IT overhead and engendered buy-in from researchers, clinicians and faculty by including them in the process of actively managing their data. The objective was to accelerate the tiering of cold data that is no longer needed to dramatically lower costs. In a few months, departmental tagging resulted in the tiering of 3x more data than was tiered over the previous 7-month period. Now, more than 100 departmental managers are using Komprise to view and tag their data for tiering. Thanks to departmental collaboration and communication, the savings have catapulted – while creating a mutually-beneficial relationship between IT and the departments. Beyond managing data storage, the health system’s IT organization is now delivering valuable data services to researchers, furthering their productivity and cost savings.   Read Case Study “I’m working on reorganizing and cleaning up data on our NAS and with Komprise I can see details within the folders and shares, such as who’s using the data and who created the data,” said the storage director. “This visibility has been invaluable to find the cold data, tag it and execute automated plans.” Read this case study to learn how Komprise Deep Analytics was used by IT and departments to deliver cold data cost savings and self-service data tagging. More Komprise customer success stories. -------------- ### Data Tagging for AI with Komprise From customer call recordings to medical images and sensor data from self-driving cars, unstructured data holds the key to making AI work for you. Yet because unstructured data is scattered across multiple storage platforms, lacks a consistent format and contextual metadata, it’s extremely difficult to find and use the right unstructured data for AI. Komprise simplifies the process of finding and classifying unstructured data across hybrid cloud data estates so that it can be quickly leveraged by AI and analytics services. Read this solution brief to learn more about: The benefits of Komprise for data tagging Global metadata indexing Enriching and tagging data for AI with contextual metadata Searching and classifying unstructured data across storage silos at scale Feeding AI apps and agents with the right data at the right time Accelerating AI data preparation with Smart Data Workflows Read Solution Brief Watch the best practices series: Komprise for Storage as a Service (STaaS) ### Duquesne University Finds, Tags Images with Rapid Speed using Komprise and Amazon Rekognition District Medical Group (DMG) is a nonprofit integrated medical group practice in Arizona, consisting of over 650 credentialed providers representing more than 25 medical and surgical specialties and subspecialties. Duquesne deployed Komprise Smart Data Workflow Manager with Amazon Rekognition to automate the process for two use cases, driven by the library archive team. Duquesne University is a private Catholic research university in Pittsburgh, Pennsylvania, founded in 1878. The university is ranked in the prestigious Princeton Review and has achieved several rankings in the US News & World Report annual college rankings including a “Best Value School” for 2024. The library archive team wanted to search for and find specific images from the millions of files in their digital archives. Assuming each file would require at least two minutes to manually inspect, they estimated it would take at least 20,000 minutes or 333 hours to fully review and record the results. The solution with Komprise and Rekognition reduced 14 days of manual labor to only 2 hours. Read Case Study “Our digital collections are growing in leaps and bounds but budgets stay flat. AI is still new but has tremendous potential. With Komprise we’re able to improve efficiency with a systematic workflow to index data, run AI and tag data.” - Rob Behary, Head of Systems and Scholarly Communications, Gumberg Library at Duquesne University. Read this case study to learn how Komprise Smart Data Workflow Manager and Amazon Rekognition were deployed to deliver massive time savings, productivity and ongoing unstructured data management benefits. Learn more about Komprise for Duquesne. More Komprise customer success stories. -------------- ### Katten Law Saves on Data Storage and Achieves Resilient Ransomware Protection with Komprise District Medical Group (DMG) is a nonprofit integrated medical group practice in Arizona, consisting of over 650 credentialed providers representing more than 25 medical and surgical specialties and subspecialties. Global Law Firm Saves $900K on Data Storage and Achieves Resilient Ransomware Protection in Azure with Komprise Intelligent Data Management Software Solution. Katten Muchin Rosenman LLP (Katten) is a full-service law firm delivering legal services across more than a dozen practice areas and sectors, including Aviation, Construction, Energy, Education, Entertainment, Healthcare and Real Estate. Like many other large law firms, Katten has been seeing an average 20% annual growth in storage for file related data, resulting in the need to add on-premises storage capacity every 12-18 months. With a focus on managing data storage costs in an environment where data is growing exponentially annually but cannot be deleted, Katten needed a solution that could provide deep data insights and the ability to move file data as it ages to immutable object storage in the cloud for greater cost savings and ransomware protection. "Komprise has not only allowed the firm to reduce its on-premise storage requirements but has become a catalyst for our cloud strategy while at the same time providing us with a tool that enhances both our compliance and security posture, reducing our attack surface on an ongoing basis." - Alexander Diaz, Katten Law Storage: Dell EMC, Microsoft Azure Read Case Study More Komprise customer success stories. -------------- ### Lummus Technology Saves 80% and Improves Data Lifecycle Management Lummus Technology specializes in energy process technologies for clean fuels, renewables, petrochemicals, polymers, gas processing and supply lifecycle services, catalysts, proprietary equipment and digitalization to customers worldwide. Lummus Technology specializes in energy process technologies for clean fuels, renewables, petrochemicals, polymers, gas processing and supply lifecycle services, catalysts, proprietary equipment and digitalization to customers worldwide.   Lonnie Brown, IT systems administrator with Lummus, chose Komprise Intelligent Data Management for its granular reporting and analytics on unstructured data usage to inform data storage decisions and to perform the Azure file migration. “Komprise has been a wonderful solution for us to save a lot of money and analyze our data to optimize cloud migrations and archiving." With granular insight into its data assets, Lummus now has the knowledge to make smart business decisions such as consolidating shares to single disks to free up space. Read the case study to learn how Lummus estimates an 80% annual savings on data storage and backup costs with Komprise Intelligent Data Management. Read Case Study More Komprise customer success stories. -------------- ### Data on the Move: Interview with Komprise Customer Success Architect Benjamin Henry Komprise Senior Director of Marketing Communications Polly Traylor chats with Benjamin Henry about the challenges and skills required to be a Customer Success Architect.  _______________________ Komprise Senior Director of Marketing Communications Polly Traylor chats with Benjamin Henry about the challenges and skills required to be a Customer Success Architect. What does it take to work with enterprise customers on unstructured data migration and unstructured data management initiatives? You can read more about his role in this blog post: Crushing the Customer Success Role. Want more Ben? Check out this webinar discussion: Preparing for file and object data migration _______________________ ### NetApp + Komprise: Right Data. Right Place. Right Time. Reduce Data Storage Costs and Deliver Greater Unstructured Data Value with Komprise for NetApp Komprise was a Gold Sponsor at NetApp Insight 2022. On-going file and object data migration, tiering and data lifecycle management allow NetApp customers to shrink storage, backup and cloud costs with Komprise Intelligent Data Management. Right Data. Right Place. Right Time. Get a single view of how NAS data is growing across storage silos, how data is being used, and what data is hot or cold. Identify what data can move to NetApp – whether your use case is NetApp cloud tiering, data migrating or tiering data. Get maximum NetApp performance, cost savings and unstructured data value with Komprise. Learn more about Komprise data management for NetApp. ### Data on the Move: Cloud File Migration Preparation In this Data on the Move discussion we interview Benjamin Henry, Customer Success Architect at Komprise.  _______________________ Planning Your Cloud NAS Migration In this Data on the Move discussion we interview Benjamin Henry, Customer Success Architect at Komprise about cloud file migration preparation and planning. Ben works with enterprise IT organizations to help them plan and prepare for NAS migrations to the cloud. In this discussion Benjamin reviews a few key considerations before you begin your file data migration. Learn more about why Komprise is the fast, no lock-in and smarter approach to data management, data migration and data mobility: Komprise Smart Data Migration. _______________________ ### District Medical Group of Arizona Customer Story District Medical Group (DMG) is a nonprofit integrated medical group practice in Arizona, consisting of over 650 credentialed providers representing more than 25 medical and surgical specialties and subspecialties. District Medical Group (DMG) is a nonprofit integrated medical group practice in Arizona, consisting of over 650 credentialed providers representing more than 25 medical and surgical specialties and subspecialties. Kevin Rhode, CIO of DMG, did an assessment of the organization’s disaster recovery, business continuity, backup and unstructured data management capabilities when he joined the organization in 2021. He discovered that data volumes were piling up fast and backups were slow and inefficient, taking 36 hours to complete on average. DMG’s two Windows file servers contained a preponderance of data that was more than 15 years old, including a lot of duplicate data, all of which was straining backups. Read the case study to learn how Komprise and Wasabi helped DMG achieve data storage cost savings of $100,000 over three years, 5.5TB reduction of backup data and 75% faster backup processes. Read Case Study More Komprise customer success stories. -------------- ### Building a Modern Data Strategy for the Automotive Industry with Komprise and AWS Komprise Intelligent Data Management for AWS Automotive Industry Customers Automotive customers need the ability to analyze and filter data at the edge. Giving customers the ability to save on infrastructure and overall data storage costs by "right-placing" critical data sets is a competitive differentiator. This webinar reviews how to intelligently tier data from on premises storage to the cloud (or back again) with Komprise Intelligent Data Management. ### Komprise Intelligent Data Management for Azure File Migrations Komprise has been selected for the Microsoft Azure File Migration Program, which gives customers access to industry leading file-migration at no cost. _______________________ Azure File Migration Demonstration Komprise has been selected for the Microsoft Azure File Migration Program, which gives customers access to industry-leading file migration at no cost and complements the Azure Migrate portfolio which customers use to automate and orchestrate the migration of servers, desktops, databases and web applications to Azure. Komprise Elastic Data Migration eliminates the cost and complexity of managing and moving file data to the cloud by providing analytics-driven data migration to Azure without creating any vendor lock-in: Provides analytics across existing NAS (eg NetApp, Dell, Windows) to identify which data sets to migrate and to which tier of Azure. Systematically migrates files 27 times faster. Komprise Elastic Data Migration scales elastically according to the distribution of your shares. directories and files. Ensures full data integrity by migrating all file attributes and permissions with full MD5 checksums on every file Learn More Learn more about Azure file migration Komprise Smart Data Migration. _______________________ ### Accelerating Microsoft Azure File Migrations with Komprise Elastic Data Migration Cloud data migrations are complex and can be costly, time consuming, and error-prone —often with the pressure of a fixed deadline. The Azure File Data Migration program brings the ease of Komprise Elastic Data Migration to Azure customers at no cost. Customers can now migrate file data to the right Azure tier for cost savings and to drive value from their data, while eliminating the complexities of cloud data migrations. Read Solution Brief Learn more about Komprise for Azure. ### Migrate Data to Microsoft Azure with Komprise Komprise helps customers reduce over 70% of costs while managing data growth. eBook: Elastic Data Migration for Microsoft Azure Komprise gives enterprises visibility into their file and object data, assessing how much you can save on data storage by migrating your data to Microsoft Azure. Komprise Elastic Data Migration moves enterprise data seamlessly and transparently, and tiers and archives cold data to the cloud to cut up to 70 percent of enterprise storage budget costs without disrupting users and applications. Eliminate the fear of migrating files and data to Azure Storage. Read the eBook ### Komprise - Why Data Management Should be an Independent Layer    Why Independent Unstructured Data Management In this Storage Field Day session, Komprise CEO and co-founder Kumar Goswami reviews the unstructured data management challenges they see at Komprise and shares their recent momentum and customer stories (including how Pfizer saved 75% on storage costs by tiering cold data to AWS). Kumar goes on to make the case for data management being established as a separate, independent layer from data storage. He share his five principles of analytics-centric data management. Presented by Kumar Goswami, CEO & Co-Founder, Komprise. Read the blog post: Why Data Management Must Be Independent from Storage ### Komprise Intelligent Data Management Overview Komprise helps customers reduce over 70% of costs while managing data growth. Unstructured Data Management as a Service: Optimize Costs and Deliver Data Value Komprise is the industry’s only hybrid, storage-agnostic unstructured data management solution that frees you to analyze, mobilize, and access the right file and object data across clouds without shackling your data to any vendor. Cut 70% of your data storage, backup and ransomware costs by right-sizing and right-placing data, while making it easy for users to unlock data value for AI and analytics use cases. Read Datasheet What is KDX? ### Komprise 4.0: Simplify Global Data Management with Multisite Controls As infrastructure becomes more distributed, visibility can suffer. IT and storage managers want centralized management across all sites with a single dashboard. They also need to retain control at the site level to comply with local security, customer and data requirements. ### Case Study: Municipality Manages 3,000% Data Growth with Komprise and Microsoft Azure “We were at an unsustainable data growth rate… Now we can move large amounts of data to the cloud without any disruption to employees.” Sean Horan Sr. Network Systems Specialist, GUTS The Data Storage Challenge Boone County, Indiana was facing massive influx of data, primarily from evidentiary data. The Sheriff’s Department had adopted body and dash cameras for all its officers and vehicles. And the Boone County Courthouse was mandated to indefinitely retain accessible case files on both ongoing cases and all historical cases—most of which are inactive after being closed. In the five years since deploying the cameras, evidentiary data grew by 3000%. “We were at an unsustainable growth rate,” said Sean Horan, manager of IT services for Boone County at Government Utilities Technology Service, Inc. (GUTS), its managed IT services provider. As the sheer volume of backed up data continued to grow, so did the time and cost to complete their weekly off-site tape backups, and the recovery time from them in the event of a disaster. They needed a plan to: Lower their storage costs Modernize their existing SAN environment Improve weekly backup and restore times And do it all without any data disruption to the county employees or applications. The Data Management Solution Komprise Intelligent Data Management Microsoft Azure Blob Storage Boone County wanted to leverage Microsoft Azure Blob Storage to lower costs, but it didn’t know what data to migrate to the cloud or how to manage it without affecting user and application access. GUTS used Komprise to run an analysis that showed 83% of the county’s data was cold and hadn’t been accessed in over six months. Using the ROI reporting from the Komprise Intelligent Data Management Platform, the GUTS team built a five year plan that projected 70% savings by using Komprise to transparently archive cold data into Azure Blob. Their convincing proposal quickly secured plan approval. Moving to Azure Blob GUTS set policies in Komprise to archive each departments’ data to Azure Blob. Because data moved by Komprise still appears on Boone County’s SAN, there was zero disruption to users. Intelligent Data Management Results By using Komprise to archive cold data off the SAN to Azure Blob, GUTS was able to achieve the following for Boone County: Reduce the size of their weekly backups by 88% Reduce the cost associated with backups by 72%: purchasing less tape, taking tape loaders offline, and reducing the tape stored off-site Reduce the disaster recovery time from a week to a day and half Purchase a smaller all-flash SAN for hot data storage at 42% of the cost Retained file-based access from the original storage to data stored as objects on Azure Blob About the Solutions Microsoft Azure Blob Azure Blob enables organizations to store any type of unstructured data—images, videos, audio, documents, and more—with proven technology at exabyte scale. Blob storage handles trillions of stored objects, with millions of average requests per second, for customers around the world. Komprise Intelligent Data Management Komprise empowers businesses to take control of their data with no interference to applications, users, or hot data. Komprise Intelligent Data Management is the foundation for analytics-driven data management which is key to putting data in the right place at the right time across all storage. Analyze, move, and easily find your data with Komprise. Download PDF Learn More Experience a complimentary trial of Komprise in your own environment to see the true picture of your data and how much you can save. Schedule a Demo Read the blog post: Boone County and Komprise: Preserving Bodycam Footage More Komprise customer success stories. ### Case Study: Major West Coast University Cuts Data Replication Costs “Now our faculty can be up and running in minutes in an emergency. With Komprise we can have DR in the cloud at a fraction of the cost.” CTO Major Research University The Data Storage Challenge A major West Coast research university had accumulated petabytes of data on their Network Attached Storage (NAS). Protecting this data by keeping a Data Replication (DR) copy on a mirrored environment was too expensive. As a result, most of the NAS data was not replicated, putting the University at risk of data loss. The university wanted to see how they could leverage the public cloud to store a DR copy of their NAS data, without having to upgrade their network or incur sizable backup software costs. The Data Management Solution Using Komprise, the university was able to see that over 60% of the data on the NAS had not been accessed in over a year. They were able to calculate that keeping a DR copy in the cloud vs. on premises would be 70% less expensive. Komprise used spare network availability and storage cycles to copy data from their NAS into Google Cloud. The university experienced no changes or impact to their network and storage infrastructure or to users or applications. Intelligent Data Management Results The university now has a replicate copy of data in the cloud, which can be accessed in a DR scenario or by students and faculty for an application in the cloud. With Komprise they were able to: Lower costs - They achieved a DR SLA without additional costs, and at 30% of what it would have cost on-premises. Achieve cloud DR without affecting network, storage or users - They were able to get a DR copy in the cloud without any interruption to students, faculty, network activities, and without any changes to network or storage infrastructure. Dynamically scale resources without over-provisioning - Komprise was deployed without any additional hardware or storage investments. They were able to scale Komprise up and down as needed and optimize their virtual machine resources. Protect data against disaster - Students and faculty now have a DR copy in the cloud in the event of a disaster and for cloud-native access. Other sites can run apps in the cloud on that data without reaching back on-premises. Download PDF About Komprise Komprise empowers businesses to take control of their data with no interference to applications, users, or hot data. Komprise Intelligent Data Management is the foundation for analytics-driven data management which is key to putting data in the right place at the right time across all storage. Analyze, move, and easily find your data with Komprise. Learn More Experience a complimentary trial of Komprise in your own environment to see the true picture of your data and how much you can save. Schedule a Demo ### Demo: Save on Data Storage Costs with No Disruption This demo shows how Komprise helps customers cut costs and better manage their data by empowering them to know and understand their data, better plan their multicloud data management, and transparently move and manage data across all silos without any disruption to users. Save on Data Storage Costs with Zero Disruption to Users and Applications This demo shows how Komprise helps customers cut data storage costs and better manage their data by empowering them to know and understand their data, better plan their multi-cloud unstructured data management, and transparently move and manage data across all silos without any disruption to users. Rein in Data Storage and Backup Costs Quantify the Business Value of Komprise Intelligent Data Management   KNOW FIRST. MOVE SMART. SAVE MORE. Smart Data Migration Smart Data Management Smart Data Workflows ### How to save costs and manage your multicloud strategy This demo shows how customers can cut costs, modernize their storage, and better manage their multicloud strategy with Komprise Intelligent Data Management. This demo shows how customers can cut costs, modernize their storage, and better manage their multi-cloud strategy with Komprise Intelligent Data Management. See how you can know and understand your data, better plan your hybrid, multi-cloud unstructured data management strategy, and transparently move and manage your data across all silos without any disruption to users. Why Komprise? Smarter, Faster Data Management The Easy, Fast, No Lock-In Path to the Cloud ------------------------------ ### Komprise Analytics-Driven Data Management    This overview shows how Komprise analytics-driven data management platform helps customers address the challenges and costs of exponential data growth. CEO Kumar Goswami explains the platform’s three pillars, Dynamic Data Analytics, Transparent Move Technology, and Direct Data Access followed by a live demo. ### Know Your Data to Manage it Better with HPE and Komprise As data footprints quickly expand, managing data more easily and intelligently becomes critical. Understanding your data is key to managing it in a more strategic and efficient way. This brief explains how Komprise and Hewlett Packard Enterprise (HPE) are working together with customers to help streamline costs, create more efficient, capacity-enhanced storage, and increase the resiliency of their data in active archiving, replication, and disaster recovery. Read Solution Brief ### Know Your Data to Manage it Better with HPE and Komprise As data footprints quickly expand, managing data more easily and intelligently becomes critical. Understanding your data is key to managing it in a more strategic and efficient way. This brief explains how Komprise and Hewlett Packard Enterprise (HPE) are working together with customers to help streamline data storage costs, create more efficient, capacity-enhanced storage, and increase the resiliency of their data in active archiving, replication, and disaster recovery. Read Solution Brief ### Northwestern Cuts Data Storage Costs in Half with Komprise With its strong research focus, Northwestern University generates massive data. Research data holdings had extended to billions of files and petabytes of unstructured data, such as medical imaging and data from other fields. Read how Komprise helped the university’s growing challenges of storage capacity and classification and helped them save $330K a year on storage costs. With its strong research focus, Northwestern University generates massive data. Research data holdings had extended to billions of files and petabytes of unstructured data, such as medical imaging and data from other fields. Read how Komprise helped the university’s growing challenges of storage capacity and classification and helped them save $330K a year on storage costs. Read Case Study ### NetApp Cloud Volumes ONTAP with Komprise Deep Analytics extends its support of NetApp ONTAP sources to now also include NetApp Cloud Volumes ONTAP. Customers now have a single place to view, find, and tag data regardless of where it lives, and export this virtual data lake to any analytics application or destination of their choice, such as AWS Lambda. ### NetApp Cloud Volumes ONTAP with Komprise Deep Analytics extends its support of NetApp ONTAP sources to now also include NetApp Cloud Volumes ONTAP. Customers now have a single place to view, find, and tag data regardless of where it lives, and export this virtual data lake to any analytics application or destination of their choice, such as AWS Lambda. ### How an Ivy-League University Enabled Departments to Keep More Data with Flat Budgets. With storage already consuming anywhere from 25% to 50% of an average Educational Institution’s IT budget, major universities are asking how they can do more with less? The challenge? The majority of Education IT budgets are staying flat or in some cases, even shrinking! Meanwhile, the data deluge still needs to be stored, managed, protected, and harnessed. ### Duquesne University Leverages Komprise to Tier Cold Data Smart Cold Data Tiering at Duquesne Matt Madill, Storage Systems Administrator at Duquesne University, as he shares his higher education unstructured data management journey with Komprise and how his team: Identified and tiered years of cold data to enable the move to an all-flash array Build data management policies that meet the unique needs of each department as well as the university as a whole Plan to offer cloud services and infrastructure Watch the webinar How Duquesne University Got Its Growing Data Under Control Learn more about Komprise for Higher Education Duquesne data management with Komprise ### Major University spart Abteilungskosten Erfahren Sie, wie eine Ivy League-Universität Komprise Dynamic Data Analytics verwendet, um mit Abteilungen zusammenzuarbeiten und Datenverwaltungsstrategien zu entwickeln, die erhebliche Kosten für Abteilungen und die Universität insgesamt einsparen. Fallstudie lesen ### Major University Saves Departmental Storage Costs Learn how an Ivy League University uses Komprise Intelligent Data Management to work with departments and build data management strategies that save significant data storage costs for departments and the university as a whole. Read Case Study Learn more about Komprise for Higher Education Read Komprise Case Studies ### Municipality Cuts 70% of NAS Costs With Komprise and Azure State and local governments face a massive influx of data with new technologies such as body and dash cameras. Using Komprise, Boone County cut 70% of their NAS costs by migrating data to Microsoft Azure Blob with zero disruption to users and applications. State and local governments face a massive influx of data with new technologies such as body and dash cameras. Using Komprise, Boone County cut 70% of their NAS costs by migrating data to Microsoft Azure Blob with zero disruption to users and applications. Read Case Study ### Top 3 Roadblocks to Managing Storage Capacity Data growth is exploding, and you are being asked to address growing capacity needs within tight budgets. In this webinar, we will cover the top 3 roadblocks we repeatedly hear from customers in managing capacity growth efficiently – and, how to overcome them. Download Slides ### Pacific Biosciences: Komprise Customer Journey Using Komprise, PacBio now handles massive 7x YOY data growth on a flat IT budget by moving cold data to an active archive on cost-effective secondary storage. Using Komprise’s analysis, PacBio began standardizing its primary environment on NetApp FAS systems and added a petabyte of ultra-dense NetApp E-Series storage for archiving. Komprise transparently moved data to and from the archive based on predetermined policies, keeping only active data on primary storage. Read the Life Sciences use cases and customer stories. ### Genomics Firm Saves 60% Managing Its Sequencing Data Growth Pacific BioSciences uses Komprise to gain insight into data usage and growth and saves costs moving to lower-tier storage. Pacific BioSciences uses Komprise to gain insight into data usage and growth and saves data storage costs moving to lower-tier storage. Learn more about Komprise for Genomics and Healthcare. Watch a video of the PacBio customer journey on YouTube. ## Product Content and Solution Briefs > Technical overviews, solution briefs, and datasheets covering Komprise capabilities and partner integrations. ### 5 Ways to Use Analytics for Cloud File Data Migrations 5 Ways to Use Analytics for Cloud File Data Migrations Why Cold Data Management is an Easy Path to the Cloud As unstructured data continues to grow exponentially, organizations struggle to control costs for file data storage. Many are turning to the cloud to scale and manage spend. This Komprise and AWS eBook examines the 5 ways to use analytics to drive your cloud file and object data migration and data management strategy: Understand your data patterns Plan using a cost model Use data to drive stakeholder buy-in Eliminate user disruption Create a systematic plan for on-going data management Also learn how Pfizer used Komprise analytics to accelerate their cloud data migration to AWS. Download eBook Thanks for reaching out. We'll be in touch "Komprise helps us make razor sharp business decisions based on data so we can reinvest in areas that are more important to patients." – Matthew Braunstein, Director Hosting Data Services, Pfizer ### Introducing the Komprise Data Experience Watch this video to get an overview of the Komprise Data Experience (KDX) - a better way to analyze, manage, move and prepare unstructured data for AI. Start experiencing your data in an entirely new way with the Komprise Data Experience.  _______________________ 5 Ways to Boost Unstructured Data Value Learn more about the Komprise Data Experience Unstructured data is 90% of your organization’s data, and your organization depends on IT to effectively manage it for fast access, simple search, long-term value and to feed AI pipelines. Your executives, meanwhile, need you to pay close attention to the bottom line while ensuring that you are not jeopardizing data security and compliance with your unstructured data management strategy. The Komprise Data Experience (KDX) draws upon a storage-agnostic unstructured data management solution that prioritizes analytics and visibility, scalability and the ultimate flexibility. This experience means that you can meet the needs of your various stakeholders with the best cost economics, the lowest risk, and the best pathway to leverage unstructured data for long-term value and AI initiatives. STOP Making storage decisions in the dark. STOP Overspending on data migrations. STOP Paying the rehydration penalty. STOP Exposing your weakest link to ransomware. STOP Sharing sensitive data with AI. _______________________ ### The Komprise Data Experience The Komprise Data Experience   Komprise never gets in the way, never locks your data up, and always keeps you in control. The Komprise Data Experience (KDX) draws upon a storage-agnostic unstructured data management solution that prioritizes analytics and visibility, scalability and the ultimate flexibility. This experience means that you can meet the needs of your various stakeholders with the best cost economics, the lowest risk, and the best pathway to leverage unstructured data for long-term value and AI initiatives. Learn how the Komprise Data Experience helps enterprise IT: STOP Making Storage Decisions in the Dark. STOP Overspending on Data Migrations. STOP Paying the Rehydration Penalty. STOP Exposing Your Weakest Link to Ransomware. STOP Sharing Sensitive Data with AI. START experiencing your unstructured data in an entirely new way with Komprise. Download White Paper First Name* Last Name* Work Email* Company Name* Job Title* Phone* Country* --United StatesCanadaAfghanistanÅland IslandsAlbaniaAlgeriaAmerican SamoaAndorraAngolaAnguillaAntarcticaAntigua and BarbudaArgentinaArmeniaArubaAustraliaAustriaAzerbaijanBahamasBahrainBangladeshBarbadosBelarusBelgiumBelizeBeninBermudaBhutanBolivia, Plurinational State ofBosnia and HerzegovinaBotswanaBouvet IslandBrazilBritish Indian Ocean TerritoryBrunei DarussalamBulgariaBurkina FasoBurundiCambodiaCameroonCape VerdeCayman IslandsCentral African RepublicChadChileChinaChristmas IslandCocos (Keeling) IslandsColombiaComorosCongoCongo, The Democratic Republic of theCook IslandsCosta RicaCôte dIvoireCroatiaCubaCuraçaoCyprusCzechiaDenmarkDjiboutiDominicaDominican RepublicEcuadorEgyptEl SalvadorEquatorial GuineaEritreaEstoniaEthiopiaFalkland IslandsFaroe IslandsFijiFinlandFranceFrench GuianaFrench PolynesiaFrench Southern TerritoriesGabonGambiaGeorgiaGermanyGhanaGibraltarGreeceGreenlandGrenadaGuadeloupeGuamGuatemalaGuineaGuinea-BissauGuyanaHaitiHeard Island and McDonald IslandsHondurasHong KongHungaryIcelandIndiaIndonesiaIranIraqIrelandIsle of ManIsraelItalyJamaicaJapanJerseyJordanKazakhstanKenyaKiribatiKorea, Republic ofKuwaitKyrgyzstanLao People's Democratic RepublicLatviaLebanonLesothoLiberiaLibyan Arab JamahiriyaLiechtensteinLithuaniaLuxembourgMacaoMadagascarMalawiMalaysiaMaldivesMaliMaltaMarshall IslandsMartiniqueMauritaniaMauritiusMayotteMexicoMicronesia, Federated States ofMoldova, Republic ofMonacoMongoliaMontenegroMontserratMoroccoMozambiqueMyanmarNamibiaNauruNepalNetherlandsNetherlands AntillesNew CaledoniaNew ZealandNicaraguaNigerNigeriaNiueNorfolk IslandNorth MacedoniaNorthern Mariana IslandsNorwayOmanPakistanPalauPalestinian Territory, OccupiedPanamaPapua New GuineaParaguayPeruPhilippinesPitcairnPolandPortugalPuerto RicoQatarRéunionRomaniaRussiaRwandaSaint BarthélemySaint HelenaSaint Kitts and NevisSaint MartinSaint LuciaSaint Pierre and MiquelonSaint Vincent and the GrenadinesSamoaSan MarinoSão Tomé and PríncipeSaudi ArabiaSenegalSerbiaSeychellesSierra LeoneSingaporeSlovakiaSloveniaSolomon IslandsSomaliaSouth AfricaSouth SudanSpainSri LankaSudanSurinameSvalbard and Jan MayenSwazilandSwedenSwitzerlandSyrian Arab RepublicTaiwanTajikistanTanzania, United Republic ofThailandTimor-LesteTogoTokelauTongaTrinidad and TobagoTunisiaTurkeyTurkmenistanTurks and Caicos IslandsTuvaluUgandaUkraineUnited Arab EmiratesUnited KingdomUnited States Minor Outlying IslandsUruguayUzbekistanVanuatuVatican CityVenezuela, Bolivarian Republic ofVietnamVirgin Islands, BritishVirgin Islands, USWallis and FutunaWestern SaharaYemenZambiaZimbabwe State —Please choose an option— How did you hear about Komprise?*Search EngineSocial MediaMarketing EventBlog or PublicationRecommended by Friend or ColleagueRecommended by Industry AnalystRecommended by a PartnerOther Other By submitting this form, I confirm that I have read and agree to the Privacy Statement. Δ Read this paper to learn about what KDX delivers and the power of Komprise Intelligent Data Management. ### Komprise File Analysis for AWS Komprise File Analysis for AWS provides strategic insight into the unstructured file and object data across your data centers and clouds. Know first and make the right data storage, cloud, backup and ransomware investments. Analyze across your NAS, NFS, SMB, dual shares. See how much data you have, how fast it's growing, what is hot and cold data. File analysis of data types, top users, top groups, top directories. Cost / benefit analysis of tiering and unstructured data management. Read Solution Brief Learn more about Komprise for AWS. ### Protect Sensitive Unstructured Data with Komprise In enterprise IT, security is now everyone’s responsibility. Storage IT professionals need to ensure that sensitive data — such as PII and IP—are protected from unauthorized access and unavailable for ingestion into AI. However, as unstructured data has exploded across many different silos in the enterprise, the task has become increasingly difficult and impossible to do manually. The risks are irrefutable: attempts to input PII into GenAI platforms represent over half (55%) of data loss prevention (DLP) events, according to research by Menlo Security. Roughly 80% of data breaches involve sensitive data, according to Verizon’s Data Breach Investigations report and other sources. Komprise Smart Data Workflow Manager, a simple interface for configuring and automating the discovery, tagging and classification, and movement of data between different storage platforms, includes built-in scanners for PII and other sensitive data. Komprise Sensitive Data Management Find Sensitive Data Across Hybrid Storage Silos Tag, Move and Remediate Sensitive Data Exclude Data and Audit Workflows for AI Read Solution Brief ### Do More with Less: How to cut costs, shrink ransomware risk and leverage AI for your file data with Azure and Komprise Learn more about Komprise for Azure Get started with Komprise Intelligent Tiering for Azure Get started with Azure File Migration Read the Azure Storage Blog: Hybrid File Tiering addresses top CIO priorities of risk control and cost optimization ### Storage as a Service (STaaS) and Komprise Best Practices In this best practice series, we focus on strategies to deliver a successful STaaS model with Komprise Intelligent Data Management. Hear practical tips and guidance from from Komprise Field CTO Benjamin Henry, who has years of experience working with some of the largest global enterprise organizations 1. What is the role of Komprise for STaaS? In this first Storage as a Service (STaaS) best practice video Benjamin Henry, Field CTO @ Komprise, discusses why STaaS has gained such traction in the enterprise and the important role of storage-agnostic unstructured data management from Komprise in this model.    _______________________ 2. Setting up the STaaS Cost Model and Reporting In this second Storage as a Service (STaaS) best practice video Ben reviews how to set up the cost model and reviews the Orphan Data Report and Potential Duplicates Komprise report templates. The video includes a demonstration of Deep Analytics and discussion on how to optimize data storage costs.    _______________________ 3. Diving into Deep Analytics for STaaS In this 3rd Storage as a Service (STaaS) best practice video Ben reviews key concepts using Komprise Deep Analytics. He demonstrates the power of custom queries to help you get more surgical with your data. He also reviews setting up tags in Komprise and the Deep Analytics role so some users can only create queries on specific data sets but not have any data mobility or unstructured data management capabilities. _______________________              _______________________   4. The Role of Tiering and Archiving in the STaaS Model In this 4th Storage as a Service (STaaS) best practice video Ben reviews data tiering and data archiving. He introduces the power of Komprise Transparent Move Technology (TMT) and discusses the importance of right sizing in an enterprise deployment. In the demonstration he connects the dots between analytics and unstructured data mobility. _______________________                _______________________ 5. Target Portability – Cloud Native, AI-Ready Data In this first Storage as a Service (STaaS) best practice video Benjamin Henry, Field CTO @ Komprise, discusses target portability, cloud native AI ready data. To start effective data access, a primary benefit of Komprise is how we keep data in native format no matter where you put it so that you can access the data on prem, in the cloud, 40 miles away, 3000 miles away...we don't care. We want you to have access to your data and leverage it where it's most cost effective. We also want you to have data portability without penalty, so that you always have the most cost effective approach to your cloud strategy, to your on prem strategy, whatever it might be.    _______________________ 6. Smart Data Workflows – Sensitive Data Detection In this final Storage as a Service (STaaS) best practice video Benjamin Henry, Field CTO @ Komprise discusses and demonstrations sensitive data detection. It’s all about establishing confidence in trusted data How do I find sensitive data where it shouldn’t be? How do I move data when PII is found? Review the challenges and see a demonstration of Komprise Smart Data Workflows for PII detection in your unstructured file and object data and how to take action with data mobility.  Unstructured Data Migration Best Practices Watch More TechKrunch Webinars and Videos ### Komprise Hybrid Tiering: Unlock the Potential of Your Unstructured Data Komprise Hybrid Tiering: Unlock the Potential of Your Unstructured Data 5 considerations when picking a storage tiering solution This paper introduces hybrid tiering, which tiers unstructured data across your entire hybrid storage infrastructure including on-premises and cloud, file and object storage. Hybrid tiering is an independent, storage-agnostic solution which can: Reduce your ransomware attack surface; Avoid rehydration when refreshing your storage; Provide flexible data management polices while maintaining transparent access; Ensure innovation and choice with cloud native data access; Ensure your unstructured data is ready for AI. This paper reviews these five considerations when evaluating data tiering options and presents a cost model that outlines the benefits of hybrid tiering as both a complement and an alternative to built-in tiering capabilities from data storage vendors. Download White Paper First Name* Last Name* Work Email* Company Name* Job Title* Phone* Country* --United StatesCanadaAfghanistanÅland IslandsAlbaniaAlgeriaAmerican SamoaAndorraAngolaAnguillaAntarcticaAntigua and BarbudaArgentinaArmeniaArubaAustraliaAustriaAzerbaijanBahamasBahrainBangladeshBarbadosBelarusBelgiumBelizeBeninBermudaBhutanBolivia, Plurinational State ofBosnia and HerzegovinaBotswanaBouvet IslandBrazilBritish Indian Ocean TerritoryBrunei DarussalamBulgariaBurkina FasoBurundiCambodiaCameroonCape VerdeCayman IslandsCentral African RepublicChadChileChinaChristmas IslandCocos (Keeling) IslandsColombiaComorosCongoCongo, The Democratic Republic of theCook IslandsCosta RicaCôte dIvoireCroatiaCubaCuraçaoCyprusCzechiaDenmarkDjiboutiDominicaDominican RepublicEcuadorEgyptEl SalvadorEquatorial GuineaEritreaEstoniaEthiopiaFalkland IslandsFaroe IslandsFijiFinlandFranceFrench GuianaFrench PolynesiaFrench Southern TerritoriesGabonGambiaGeorgiaGermanyGhanaGibraltarGreeceGreenlandGrenadaGuadeloupeGuamGuatemalaGuineaGuinea-BissauGuyanaHaitiHeard Island and McDonald IslandsHondurasHong KongHungaryIcelandIndiaIndonesiaIranIraqIrelandIsle of ManIsraelItalyJamaicaJapanJerseyJordanKazakhstanKenyaKiribatiKorea, Republic ofKuwaitKyrgyzstanLao People's Democratic RepublicLatviaLebanonLesothoLiberiaLibyan Arab JamahiriyaLiechtensteinLithuaniaLuxembourgMacaoMadagascarMalawiMalaysiaMaldivesMaliMaltaMarshall IslandsMartiniqueMauritaniaMauritiusMayotteMexicoMicronesia, Federated States ofMoldova, Republic ofMonacoMongoliaMontenegroMontserratMoroccoMozambiqueMyanmarNamibiaNauruNepalNetherlandsNetherlands AntillesNew CaledoniaNew ZealandNicaraguaNigerNigeriaNiueNorfolk IslandNorth MacedoniaNorthern Mariana IslandsNorwayOmanPakistanPalauPalestinian Territory, OccupiedPanamaPapua New GuineaParaguayPeruPhilippinesPitcairnPolandPortugalPuerto RicoQatarRéunionRomaniaRussiaRwandaSaint BarthélemySaint HelenaSaint Kitts and NevisSaint MartinSaint LuciaSaint Pierre and MiquelonSaint Vincent and the GrenadinesSamoaSan MarinoSão Tomé and PríncipeSaudi ArabiaSenegalSerbiaSeychellesSierra LeoneSingaporeSlovakiaSloveniaSolomon IslandsSomaliaSouth AfricaSouth SudanSpainSri LankaSudanSurinameSvalbard and Jan MayenSwazilandSwedenSwitzerlandSyrian Arab RepublicTaiwanTajikistanTanzania, United Republic ofThailandTimor-LesteTogoTokelauTongaTrinidad and TobagoTunisiaTurkeyTurkmenistanTurks and Caicos IslandsTuvaluUgandaUkraineUnited Arab EmiratesUnited KingdomUnited States Minor Outlying IslandsUruguayUzbekistanVanuatuVatican CityVenezuela, Bolivarian Republic ofVietnamVirgin Islands, BritishVirgin Islands, USWallis and FutunaWestern SaharaYemenZambiaZimbabwe State —Please choose an option— How did you hear about Komprise?*Search EngineSocial MediaMarketing EventBlog or PublicationRecommended by Friend or ColleagueRecommended by Industry AnalystRecommended by a PartnerOther Other By submitting this form, I confirm that I have read and agree to the Privacy Statement. Δ Read this paper to understand the benefits of Komprise hybrid tiering vs. storage-based tiering. ### Protect Unstructured Data from Ransomware Ransomware can enter your organization by infecting any data, not just your mission critical data. This poses a challenge for infrastructure and storage managers who typically focus the best data protection strategies on mission-critical data which is often block data. The large volume, variety and velocity of file data in the enterprise makes this unstructured data the hardest to defend against ransomware attacks and leaves the organization vulnerable. Komprise Hybrid Tiering Benefits for Ransomware Removes the files from the ransomware attack surface, unlike storage- based tiering Tiering to an immutable location adds another layer of defense from potential attacks Transparent to the snapshot mechanisms, supporting tamperproof snapshots Shrinks your storage, backup and DR footprints, reducing costs Read Solution Brief Read the Katten Law case study Read: The File Data Problem for Ransomware Learn more about Komprise for ransomware cost savings ### Unstructured Data Migration Best Practices In this best practice series, we focus on strategies to deliver a successful file and object data migration. Hear practical tips and guidance from from Komprise Field CTO Benjamin Henry, who has years of experience working with some of the largest global enterprise organizations 1. How do I plan for an unstructured data migration? In this first unstructured data migration best practice video Benjamin Henry, Field CTO @ Komprise, reviews planning your unstructured data migration and spends the majority of the video demonstrating Komprise Analysis and reporting features. Know First. Move Smart. Take Control.  _______________________ 2. How do I reduce my data footprint prior to an unstructured data migration? In this 2nd unstructured data migration best practice video Benjamin Henry reviews how to analyze and tier off cold data prior to an unstructured data migration. See a demonstration of automated Komprise cold data tiering as part of a Smart Data Migration strategy and how to use the Showback report to view and share results. _______________________ 3. How do I set up my data migration? In this 3rd best practice video Benjamin Henry reviews how to set up a data migration with Komprise Elastic Data Migration. He reviews demonstrates some of the advanced features and discusses the use cases Komprise supports: NAS to NAS, Object to Object, NAS to Object and Object to NAS. _______________________ 4. How do I minimize data migration downtime? In this 4th best practice video Benjamin reviews strategies to minimize downtime and demonstrates the warm cutover feature of Komprise Elastic Data Migration. Read the blog post to learn more about the warm cutover or "zero-downtime" feature.  5. How do I troubleshoot migrations? You've started your migration and you're facing some issues - is it performance related? Is it something to do with permissions? Or is it the data set - source or target? In this 5th and final best practice video, Benjamin reviews strategies to troubleshoot your unstructured data migrations.  _______________________ _______________________ Watch More TechKrunch Webinars and Videos Watch the Series: STaaS and Unstructured Data Management ### Rapidly Migrate Petabytes to NetApp with Efficiency Migrating file and object data to NetApp doesn’t have to be costly, time consuming and error prone. Komprise Elastic Data Migration migrates 27 times faster with an elastic architecture to reliably migrate petabytes of data to NetApp. Why Komprise for NetApp Unstructured Data Migration? Analyze Before You Migrate Migrate Fast Migrate NAS Reliably Migrate Across Clouds Simplify Management A Fast, No Lock-In Path to the Cloud Read Solution Brief Learn more about Komprise for NetApp Learn more about Komprise Elastic Data Migration ### IDC Innovators: Komprise Included in Knowledge Management Technologies IDC Innovators Vendor Profile: Komprise IDC Innovators Excerpt Features Komprise Komprise's Intelligent Data Management helps the enterprise do two valuable things: unlock the value hidden in unstructured data and reduce storage costs. This is especially relevant for data-intensive industries like pharma/biotech, research, financial, and public sector. Proper metadata tagging and access ensures AI solutions can extract and present the right data, at the right time, and to the right person. Komprise is SaaS solution that is typically run in a hybrid deployment. Read this IDC Innovator Assessment of Komprise Intelligent Data Management. You can also download the one page summary here. Key Komprise Differentiators: Analytics UI Global Search capabilities Smart Data Workflows Read the press release. Download White Paper First Name* Last Name* Work Email* Company Name* Job Title* Phone* Country* --United StatesCanadaAfghanistanÅland IslandsAlbaniaAlgeriaAmerican SamoaAndorraAngolaAnguillaAntarcticaAntigua and BarbudaArgentinaArmeniaArubaAustraliaAustriaAzerbaijanBahamasBahrainBangladeshBarbadosBelarusBelgiumBelizeBeninBermudaBhutanBolivia, Plurinational State ofBosnia and HerzegovinaBotswanaBouvet IslandBrazilBritish Indian Ocean TerritoryBrunei DarussalamBulgariaBurkina FasoBurundiCambodiaCameroonCape VerdeCayman IslandsCentral African RepublicChadChileChinaChristmas IslandCocos (Keeling) IslandsColombiaComorosCongoCongo, The Democratic Republic of theCook IslandsCosta RicaCôte dIvoireCroatiaCubaCuraçaoCyprusCzechiaDenmarkDjiboutiDominicaDominican RepublicEcuadorEgyptEl SalvadorEquatorial GuineaEritreaEstoniaEthiopiaFalkland IslandsFaroe IslandsFijiFinlandFranceFrench GuianaFrench PolynesiaFrench Southern TerritoriesGabonGambiaGeorgiaGermanyGhanaGibraltarGreeceGreenlandGrenadaGuadeloupeGuamGuatemalaGuineaGuinea-BissauGuyanaHaitiHeard Island and McDonald IslandsHondurasHong KongHungaryIcelandIndiaIndonesiaIranIraqIrelandIsle of ManIsraelItalyJamaicaJapanJerseyJordanKazakhstanKenyaKiribatiKorea, Republic ofKuwaitKyrgyzstanLao People's Democratic RepublicLatviaLebanonLesothoLiberiaLibyan Arab JamahiriyaLiechtensteinLithuaniaLuxembourgMacaoMadagascarMalawiMalaysiaMaldivesMaliMaltaMarshall IslandsMartiniqueMauritaniaMauritiusMayotteMexicoMicronesia, Federated States ofMoldova, Republic ofMonacoMongoliaMontenegroMontserratMoroccoMozambiqueMyanmarNamibiaNauruNepalNetherlandsNetherlands AntillesNew CaledoniaNew ZealandNicaraguaNigerNigeriaNiueNorfolk IslandNorth MacedoniaNorthern Mariana IslandsNorwayOmanPakistanPalauPalestinian Territory, OccupiedPanamaPapua New GuineaParaguayPeruPhilippinesPitcairnPolandPortugalPuerto RicoQatarRéunionRomaniaRussiaRwandaSaint BarthélemySaint HelenaSaint Kitts and NevisSaint MartinSaint LuciaSaint Pierre and MiquelonSaint Vincent and the GrenadinesSamoaSan MarinoSão Tomé and PríncipeSaudi ArabiaSenegalSerbiaSeychellesSierra LeoneSingaporeSlovakiaSloveniaSolomon IslandsSomaliaSouth AfricaSouth SudanSpainSri LankaSudanSurinameSvalbard and Jan MayenSwazilandSwedenSwitzerlandSyrian Arab RepublicTaiwanTajikistanTanzania, United Republic ofThailandTimor-LesteTogoTokelauTongaTrinidad and TobagoTunisiaTurkeyTurkmenistanTurks and Caicos IslandsTuvaluUgandaUkraineUnited Arab EmiratesUnited KingdomUnited States Minor Outlying IslandsUruguayUzbekistanVanuatuVatican CityVenezuela, Bolivarian Republic ofVietnamVirgin Islands, BritishVirgin Islands, USWallis and FutunaWestern SaharaYemenZambiaZimbabwe State —Please choose an option— How did you hear about Komprise?*Search EngineSocial MediaMarketing EventBlog or PublicationRecommended by Friend or ColleagueRecommended by Industry AnalystRecommended by a PartnerOther Other By submitting this form, I confirm that I have read and agree to the Privacy Statement. Δ "Komprise's Intelligent Data Management helps the enterprise do two valuable things: unlock the value hidden in unstructured data and reduce storage costs. This is especially relevant for data-intensive industries like pharma/biotech, research, financial, and public sector. Proper metadata tagging and access ensures AI solutions can extract and present the right data, at the right time, and to the right person. Komprise is SaaS solution that is typically run in a hybrid deployment."   Read this paper to see why IDC has recognized Komprise as a 2024 Innovator! ### Komprise Intelligent Data Management for VAST Data Komprise delivers the fastest, simplest, most cost-effective way to find and move petabytes of file and object data into VAST. An analysis-first software solution for data migration and ongoing data management, with Komprise you’ll always get the right data to the VAST data platform and ensure you’re achieving maximum value from your VAST Data investment. Know First with Analysis: Ensure customers have a complete picture across silos. Move Smart with Migration: 2x faster NFS, SMB vs. point tools. No PS required. Save More with Tiering: No disruption with Transparent Move Technology (TMT). Read Solution Brief Learn more about Komprise for VAST Data. ### Komprise: Know First. Move Smart. Take Control. In this brief overview of Komprise, see some of the analysis capabilities of the Intelligent Data Management platform.  _______________________ Komprise Intelligent Data Management Komprise delivers an analytics-driven SaaS platform to manage and mobilize all your unstructured data. With Komprise Intelligent Data Management, enterprise IT teams can easily analyze, search and use unstructured data across silos to deliver greater visibility, mobility and value. They can create policy-driven automated workflows to find, tag and move data to the right storage at the right time to meet a variety of needs from data protection and compliance, to cost savings and to feed AI applications. KNOW FIRST: Komprise customers save on average 70% on storage, backup, ransomware and cloud costs. Get started with powerful analysis. MOVE SMART: Move to the cloud 27x faster with smart data migration and see a dramatic reduction in time spent preparing data for analytics workflows with our Global File Index and intelligent data tiering. TAKE CONTROL: Save and make money on your unstructured data with Komprise. No lock-in. Never in the hot data path. No disruption, all value. Read the Solution Brief ### Komprise Elastic Data Migration Overview Komprise Elastic Data Migration Overview Accelerate NAS And Cloud Data Migrations As enterprise IT organizations evolve to faster, flash-based NAS and cloud storage, migrating unstructured data into these environments can be difficult. The goal is to migrate large production data sets quickly, without errors, and without user disruption. With Komprise Elastic Data Migration that’s possible. This white paper provides an overview of the fast, reliable, and cost-efficient unstructured migration solution from Komprise. Komprise delivers 27x faster NFS migrations.  Also learn more about 25x faster SMB migrations with Hypertransfer. Download the paper to learn more. Download White Paper First Name* Last Name* Work Email* Company Name* Job Title* Phone* Country* --United StatesCanadaAfghanistanÅland IslandsAlbaniaAlgeriaAmerican SamoaAndorraAngolaAnguillaAntarcticaAntigua and BarbudaArgentinaArmeniaArubaAustraliaAustriaAzerbaijanBahamasBahrainBangladeshBarbadosBelarusBelgiumBelizeBeninBermudaBhutanBolivia, Plurinational State ofBosnia and HerzegovinaBotswanaBouvet IslandBrazilBritish Indian Ocean TerritoryBrunei DarussalamBulgariaBurkina FasoBurundiCambodiaCameroonCape VerdeCayman IslandsCentral African RepublicChadChileChinaChristmas IslandCocos (Keeling) IslandsColombiaComorosCongoCongo, The Democratic Republic of theCook IslandsCosta RicaCôte dIvoireCroatiaCubaCuraçaoCyprusCzechiaDenmarkDjiboutiDominicaDominican RepublicEcuadorEgyptEl SalvadorEquatorial GuineaEritreaEstoniaEthiopiaFalkland IslandsFaroe IslandsFijiFinlandFranceFrench GuianaFrench PolynesiaFrench Southern TerritoriesGabonGambiaGeorgiaGermanyGhanaGibraltarGreeceGreenlandGrenadaGuadeloupeGuamGuatemalaGuineaGuinea-BissauGuyanaHaitiHeard Island and McDonald IslandsHondurasHong KongHungaryIcelandIndiaIndonesiaIranIraqIrelandIsle of ManIsraelItalyJamaicaJapanJerseyJordanKazakhstanKenyaKiribatiKorea, Republic ofKuwaitKyrgyzstanLao People's Democratic RepublicLatviaLebanonLesothoLiberiaLibyan Arab JamahiriyaLiechtensteinLithuaniaLuxembourgMacaoMadagascarMalawiMalaysiaMaldivesMaliMaltaMarshall IslandsMartiniqueMauritaniaMauritiusMayotteMexicoMicronesia, Federated States ofMoldova, Republic ofMonacoMongoliaMontenegroMontserratMoroccoMozambiqueMyanmarNamibiaNauruNepalNetherlandsNetherlands AntillesNew CaledoniaNew ZealandNicaraguaNigerNigeriaNiueNorfolk IslandNorth MacedoniaNorthern Mariana IslandsNorwayOmanPakistanPalauPalestinian Territory, OccupiedPanamaPapua New GuineaParaguayPeruPhilippinesPitcairnPolandPortugalPuerto RicoQatarRéunionRomaniaRussiaRwandaSaint BarthélemySaint HelenaSaint Kitts and NevisSaint MartinSaint LuciaSaint Pierre and MiquelonSaint Vincent and the GrenadinesSamoaSan MarinoSão Tomé and PríncipeSaudi ArabiaSenegalSerbiaSeychellesSierra LeoneSingaporeSlovakiaSloveniaSolomon IslandsSomaliaSouth AfricaSouth SudanSpainSri LankaSudanSurinameSvalbard and Jan MayenSwazilandSwedenSwitzerlandSyrian Arab RepublicTaiwanTajikistanTanzania, United Republic ofThailandTimor-LesteTogoTokelauTongaTrinidad and TobagoTunisiaTurkeyTurkmenistanTurks and Caicos IslandsTuvaluUgandaUkraineUnited Arab EmiratesUnited KingdomUnited States Minor Outlying IslandsUruguayUzbekistanVanuatuVatican CityVenezuela, Bolivarian Republic ofVietnamVirgin Islands, BritishVirgin Islands, USWallis and FutunaWestern SaharaYemenZambiaZimbabwe State —Please choose an option— How did you hear about Komprise?*Search EngineSocial MediaMarketing EventBlog or PublicationRecommended by Friend or ColleagueRecommended by Industry AnalystRecommended by a PartnerOther Other By submitting this form, I confirm that I have read and agree to the Privacy Statement. Δ ### Komprise Intelligent Data Management Architecture Overview Komprise Intelligent Data Management Architecture Overview Explosive data growth requires a re-think of how data is managed. Storage capacity is running out, backups are taking longer, and budgets can’t keep up with the unstructured data deluge. Managing data within vendor silos leads to poor visibility, proprietary lock-in, and ballooning costs. Komprise provides a standards-based, modern data management solution architected to put you in control of your data with unprecedented simplicity – by giving you visibility into all your data, moving data to the right place at the right time efficiently, and providing native access to data at every tier without proprietary lock-in. Komprise Intelligent Data Management white paper highlights: Today’s Data Management Challenges The Principles of Komprise Technology The 7 Components of the Komprise Architecture How it Works Download White Paper First Name* Last Name* Work Email* Company Name* Job Title* Phone* Country* --United StatesCanadaAfghanistanÅland IslandsAlbaniaAlgeriaAmerican SamoaAndorraAngolaAnguillaAntarcticaAntigua and BarbudaArgentinaArmeniaArubaAustraliaAustriaAzerbaijanBahamasBahrainBangladeshBarbadosBelarusBelgiumBelizeBeninBermudaBhutanBolivia, Plurinational State ofBosnia and HerzegovinaBotswanaBouvet IslandBrazilBritish Indian Ocean TerritoryBrunei DarussalamBulgariaBurkina FasoBurundiCambodiaCameroonCape VerdeCayman IslandsCentral African RepublicChadChileChinaChristmas IslandCocos (Keeling) IslandsColombiaComorosCongoCongo, The Democratic Republic of theCook IslandsCosta RicaCôte dIvoireCroatiaCubaCuraçaoCyprusCzechiaDenmarkDjiboutiDominicaDominican RepublicEcuadorEgyptEl SalvadorEquatorial GuineaEritreaEstoniaEthiopiaFalkland IslandsFaroe IslandsFijiFinlandFranceFrench GuianaFrench PolynesiaFrench Southern TerritoriesGabonGambiaGeorgiaGermanyGhanaGibraltarGreeceGreenlandGrenadaGuadeloupeGuamGuatemalaGuineaGuinea-BissauGuyanaHaitiHeard Island and McDonald IslandsHondurasHong KongHungaryIcelandIndiaIndonesiaIranIraqIrelandIsle of ManIsraelItalyJamaicaJapanJerseyJordanKazakhstanKenyaKiribatiKorea, Republic ofKuwaitKyrgyzstanLao People's Democratic RepublicLatviaLebanonLesothoLiberiaLibyan Arab JamahiriyaLiechtensteinLithuaniaLuxembourgMacaoMadagascarMalawiMalaysiaMaldivesMaliMaltaMarshall IslandsMartiniqueMauritaniaMauritiusMayotteMexicoMicronesia, Federated States ofMoldova, Republic ofMonacoMongoliaMontenegroMontserratMoroccoMozambiqueMyanmarNamibiaNauruNepalNetherlandsNetherlands AntillesNew CaledoniaNew ZealandNicaraguaNigerNigeriaNiueNorfolk IslandNorth MacedoniaNorthern Mariana IslandsNorwayOmanPakistanPalauPalestinian Territory, OccupiedPanamaPapua New GuineaParaguayPeruPhilippinesPitcairnPolandPortugalPuerto RicoQatarRéunionRomaniaRussiaRwandaSaint BarthélemySaint HelenaSaint Kitts and NevisSaint MartinSaint LuciaSaint Pierre and MiquelonSaint Vincent and the GrenadinesSamoaSan MarinoSão Tomé and PríncipeSaudi ArabiaSenegalSerbiaSeychellesSierra LeoneSingaporeSlovakiaSloveniaSolomon IslandsSomaliaSouth AfricaSouth SudanSpainSri LankaSudanSurinameSvalbard and Jan MayenSwazilandSwedenSwitzerlandSyrian Arab RepublicTaiwanTajikistanTanzania, United Republic ofThailandTimor-LesteTogoTokelauTongaTrinidad and TobagoTunisiaTurkeyTurkmenistanTurks and Caicos IslandsTuvaluUgandaUkraineUnited Arab EmiratesUnited KingdomUnited States Minor Outlying IslandsUruguayUzbekistanVanuatuVatican CityVenezuela, Bolivarian Republic ofVietnamVirgin Islands, BritishVirgin Islands, USWallis and FutunaWestern SaharaYemenZambiaZimbabwe State —Please choose an option— How did you hear about Komprise?*Search EngineSocial MediaMarketing EventBlog or PublicationRecommended by Friend or ColleagueRecommended by Industry AnalystRecommended by a PartnerOther Other By submitting this form, I confirm that I have read and agree to the Privacy Statement. Δ Download this white paper to learn more about Komprise Intelligent Data Management From dynamic data analytics, to Transparent Movement Technology (TMT), to direct data access, with Komprise Intelligent Data Management, you are able to know first, move smart, and take control of massive unstructured data growth while cutting 70% of enterprise storage, backup, and cloud costs. Know First. Move Smart. Take Control of Unstructured Data Growth and Costs. Your data will outlive your storage infrastructure. A storage-centric approach to data management misses the point. Read more. ### Cloud Tiering: Storage-Based vs Gateways vs File-Based Cloud Tiering: Storage-Based vs Gateways vs File-Based Which is Better for Cloud Data Management and Why? Cloud tiering enables enterprises to offload unused cold data into cost-efficient cloud storage and should yield significant savings. Most enterprises today have a corporate cloud strategy and are now looking to move file workloads to the cloud. When done correctly, cloud tiering is an easy path to the cloud. But not all cloud tiering and archiving solutions are the same. You may end up paying more in cloud egress and storage licensing costs by picking the wrong strategy. This paper compares three alternatives to move file data to the cloud: Built-in storage cloud tiering (aka “pool” solutions) Cloud storage gateways File-level cloud tiering Download White Paper First Name* Last Name* Work Email* Company Name* Job Title* Phone* Country* --United StatesCanadaAfghanistanÅland IslandsAlbaniaAlgeriaAmerican SamoaAndorraAngolaAnguillaAntarcticaAntigua and BarbudaArgentinaArmeniaArubaAustraliaAustriaAzerbaijanBahamasBahrainBangladeshBarbadosBelarusBelgiumBelizeBeninBermudaBhutanBolivia, Plurinational State ofBosnia and HerzegovinaBotswanaBouvet IslandBrazilBritish Indian Ocean TerritoryBrunei DarussalamBulgariaBurkina FasoBurundiCambodiaCameroonCape VerdeCayman IslandsCentral African RepublicChadChileChinaChristmas IslandCocos (Keeling) IslandsColombiaComorosCongoCongo, The Democratic Republic of theCook IslandsCosta RicaCôte dIvoireCroatiaCubaCuraçaoCyprusCzechiaDenmarkDjiboutiDominicaDominican RepublicEcuadorEgyptEl SalvadorEquatorial GuineaEritreaEstoniaEthiopiaFalkland IslandsFaroe IslandsFijiFinlandFranceFrench GuianaFrench PolynesiaFrench Southern TerritoriesGabonGambiaGeorgiaGermanyGhanaGibraltarGreeceGreenlandGrenadaGuadeloupeGuamGuatemalaGuineaGuinea-BissauGuyanaHaitiHeard Island and McDonald IslandsHondurasHong KongHungaryIcelandIndiaIndonesiaIranIraqIrelandIsle of ManIsraelItalyJamaicaJapanJerseyJordanKazakhstanKenyaKiribatiKorea, Republic ofKuwaitKyrgyzstanLao People's Democratic RepublicLatviaLebanonLesothoLiberiaLibyan Arab JamahiriyaLiechtensteinLithuaniaLuxembourgMacaoMadagascarMalawiMalaysiaMaldivesMaliMaltaMarshall IslandsMartiniqueMauritaniaMauritiusMayotteMexicoMicronesia, Federated States ofMoldova, Republic ofMonacoMongoliaMontenegroMontserratMoroccoMozambiqueMyanmarNamibiaNauruNepalNetherlandsNetherlands AntillesNew CaledoniaNew ZealandNicaraguaNigerNigeriaNiueNorfolk IslandNorth MacedoniaNorthern Mariana IslandsNorwayOmanPakistanPalauPalestinian Territory, OccupiedPanamaPapua New GuineaParaguayPeruPhilippinesPitcairnPolandPortugalPuerto RicoQatarRéunionRomaniaRussiaRwandaSaint BarthélemySaint HelenaSaint Kitts and NevisSaint MartinSaint LuciaSaint Pierre and MiquelonSaint Vincent and the GrenadinesSamoaSan MarinoSão Tomé and PríncipeSaudi ArabiaSenegalSerbiaSeychellesSierra LeoneSingaporeSlovakiaSloveniaSolomon IslandsSomaliaSouth AfricaSouth SudanSpainSri LankaSudanSurinameSvalbard and Jan MayenSwazilandSwedenSwitzerlandSyrian Arab RepublicTaiwanTajikistanTanzania, United Republic ofThailandTimor-LesteTogoTokelauTongaTrinidad and TobagoTunisiaTurkeyTurkmenistanTurks and Caicos IslandsTuvaluUgandaUkraineUnited Arab EmiratesUnited KingdomUnited States Minor Outlying IslandsUruguayUzbekistanVanuatuVatican CityVenezuela, Bolivarian Republic ofVietnamVirgin Islands, BritishVirgin Islands, USWallis and FutunaWestern SaharaYemenZambiaZimbabwe State —Please choose an option— How did you hear about Komprise?*Search EngineSocial MediaMarketing EventBlog or PublicationRecommended by Friend or ColleagueRecommended by Industry AnalystRecommended by a PartnerOther Other By submitting this form, I confirm that I have read and agree to the Privacy Statement. Δ Do you have the right Cloud Tiering strategy? A petabyte of file data can easily be a few billion files, each with their metadata, of varying sizes, and varying formats. The approach you take to cloud data tiering could either save you millions or cost you 75%+ higher cloud costs. Download this unstructured data management white paper to learn more. ### Global Namespace vs Global File System Global Namespace vs Global File System  What's the Difference and Why Does it Matter? It’s easy to see the appeal of a single control plane to access and manage data no matter where it lives. But investing in the right technology to deliver data visibility, access and management without lock-in, and unnecessary performance overhead and costs can be difficult without a clear set of requirements. This paper summarizes the differences between a global namespace and a global file system (GFS) and reviews the benefits that Komprise Intelligent Data Management delivers sitting outside of the hot data path. The paper reviews: Why a Global Namespace? Differences Between a Global Namespace and a Global File System When to Use a Global File System vs a Global Namespace Questions to Ask Vendors Download White Paper First Name* Last Name* Work Email* Company Name* Job Title* Phone* Country* --United StatesCanadaAfghanistanÅland IslandsAlbaniaAlgeriaAmerican SamoaAndorraAngolaAnguillaAntarcticaAntigua and BarbudaArgentinaArmeniaArubaAustraliaAustriaAzerbaijanBahamasBahrainBangladeshBarbadosBelarusBelgiumBelizeBeninBermudaBhutanBolivia, Plurinational State ofBosnia and HerzegovinaBotswanaBouvet IslandBrazilBritish Indian Ocean TerritoryBrunei DarussalamBulgariaBurkina FasoBurundiCambodiaCameroonCape VerdeCayman IslandsCentral African RepublicChadChileChinaChristmas IslandCocos (Keeling) IslandsColombiaComorosCongoCongo, The Democratic Republic of theCook IslandsCosta RicaCôte dIvoireCroatiaCubaCuraçaoCyprusCzechiaDenmarkDjiboutiDominicaDominican RepublicEcuadorEgyptEl SalvadorEquatorial GuineaEritreaEstoniaEthiopiaFalkland IslandsFaroe IslandsFijiFinlandFranceFrench GuianaFrench PolynesiaFrench Southern TerritoriesGabonGambiaGeorgiaGermanyGhanaGibraltarGreeceGreenlandGrenadaGuadeloupeGuamGuatemalaGuineaGuinea-BissauGuyanaHaitiHeard Island and McDonald IslandsHondurasHong KongHungaryIcelandIndiaIndonesiaIranIraqIrelandIsle of ManIsraelItalyJamaicaJapanJerseyJordanKazakhstanKenyaKiribatiKorea, Republic ofKuwaitKyrgyzstanLao People's Democratic RepublicLatviaLebanonLesothoLiberiaLibyan Arab JamahiriyaLiechtensteinLithuaniaLuxembourgMacaoMadagascarMalawiMalaysiaMaldivesMaliMaltaMarshall IslandsMartiniqueMauritaniaMauritiusMayotteMexicoMicronesia, Federated States ofMoldova, Republic ofMonacoMongoliaMontenegroMontserratMoroccoMozambiqueMyanmarNamibiaNauruNepalNetherlandsNetherlands AntillesNew CaledoniaNew ZealandNicaraguaNigerNigeriaNiueNorfolk IslandNorth MacedoniaNorthern Mariana IslandsNorwayOmanPakistanPalauPalestinian Territory, OccupiedPanamaPapua New GuineaParaguayPeruPhilippinesPitcairnPolandPortugalPuerto RicoQatarRéunionRomaniaRussiaRwandaSaint BarthélemySaint HelenaSaint Kitts and NevisSaint MartinSaint LuciaSaint Pierre and MiquelonSaint Vincent and the GrenadinesSamoaSan MarinoSão Tomé and PríncipeSaudi ArabiaSenegalSerbiaSeychellesSierra LeoneSingaporeSlovakiaSloveniaSolomon IslandsSomaliaSouth AfricaSouth SudanSpainSri LankaSudanSurinameSvalbard and Jan MayenSwazilandSwedenSwitzerlandSyrian Arab RepublicTaiwanTajikistanTanzania, United Republic ofThailandTimor-LesteTogoTokelauTongaTrinidad and TobagoTunisiaTurkeyTurkmenistanTurks and Caicos IslandsTuvaluUgandaUkraineUnited Arab EmiratesUnited KingdomUnited States Minor Outlying IslandsUruguayUzbekistanVanuatuVatican CityVenezuela, Bolivarian Republic ofVietnamVirgin Islands, BritishVirgin Islands, USWallis and FutunaWestern SaharaYemenZambiaZimbabwe State —Please choose an option— How did you hear about Komprise?*Search EngineSocial MediaMarketing EventBlog or PublicationRecommended by Friend or ColleagueRecommended by Industry AnalystRecommended by a PartnerOther Other By submitting this form, I confirm that I have read and agree to the Privacy Statement. Δ Do you need to be able to analyze, move and manage data across systems and right place data based on policy? Or is your primary requirement to collaborate across teams and locations? Knowing this will determine if a global file system that fronts all of your data is needed or if storage-agnostic unstructured data management that is never in the hot data path is a better solution. Download this unstructured data management white paper to learn more. ### Komprise Intelligent Tiering for Pure FlashBlade Solution Brief Manage unstructured data growth sustainably, efficiently, and cost effectively with Pure FlashBlade and Komprise. By leveraging the power of FlashBlade all-flash scale-out file and object storage platform and the Intelligent Data Management capabilities of Komprise, data-heavy organizations in all industries can optimize their storage infrastructure - improving data accessibility and streamlining data management processes. Visualize: Assess, Manage & Scale Flexible Policies to Meet Your Needs Patented File Tiering Maximizes Savings No Disruption Read Solution Brief Read the technical white paper: Transparent data tiering between FlashBlade//S and FlashBlade//E with Komprise Learn more about Komprise for Pure Storage. ### Unstructured Data Management Strategies in the GenAI Age Unstructured Data Management In the Age of Generative AI  Guidance for the Next Generation of Unstructured Data Management Challenges Despite the fact that rules and standards remain in development, it’s possible to establish basic data management principles when working with the unstructured data that feeds AI tools. The paper reviews: AI data management principles included in the security, privacy, lineage, ownership, governance (SPLOG) framework; How to protect and segment data in generative AI; How to track and audit data in generative AI; The importance of guardrails for employees; The role of unstructured data management in AI. Download White Paper First Name* Last Name* Work Email* Company Name* Job Title* Phone* Country* --United StatesCanadaAfghanistanÅland IslandsAlbaniaAlgeriaAmerican SamoaAndorraAngolaAnguillaAntarcticaAntigua and BarbudaArgentinaArmeniaArubaAustraliaAustriaAzerbaijanBahamasBahrainBangladeshBarbadosBelarusBelgiumBelizeBeninBermudaBhutanBolivia, Plurinational State ofBosnia and HerzegovinaBotswanaBouvet IslandBrazilBritish Indian Ocean TerritoryBrunei DarussalamBulgariaBurkina FasoBurundiCambodiaCameroonCape VerdeCayman IslandsCentral African RepublicChadChileChinaChristmas IslandCocos (Keeling) IslandsColombiaComorosCongoCongo, The Democratic Republic of theCook IslandsCosta RicaCôte dIvoireCroatiaCubaCuraçaoCyprusCzechiaDenmarkDjiboutiDominicaDominican RepublicEcuadorEgyptEl SalvadorEquatorial GuineaEritreaEstoniaEthiopiaFalkland IslandsFaroe IslandsFijiFinlandFranceFrench GuianaFrench PolynesiaFrench Southern TerritoriesGabonGambiaGeorgiaGermanyGhanaGibraltarGreeceGreenlandGrenadaGuadeloupeGuamGuatemalaGuineaGuinea-BissauGuyanaHaitiHeard Island and McDonald IslandsHondurasHong KongHungaryIcelandIndiaIndonesiaIranIraqIrelandIsle of ManIsraelItalyJamaicaJapanJerseyJordanKazakhstanKenyaKiribatiKorea, Republic ofKuwaitKyrgyzstanLao People's Democratic RepublicLatviaLebanonLesothoLiberiaLibyan Arab JamahiriyaLiechtensteinLithuaniaLuxembourgMacaoMadagascarMalawiMalaysiaMaldivesMaliMaltaMarshall IslandsMartiniqueMauritaniaMauritiusMayotteMexicoMicronesia, Federated States ofMoldova, Republic ofMonacoMongoliaMontenegroMontserratMoroccoMozambiqueMyanmarNamibiaNauruNepalNetherlandsNetherlands AntillesNew CaledoniaNew ZealandNicaraguaNigerNigeriaNiueNorfolk IslandNorth MacedoniaNorthern Mariana IslandsNorwayOmanPakistanPalauPalestinian Territory, OccupiedPanamaPapua New GuineaParaguayPeruPhilippinesPitcairnPolandPortugalPuerto RicoQatarRéunionRomaniaRussiaRwandaSaint BarthélemySaint HelenaSaint Kitts and NevisSaint MartinSaint LuciaSaint Pierre and MiquelonSaint Vincent and the GrenadinesSamoaSan MarinoSão Tomé and PríncipeSaudi ArabiaSenegalSerbiaSeychellesSierra LeoneSingaporeSlovakiaSloveniaSolomon IslandsSomaliaSouth AfricaSouth SudanSpainSri LankaSudanSurinameSvalbard and Jan MayenSwazilandSwedenSwitzerlandSyrian Arab RepublicTaiwanTajikistanTanzania, United Republic ofThailandTimor-LesteTogoTokelauTongaTrinidad and TobagoTunisiaTurkeyTurkmenistanTurks and Caicos IslandsTuvaluUgandaUkraineUnited Arab EmiratesUnited KingdomUnited States Minor Outlying IslandsUruguayUzbekistanVanuatuVatican CityVenezuela, Bolivarian Republic ofVietnamVirgin Islands, BritishVirgin Islands, USWallis and FutunaWestern SaharaYemenZambiaZimbabwe State —Please choose an option— How did you hear about Komprise?*Search EngineSocial MediaMarketing EventBlog or PublicationRecommended by Friend or ColleagueRecommended by Industry AnalystRecommended by a PartnerOther Other By submitting this form, I confirm that I have read and agree to the Privacy Statement. Δ How generative AI technology and the laws and industry standards relating to it will evolve is a work in progress. Yet it’s clear that the ability to define and enforce basic data governance standards will be paramount for taking full advantage of AI solutions without assuming unnecessary risks. Read this paper to review data management strategies for successful GenAI initiatives. Download this unstructured data management white paper to learn more. ### 8 Ways to Save on File Storage and Backup Costs 8 Ways to Save on File Storage and Backup Costs Make better investments with analytics-driven unstructured data management. High file storage costs are pushing organizations to seek out smarter strategies to store data — but moving data once or twice over its life isn’t the point. With new, more affordable options and new cloud storage tiers hitting the market regularly, IT organizations need intelligence and the flexibility to manage and move data repeatedly. The right approach to unstructured data management can maximize cost savings, improve ransomware protection and better serve enterprise/user needs. In this eBook, we delve into why file storage can be more expensive than it needs to be and what to do about it. Adopt a data services mindset. Adopt new unstructured data management metrics. Introduce an analytics approach for departments and users. And more... Also learn how Komprise customers save on average 70% with smart (and fast) data migration, cold data tiering and Intelligent Data Management. Download eBook Thanks for reaching out. We'll be in touch "Komprise helps us make razor sharp business decisions based on data so we can reinvest in areas that are more important to patients." – Director Hosting Data Services, Pfizer ### Komprise Intelligent Data Management Pre-Installation Review Watch an overview of the Komprise Intelligent Data Management installation process.  Getting Started with Komprise This video walks through the components and the requirements for a successful installation of the Komprise Intelligent Data Management platform as well as resources and reference materials to get started. Getting Started with Komprise Komprise University     On-Demand Videos _______________________ ### Komprise Intelligent Tiering for Azure Save 70% on File Storage Costs Komprise Intelligent Tiering for Azure analyzes data across on-prem and cloud file storage to identify cold data, and tiers cold files based on the policies you set to the appropriate Azure Blob tier. Komprise Intelligent Tiering for Azure is the only Azure Marketplace solution that gives customers access to file analysis and tiering to and within Azure. Break Down Silos Do More with Less No Disruption No Stubs, Agents or Lock-in Optimize Cloud Costs Exclusive Azure Offer Read Solution Brief Learn more about Komprise for Azure. ### How Pfizer Reversed Two Decades of Rising Storage Costs Pfizer optimizes data storage spending with Komprise Intelligent Data Management Pfizer set out to analyze the petabytes of unstructured files it had on high-performance, on-premises storage to identify what could be moved to the cloud and migrate it there. But Pfizer’s IT leaders needed to do this without compromising how users and applications access the data nor affecting the performance of its existing storage infrastructure. Data-heavy enterprises have mountains of cold data clogging their storage and backups. Yet most IT teams don’t know what can be moved to lower-tier storage or how to find it, because they never know when that data may become mission-critical again. When the company is pharmaceutical giant Pfizer, the issue of “right-placing” aging, cold data is not just a data hygiene challenge, but one that could affect the health of millions. Pfizer is saving 75% on storage using Komprise to analyze and continuously tier and migrate cold data to Amazon S3 as it ages. Storage managers and researchers are finding additional benefits from analytics-driven unstructured data management, including zero user disruption and a foundation for delivering self-service to line of business teams. Read Case Study Komprise cloud tiering helped Pfizer turn the tide on 20 years of increasing data storage costs. The strategy included tiering colder data to AWS, while keeping it instantly available for research, without changing how users and applications access their files. Download the case study and watch the webinar to learn: How to leverage analytics across multi-vendor storage environments to better plan data management and map out a path for moving data to the cloud. How to do this in line with your existing storage policies, while keeping both storage and migration costs under control and avoiding vendor lock-in. How to benefit from access to cloud-native data using cloud-based Big Data, AI and scalable data lakes. Watch the Komprise and AWS webinar to find out how they did it. Read the blog post for more details. Whether you’re an IT practitioner needing continued access to cold data, a CIO looking to balance the needs of your in-house researchers and your finance department, or a CISO looking to ensure that your data reserves are safe, learn more about Komprise and Pfizer and be sure to schedule a demonstration of Komprise Intelligent Data Management for your team today. Komprise for Healthcare and Life Sciences Komprise for Genomics and Pharmaceuticals More Komprise customer success stories. Learn more about Komprise and AWS. ### Azure Expert Series: The Azure File Migration Program and Komprise Overview of the Azure File Migration Program and Komprise data management solutions for Microsoft Azure Storage  Azure Expert Series: Overview of Komprise data management solutions for Microsoft Azure Storage Learn more about data management solutions from Komprise for Microsoft Azure Storage and how to leverage these solutions to optimize your data estate within Azure Storage. Topics include: Overview of Azure file migration program How Komprise helps with file migration program Trends and adoption of the Azure file migration program Data management best practices after you migrate your file data to Azure Presenters: • Vamshidhar Kommineni, Group Product Manager, Microsoft Azure Storage • Krishna Subramanian, Co-founder and COO, Komprise • Darren Cunningham, VP of Marketing, Komprise Recommended Resources: Analyze and migrate to Azure with Komprise: https://aka.ms/KompriseGuide Overview of Komprise solutions for Azure storage: https://www.komprise.com/azure ### Introducing Komprise Analysis In this video, Komprise COO and co-founder Krishna Subramanian introduces Komprise Analysis, a new subscription available to provide visibility across disparate file and object data storage systems so organizations can make smarter investment decisions and save on data storage spending.  Know More. Save More. Do More. Komprise COO and co-founder Krishna Subramanian introduces Komprise Analysis, a new subscription available to provide visibility across disparate file and object data storage systems so organizations can make smarter investment decisions and save on data storage spending. Learn more about Komprise Analysis. ### Komprise Analysis Solution Brief Komprise Analysis provides strategic insights into the unstructured file and object data across your data centers and clouds: Analyze across all your NAS, NFS, SMB, dual shares. See how much data you have, how fast it is growing, what is hot/cold. Quickly see top data types, top users, top groups, top directories. Understand cost/benefit modeling of tiering and data management. Read Solution Brief Learn more about Komprise Analysis. ### Energy Company Modernizes Data Center Operations & Unstructured Data Management Global oil and gas services provider modernizes & optimizes IT infrastructure, transitions workloads and data storage to multiple cloud providers and shrinks data center space, reduces CAPEX spending. Global oil and gas services provider modernizes and optimizes IT infrastructure and digital services, transitions workloads and data storage to multiple cloud service providers and shrinks data center space, reduces capital expense (CAPEX) spending. After a merger and divestiture, an IT operations leader at a global energy company was looking for ways to see across data storage silos to understand utilization, shrink their data center footprint, consolidate tools and make better business decisions about data storage. They needed a solution that was storage-agnostic and universal in order to provide maximum flexibility and avoid storage vendor lock-in as they pivoted to a more OPEX-driven environment and reduce CAPEX IT spending. "This pays off for us over and over again and 85% of our data is cold, just like the Komprise literature says," the data center manager says. One factor that led them to select Komprise is how the software solution can support many storage platforms, enabling one interface to achieve the same result universally regardless of what hardware OEM is in place. With the experience of divestitures and mergers in the past, having a tool that can both consolidate and separate data was key to selecting a partner and a solution. Storage: NetApp, Azure Read Case Study More Komprise customer success stories. -------------- ### Introducing Komprise Hypertransfer for Faster File Data Migration Komprise Hypertransfer for Elastic Data Migration accelerates data transfer to the cloud while strengthening cloud security. As enterprises migrate more file data to the cloud, IT organizations face many barriers which cause migrations to often take weeks to months.  _______________________ 25x Faster File Data Migration to the Cloud Komprise Hypertransfer for Elastic Data Migration accelerates unstructured data transfer to the cloud while strengthening cloud security. As enterprises migrate more file data to the cloud, IT organizations face many barriers which cause migrations to often take weeks to months. SMB workloads such as user data, electronic design automation (EDA) and other multimedia workloads contain lots of small files and are a particular challenge since the protocol requires many back-and-forth handshakes that increase administrative traffic over the network. Komprise Hypertransfer optimizes cloud data migration performance by minimizing the WAN roundtrips using dedicated channels to send data, mitigating the SMB protocol issues. According to recent tests performed by Komprise, Hypertransfer improves data transfer rates across the WAN by 25x over other alternatives for SMB datasets with predominantly small files. Komprise already delivers 27x faster performance for NFS migrations. Now with Hypertransfer, customers benefit from faster SMB migration to the cloud, while also strengthening security and defense against ransomware by not accessing cloud file storage over the network during data migrations, since data transfers from source to target over private channels. Learn More     Read White Paper _______________________ ### Komprise Hypertransfer Migrates Data to the Cloud 25x Faster Komprise Hypertransfer Migrate File Data to the Cloud 25x Faster Accelerate WAN file migrations with Komprise Hypertransfer for Elastic Data Migration Komprise delivers groundbreaking performance improvements for common cloud data migrations with the SMB protocol. Komprise Hypertransfer solves the vexing challenge of WAN migrations of large-scale SMB data sets: chatty protocols causing high overhead, WAN latency and network bandwidth availability issues. This paper reviews the results of Komprise Hypertransfer performance testing and outlines how Komprise Elastic Data Migration functionality delivers 25x performance gains compared to other tools. Download White Paper First Name* Last Name* Work Email* Company Name* Job Title* Phone* Country* --United StatesCanadaAfghanistanÅland IslandsAlbaniaAlgeriaAmerican SamoaAndorraAngolaAnguillaAntarcticaAntigua and BarbudaArgentinaArmeniaArubaAustraliaAustriaAzerbaijanBahamasBahrainBangladeshBarbadosBelarusBelgiumBelizeBeninBermudaBhutanBolivia, Plurinational State ofBosnia and HerzegovinaBotswanaBouvet IslandBrazilBritish Indian Ocean TerritoryBrunei DarussalamBulgariaBurkina FasoBurundiCambodiaCameroonCape VerdeCayman IslandsCentral African RepublicChadChileChinaChristmas IslandCocos (Keeling) IslandsColombiaComorosCongoCongo, The Democratic Republic of theCook IslandsCosta RicaCôte dIvoireCroatiaCubaCuraçaoCyprusCzechiaDenmarkDjiboutiDominicaDominican RepublicEcuadorEgyptEl SalvadorEquatorial GuineaEritreaEstoniaEthiopiaFalkland IslandsFaroe IslandsFijiFinlandFranceFrench GuianaFrench PolynesiaFrench Southern TerritoriesGabonGambiaGeorgiaGermanyGhanaGibraltarGreeceGreenlandGrenadaGuadeloupeGuamGuatemalaGuineaGuinea-BissauGuyanaHaitiHeard Island and McDonald IslandsHondurasHong KongHungaryIcelandIndiaIndonesiaIranIraqIrelandIsle of ManIsraelItalyJamaicaJapanJerseyJordanKazakhstanKenyaKiribatiKorea, Republic ofKuwaitKyrgyzstanLao People's Democratic RepublicLatviaLebanonLesothoLiberiaLibyan Arab JamahiriyaLiechtensteinLithuaniaLuxembourgMacaoMadagascarMalawiMalaysiaMaldivesMaliMaltaMarshall IslandsMartiniqueMauritaniaMauritiusMayotteMexicoMicronesia, Federated States ofMoldova, Republic ofMonacoMongoliaMontenegroMontserratMoroccoMozambiqueMyanmarNamibiaNauruNepalNetherlandsNetherlands AntillesNew CaledoniaNew ZealandNicaraguaNigerNigeriaNiueNorfolk IslandNorth MacedoniaNorthern Mariana IslandsNorwayOmanPakistanPalauPalestinian Territory, OccupiedPanamaPapua New GuineaParaguayPeruPhilippinesPitcairnPolandPortugalPuerto RicoQatarRéunionRomaniaRussiaRwandaSaint BarthélemySaint HelenaSaint Kitts and NevisSaint MartinSaint LuciaSaint Pierre and MiquelonSaint Vincent and the GrenadinesSamoaSan MarinoSão Tomé and PríncipeSaudi ArabiaSenegalSerbiaSeychellesSierra LeoneSingaporeSlovakiaSloveniaSolomon IslandsSomaliaSouth AfricaSouth SudanSpainSri LankaSudanSurinameSvalbard and Jan MayenSwazilandSwedenSwitzerlandSyrian Arab RepublicTaiwanTajikistanTanzania, United Republic ofThailandTimor-LesteTogoTokelauTongaTrinidad and TobagoTunisiaTurkeyTurkmenistanTurks and Caicos IslandsTuvaluUgandaUkraineUnited Arab EmiratesUnited KingdomUnited States Minor Outlying IslandsUruguayUzbekistanVanuatuVatican CityVenezuela, Bolivarian Republic ofVietnamVirgin Islands, BritishVirgin Islands, USWallis and FutunaWestern SaharaYemenZambiaZimbabwe State —Please choose an option— How did you hear about Komprise?*Search EngineSocial MediaMarketing EventBlog or PublicationRecommended by Friend or ColleagueRecommended by Industry AnalystRecommended by a PartnerOther Other By submitting this form, I confirm that I have read and agree to the Privacy Statement. Δ Schedule a Cloud File Migration Demonstration “Komprise made our data migration to the cloud as seamless as possible. While researching ways to move large amounts of data, we were confronted time and time again with limitations that made us believe the migration would never happen. Komprise was a sigh of relief for our entire team.” David Passamonte IT Manager Molecular Pathology Lab Network, Inc ### Expert Panel: Data Analytics in 2023 and Beyond Industry leaders discuss the future of the data analytics sector. On the panel were: Radhika Krishnan, Chief Product Officer, Hitachi Vantara / Torsten Grabs, Director of Product Management, Snowflake / Krishna Subramanian, Chief Operating Officer, Komprise / Barry McCardel, CEO, Hex Technologies. ### Managing Unstructured Data in Healthcare, Life Sciences and Genomics Gartner predicts that healthcare and life sciences will continue to outpace the average IT spending growth. ​Spending is expected to grow at ~ 10% in 2022 to reach an estimated $317.4 billion by 2026.​ This investment is driven by initiatives for: ​ Digital transformation of care delivery​ Transitioning to the cloud​ Virtual care​ The increasing investments in data and analytics​: “Life science CIOs continue to invest in data and analytics tools that will help their organizations fully harness the value of data.” Stop Overspending on Storage. Unstructured Data Management for Healthcare and Life Sciences Komprise works with some of the data-heavy enterprise IT organizations in Healthcare, Genomics, Pharmaceuticals, Biotech and Higher Education research institutions. Learn more about Unstructured Data Management for Healthcare and Life Sciences Learn more about Unstructured Data Management for Genomics, Pharmaceuticals, Biotech Read the white paper: How to Medical Imaging Data Growth Costs ### Komprise Analysis Overview Komprise Analysis Overview See Across Storage Silos and Make Data-Driven Investment Decisions. With Komprise Analysis, you quickly gain visibility across storage silos to make data-driven decisions. Plan what to migrate, what to tier, and understand the financial impact with an analytics-driven approach to data management and mobility. Komprise Analysis provides strategic insights into unstructured file and object data across your on-premises and cloud enterprise IT infrastructure: Analyze across all your NAS, NFS, SMB, dual shares. See how much data you have, growth rate, hot vs cold. Understand file types, top users, groups, directories. Perform cost/benefit modeling. That's the power of Komprise Intelligent Data Management. Know First. Move Smart. Stop Overspending on Data Storage. Download White Paper First Name* Last Name* Work Email* Company Name* Job Title* Phone* Country* --United StatesCanadaAfghanistanÅland IslandsAlbaniaAlgeriaAmerican SamoaAndorraAngolaAnguillaAntarcticaAntigua and BarbudaArgentinaArmeniaArubaAustraliaAustriaAzerbaijanBahamasBahrainBangladeshBarbadosBelarusBelgiumBelizeBeninBermudaBhutanBolivia, Plurinational State ofBosnia and HerzegovinaBotswanaBouvet IslandBrazilBritish Indian Ocean TerritoryBrunei DarussalamBulgariaBurkina FasoBurundiCambodiaCameroonCape VerdeCayman IslandsCentral African RepublicChadChileChinaChristmas IslandCocos (Keeling) IslandsColombiaComorosCongoCongo, The Democratic Republic of theCook IslandsCosta RicaCôte dIvoireCroatiaCubaCuraçaoCyprusCzechiaDenmarkDjiboutiDominicaDominican RepublicEcuadorEgyptEl SalvadorEquatorial GuineaEritreaEstoniaEthiopiaFalkland IslandsFaroe IslandsFijiFinlandFranceFrench GuianaFrench PolynesiaFrench Southern TerritoriesGabonGambiaGeorgiaGermanyGhanaGibraltarGreeceGreenlandGrenadaGuadeloupeGuamGuatemalaGuineaGuinea-BissauGuyanaHaitiHeard Island and McDonald IslandsHondurasHong KongHungaryIcelandIndiaIndonesiaIranIraqIrelandIsle of ManIsraelItalyJamaicaJapanJerseyJordanKazakhstanKenyaKiribatiKorea, Republic ofKuwaitKyrgyzstanLao People's Democratic RepublicLatviaLebanonLesothoLiberiaLibyan Arab JamahiriyaLiechtensteinLithuaniaLuxembourgMacaoMadagascarMalawiMalaysiaMaldivesMaliMaltaMarshall IslandsMartiniqueMauritaniaMauritiusMayotteMexicoMicronesia, Federated States ofMoldova, Republic ofMonacoMongoliaMontenegroMontserratMoroccoMozambiqueMyanmarNamibiaNauruNepalNetherlandsNetherlands AntillesNew CaledoniaNew ZealandNicaraguaNigerNigeriaNiueNorfolk IslandNorth MacedoniaNorthern Mariana IslandsNorwayOmanPakistanPalauPalestinian Territory, OccupiedPanamaPapua New GuineaParaguayPeruPhilippinesPitcairnPolandPortugalPuerto RicoQatarRéunionRomaniaRussiaRwandaSaint BarthélemySaint HelenaSaint Kitts and NevisSaint MartinSaint LuciaSaint Pierre and MiquelonSaint Vincent and the GrenadinesSamoaSan MarinoSão Tomé and PríncipeSaudi ArabiaSenegalSerbiaSeychellesSierra LeoneSingaporeSlovakiaSloveniaSolomon IslandsSomaliaSouth AfricaSouth SudanSpainSri LankaSudanSurinameSvalbard and Jan MayenSwazilandSwedenSwitzerlandSyrian Arab RepublicTaiwanTajikistanTanzania, United Republic ofThailandTimor-LesteTogoTokelauTongaTrinidad and TobagoTunisiaTurkeyTurkmenistanTurks and Caicos IslandsTuvaluUgandaUkraineUnited Arab EmiratesUnited KingdomUnited States Minor Outlying IslandsUruguayUzbekistanVanuatuVatican CityVenezuela, Bolivarian Republic ofVietnamVirgin Islands, BritishVirgin Islands, USWallis and FutunaWestern SaharaYemenZambiaZimbabwe State —Please choose an option— How did you hear about Komprise?*Search EngineSocial MediaMarketing EventBlog or PublicationRecommended by Friend or ColleagueRecommended by Industry AnalystRecommended by a PartnerOther Other By submitting this form, I confirm that I have read and agree to the Privacy Statement. Δ Schedule a Komprise Analysis Demonstration ### The Unstructured Data Management Maturity Index The Unstructured Data Management Maturity Index As data management matures, unstructured (file and object) data evolves from being a storage cost center to sitting at the epicenter of value creation. To make use of unstructured data for competitive gain, it’s important to develop a strategy for managing data to meet the dual needs of cost efficiency and monetization. This 5-stage maturity model can help organizations looking to modernize unstructured data management practices. Read this unstructured data Management maturity ebook to get an overview of: The 5 stages of unstructured data management maturity. Characteristics of each stage to help identify where you are today. Key takeaways on your journey from storage-centric to data-centric unstructured data management. Download eBook  First Name* Last Name* Work Email* Company Name* Job Title* Phone* Country* --United StatesCanadaAfghanistanÅland IslandsAlbaniaAlgeriaAmerican SamoaAndorraAngolaAnguillaAntarcticaAntigua and BarbudaArgentinaArmeniaArubaAustraliaAustriaAzerbaijanBahamasBahrainBangladeshBarbadosBelarusBelgiumBelizeBeninBermudaBhutanBolivia, Plurinational State ofBosnia and HerzegovinaBotswanaBouvet IslandBrazilBritish Indian Ocean TerritoryBrunei DarussalamBulgariaBurkina FasoBurundiCambodiaCameroonCape VerdeCayman IslandsCentral African RepublicChadChileChinaChristmas IslandCocos (Keeling) IslandsColombiaComorosCongoCongo, The Democratic Republic of theCook IslandsCosta RicaCôte dIvoireCroatiaCubaCuraçaoCyprusCzechiaDenmarkDjiboutiDominicaDominican RepublicEcuadorEgyptEl SalvadorEquatorial GuineaEritreaEstoniaEthiopiaFalkland IslandsFaroe IslandsFijiFinlandFranceFrench GuianaFrench PolynesiaFrench Southern TerritoriesGabonGambiaGeorgiaGermanyGhanaGibraltarGreeceGreenlandGrenadaGuadeloupeGuamGuatemalaGuineaGuinea-BissauGuyanaHaitiHeard Island and McDonald IslandsHondurasHong KongHungaryIcelandIndiaIndonesiaIranIraqIrelandIsle of ManIsraelItalyJamaicaJapanJerseyJordanKazakhstanKenyaKiribatiKorea, Republic ofKuwaitKyrgyzstanLao People's Democratic RepublicLatviaLebanonLesothoLiberiaLibyan Arab JamahiriyaLiechtensteinLithuaniaLuxembourgMacaoMadagascarMalawiMalaysiaMaldivesMaliMaltaMarshall IslandsMartiniqueMauritaniaMauritiusMayotteMexicoMicronesia, Federated States ofMoldova, Republic ofMonacoMongoliaMontenegroMontserratMoroccoMozambiqueMyanmarNamibiaNauruNepalNetherlandsNetherlands AntillesNew CaledoniaNew ZealandNicaraguaNigerNigeriaNiueNorfolk IslandNorth MacedoniaNorthern Mariana IslandsNorwayOmanPakistanPalauPalestinian Territory, OccupiedPanamaPapua New GuineaParaguayPeruPhilippinesPitcairnPolandPortugalPuerto RicoQatarRéunionRomaniaRussiaRwandaSaint BarthélemySaint HelenaSaint Kitts and NevisSaint MartinSaint LuciaSaint Pierre and MiquelonSaint Vincent and the GrenadinesSamoaSan MarinoSão Tomé and PríncipeSaudi ArabiaSenegalSerbiaSeychellesSierra LeoneSingaporeSlovakiaSloveniaSolomon IslandsSomaliaSouth AfricaSouth SudanSpainSri LankaSudanSurinameSvalbard and Jan MayenSwazilandSwedenSwitzerlandSyrian Arab RepublicTaiwanTajikistanTanzania, United Republic ofThailandTimor-LesteTogoTokelauTongaTrinidad and TobagoTunisiaTurkeyTurkmenistanTurks and Caicos IslandsTuvaluUgandaUkraineUnited Arab EmiratesUnited KingdomUnited States Minor Outlying IslandsUruguayUzbekistanVanuatuVatican CityVenezuela, Bolivarian Republic ofVietnamVirgin Islands, BritishVirgin Islands, USWallis and FutunaWestern SaharaYemenZambiaZimbabwe State —Please choose an option— How did you hear about Komprise?*Search EngineSocial MediaMarketing EventBlog or PublicationRecommended by Friend or ColleagueRecommended by Industry AnalystRecommended by a PartnerOther Other By submitting this form, I confirm that I have read and agree to the Privacy Statement. Δ How is your unstructured data management maturity? Download this ebook to learn more. Gartner noted that our industry doesn't have a data storage problem, it has a data management problem. That's why we founded Komprise. We see the world moving from storage administration to strategic data management and analytics. From data locked away to data free for users to securely access when needed. Read the Maturity Index and build your plan to move forward. ### Smart Data Workflows Chalk Talk An overview of how Komprise delivers the right data to the right location and teams at the right time.  _______________________ Smart Data Workflows for Unstructured Data In this session Komprise CTO Mike Peercy digs into the Komprise Intelligent Data Management architecture and walks through how Komprise delivers the right data to the right location and teams at the right time with Smart Data Workflows. Mike is known for his whiteboard chalk talks. You don’t want to miss this session. Recorded in Santa Clara, CA on June 24, 2022 as part of Cloud Field Day 14. _______________________ ### Cloud Tiering Done Right with Komprise There's a rush to the cloud for file and object data, but Smart Data Migrations and Tiering to the Cloud require a data-centric approach. _______________________ Smart Data Migration and Tiering to the Cloud Everybody is talking about cloud tiering. But, did you know you may end up paying 75% more in cloud file storage and egress costs and 300% more in ongoing data storage costs by picking the wrong strategy? Smart Data Migrations and Tiering to the Cloud require a data-centric approach. In this session, Kumar K. Goswami, CEO & Co-Founder of Komprise, will discuss the rush to the cloud for file and object data and introduce the Komprise Intelligent Data Management Platform. The patented Komprise Transparent Move Technology™ goes beyond storage-based tiering to analyze, migrate, tier and replicate data across multi-vendor storage and clouds while enabling native use of the data at each layer. Recorded in Santa Clara, CA on June 24, 2022 as part of Cloud Field Day 14. Komprise at Tech Field Day. _______________________ ### Amazon FSx for NetApp ONTAP Migration with Komprise Migrating to the cloud with Amazon FSx for NetApp ONTAP and Komprise gets you out of the storage management business, so you can ensure your data is in the right place at the right time to lower costs and increase value from your data. _______________________ Amazon FSx for NetApp ONTAP File Migration with Komprise Migrating to the cloud with Amazon FSx for NetApp ONTAP and Komprise gets you out of the storage management business so you can ensure your data is in the right place at the right time to lower costs and increase value from your data. Learn more about Komprise for AWS data management Learn more about Komprise for NetApp data management Learn more about Komprise Elastic Data Migration Read Solution Brief Watch Webinar _______________________ ### Komprise Smart Data Workflows Smart Data Workflows allow you to define and execute automated processes to visualize, mobilize and get greater value from unstructured data. _______________________ Komprise Smart Data Workflows allow you to define and execute automated processes, which are often industry and domain specific, to visualize, mobilize and get greater value from unstructured data. With Smart Data Workflows for your massive volumes of unstructured data you can create custom queries across on-premises, edge and cloud data storage silos to find the data you need, execute Komprise or external functions on a subset of data and tag the data with additional metadata. ​Move only the data you need and manage the lifecycle of unstructured data intelligently. Learn More _______________________ ### Smartest Path to the Cloud for File Data in the Public Sector Smartest Path to the Cloud for File Data in the Public Sector Storage-Driven vs File-Driven - Which is Better & Why? Government agencies, healthcare organizations, and educational institutions are all under tremendous pressure to manage unprecedented data growth within nearly flat budgets and limited resources. A simple way to squeeze growth without breaking the bank is to manage hot and cold data differently. Offloading cold data to the cloud by using cloud tiering and cloud archiving solutions can cut 80% of storage and backup costs if done correctly. Read this paper to understand the three alternatives to tier and archive file data to the cloud: Built-in storage cloud tiering (aka “pool” solutions) Distributed global file storage systems File-level cloud tiering Not all cloud tiering and archiving solutions are the same. You may end up paying more in cloud egress and storage licensing costs by picking the wrong strategy. Download White Paper First Name* Last Name* Work Email* Company Name* Job Title* Phone* Country* --United StatesCanadaAfghanistanÅland IslandsAlbaniaAlgeriaAmerican SamoaAndorraAngolaAnguillaAntarcticaAntigua and BarbudaArgentinaArmeniaArubaAustraliaAustriaAzerbaijanBahamasBahrainBangladeshBarbadosBelarusBelgiumBelizeBeninBermudaBhutanBolivia, Plurinational State ofBosnia and HerzegovinaBotswanaBouvet IslandBrazilBritish Indian Ocean TerritoryBrunei DarussalamBulgariaBurkina FasoBurundiCambodiaCameroonCape VerdeCayman IslandsCentral African RepublicChadChileChinaChristmas IslandCocos (Keeling) IslandsColombiaComorosCongoCongo, The Democratic Republic of theCook IslandsCosta RicaCôte dIvoireCroatiaCubaCuraçaoCyprusCzechiaDenmarkDjiboutiDominicaDominican RepublicEcuadorEgyptEl SalvadorEquatorial GuineaEritreaEstoniaEthiopiaFalkland IslandsFaroe IslandsFijiFinlandFranceFrench GuianaFrench PolynesiaFrench Southern TerritoriesGabonGambiaGeorgiaGermanyGhanaGibraltarGreeceGreenlandGrenadaGuadeloupeGuamGuatemalaGuineaGuinea-BissauGuyanaHaitiHeard Island and McDonald IslandsHondurasHong KongHungaryIcelandIndiaIndonesiaIranIraqIrelandIsle of ManIsraelItalyJamaicaJapanJerseyJordanKazakhstanKenyaKiribatiKorea, Republic ofKuwaitKyrgyzstanLao People's Democratic RepublicLatviaLebanonLesothoLiberiaLibyan Arab JamahiriyaLiechtensteinLithuaniaLuxembourgMacaoMadagascarMalawiMalaysiaMaldivesMaliMaltaMarshall IslandsMartiniqueMauritaniaMauritiusMayotteMexicoMicronesia, Federated States ofMoldova, Republic ofMonacoMongoliaMontenegroMontserratMoroccoMozambiqueMyanmarNamibiaNauruNepalNetherlandsNetherlands AntillesNew CaledoniaNew ZealandNicaraguaNigerNigeriaNiueNorfolk IslandNorth MacedoniaNorthern Mariana IslandsNorwayOmanPakistanPalauPalestinian Territory, OccupiedPanamaPapua New GuineaParaguayPeruPhilippinesPitcairnPolandPortugalPuerto RicoQatarRéunionRomaniaRussiaRwandaSaint BarthélemySaint HelenaSaint Kitts and NevisSaint MartinSaint LuciaSaint Pierre and MiquelonSaint Vincent and the GrenadinesSamoaSan MarinoSão Tomé and PríncipeSaudi ArabiaSenegalSerbiaSeychellesSierra LeoneSingaporeSlovakiaSloveniaSolomon IslandsSomaliaSouth AfricaSouth SudanSpainSri LankaSudanSurinameSvalbard and Jan MayenSwazilandSwedenSwitzerlandSyrian Arab RepublicTaiwanTajikistanTanzania, United Republic ofThailandTimor-LesteTogoTokelauTongaTrinidad and TobagoTunisiaTurkeyTurkmenistanTurks and Caicos IslandsTuvaluUgandaUkraineUnited Arab EmiratesUnited KingdomUnited States Minor Outlying IslandsUruguayUzbekistanVanuatuVatican CityVenezuela, Bolivarian Republic ofVietnamVirgin Islands, BritishVirgin Islands, USWallis and FutunaWestern SaharaYemenZambiaZimbabwe State —Please choose an option— How did you hear about Komprise?*Search EngineSocial MediaMarketing EventBlog or PublicationRecommended by Friend or ColleagueRecommended by Industry AnalystRecommended by a PartnerOther Other By submitting this form, I confirm that I have read and agree to the Privacy Statement. Δ Komprise and AWS: Easy Path for File Data to the Cloud Moving the right file data to the right place in the cloud while saving costs is easy with Komprise and AWS. Know first, move smart, and take control of massive unstructured data growth while cutting enterprise storage, backup, and cloud costs. Komprise is an AWS Migration and Modernization competency Partner. Learn more at komprise.com/aws ### Smart Data Migration for Azure Smart File Data Migration for Azure File and objects data’s time for the cloud has come, but the wrong move can cost you millions. Migrating on-premises applications such as file workloads, high-performance computing (HPC) and analytics requires identifying and migrating tens of terabytes to several petabytes of file data stored on NAS appliances and other on- premises storage to the right tier of Azure Files, Azure NetApp Files and Azure Blob Storage. News: Komprise Hypertransfer Migrates Data to the Cloud 25x Faster This paper introduces the benefits of a Smart Data Migration strategy for file workloads to reliable, scalable, and secure cloud storage services on Azure. Together, Komprise and Azure enable your organization to:  Understand your NAS & object data usage and growth.  Estimate the ROI of Azure in your environment.  Migrate smarter to Azure File and Azure NetApp Files.  Easily integrate ongoing data lifecycle management.  Access moved data as files without stubs or agents.  Reduce complexity and scale on-demand.  Deliver native data access in the cloud without lock-in. Download White Paper First Name* Last Name* Work Email* Company Name* Job Title* Phone* Country* --United StatesCanadaAfghanistanÅland IslandsAlbaniaAlgeriaAmerican SamoaAndorraAngolaAnguillaAntarcticaAntigua and BarbudaArgentinaArmeniaArubaAustraliaAustriaAzerbaijanBahamasBahrainBangladeshBarbadosBelarusBelgiumBelizeBeninBermudaBhutanBolivia, Plurinational State ofBosnia and HerzegovinaBotswanaBouvet IslandBrazilBritish Indian Ocean TerritoryBrunei DarussalamBulgariaBurkina FasoBurundiCambodiaCameroonCape VerdeCayman IslandsCentral African RepublicChadChileChinaChristmas IslandCocos (Keeling) IslandsColombiaComorosCongoCongo, The Democratic Republic of theCook IslandsCosta RicaCôte dIvoireCroatiaCubaCuraçaoCyprusCzechiaDenmarkDjiboutiDominicaDominican RepublicEcuadorEgyptEl SalvadorEquatorial GuineaEritreaEstoniaEthiopiaFalkland IslandsFaroe IslandsFijiFinlandFranceFrench GuianaFrench PolynesiaFrench Southern TerritoriesGabonGambiaGeorgiaGermanyGhanaGibraltarGreeceGreenlandGrenadaGuadeloupeGuamGuatemalaGuineaGuinea-BissauGuyanaHaitiHeard Island and McDonald IslandsHondurasHong KongHungaryIcelandIndiaIndonesiaIranIraqIrelandIsle of ManIsraelItalyJamaicaJapanJerseyJordanKazakhstanKenyaKiribatiKorea, Republic ofKuwaitKyrgyzstanLao People's Democratic RepublicLatviaLebanonLesothoLiberiaLibyan Arab JamahiriyaLiechtensteinLithuaniaLuxembourgMacaoMadagascarMalawiMalaysiaMaldivesMaliMaltaMarshall IslandsMartiniqueMauritaniaMauritiusMayotteMexicoMicronesia, Federated States ofMoldova, Republic ofMonacoMongoliaMontenegroMontserratMoroccoMozambiqueMyanmarNamibiaNauruNepalNetherlandsNetherlands AntillesNew CaledoniaNew ZealandNicaraguaNigerNigeriaNiueNorfolk IslandNorth MacedoniaNorthern Mariana IslandsNorwayOmanPakistanPalauPalestinian Territory, OccupiedPanamaPapua New GuineaParaguayPeruPhilippinesPitcairnPolandPortugalPuerto RicoQatarRéunionRomaniaRussiaRwandaSaint BarthélemySaint HelenaSaint Kitts and NevisSaint MartinSaint LuciaSaint Pierre and MiquelonSaint Vincent and the GrenadinesSamoaSan MarinoSão Tomé and PríncipeSaudi ArabiaSenegalSerbiaSeychellesSierra LeoneSingaporeSlovakiaSloveniaSolomon IslandsSomaliaSouth AfricaSouth SudanSpainSri LankaSudanSurinameSvalbard and Jan MayenSwazilandSwedenSwitzerlandSyrian Arab RepublicTaiwanTajikistanTanzania, United Republic ofThailandTimor-LesteTogoTokelauTongaTrinidad and TobagoTunisiaTurkeyTurkmenistanTurks and Caicos IslandsTuvaluUgandaUkraineUnited Arab EmiratesUnited KingdomUnited States Minor Outlying IslandsUruguayUzbekistanVanuatuVatican CityVenezuela, Bolivarian Republic ofVietnamVirgin Islands, BritishVirgin Islands, USWallis and FutunaWestern SaharaYemenZambiaZimbabwe State —Please choose an option— How did you hear about Komprise?*Search EngineSocial MediaMarketing EventBlog or PublicationRecommended by Friend or ColleagueRecommended by Industry AnalystRecommended by a PartnerOther Other By submitting this form, I confirm that I have read and agree to the Privacy Statement. Δ Learn more about Azure File Migration “We are excited to work with Komprise on delivering a valuable service so that our customers can more easily and reliably move file data from expensive on-premises NAS devices to the cloud native storage services on Azure.” Azure File Migration Customer Feedback “Komprise made our data migration to the cloud as seamless as possible. While researching ways to move large amounts of data, we were confronted time and time again with limitations that made us believe the migration would never happen. Komprise was a sigh of relief for our entire team.” David Passamonte IT Manager Molecular Pathology Lab Network, Inc ### Smart File Data Migration for AWS Smart File Data Migration for AWS File and objects data’s time for the cloud has come, but the wrong move can cost you millions. Data-heavy enterprise IT organizations typically have petabytes of file data, which can consist of billions of files scattered across different storage vendors, architectures and locations. Cloud storage on AWS has more than 16 classes of file and object storage and is continually launching new options for enterprise customers—including a rich array of third-party solutions, including Amazon FSX for NetApp ONTAP. Migrate and Manage AWS File and Object Data This paper introduces the benefits of a Smart Data Migration strategy for file workloads to reliable, scalable, and secure cloud storage services on AWS. Together, Komprise and AWS enable your organization to: Understand your NAS & object data usage and growth. Estimate the ROI of AWS storage in your environment. Migrate smarter to Amazon FSx for NetApp ONTAP. Access moved data as files without stubs or agents. Reduce complexity and scale on-demand. Deliver native data access in the cloud without lock-in. Download White Paper First Name* Last Name* Work Email* Company Name* Job Title* Phone* Country* --United StatesCanadaAfghanistanÅland IslandsAlbaniaAlgeriaAmerican SamoaAndorraAngolaAnguillaAntarcticaAntigua and BarbudaArgentinaArmeniaArubaAustraliaAustriaAzerbaijanBahamasBahrainBangladeshBarbadosBelarusBelgiumBelizeBeninBermudaBhutanBolivia, Plurinational State ofBosnia and HerzegovinaBotswanaBouvet IslandBrazilBritish Indian Ocean TerritoryBrunei DarussalamBulgariaBurkina FasoBurundiCambodiaCameroonCape VerdeCayman IslandsCentral African RepublicChadChileChinaChristmas IslandCocos (Keeling) IslandsColombiaComorosCongoCongo, The Democratic Republic of theCook IslandsCosta RicaCôte dIvoireCroatiaCubaCuraçaoCyprusCzechiaDenmarkDjiboutiDominicaDominican RepublicEcuadorEgyptEl SalvadorEquatorial GuineaEritreaEstoniaEthiopiaFalkland IslandsFaroe IslandsFijiFinlandFranceFrench GuianaFrench PolynesiaFrench Southern TerritoriesGabonGambiaGeorgiaGermanyGhanaGibraltarGreeceGreenlandGrenadaGuadeloupeGuamGuatemalaGuineaGuinea-BissauGuyanaHaitiHeard Island and McDonald IslandsHondurasHong KongHungaryIcelandIndiaIndonesiaIranIraqIrelandIsle of ManIsraelItalyJamaicaJapanJerseyJordanKazakhstanKenyaKiribatiKorea, Republic ofKuwaitKyrgyzstanLao People's Democratic RepublicLatviaLebanonLesothoLiberiaLibyan Arab JamahiriyaLiechtensteinLithuaniaLuxembourgMacaoMadagascarMalawiMalaysiaMaldivesMaliMaltaMarshall IslandsMartiniqueMauritaniaMauritiusMayotteMexicoMicronesia, Federated States ofMoldova, Republic ofMonacoMongoliaMontenegroMontserratMoroccoMozambiqueMyanmarNamibiaNauruNepalNetherlandsNetherlands AntillesNew CaledoniaNew ZealandNicaraguaNigerNigeriaNiueNorfolk IslandNorth MacedoniaNorthern Mariana IslandsNorwayOmanPakistanPalauPalestinian Territory, OccupiedPanamaPapua New GuineaParaguayPeruPhilippinesPitcairnPolandPortugalPuerto RicoQatarRéunionRomaniaRussiaRwandaSaint BarthélemySaint HelenaSaint Kitts and NevisSaint MartinSaint LuciaSaint Pierre and MiquelonSaint Vincent and the GrenadinesSamoaSan MarinoSão Tomé and PríncipeSaudi ArabiaSenegalSerbiaSeychellesSierra LeoneSingaporeSlovakiaSloveniaSolomon IslandsSomaliaSouth AfricaSouth SudanSpainSri LankaSudanSurinameSvalbard and Jan MayenSwazilandSwedenSwitzerlandSyrian Arab RepublicTaiwanTajikistanTanzania, United Republic ofThailandTimor-LesteTogoTokelauTongaTrinidad and TobagoTunisiaTurkeyTurkmenistanTurks and Caicos IslandsTuvaluUgandaUkraineUnited Arab EmiratesUnited KingdomUnited States Minor Outlying IslandsUruguayUzbekistanVanuatuVatican CityVenezuela, Bolivarian Republic ofVietnamVirgin Islands, BritishVirgin Islands, USWallis and FutunaWestern SaharaYemenZambiaZimbabwe State —Please choose an option— How did you hear about Komprise?*Search EngineSocial MediaMarketing EventBlog or PublicationRecommended by Friend or ColleagueRecommended by Industry AnalystRecommended by a PartnerOther Other By submitting this form, I confirm that I have read and agree to the Privacy Statement. Δ Faster, Smarter AWS File Migration Download this white paper to learn more about Komprise for AWS “Komprise offers organizations a simple way to go beyond migrating data to gaining business value in the cloud.” Komprise is an AWS File Migration and Modernization competency Partner. Learn more at komprise.com/aws ### Transparent Move Technology (TMT) Transparent Move Technology (TMT) Leverage the Full Power of the Cloud without Disrupting Users with Komprise TMT Komprise Transparent Move Technology delivers transparent data tiering and allows you to use any of the compute capabilities of the cloud on your data. Using Komprise TMT, you can take control of rampant data growth with a strategic approach, allowing you to: Move data without stubs or agents. Minimize impact on users and applications of tiered data. Retain full data access from source or target. Avoid vendor lock-in to storage devices or to Komprise. Eliminate obstruction to hot data and enable faster recall of cold data. Get more value from your data by leveraging native cloud capabilities. Deliver efficient ransomware protection by using expensive protection on hot data only and storing cold data in immutable S3 storage. Dramatically reduce storage costs by reducing backup and DR footprint. Download White Paper First Name* Last Name* Work Email* Company Name* Job Title* Phone* Country* --United StatesCanadaAfghanistanÅland IslandsAlbaniaAlgeriaAmerican SamoaAndorraAngolaAnguillaAntarcticaAntigua and BarbudaArgentinaArmeniaArubaAustraliaAustriaAzerbaijanBahamasBahrainBangladeshBarbadosBelarusBelgiumBelizeBeninBermudaBhutanBolivia, Plurinational State ofBosnia and HerzegovinaBotswanaBouvet IslandBrazilBritish Indian Ocean TerritoryBrunei DarussalamBulgariaBurkina FasoBurundiCambodiaCameroonCape VerdeCayman IslandsCentral African RepublicChadChileChinaChristmas IslandCocos (Keeling) IslandsColombiaComorosCongoCongo, The Democratic Republic of theCook IslandsCosta RicaCôte dIvoireCroatiaCubaCuraçaoCyprusCzechiaDenmarkDjiboutiDominicaDominican RepublicEcuadorEgyptEl SalvadorEquatorial GuineaEritreaEstoniaEthiopiaFalkland IslandsFaroe IslandsFijiFinlandFranceFrench GuianaFrench PolynesiaFrench Southern TerritoriesGabonGambiaGeorgiaGermanyGhanaGibraltarGreeceGreenlandGrenadaGuadeloupeGuamGuatemalaGuineaGuinea-BissauGuyanaHaitiHeard Island and McDonald IslandsHondurasHong KongHungaryIcelandIndiaIndonesiaIranIraqIrelandIsle of ManIsraelItalyJamaicaJapanJerseyJordanKazakhstanKenyaKiribatiKorea, Republic ofKuwaitKyrgyzstanLao People's Democratic RepublicLatviaLebanonLesothoLiberiaLibyan Arab JamahiriyaLiechtensteinLithuaniaLuxembourgMacaoMadagascarMalawiMalaysiaMaldivesMaliMaltaMarshall IslandsMartiniqueMauritaniaMauritiusMayotteMexicoMicronesia, Federated States ofMoldova, Republic ofMonacoMongoliaMontenegroMontserratMoroccoMozambiqueMyanmarNamibiaNauruNepalNetherlandsNetherlands AntillesNew CaledoniaNew ZealandNicaraguaNigerNigeriaNiueNorfolk IslandNorth MacedoniaNorthern Mariana IslandsNorwayOmanPakistanPalauPalestinian Territory, OccupiedPanamaPapua New GuineaParaguayPeruPhilippinesPitcairnPolandPortugalPuerto RicoQatarRéunionRomaniaRussiaRwandaSaint BarthélemySaint HelenaSaint Kitts and NevisSaint MartinSaint LuciaSaint Pierre and MiquelonSaint Vincent and the GrenadinesSamoaSan MarinoSão Tomé and PríncipeSaudi ArabiaSenegalSerbiaSeychellesSierra LeoneSingaporeSlovakiaSloveniaSolomon IslandsSomaliaSouth AfricaSouth SudanSpainSri LankaSudanSurinameSvalbard and Jan MayenSwazilandSwedenSwitzerlandSyrian Arab RepublicTaiwanTajikistanTanzania, United Republic ofThailandTimor-LesteTogoTokelauTongaTrinidad and TobagoTunisiaTurkeyTurkmenistanTurks and Caicos IslandsTuvaluUgandaUkraineUnited Arab EmiratesUnited KingdomUnited States Minor Outlying IslandsUruguayUzbekistanVanuatuVatican CityVenezuela, Bolivarian Republic ofVietnamVirgin Islands, BritishVirgin Islands, USWallis and FutunaWestern SaharaYemenZambiaZimbabwe State —Please choose an option— How did you hear about Komprise?*Search EngineSocial MediaMarketing EventBlog or PublicationRecommended by Friend or ColleagueRecommended by Industry AnalystRecommended by a PartnerOther Other By submitting this form, I confirm that I have read and agree to the Privacy Statement. Δ The patented Komprise Transparent Move Technology (TMT)™ goes beyond storage tiering to analyze, migrate, tier and replicate data across multi-vendor storage and clouds while enabling native use of the data at each layer. This is possible without disrupting users and without vendor lock-in. Download this unstructured data management white paper to learn more. ### Data Management Must Replace Storage Management Data Management Must Replace Storage Management Moving from Storage Management to Data Management Unstructured data growth and storage proliferation are urgent problems that IT organizations can no longer ignore. Data outlives storage—so why manage data through a storage silo? IT organizations need data-centric management that is a separate layer working across storage and cloud to analyze and move data without creating lock-in. The right approach to unstructured data management provides the visibility required to gain a holistic understanding of the storage landscape. This means that IT can make storage decisions that maximize the value of data, optimize storage costs, and increase agility. Storage Management to Data Management Highlights The limits of storage management for data value Why it’s time to manage data, not storage Data management software benefits and capabilities Key criteria for evaluating data management software Download White Paper First Name* Last Name* Work Email* Company Name* Job Title* Phone* Country* --United StatesCanadaAfghanistanÅland IslandsAlbaniaAlgeriaAmerican SamoaAndorraAngolaAnguillaAntarcticaAntigua and BarbudaArgentinaArmeniaArubaAustraliaAustriaAzerbaijanBahamasBahrainBangladeshBarbadosBelarusBelgiumBelizeBeninBermudaBhutanBolivia, Plurinational State ofBosnia and HerzegovinaBotswanaBouvet IslandBrazilBritish Indian Ocean TerritoryBrunei DarussalamBulgariaBurkina FasoBurundiCambodiaCameroonCape VerdeCayman IslandsCentral African RepublicChadChileChinaChristmas IslandCocos (Keeling) IslandsColombiaComorosCongoCongo, The Democratic Republic of theCook IslandsCosta RicaCôte dIvoireCroatiaCubaCuraçaoCyprusCzechiaDenmarkDjiboutiDominicaDominican RepublicEcuadorEgyptEl SalvadorEquatorial GuineaEritreaEstoniaEthiopiaFalkland IslandsFaroe IslandsFijiFinlandFranceFrench GuianaFrench PolynesiaFrench Southern TerritoriesGabonGambiaGeorgiaGermanyGhanaGibraltarGreeceGreenlandGrenadaGuadeloupeGuamGuatemalaGuineaGuinea-BissauGuyanaHaitiHeard Island and McDonald IslandsHondurasHong KongHungaryIcelandIndiaIndonesiaIranIraqIrelandIsle of ManIsraelItalyJamaicaJapanJerseyJordanKazakhstanKenyaKiribatiKorea, Republic ofKuwaitKyrgyzstanLao People's Democratic RepublicLatviaLebanonLesothoLiberiaLibyan Arab JamahiriyaLiechtensteinLithuaniaLuxembourgMacaoMadagascarMalawiMalaysiaMaldivesMaliMaltaMarshall IslandsMartiniqueMauritaniaMauritiusMayotteMexicoMicronesia, Federated States ofMoldova, Republic ofMonacoMongoliaMontenegroMontserratMoroccoMozambiqueMyanmarNamibiaNauruNepalNetherlandsNetherlands AntillesNew CaledoniaNew ZealandNicaraguaNigerNigeriaNiueNorfolk IslandNorth MacedoniaNorthern Mariana IslandsNorwayOmanPakistanPalauPalestinian Territory, OccupiedPanamaPapua New GuineaParaguayPeruPhilippinesPitcairnPolandPortugalPuerto RicoQatarRéunionRomaniaRussiaRwandaSaint BarthélemySaint HelenaSaint Kitts and NevisSaint MartinSaint LuciaSaint Pierre and MiquelonSaint Vincent and the GrenadinesSamoaSan MarinoSão Tomé and PríncipeSaudi ArabiaSenegalSerbiaSeychellesSierra LeoneSingaporeSlovakiaSloveniaSolomon IslandsSomaliaSouth AfricaSouth SudanSpainSri LankaSudanSurinameSvalbard and Jan MayenSwazilandSwedenSwitzerlandSyrian Arab RepublicTaiwanTajikistanTanzania, United Republic ofThailandTimor-LesteTogoTokelauTongaTrinidad and TobagoTunisiaTurkeyTurkmenistanTurks and Caicos IslandsTuvaluUgandaUkraineUnited Arab EmiratesUnited KingdomUnited States Minor Outlying IslandsUruguayUzbekistanVanuatuVatican CityVenezuela, Bolivarian Republic ofVietnamVirgin Islands, BritishVirgin Islands, USWallis and FutunaWestern SaharaYemenZambiaZimbabwe State —Please choose an option— How did you hear about Komprise?*Search EngineSocial MediaMarketing EventBlog or PublicationRecommended by Friend or ColleagueRecommended by Industry AnalystRecommended by a PartnerOther Other By submitting this form, I confirm that I have read and agree to the Privacy Statement. Δ About DCIG The Data Center Intelligence Group (DCIG) empowers the IT industry with actionable analysis. DCIG analysts provide informed third-party analysis of various cloud, data protection, and data storage technologies. DCIG independently develops licensed content in the form of TOP 5 Reports and Solution Profiles. More information is available at www.dcig.com. ### Komprise Transparent Move Technology Chalk Talk Transparent Move Technology: Komprise CTO Chalk Talk In this Storage Field Day whiteboard session with Komprise CTO and co-founder Mike Peercy, he reviews the power of Komprise Transparent Move Technology (TMT) and how Komprise transparently extends your NAS to any storage while: 1. Keeping native access in the cloud 2. Being open (non-proprietary) 3. Not using agents or stubs 4. Staying outside of the hot data path Read the blog post: Why Data Management Must Be Independent from Storage ### Überblick über das intelligente Datenmanagement von Komprise Komprise hilft Kunden, über 70 % der Kosten zu senken und gleichzeitig das Datenwachstum zu verwalten. Einer Datenverwaltungsplattform für alle Ihre NAS- und Cloud-Daten Komprise ist der branchenweit einzige Multi-Cloud-Datenmanagement-as-a-Service, der es Ihnen ermöglicht, die richtigen Datei- und Objektdaten über Clouds hinweg zu analysieren, zu mobilisieren und darauf zuzugreifen, ohne Ihre Daten an einen Anbieter zu binden. Komprise hilft Ihnen, 70 % der Speicherkosten zu senken, indem Sie Daten richtig dimensionieren und platzieren, während es den Benutzern leicht gemacht wird, den Datenwert zu erschließen. Datenblatt lesen ### How to Manage Medical Imaging Data Growth and Costs How to Manage Medical Imaging Data Growth and Costs Medical images contain untold value for healthcare organizations, yet they’re exceeding the limits of on-premises storage. It’s time to take control with a new data management strategy. Digital PACS, digital pathology and VNA systems are all generating and now storing petabytes of medical imaging data—lab slides, X-rays, MRIs, CT scans and more. These ever-expanding datasets are pushing the limitations of storage systems and challenging IT department’s ability to effectively manage data. To get more flexibility and cost savings from storage, healthcare organizations are adopting unstructured data management software to tier cold medical imaging data out of expensive storage to cost-effective environments such as the cloud. This white paper examines the benefits of augmenting your medical imaging solution with data management software that transparently tiers cold data from your storage and backups, explains what you should look for in a solution, and includes a case study of a healthcare provider that did this successfully. Download White Paper First Name* Last Name* Work Email* Company Name* Job Title* Phone* Country* --United StatesCanadaAfghanistanÅland IslandsAlbaniaAlgeriaAmerican SamoaAndorraAngolaAnguillaAntarcticaAntigua and BarbudaArgentinaArmeniaArubaAustraliaAustriaAzerbaijanBahamasBahrainBangladeshBarbadosBelarusBelgiumBelizeBeninBermudaBhutanBolivia, Plurinational State ofBosnia and HerzegovinaBotswanaBouvet IslandBrazilBritish Indian Ocean TerritoryBrunei DarussalamBulgariaBurkina FasoBurundiCambodiaCameroonCape VerdeCayman IslandsCentral African RepublicChadChileChinaChristmas IslandCocos (Keeling) IslandsColombiaComorosCongoCongo, The Democratic Republic of theCook IslandsCosta RicaCôte dIvoireCroatiaCubaCuraçaoCyprusCzechiaDenmarkDjiboutiDominicaDominican RepublicEcuadorEgyptEl SalvadorEquatorial GuineaEritreaEstoniaEthiopiaFalkland IslandsFaroe IslandsFijiFinlandFranceFrench GuianaFrench PolynesiaFrench Southern TerritoriesGabonGambiaGeorgiaGermanyGhanaGibraltarGreeceGreenlandGrenadaGuadeloupeGuamGuatemalaGuineaGuinea-BissauGuyanaHaitiHeard Island and McDonald IslandsHondurasHong KongHungaryIcelandIndiaIndonesiaIranIraqIrelandIsle of ManIsraelItalyJamaicaJapanJerseyJordanKazakhstanKenyaKiribatiKorea, Republic ofKuwaitKyrgyzstanLao People's Democratic RepublicLatviaLebanonLesothoLiberiaLibyan Arab JamahiriyaLiechtensteinLithuaniaLuxembourgMacaoMadagascarMalawiMalaysiaMaldivesMaliMaltaMarshall IslandsMartiniqueMauritaniaMauritiusMayotteMexicoMicronesia, Federated States ofMoldova, Republic ofMonacoMongoliaMontenegroMontserratMoroccoMozambiqueMyanmarNamibiaNauruNepalNetherlandsNetherlands AntillesNew CaledoniaNew ZealandNicaraguaNigerNigeriaNiueNorfolk IslandNorth MacedoniaNorthern Mariana IslandsNorwayOmanPakistanPalauPalestinian Territory, OccupiedPanamaPapua New GuineaParaguayPeruPhilippinesPitcairnPolandPortugalPuerto RicoQatarRéunionRomaniaRussiaRwandaSaint BarthélemySaint HelenaSaint Kitts and NevisSaint MartinSaint LuciaSaint Pierre and MiquelonSaint Vincent and the GrenadinesSamoaSan MarinoSão Tomé and PríncipeSaudi ArabiaSenegalSerbiaSeychellesSierra LeoneSingaporeSlovakiaSloveniaSolomon IslandsSomaliaSouth AfricaSouth SudanSpainSri LankaSudanSurinameSvalbard and Jan MayenSwazilandSwedenSwitzerlandSyrian Arab RepublicTaiwanTajikistanTanzania, United Republic ofThailandTimor-LesteTogoTokelauTongaTrinidad and TobagoTunisiaTurkeyTurkmenistanTurks and Caicos IslandsTuvaluUgandaUkraineUnited Arab EmiratesUnited KingdomUnited States Minor Outlying IslandsUruguayUzbekistanVanuatuVatican CityVenezuela, Bolivarian Republic ofVietnamVirgin Islands, BritishVirgin Islands, USWallis and FutunaWestern SaharaYemenZambiaZimbabwe State —Please choose an option— How did you hear about Komprise?*Search EngineSocial MediaMarketing EventBlog or PublicationRecommended by Friend or ColleagueRecommended by Industry AnalystRecommended by a PartnerOther Other By submitting this form, I confirm that I have read and agree to the Privacy Statement. Δ It's Time Medical Imaging Data Management "Internal storage for large image files is expensive—costing millions a year for some organizations on Porsche-grade NAS devices. The data must be secured, replicated and backed up. Meanwhile, in most cases, imaging data is rarely accessed after a few days." Read the blog post: Unstructured Data in Healthcare ### 5 Ways to Use Analytics for Cloud File Data Migrations 5 Ways to Use Analytics for Cloud File Data Migrations Why Cold Data Management is an Easy Path to the Cloud As unstructured data continues to grow exponentially, organizations struggle to control costs for file data storage. Many are turning to the cloud to scale and manage spend. This Komprise and AWS eBook examines the 5 ways to use analytics to drive your cloud file and object data migration and data management strategy: Understand your data patterns Plan using a cost model Use data to drive stakeholder buy-in Eliminate user disruption Create a systematic plan for on-going data management How Pfizer used Komprise to accelerate their cloud data migration. Download eBook Thanks for reaching out. We'll be in touch "Komprise helps us make razor sharp business decisions based on data so we can reinvest in areas that are more important to patients." – Director Hosting Data Services, Pfizer ### Status der unstrukturierten Datenverwaltungsbericht Der Stand der unstrukturierten Datenverwaltung Komprise-Umfrage stellt fest, dass IT-Führungskräfte keine Einblicke in das unstrukturierte Datenmanagement in der Hybrid Cloud haben Das anhaltende Datenwachstum belastet die IT-Budgets und veranlasst immer mehr Unternehmen, Cloud-Datenmigrationen Priorität einzuräumen – aber Datentransparenz, -planung und -verwaltung über Hybrid-Clouds hinweg bleibt ein wichtiges Hindernis. Der Komprise-Managementbericht für unstrukturierte Daten 2021 untersucht die Herausforderungen und Chancen mit unstrukturierten Daten im Unternehmen – von der Datenmenge, die Unternehmen verwalten, über Cloud-Datenprioritäten bis hin zu zukünftigen Ansätzen für das Datenmanagement. Dieser Bericht fasst die Antworten von 300 globalen IT-Entscheidungsträgern im Bereich Enterprise Storage in Unternehmen mit mehr als 1.000 Mitarbeitern in den USA und Großbritannien zusammen. Alle Befragten arbeiten auf IT-Manager-Ebene oder höher in allen IT-/Technologie-Betriebsteams. Zu den Highlights der Umfrage gehören: 65,5% der Unternehmen geben +30% des IT-Budgets für Datenspeicherung und -verwaltung aus. 44,5% wünschen sich eine bessere Sichtbarkeit für die Planung. Investitionen in Analysetools haben höchste Priorität (45 %) gegenüber dem Kauf von mehr Cloud- oder On-Premise-Speicher oder der Modernisierung von Backups. Bericht herunterladen Thanks for reaching out. We'll be in touch “Die Umfrage zeigt, dass Unternehmen Analysen und systematisches Datenmanagement wünschen, um die besten Entscheidungen bei Cloud-Migrationen und Archivierung zu treffen. Das Endziel besteht darin, die Speicherkosten zu senken und im Laufe der Zeit aus unstrukturierten Daten einen neuen Wert zu schaffen.” -Krishna Subramanian, President and COO of Komprise ### Fast, Reliable, Intelligent Data Migrations from Any NAS to Qumulo Qumulo helps organizations easily store and manage file data with unrivaled freedom, control and real-time visibility. With analytics-driven Komprise Elastic Data Migration, you can accelerate data migrations from any NAS or file server into Qumulo’s file data platform. ### Migrating NFS & SMB Data with Komprise Whether it's SMB or NFS or object storage protocol, Komprise Elastic Migration, now with Hypertransfer for faster WAN transfers, is the easy, fast, no lock-in path to the cloud for file and object data. Get started with Komprise Intelligent Data Management Learn more about Komprise Elastic Data Migration Check out all of our TechKrunch sessions ### Komprise Intelligent Data Management: Global Data Management with Multisite Controls Komprise Intelligent Data Management now supports using a single administrative console to manage multiple sites, across file storage, object storage, and cloud storage. _______________________ Multisite Controls with Komprise Intelligent Data Management Komprise Intelligent Data Management now supports using a single administrative console to manage multiple sites, across file storage, object storage, and cloud storage. This is the same Komprise solution that you’re already using to analyze, move, and unlock your data’s value without proprietary lock-in – now scaling to provide global visibility with localized control across your enterprise. This demo shows how Komprise enables intelligent data management across multiple sites – and focuses on how you can create localized policies and control for each site while getting global visibility and global search across sites. Learn More About What's New Watch the Komprise 4.0 Webinar _______________________ ### Komprise Elastic Data Migration Faster than ever and just as reliable. Data migrations are often dreaded because they are costly, time-consuming and error-prone. Komprise Elastic Data Migration eliminates the costs and complexity of NAS and cloud migrations. Komprise runs smart file data migrations 27 times faster with an elastic architecture and reliably migrates petabytes of file and object data. Elastic Data Migration is included in the Komprise Intelligent Data Management platform or available standalone. Read Solution Brief ### Nutanix and Komprise: Hybrid File Data Management Komprise enables enterprises to cut costs and take control of the massive growth in file data by finding the right data to migrate to Nutanix, managing data migrations, and transparently archiving or tiering cold data, while enabling global search across clouds. Komprise provides global data management that works across file and object storage and clouds to analyze, move and unlock data value without proprietary lock-in. Read Solution Brief Learn more about Komprise Intelligent Data Management for Nutanix. ### Cloud Data Migration and Data Tiering: Know Your Choices Unstructured data is everywhere. From genomics and medical imaging to streaming video, electric cars, and IoT products, all sectors generate unstructured file data. While file data growth is exploding, IT budgets are not. That’s why enterprises need to migrate file workloads to the cloud. The Fast, No Lock-in Path to the Cloud Why migrate file data to the cloud? What are your unstructured data migration options? What choices are there for cloud file data migrations? What choices are there for cloud date tiering? Learn more about Komprise Cloud Data Migration and Cloud Data Management ### Komprise: A Faster Path to the Cloud for Unstructured Data The opposite of structured data, unstructured data is that which doesn't fit neatly into a database; it includes e-mail messages, social media posts, photos, videos, etc. According to IDC, 70% of data in most organsations is unstructured and, most importantly, cold (infrequently accessed), with this number increasing year on year. ### Getting Departments To Care About Storage Savings Getting Departments To Care About Storage Savings How Showback and Simplicity Get the Archiving Buy-in You Need Many companies are choosing a Storage-as-a-Service (STaaS) approach to centralize IT’s efforts for each department. But convincing department heads to care about storage savings is a tough task without the right tools. Unstructured data management, cloud tiering and archiving are viewed by users as an extraneous hassle and potential disruption that fails to answer “What’s in it for me?” This white paper explains how to make STaaS successful by telling a compelling data story department heads can’t ignore. This coupled with transparent data archiving techniques that do not change the user experience are critical to successful systematic archiving and significant savings. Download White Paper First Name* Last Name* Work Email* Company Name* Job Title* Phone* Country* --United StatesCanadaAfghanistanÅland IslandsAlbaniaAlgeriaAmerican SamoaAndorraAngolaAnguillaAntarcticaAntigua and BarbudaArgentinaArmeniaArubaAustraliaAustriaAzerbaijanBahamasBahrainBangladeshBarbadosBelarusBelgiumBelizeBeninBermudaBhutanBolivia, Plurinational State ofBosnia and HerzegovinaBotswanaBouvet IslandBrazilBritish Indian Ocean TerritoryBrunei DarussalamBulgariaBurkina FasoBurundiCambodiaCameroonCape VerdeCayman IslandsCentral African RepublicChadChileChinaChristmas IslandCocos (Keeling) IslandsColombiaComorosCongoCongo, The Democratic Republic of theCook IslandsCosta RicaCôte dIvoireCroatiaCubaCuraçaoCyprusCzechiaDenmarkDjiboutiDominicaDominican RepublicEcuadorEgyptEl SalvadorEquatorial GuineaEritreaEstoniaEthiopiaFalkland IslandsFaroe IslandsFijiFinlandFranceFrench GuianaFrench PolynesiaFrench Southern TerritoriesGabonGambiaGeorgiaGermanyGhanaGibraltarGreeceGreenlandGrenadaGuadeloupeGuamGuatemalaGuineaGuinea-BissauGuyanaHaitiHeard Island and McDonald IslandsHondurasHong KongHungaryIcelandIndiaIndonesiaIranIraqIrelandIsle of ManIsraelItalyJamaicaJapanJerseyJordanKazakhstanKenyaKiribatiKorea, Republic ofKuwaitKyrgyzstanLao People's Democratic RepublicLatviaLebanonLesothoLiberiaLibyan Arab JamahiriyaLiechtensteinLithuaniaLuxembourgMacaoMadagascarMalawiMalaysiaMaldivesMaliMaltaMarshall IslandsMartiniqueMauritaniaMauritiusMayotteMexicoMicronesia, Federated States ofMoldova, Republic ofMonacoMongoliaMontenegroMontserratMoroccoMozambiqueMyanmarNamibiaNauruNepalNetherlandsNetherlands AntillesNew CaledoniaNew ZealandNicaraguaNigerNigeriaNiueNorfolk IslandNorth MacedoniaNorthern Mariana IslandsNorwayOmanPakistanPalauPalestinian Territory, OccupiedPanamaPapua New GuineaParaguayPeruPhilippinesPitcairnPolandPortugalPuerto RicoQatarRéunionRomaniaRussiaRwandaSaint BarthélemySaint HelenaSaint Kitts and NevisSaint MartinSaint LuciaSaint Pierre and MiquelonSaint Vincent and the GrenadinesSamoaSan MarinoSão Tomé and PríncipeSaudi ArabiaSenegalSerbiaSeychellesSierra LeoneSingaporeSlovakiaSloveniaSolomon IslandsSomaliaSouth AfricaSouth SudanSpainSri LankaSudanSurinameSvalbard and Jan MayenSwazilandSwedenSwitzerlandSyrian Arab RepublicTaiwanTajikistanTanzania, United Republic ofThailandTimor-LesteTogoTokelauTongaTrinidad and TobagoTunisiaTurkeyTurkmenistanTurks and Caicos IslandsTuvaluUgandaUkraineUnited Arab EmiratesUnited KingdomUnited States Minor Outlying IslandsUruguayUzbekistanVanuatuVatican CityVenezuela, Bolivarian Republic ofVietnamVirgin Islands, BritishVirgin Islands, USWallis and FutunaWestern SaharaYemenZambiaZimbabwe State —Please choose an option— How did you hear about Komprise?*Search EngineSocial MediaMarketing EventBlog or PublicationRecommended by Friend or ColleagueRecommended by Industry AnalystRecommended by a PartnerOther Other By submitting this form, I confirm that I have read and agree to the Privacy Statement. Δ Download this Intelligent Data Management paper to learn more. Learn how using analytics-driven showback can help secure the buy-in needed to archive more data more often. Once they understand their data—how much is cold and how much they could be saving—the conversation quickly changes. ### IDC Infobrief: How To Manage Your Data Growth Smarter With Data Literacy IDC Infobrief: How To Manage Your Data Growth Smarter With Data Literacy IDC explains why developing data literacy is imperative to better manage the challenges that unprecedented data growth has created. Learn the importance of a more proactive approach to unstructured data management—understanding your data types and access patterns, and strategically placing data in the right data storage infrastructure to save significant data storage costs and derive more value from your unstructured data. Download White Paper First Name* Last Name* Work Email* Company Name* Job Title* Phone* Country* --United StatesCanadaAfghanistanÅland IslandsAlbaniaAlgeriaAmerican SamoaAndorraAngolaAnguillaAntarcticaAntigua and BarbudaArgentinaArmeniaArubaAustraliaAustriaAzerbaijanBahamasBahrainBangladeshBarbadosBelarusBelgiumBelizeBeninBermudaBhutanBolivia, Plurinational State ofBosnia and HerzegovinaBotswanaBouvet IslandBrazilBritish Indian Ocean TerritoryBrunei DarussalamBulgariaBurkina FasoBurundiCambodiaCameroonCape VerdeCayman IslandsCentral African RepublicChadChileChinaChristmas IslandCocos (Keeling) IslandsColombiaComorosCongoCongo, The Democratic Republic of theCook IslandsCosta RicaCôte dIvoireCroatiaCubaCuraçaoCyprusCzechiaDenmarkDjiboutiDominicaDominican RepublicEcuadorEgyptEl SalvadorEquatorial GuineaEritreaEstoniaEthiopiaFalkland IslandsFaroe IslandsFijiFinlandFranceFrench GuianaFrench PolynesiaFrench Southern TerritoriesGabonGambiaGeorgiaGermanyGhanaGibraltarGreeceGreenlandGrenadaGuadeloupeGuamGuatemalaGuineaGuinea-BissauGuyanaHaitiHeard Island and McDonald IslandsHondurasHong KongHungaryIcelandIndiaIndonesiaIranIraqIrelandIsle of ManIsraelItalyJamaicaJapanJerseyJordanKazakhstanKenyaKiribatiKorea, Republic ofKuwaitKyrgyzstanLao People's Democratic RepublicLatviaLebanonLesothoLiberiaLibyan Arab JamahiriyaLiechtensteinLithuaniaLuxembourgMacaoMadagascarMalawiMalaysiaMaldivesMaliMaltaMarshall IslandsMartiniqueMauritaniaMauritiusMayotteMexicoMicronesia, Federated States ofMoldova, Republic ofMonacoMongoliaMontenegroMontserratMoroccoMozambiqueMyanmarNamibiaNauruNepalNetherlandsNetherlands AntillesNew CaledoniaNew ZealandNicaraguaNigerNigeriaNiueNorfolk IslandNorth MacedoniaNorthern Mariana IslandsNorwayOmanPakistanPalauPalestinian Territory, OccupiedPanamaPapua New GuineaParaguayPeruPhilippinesPitcairnPolandPortugalPuerto RicoQatarRéunionRomaniaRussiaRwandaSaint BarthélemySaint HelenaSaint Kitts and NevisSaint MartinSaint LuciaSaint Pierre and MiquelonSaint Vincent and the GrenadinesSamoaSan MarinoSão Tomé and PríncipeSaudi ArabiaSenegalSerbiaSeychellesSierra LeoneSingaporeSlovakiaSloveniaSolomon IslandsSomaliaSouth AfricaSouth SudanSpainSri LankaSudanSurinameSvalbard and Jan MayenSwazilandSwedenSwitzerlandSyrian Arab RepublicTaiwanTajikistanTanzania, United Republic ofThailandTimor-LesteTogoTokelauTongaTrinidad and TobagoTunisiaTurkeyTurkmenistanTurks and Caicos IslandsTuvaluUgandaUkraineUnited Arab EmiratesUnited KingdomUnited States Minor Outlying IslandsUruguayUzbekistanVanuatuVatican CityVenezuela, Bolivarian Republic ofVietnamVirgin Islands, BritishVirgin Islands, USWallis and FutunaWestern SaharaYemenZambiaZimbabwe State —Please choose an option— How did you hear about Komprise?*Search EngineSocial MediaMarketing EventBlog or PublicationRecommended by Friend or ColleagueRecommended by Industry AnalystRecommended by a PartnerOther Other By submitting this form, I confirm that I have read and agree to the Privacy Statement. Δ ### Why Data Growth is Not a Storage Problem Why Data Growth is Not a Data Storage Problem Data growth is skyrocketing. Storage capacity is running out, backups are taking longer, and budgets can’t keep up with the unstructured data deluge. The answer isn’t so much a storage issue as it is how the data in your storage is managed. Because treating all your data the same will only see your data storage costs grow. There are three key areas that set Komprise apart, offering a unique unstructured data management solution that puts data control where it belongs—with data owners. Download White Paper First Name* Last Name* Work Email* Company Name* Job Title* Phone* Country* --United StatesCanadaAfghanistanÅland IslandsAlbaniaAlgeriaAmerican SamoaAndorraAngolaAnguillaAntarcticaAntigua and BarbudaArgentinaArmeniaArubaAustraliaAustriaAzerbaijanBahamasBahrainBangladeshBarbadosBelarusBelgiumBelizeBeninBermudaBhutanBolivia, Plurinational State ofBosnia and HerzegovinaBotswanaBouvet IslandBrazilBritish Indian Ocean TerritoryBrunei DarussalamBulgariaBurkina FasoBurundiCambodiaCameroonCape VerdeCayman IslandsCentral African RepublicChadChileChinaChristmas IslandCocos (Keeling) IslandsColombiaComorosCongoCongo, The Democratic Republic of theCook IslandsCosta RicaCôte dIvoireCroatiaCubaCuraçaoCyprusCzechiaDenmarkDjiboutiDominicaDominican RepublicEcuadorEgyptEl SalvadorEquatorial GuineaEritreaEstoniaEthiopiaFalkland IslandsFaroe IslandsFijiFinlandFranceFrench GuianaFrench PolynesiaFrench Southern TerritoriesGabonGambiaGeorgiaGermanyGhanaGibraltarGreeceGreenlandGrenadaGuadeloupeGuamGuatemalaGuineaGuinea-BissauGuyanaHaitiHeard Island and McDonald IslandsHondurasHong KongHungaryIcelandIndiaIndonesiaIranIraqIrelandIsle of ManIsraelItalyJamaicaJapanJerseyJordanKazakhstanKenyaKiribatiKorea, Republic ofKuwaitKyrgyzstanLao People's Democratic RepublicLatviaLebanonLesothoLiberiaLibyan Arab JamahiriyaLiechtensteinLithuaniaLuxembourgMacaoMadagascarMalawiMalaysiaMaldivesMaliMaltaMarshall IslandsMartiniqueMauritaniaMauritiusMayotteMexicoMicronesia, Federated States ofMoldova, Republic ofMonacoMongoliaMontenegroMontserratMoroccoMozambiqueMyanmarNamibiaNauruNepalNetherlandsNetherlands AntillesNew CaledoniaNew ZealandNicaraguaNigerNigeriaNiueNorfolk IslandNorth MacedoniaNorthern Mariana IslandsNorwayOmanPakistanPalauPalestinian Territory, OccupiedPanamaPapua New GuineaParaguayPeruPhilippinesPitcairnPolandPortugalPuerto RicoQatarRéunionRomaniaRussiaRwandaSaint BarthélemySaint HelenaSaint Kitts and NevisSaint MartinSaint LuciaSaint Pierre and MiquelonSaint Vincent and the GrenadinesSamoaSan MarinoSão Tomé and PríncipeSaudi ArabiaSenegalSerbiaSeychellesSierra LeoneSingaporeSlovakiaSloveniaSolomon IslandsSomaliaSouth AfricaSouth SudanSpainSri LankaSudanSurinameSvalbard and Jan MayenSwazilandSwedenSwitzerlandSyrian Arab RepublicTaiwanTajikistanTanzania, United Republic ofThailandTimor-LesteTogoTokelauTongaTrinidad and TobagoTunisiaTurkeyTurkmenistanTurks and Caicos IslandsTuvaluUgandaUkraineUnited Arab EmiratesUnited KingdomUnited States Minor Outlying IslandsUruguayUzbekistanVanuatuVatican CityVenezuela, Bolivarian Republic ofVietnamVirgin Islands, BritishVirgin Islands, USWallis and FutunaWestern SaharaYemenZambiaZimbabwe State —Please choose an option— How did you hear about Komprise?*Search EngineSocial MediaMarketing EventBlog or PublicationRecommended by Friend or ColleagueRecommended by Industry AnalystRecommended by a PartnerOther Other By submitting this form, I confirm that I have read and agree to the Privacy Statement. Δ ### File Data Migration Isn’t File Archiving File Migration Isn’t File Archiving What’s the difference between file and object data migration and data archiving and what matters? While there are similarities, the differences between file data migration and file archiving have a big impact on your organization. Make sure your solutions have the right capabilities to save you the most costs and headaches. Download White Paper First Name* Last Name* Work Email* Company Name* Job Title* Phone* Country* --United StatesCanadaAfghanistanÅland IslandsAlbaniaAlgeriaAmerican SamoaAndorraAngolaAnguillaAntarcticaAntigua and BarbudaArgentinaArmeniaArubaAustraliaAustriaAzerbaijanBahamasBahrainBangladeshBarbadosBelarusBelgiumBelizeBeninBermudaBhutanBolivia, Plurinational State ofBosnia and HerzegovinaBotswanaBouvet IslandBrazilBritish Indian Ocean TerritoryBrunei DarussalamBulgariaBurkina FasoBurundiCambodiaCameroonCape VerdeCayman IslandsCentral African RepublicChadChileChinaChristmas IslandCocos (Keeling) IslandsColombiaComorosCongoCongo, The Democratic Republic of theCook IslandsCosta RicaCôte dIvoireCroatiaCubaCuraçaoCyprusCzechiaDenmarkDjiboutiDominicaDominican RepublicEcuadorEgyptEl SalvadorEquatorial GuineaEritreaEstoniaEthiopiaFalkland IslandsFaroe IslandsFijiFinlandFranceFrench GuianaFrench PolynesiaFrench Southern TerritoriesGabonGambiaGeorgiaGermanyGhanaGibraltarGreeceGreenlandGrenadaGuadeloupeGuamGuatemalaGuineaGuinea-BissauGuyanaHaitiHeard Island and McDonald IslandsHondurasHong KongHungaryIcelandIndiaIndonesiaIranIraqIrelandIsle of ManIsraelItalyJamaicaJapanJerseyJordanKazakhstanKenyaKiribatiKorea, Republic ofKuwaitKyrgyzstanLao People's Democratic RepublicLatviaLebanonLesothoLiberiaLibyan Arab JamahiriyaLiechtensteinLithuaniaLuxembourgMacaoMadagascarMalawiMalaysiaMaldivesMaliMaltaMarshall IslandsMartiniqueMauritaniaMauritiusMayotteMexicoMicronesia, Federated States ofMoldova, Republic ofMonacoMongoliaMontenegroMontserratMoroccoMozambiqueMyanmarNamibiaNauruNepalNetherlandsNetherlands AntillesNew CaledoniaNew ZealandNicaraguaNigerNigeriaNiueNorfolk IslandNorth MacedoniaNorthern Mariana IslandsNorwayOmanPakistanPalauPalestinian Territory, OccupiedPanamaPapua New GuineaParaguayPeruPhilippinesPitcairnPolandPortugalPuerto RicoQatarRéunionRomaniaRussiaRwandaSaint BarthélemySaint HelenaSaint Kitts and NevisSaint MartinSaint LuciaSaint Pierre and MiquelonSaint Vincent and the GrenadinesSamoaSan MarinoSão Tomé and PríncipeSaudi ArabiaSenegalSerbiaSeychellesSierra LeoneSingaporeSlovakiaSloveniaSolomon IslandsSomaliaSouth AfricaSouth SudanSpainSri LankaSudanSurinameSvalbard and Jan MayenSwazilandSwedenSwitzerlandSyrian Arab RepublicTaiwanTajikistanTanzania, United Republic ofThailandTimor-LesteTogoTokelauTongaTrinidad and TobagoTunisiaTurkeyTurkmenistanTurks and Caicos IslandsTuvaluUgandaUkraineUnited Arab EmiratesUnited KingdomUnited States Minor Outlying IslandsUruguayUzbekistanVanuatuVatican CityVenezuela, Bolivarian Republic ofVietnamVirgin Islands, BritishVirgin Islands, USWallis and FutunaWestern SaharaYemenZambiaZimbabwe State —Please choose an option— How did you hear about Komprise?*Search EngineSocial MediaMarketing EventBlog or PublicationRecommended by Friend or ColleagueRecommended by Industry AnalystRecommended by a PartnerOther Other By submitting this form, I confirm that I have read and agree to the Privacy Statement. Δ ### 3 Keys To Solving Data Growth Challenges 3 Keys To Solving Data Growth Challenges Data growth is skyrocketing. Storage capacity is running out, backups are taking longer, and budgets can’t keep up with the unstructured data deluge. The answer isn’t so much a storage issue as it is how the data in your storage is managed. Because treating all your data as if it’s the same will cost you plenty. There are three key areas that set Komprise apart, offering a unique data management solution that puts data control where it belongs—with data owners. Download White Paper Thanks for reaching out. We'll be in touch ### Archiving vs Transparent Archiving Archiving vs Transparent Archiving All Data Archiving is Not the Same Data tiering and archiving is popular for its cost-saving potential. But how much you actually save varies greatly depending on the method used. Not all tiering and archiving choices are equal. But when you know what to look for, you can avoid disruption to end-users and data access, prevent vendor lock-in and maximize savings. Read this white paper, Archiving vs. Transparent Archiving to understand the differences in archiving techniques to make the best decision for your organization and learn more about Komprise Transparent Move Technology (TMT). Read about: The 5 keys to good archiving Popular archiving and tiering approaches How they compare Download White Paper First Name* Last Name* Work Email* Company Name* Job Title* Phone* Country* --United StatesCanadaAfghanistanÅland IslandsAlbaniaAlgeriaAmerican SamoaAndorraAngolaAnguillaAntarcticaAntigua and BarbudaArgentinaArmeniaArubaAustraliaAustriaAzerbaijanBahamasBahrainBangladeshBarbadosBelarusBelgiumBelizeBeninBermudaBhutanBolivia, Plurinational State ofBosnia and HerzegovinaBotswanaBouvet IslandBrazilBritish Indian Ocean TerritoryBrunei DarussalamBulgariaBurkina FasoBurundiCambodiaCameroonCape VerdeCayman IslandsCentral African RepublicChadChileChinaChristmas IslandCocos (Keeling) IslandsColombiaComorosCongoCongo, The Democratic Republic of theCook IslandsCosta RicaCôte dIvoireCroatiaCubaCuraçaoCyprusCzechiaDenmarkDjiboutiDominicaDominican RepublicEcuadorEgyptEl SalvadorEquatorial GuineaEritreaEstoniaEthiopiaFalkland IslandsFaroe IslandsFijiFinlandFranceFrench GuianaFrench PolynesiaFrench Southern TerritoriesGabonGambiaGeorgiaGermanyGhanaGibraltarGreeceGreenlandGrenadaGuadeloupeGuamGuatemalaGuineaGuinea-BissauGuyanaHaitiHeard Island and McDonald IslandsHondurasHong KongHungaryIcelandIndiaIndonesiaIranIraqIrelandIsle of ManIsraelItalyJamaicaJapanJerseyJordanKazakhstanKenyaKiribatiKorea, Republic ofKuwaitKyrgyzstanLao People's Democratic RepublicLatviaLebanonLesothoLiberiaLibyan Arab JamahiriyaLiechtensteinLithuaniaLuxembourgMacaoMadagascarMalawiMalaysiaMaldivesMaliMaltaMarshall IslandsMartiniqueMauritaniaMauritiusMayotteMexicoMicronesia, Federated States ofMoldova, Republic ofMonacoMongoliaMontenegroMontserratMoroccoMozambiqueMyanmarNamibiaNauruNepalNetherlandsNetherlands AntillesNew CaledoniaNew ZealandNicaraguaNigerNigeriaNiueNorfolk IslandNorth MacedoniaNorthern Mariana IslandsNorwayOmanPakistanPalauPalestinian Territory, OccupiedPanamaPapua New GuineaParaguayPeruPhilippinesPitcairnPolandPortugalPuerto RicoQatarRéunionRomaniaRussiaRwandaSaint BarthélemySaint HelenaSaint Kitts and NevisSaint MartinSaint LuciaSaint Pierre and MiquelonSaint Vincent and the GrenadinesSamoaSan MarinoSão Tomé and PríncipeSaudi ArabiaSenegalSerbiaSeychellesSierra LeoneSingaporeSlovakiaSloveniaSolomon IslandsSomaliaSouth AfricaSouth SudanSpainSri LankaSudanSurinameSvalbard and Jan MayenSwazilandSwedenSwitzerlandSyrian Arab RepublicTaiwanTajikistanTanzania, United Republic ofThailandTimor-LesteTogoTokelauTongaTrinidad and TobagoTunisiaTurkeyTurkmenistanTurks and Caicos IslandsTuvaluUgandaUkraineUnited Arab EmiratesUnited KingdomUnited States Minor Outlying IslandsUruguayUzbekistanVanuatuVatican CityVenezuela, Bolivarian Republic ofVietnamVirgin Islands, BritishVirgin Islands, USWallis and FutunaWestern SaharaYemenZambiaZimbabwe State —Please choose an option— How did you hear about Komprise?*Search EngineSocial MediaMarketing EventBlog or PublicationRecommended by Friend or ColleagueRecommended by Industry AnalystRecommended by a PartnerOther Other By submitting this form, I confirm that I have read and agree to the Privacy Statement. Δ ### Block-level Tiering Vs File-Level Tiering Block-level Tiering vs File-Level Tiering Finding and tiering your cold data can save substantial costs by offloading it from expensive storage and backups. Tiering has been a solution for years, but the way it’s done can significantly change your actual savings and affect your options to access your cold data. Learn the difference between block-level tiering (NetApp FabricPool, Dell PowerScale CloudPools), which moves blocks that can no longer be directly accessed from their new location without the vendor software, and file-level tiering, which is what Komprise uses to fully preserve file access at each tier by keeping the metadata and file attributes with the file—no matter where it lives. Know the difference to make the right cloud tiering choice for your moves. Download White Paper First Name* Last Name* Work Email* Company Name* Job Title* Phone* Country* --United StatesCanadaAfghanistanÅland IslandsAlbaniaAlgeriaAmerican SamoaAndorraAngolaAnguillaAntarcticaAntigua and BarbudaArgentinaArmeniaArubaAustraliaAustriaAzerbaijanBahamasBahrainBangladeshBarbadosBelarusBelgiumBelizeBeninBermudaBhutanBolivia, Plurinational State ofBosnia and HerzegovinaBotswanaBouvet IslandBrazilBritish Indian Ocean TerritoryBrunei DarussalamBulgariaBurkina FasoBurundiCambodiaCameroonCape VerdeCayman IslandsCentral African RepublicChadChileChinaChristmas IslandCocos (Keeling) IslandsColombiaComorosCongoCongo, The Democratic Republic of theCook IslandsCosta RicaCôte dIvoireCroatiaCubaCuraçaoCyprusCzechiaDenmarkDjiboutiDominicaDominican RepublicEcuadorEgyptEl SalvadorEquatorial GuineaEritreaEstoniaEthiopiaFalkland IslandsFaroe IslandsFijiFinlandFranceFrench GuianaFrench PolynesiaFrench Southern TerritoriesGabonGambiaGeorgiaGermanyGhanaGibraltarGreeceGreenlandGrenadaGuadeloupeGuamGuatemalaGuineaGuinea-BissauGuyanaHaitiHeard Island and McDonald IslandsHondurasHong KongHungaryIcelandIndiaIndonesiaIranIraqIrelandIsle of ManIsraelItalyJamaicaJapanJerseyJordanKazakhstanKenyaKiribatiKorea, Republic ofKuwaitKyrgyzstanLao People's Democratic RepublicLatviaLebanonLesothoLiberiaLibyan Arab JamahiriyaLiechtensteinLithuaniaLuxembourgMacaoMadagascarMalawiMalaysiaMaldivesMaliMaltaMarshall IslandsMartiniqueMauritaniaMauritiusMayotteMexicoMicronesia, Federated States ofMoldova, Republic ofMonacoMongoliaMontenegroMontserratMoroccoMozambiqueMyanmarNamibiaNauruNepalNetherlandsNetherlands AntillesNew CaledoniaNew ZealandNicaraguaNigerNigeriaNiueNorfolk IslandNorth MacedoniaNorthern Mariana IslandsNorwayOmanPakistanPalauPalestinian Territory, OccupiedPanamaPapua New GuineaParaguayPeruPhilippinesPitcairnPolandPortugalPuerto RicoQatarRéunionRomaniaRussiaRwandaSaint BarthélemySaint HelenaSaint Kitts and NevisSaint MartinSaint LuciaSaint Pierre and MiquelonSaint Vincent and the GrenadinesSamoaSan MarinoSão Tomé and PríncipeSaudi ArabiaSenegalSerbiaSeychellesSierra LeoneSingaporeSlovakiaSloveniaSolomon IslandsSomaliaSouth AfricaSouth SudanSpainSri LankaSudanSurinameSvalbard and Jan MayenSwazilandSwedenSwitzerlandSyrian Arab RepublicTaiwanTajikistanTanzania, United Republic ofThailandTimor-LesteTogoTokelauTongaTrinidad and TobagoTunisiaTurkeyTurkmenistanTurks and Caicos IslandsTuvaluUgandaUkraineUnited Arab EmiratesUnited KingdomUnited States Minor Outlying IslandsUruguayUzbekistanVanuatuVatican CityVenezuela, Bolivarian Republic ofVietnamVirgin Islands, BritishVirgin Islands, USWallis and FutunaWestern SaharaYemenZambiaZimbabwe State —Please choose an option— How did you hear about Komprise?*Search EngineSocial MediaMarketing EventBlog or PublicationRecommended by Friend or ColleagueRecommended by Industry AnalystRecommended by a PartnerOther Other By submitting this form, I confirm that I have read and agree to the Privacy Statement. Δ ### Why Standards-Based File Tiering Matters Why Standards-Based File Tiering Matters Finding and tiering your cold data can save substantial costs by offloading it from expensive storage and backups. Tiering has been a solution for years, but the way it’s done can significantly change your actual savings and affect your options to access your cold data. Learn the difference between block-level tiering, which moves blocks that can no longer be directly accessed from their new location without vendor software, and file-level tiering, which is what Komprise uses to fully preserve file access at each tier by keeping the metadata and file attributes with the file—no matter where it lives. Know the difference to make the right choice for your moves. Read: Block-Level vs File-Level Data Tiering - What's the Difference? Why does it Matter? Download White Paper First Name* Last Name* Work Email* Company Name* Job Title* Phone* Country* --United StatesCanadaAfghanistanÅland IslandsAlbaniaAlgeriaAmerican SamoaAndorraAngolaAnguillaAntarcticaAntigua and BarbudaArgentinaArmeniaArubaAustraliaAustriaAzerbaijanBahamasBahrainBangladeshBarbadosBelarusBelgiumBelizeBeninBermudaBhutanBolivia, Plurinational State ofBosnia and HerzegovinaBotswanaBouvet IslandBrazilBritish Indian Ocean TerritoryBrunei DarussalamBulgariaBurkina FasoBurundiCambodiaCameroonCape VerdeCayman IslandsCentral African RepublicChadChileChinaChristmas IslandCocos (Keeling) IslandsColombiaComorosCongoCongo, The Democratic Republic of theCook IslandsCosta RicaCôte dIvoireCroatiaCubaCuraçaoCyprusCzechiaDenmarkDjiboutiDominicaDominican RepublicEcuadorEgyptEl SalvadorEquatorial GuineaEritreaEstoniaEthiopiaFalkland IslandsFaroe IslandsFijiFinlandFranceFrench GuianaFrench PolynesiaFrench Southern TerritoriesGabonGambiaGeorgiaGermanyGhanaGibraltarGreeceGreenlandGrenadaGuadeloupeGuamGuatemalaGuineaGuinea-BissauGuyanaHaitiHeard Island and McDonald IslandsHondurasHong KongHungaryIcelandIndiaIndonesiaIranIraqIrelandIsle of ManIsraelItalyJamaicaJapanJerseyJordanKazakhstanKenyaKiribatiKorea, Republic ofKuwaitKyrgyzstanLao People's Democratic RepublicLatviaLebanonLesothoLiberiaLibyan Arab JamahiriyaLiechtensteinLithuaniaLuxembourgMacaoMadagascarMalawiMalaysiaMaldivesMaliMaltaMarshall IslandsMartiniqueMauritaniaMauritiusMayotteMexicoMicronesia, Federated States ofMoldova, Republic ofMonacoMongoliaMontenegroMontserratMoroccoMozambiqueMyanmarNamibiaNauruNepalNetherlandsNetherlands AntillesNew CaledoniaNew ZealandNicaraguaNigerNigeriaNiueNorfolk IslandNorth MacedoniaNorthern Mariana IslandsNorwayOmanPakistanPalauPalestinian Territory, OccupiedPanamaPapua New GuineaParaguayPeruPhilippinesPitcairnPolandPortugalPuerto RicoQatarRéunionRomaniaRussiaRwandaSaint BarthélemySaint HelenaSaint Kitts and NevisSaint MartinSaint LuciaSaint Pierre and MiquelonSaint Vincent and the GrenadinesSamoaSan MarinoSão Tomé and PríncipeSaudi ArabiaSenegalSerbiaSeychellesSierra LeoneSingaporeSlovakiaSloveniaSolomon IslandsSomaliaSouth AfricaSouth SudanSpainSri LankaSudanSurinameSvalbard and Jan MayenSwazilandSwedenSwitzerlandSyrian Arab RepublicTaiwanTajikistanTanzania, United Republic ofThailandTimor-LesteTogoTokelauTongaTrinidad and TobagoTunisiaTurkeyTurkmenistanTurks and Caicos IslandsTuvaluUgandaUkraineUnited Arab EmiratesUnited KingdomUnited States Minor Outlying IslandsUruguayUzbekistanVanuatuVatican CityVenezuela, Bolivarian Republic ofVietnamVirgin Islands, BritishVirgin Islands, USWallis and FutunaWestern SaharaYemenZambiaZimbabwe State —Please choose an option— How did you hear about Komprise?*Search EngineSocial MediaMarketing EventBlog or PublicationRecommended by Friend or ColleagueRecommended by Industry AnalystRecommended by a PartnerOther Other By submitting this form, I confirm that I have read and agree to the Privacy Statement. Δ ### AWS, Komprise, & Red River: Advancing Cloud Data Strategies with Efficiency and Ease in the Public Sector Unsure of how best to leverage data storage in the cloud? Not sure what your cloud data strategy should be? Are slow cloud migrations hindering decisions? Cloud First is a strategy all entities are working toward. But, how do we know what data we have? Where it lives? How old it is? Where it should go? And how it should get there? ### Krishna Subramanian of Komprise: Managing the Exponential Growth of Data  Krishna Subramanian, Co-founder and COO of Komprise shares with Lee Razo some tips and advice on managing the exponential pace and scale of data growth while maintaining control of your own data in the process. Krishna also walks us through some relevant use cases and examples of real-world data value extraction in multiple industries. Find out more about Komprise at https://go.cloudnativex.io/3ojFxFA​ Be sure to also visit us at https://cloudnativex.io​​​ and subscribe to the newsletter! ### Grow NAS Capacity while Cutting Costs with Azure and Komprise Data is growing exponentially, yet your budgets remain flat. Since over 70% of unstructured data is rarely accessed, continuing to store and replicate it on expensive primary storage systems is cost prohibitive. With Komprise Intelligent Data Management, you can know your data and transparently migrate, tier, and archive cold data to the Microsoft Azure cloud for significant data storage and backup savings—all without any access disruptions. Read Solution Brief ### TechKrunch: Data Migration or Data Tiering/Archiving At Komprise we like to say Know First and Move Smart. But when it comes to NFS and SMB, what is the difference between copy, migration (replication), and migration (cutover)? Which one would you us and why? What are the different use cases? ### Faster Recovery: File Replication for Pure FlashArray with Komprise If your disaster recovery plan for unstructured data doesn’t offer fast recovery, your phone could be buzzing with complaints for hours. Recovering data from backups can be much slower than your users can tolerate. That’s why data replication, not backup, is the strategy you need for business continuity. ### Komprise Replication for Pure FlashArray Keeping your business running without disruption requires comprehensive disaster recovery services for all your data. Komprise and Pure Storage provide a file-based replication solution to ensure business continuity. Protect Unstructured Data with Robust, Flexible Replication With the ever-present threat of natural disasters, failures, and outages, keeping your business running without disruption requires comprehensive disaster recovery services for all your data. If you use FlashArray File Services, Komprise and Pure Storage provide a file-based replication solution jointly developed to ensure business continuity. Read Solution Brief ### AWS & Komprise: Efficiently Manage Your Hybrid Cloud Data Strategy Wish cloud migrations were easier? Want to speed up data migrations to AWS without the headaches? It’s not enough to just copy data to the cloud. You need to preserve full file-based access to data in S3 and be able to easily manage it once it’s there to avoid data sprawl. It’s all possible with AWS and Komprise Intelligent Data Management. ### Carhartt shifts old data to the cloud with Komprise Michigan-headquartered Carhartt makes clothing at nine manufacturing plants in the US and Mexico, with satellite locations in Europe and the Far East. It has two primary datacenters with 3,200 staff and more than 1,000 contractors who access its systems. It is primarily a Microsoft user, with SAP as its enterprise resource planning (ERP) provider. The key issues that faced the company were a great deal of “data sprawl”, said systems engineer, Earl Williams. Read Case Study ### Komprise Analytics-Driven Data Management for AWS Outposts Managing file and object data on AWS Outposts is a snap with Komprise. You can now find and migrate the right data into AWS Outposts, archive, and tier data both within AWS Outposts and into AWS, and manage all your hybrid cloud data from a single pane of glass. Read Solution Brief Learn more about Komprise for AWS. ### Accelerate NAS and Cloud Data Migrations to NetApp CVO and ANF NetApp Cloud Migration: Know First. Move Smart. Take Control. As businesses evolve to faster, flash-based NAS and cloud storage, migrating data into these environments can be tough. The goal is to migrate large production data sets quickly, without errors, and without user disruption. Now with Komprise Elastic Data Migration that’s possible. This white paper explains how the new fast, reliable, and cost-efficient migration solution from Komprise will turn your next NAS and cloud migration to NetApp CVO and ANF from a dreaded chore to “Done already?” Read White Paper Additional NetApp cloud migration resources: Komprise for NetApp Demo: Fast, Reliable Data Migration to NetApp Solution Brief: Accelerate Cloud Migration to Azure NetApp Files Blog Post: Komprise and Amazon FSx for NetApp OnTap ### Accelerate Cloud Migration to Azure NetApp Files Cloud migrations do not have to be risky, time-consuming, expensive and laborious. With Komprise, you can quickly identify and migrate the right data to Microsoft Azure. You can “lift-and-shift”entire file sets into Azure NetApp Files (ANF) or optimize clod costs by intelligently migrating hot data to ANF and transparently tiering cold data to Azure Blob—without any changes to user and application access. Read Solution Brief Learn more about Komprise Intelligent Data Management for Microsoft Azure Learn more about Komprise Intelligent Data Management for NetApp Komprise Smart Data Migration for Azure. Smarter. Faster. Proven. ### Versnel clouddatamigraties naar Azure met Komprise Vereenvoudig uw NAS-migraties naar Azure met Komprise. U hoeft niet langer te worstelen met het migreren van bestandsgegevens naar Azure – geen giswerk meer, geen lange nachten meer worstelen met foutgevoelige, trage tools, geen verloren migratiekosten meer. ### Accelerate Cloud Data Migrations to Azure with Komprise Simplify your NAS migrations to Azure with Komprise. You no longer have to struggle with migrating file data to Azure – no more guesswork, no more late nights battling with error prone, slow tools, no more sunk migration costs. ### How to Cut Spiraling NAS Costs: IBM & Komprise - Data Management for the new era In these unprecedented times, one thing is certain: the continued growth of NAS data and the rising costs to manage it. The key to cutting costs is understanding your data across your storage silos to make the right decisions. ### How to Cut Spiraling NAS Costs: IBM & Komprise - Data Management for the new era In these unprecedented times, one thing is certain: the continued growth of NAS data and the rising costs to manage it. The key to cutting costs is understanding your data across your storage silos to make the right decisions. ### 3 Ways to Control Data Costs with Analytics-driven Data Management Data growth is skyrocketing. Storage capacity is running out, backups are taking longer, and budgets can’t keep up with the unstructured data deluge. The answer isn’t so much a storage issue as it is how the data in your storage is managed. Because treating all your data the same is a costly error. ### 3 Ways to Control Data Costs with Analytics-driven Data Management Data growth is skyrocketing. Storage capacity is running out, backups are taking longer, and budgets can’t keep up with the unstructured data deluge. The answer isn’t so much a storage issue as it is how the data in your storage is managed. Because treating all your data the same is a costly error. ### 4 Surefire Ways to Control Cloud Storage Costs with Komprise Gartner predicts that 80% of businesses will overspend their cloud infrastructure budgets. And since storage is the second largest spend item in the cloud, isn’t it time to start containing those spiraling costs? ### 4 Surefire Ways to Control Cloud Storage Costs with Komprise Gartner predicts that 80% of businesses will overspend their cloud infrastructure budgets. And since storage is the second largest spend item in the cloud, isn’t it time to start containing those spiraling costs? ### Eliminate Roadblocks and Challenges of Cloud File Data Migration Migrating large data sets to the cloud quickly, accurately and without errors or disruption is notoriously painful. Check out this infographic to learn how to eliminate the 7 most common roadblocks to cloud data migration with an analytics-driven approach to unstructured data management. View Infographic What are the primary cloud file and object migration challenges? Read the blog post ### Maximize Cloud Savings with IBM & Komprise Is your cloud strategy keeping up with your data growth? The pressure to save is on, and this infographic shows how IBM and Komprise can maximize data storage savings and become a data-driven, hybrid multicloud enterprise. View Infographic Learn more about Komprise and IBM unstructured data management. ### 4 Surefire Ways to Control Cloud Storage Costs Cloud budgets are straining, and the second biggest line item is storage.  The toughest challenges to controlling these costs are poor visibility into cloud data and the complex, multiple factors involved in managing it.  This contributes to over 80% of organizations missing out on available cost-saving options in the cloud. Challenges of Cloud Data Management Cloud administrators’ biggest challenges are poor visibility and the complexity of cloud data management. IT has no way of knowing even the basic information to make better storage decisions: How fast is cloud data growing and who’s using it? How much is active hot data how much is less accessed cold data? How can you dig deeper to plan the best way to optimize cloud storage costs and minimize retrieval fees? Cloud management administrators are responsible for a multi-factored billing headache: Cloud storage pricing can vary widely based on needs for storage, access, retrievals, API, transitions, initial transfer, and minimal storage-time fees Unexpected costs of moving data across different cloud storage providers or classes Steps to Control Cloud Storage Costs 1. Gain Accurate Visibility Across Cloud Accounts into Actual Usage Learn how to get your true cloud picture—across all your cloud accounts and services—no matter what vendors and cloud services you use. 2. Forecast Savings and Plan Cloud Data Management Strategies Find out how to better understand your current cloud costs and establish a baseline with “what-if” scenarios to accurately project your company’s savings with cloud data analytics. 3. Archive Data in Cloud Storage Based on Data Usage to Avoid Surprises Learn the difference between basing cloud management policies on when data was last accessed (last read or written) vs. last modified. 4. Easier Cloud Migrations Cloud migration tools need to simplify the time-consuming, error-prone task for you. The brief explains the essential features you should demand in a solution. Download Report Thanks for reaching out. We'll be in touch ### 4 Surefire Ways to Control Cloud Storage Costs Cloud budgets are straining, and the second biggest line item is storage. The toughest challenges to controlling these costs are poor visibility into cloud data and the complex, multiple factors involved in managing it. This contributes to over 80% of organizations missing out on available cost-saving options in the cloud. Challenges of Cloud Data Management Cloud administrators’ biggest challenges are poor visibility and the complexity of cloud data management. IT has no way of knowing even the basic unstructured data information to make better storage decisions: How fast is cloud data growing and who’s using it? How much is active hot data how much is less accessed cold data? How can you dig deeper to plan the best way to optimize cloud storage costs and minimize retrieval fees? Cloud management administrators are responsible for a multi-factored billing headache: Cloud storage pricing can vary widely based on needs for storage, access, retrievals, API, transitions, initial transfer, and minimal storage-time fees Unexpected costs of moving data across different cloud storage providers or classes Steps to Control Cloud Storage Costs 1. Gain Accurate Visibility Across Cloud Accounts into Actual Usage Learn how to get your true cloud picture—across all your cloud accounts and services—no matter what vendors and cloud services you use. Learn more about Komprise Analysis. 2. Forecast Savings and Plan Cloud Data Management Strategies Find out how to better understand your current cloud costs and establish a baseline with “what-if” scenarios to accurately project your company’s savings with cloud data analytics. Watch the webinar: What can Komprise Analysis Do For You? 3. Tier Data to Cloud Storage Based on Data Usage to Avoid Surprises Learn the difference between basing cloud management policies on when data was last accessed (last read or written) vs. last modified. Learn more about Cold Data Tiering with Komprise. 4. Easier Cloud Migrations Cloud migration tools need to simplify the time-consuming, error-prone task for you. The brief explains the essential features you should demand in a solution. Download Cloud Cost Control Report Thanks for reaching out. We'll be in touch ### How to Cut NAS costs without Users Noticing Any Difference One constant you can continue to count on? The growth of unstructured data and the costs to manage it. Discover a cost-effective strategy to quickly lower NAS and cloud storage costs with an analytics-driven approach to data management. ### How to Cut NAS costs without Users Noticing Any Difference One constant you can continue to count on? The growth of unstructured data and the costs to manage it. Discover a cost-effective strategy to quickly lower NAS and cloud storage costs with an analytics-driven approach to data management. ### Senken Sie 80 % der NAS- und Cloud-Kosten mit Wasabi und Komprise Ändern Sie ständig Ihre Meinung darüber, wie Sie die Cloud nutzen können? Sie fragen sich, wie Sie: - die Kosten niedrig halten, wenn NAS und Backups wachsen? - Verstehen Sie Daten in all Ihren Silos? ### 7 Archiving Pitfalls That Reduce Your Savings Are you being robbed of valuable archiving savings? The differences in archiving approaches can introduce unexpected disruption that affects the amount of your savings. Read how to avoid these common pitfalls and maximize your archiving cost savings. Read Brief ### Optimize Cloud Storage Costs with Intelligent Data Management Smarter, Faster, Proven Cloud File and Object Data Management Learn a simpler way to optimize cloud spend, manage cloud costs and speed cloud migrations that addresses low visibility of data usage, cloud cost complexity, and cloud bucket sprawl. Read Solution Brief Learn more about optimizing cloud data costs with Komprise. Watch a demonstration: Komprise for Multicloud Data Management. ### 7 Common Errors to Avoid when Archiving Data Archiving unstructured data can deliver 70% of cost savings when done right. Unfortunately, most archiving solutions create unnecessary rehydration and disruption that eliminates most of this savings. Find out how you can avoid this, deliver 70% storage cost savings without disrupting your users or your existing data protection workflow. ### 7 Common Errors to Avoid when Archiving Data Archiving unstructured data can deliver 70% of cost savings when done right. Unfortunately, most archiving solutions create unnecessary rehydration and disruption that eliminates most of this savings. Find out how you can avoid this, deliver 70% storage cost savings without disrupting your users or your existing data protection workflow. ### How to Maximize Cloud Cost Savings Learn why 80% of companies will blow their cloud budget. This infographic shows the challenges of cloud cost optimization and how to reduce data storage costs with Komprise Intelligent Data Management. View Infographic ### Migrate, Replicate, Manage File Data on Pure FlashArray with Komprise Migrations needn’t be expensive, error-prone, and labor-intensive. This brief explains how to easily find and migrate the right data from any NAS to Pure FlashArray. Data migrations needn’t be expensive, error-prone, and labor-intensive. This brief explains how to easily find and migrate the right data from any NAS to Pure FlashArray. Learn how Komprise quickly analyzes data across your NAS, so you can choose the right data to be migrated and then migrate it, using the same software—with a click of a button. You can also maximize your Flash investment and performance by using Komprise to continually offload cold data. Read Solution Brief Learn more about Komprise for Pure Storage. ### Episode 44 of The Andy Show On episode 44 of The Andy Show, Nicky is joined by Krishna Subramanian, COO at Komprise. Krishna discusses how companies can analyze and manage data smarter to cut costs with Komprise...  On episode 44 of The Andy Show, Nicky is joined by Krishna Subramanian, COO at Komprise. Krishna discusses how companies can analyze and manage data smarter to cut costs with Komprise. She discusses why cloud is so important right now and how Komprise Intelligent Data Management helps customers handle multicloud challenges so they can save optimize cloud costs and get more from their data, whether on-prem or in any cloud. ### Why Standards-Based File Tiering Matters Finding and tiering your cold data can save substantial costs by offloading it from expensive storage and backups. Tiering has been a solution for years, but the way it’s done can significantly change your actual savings and affect your options to access your cold data. Learn the difference between block-level tiering, which moves blocks that can no longer be directly accessed from their new location without vendor software, and file-level tiering, which is what Komprise uses to fully preserve file access at each tier by keeping the metadata and file attributes with the file—no matter where it lives. Know the difference to make the right choice for your moves. Download Report Thanks for reaching out. We'll be in touch ### Komprise Overview Video  Stop Overspending on Data Storage. Be Wise. Komprise. Want to save storage and backup costs? Migrate file and object data faster? Tier to the cloud without disruption? It’s all possible with Komprise Intelligent Data Management. With Komprise you can easily analyze, mobilize, and monetize the right file and object data across clouds, on-premises and multi-cloud data storage silos. Cut 70% of your enterprise data storage, backup and cloud costs while making unstructured data easily available to cloud-based data lakes and analytics tools. ### IBM COS and Komprise Intelligent Data Management Learn how to analyze and optimize data across the hybrid cloud with IBM Cloud Object Storage and Komprise. A Smart, Fast, Proven Path to the IBM Cloud Learn how to analyze and optimize data across hybrid cloud data storage, reduce data storage costs and accelerate file and object data migrations with Komprise for IBM Cloud Object Storage (COS). Read Solution Brief Learn more about Komprise for IBM. ### Data’s Doubling, Budget Isn’t: Efficiently manage your multi cloud data strategy In these unprecedented times, cost savings has become a white-hot focus for organizations. A prime target for uncovering significant savings can be found in the way unstructured data growth is managed. Choosing the right data management platform and storage solution will allow you to save over 70%+. ### Data’s Doubling, Budget Isn’t: Efficiently manage your multi cloud data strategy In these unprecedented times, cost savings has become a white-hot focus for organizations. A prime target for uncovering significant savings can be found in the way unstructured data growth is managed. Choosing the right data management platform and storage solution will allow you to save over 70%+. ### Archivierung vs transparente Archivierung Die gesamte Datenarchivierung ist nicht gleich Die Archivierung wird aufgrund ihres Kosteneinsparungspotenzials immer beliebter. Wie viel Sie tatsächlich sparen, hängt jedoch stark von der verwendeten Methode ab. Nicht alle Archivierungsoptionen sind gleich. Wenn Sie jedoch wissen, wonach Sie suchen müssen, können Sie Störungen des Endbenutzers und des Datenzugriffs vermeiden, eine Lieferantenbindung verhindern und Einsparungen maximieren. Lesen Sie dieses Whitepaper Archivierung vs. transparente Archivierung, um die Unterschiede bei den Archivierungstechniken zu verstehen und die beste Entscheidung für Ihr Unternehmen zu treffen. Lesen über: Die 5 Schlüssel zu einer guten Archivierung Beliebte Archivierungs- und Tiering-Ansätze Wie sie vergleichen Füllen Sie die folgenden Felder aus, um das Whitepaper herunterzuladen Thanks for reaching out. We'll be in touch ### Your Data’s Doubling, But Your Budget Isn’t. IBM & Komprise: Data Management for the new era In these unprecedented times, one thing is certain: the continued growth of unstructured data and the rising costs to manage it. The key to cutting costs is understanding your data across your storage silos to make the right decisions. With an analytics-driven approach to data management, you can move your cold data to less expensive storage, which significantly lowers the cost of both storage, back-up, and DR replication. ### Your Data’s Doubling, But Your Budget Isn’t. IBM & Komprise: Data Management for the new era In these unprecedented times, one thing is certain: the continued growth of unstructured data and the rising costs to manage it. The key to cutting costs is understanding your data across your storage silos to make the right decisions. With an analytics-driven approach to data management, you can move your cold data to less expensive storage, which significantly lowers the cost of both storage, back-up, and DR replication. ### Your NAS is full of cold data. Identify, archive, and save with Komprise Cost savings has become a white-hot focus for organizations. A prime target for uncovering significant savings can be found in the way unstructured data growth is managed. Most aren’t aware that over 70% of their NAS is filled with cold data—a needless waste of budget. ### Rein in Storage Backup Costs Storing and managing all your unstructured data is crippling IT budgets. Learn how to stop treating all data the same to realize significant cost reductions. Reduce Storage Backup Costs with Analysis-First Data Management Stop backing up all of your file data without insight. Backup first is backwards. When you identify and easily tier and archive your cold data to secondary storage—without disrupting users or apps—you can shrink your NAS, data backup, data replication data storage costs quickly. And when that same unstructured data management solution allows you to get even more from your data—with migration and AI applications -- the value grows. With Komprise you can protect file data from ransomware at 80% lower costs. Read Solution Brief ### AWS & Komprise: Efficiently Manage Your Hybrid Cloud Data Strategy Wish cloud migrations were easier? Want to speed up data migrations to AWS without the headaches? It’s not enough to just copy data to the cloud. You need to preserve full file-based access to data in S3 and be able to easily manage it once it’s there to avoid data sprawl. It’s all possible with AWS and Komprise Intelligent Data Management. ### Infographic: Smarter Cold Data Management Hot Tips to Cut Cold Data Costs with Intelligent Data Management Stop Treating Hot and Cold Data the Same Way!   Struggling with growing data storage costs? Do you have a cold data management strategy? This infographic shows how to quickly cut the costs of storing and managing your unstructured data with Komprise Intelligent Data Management. Got cold data? Be wise. Komprise. Read Infographic ### Know Your Data to Slow Your Storage Costs The costs of unstructured data growth continue to cripple IT budgets—wherever you’re working from these days. Your next storage refresh is a chance to stop the cycle and make smarter data decisions to save significant costs. ### BESCHLEUNIGEN SIE NAS UND CLOUD DATENMIGRATION Da Unternehmen schnelleres, Flash-basierendes NAS und Cloud Storage einsetzen, kann die Migration in solche Umgebungen schwierig werden. Das Ziel ist es, große Produktionsdatensätze schnell, fehlerfrei und ohne Störung der Anwender zu migrieren. Das ist jetzt mit Komprise Elastic Data Migration möglich. Diese White Paper erklärt, wie die neue schnelle, zuverlässige und kosteneffiziente Migrationslösung von Komprise Ihre nächste NAS Migration von einer gefürchteten lästigen Pflicht in ein „schon fertig?“verwandelt. Füllen Sie die Felder unten aus für den Download des White Papers Thanks for reaching out. We'll be in touch ### Fast, Painless Data Migrations with Komprise Elastic Data Migration Data migrations are notoriously laborious. Complex, error-prone, and time-consuming—they’re especially tough for unstructured data over WAN environments, such as data going to the cloud. Analytics-First Unstructured Data Migration as a Service Learn more about Komprise Elastic Data Migration What is a Smart Data Migration for File and Object Data? Closer Look: Komprise Unstructured Data Migration ### Get 2020 Vision Into Your Cold Data: Know Before You Act By 2025, 175 ZB of data will be created, up from just 33 ZB in 2018, including structured, semi-structured and unstructured data. The result? Costs to store and transmit data , while always a concern, has taken on increased importance in this era of explosive data growth and pressure to become data-driven. ### IDC Infobrief: So managen Sie Ihr Datenwachstum intelligenter mit Data Literacy - Komprise + IBM IDC explains why developing data literacy is imperative to better manage the challenges that unprecedented data growth has created. Learn how Komprise Intelligent Data Management and IBM Cloud Object Storage can boost storage efficiency and cut costs with a more proactive approach to data management. Bericht herunterladen Thanks for reaching out. We'll be in touch ### IDC Infobrief: How to Manage Your Data Growth Smarter with Data Literacy - Komprise + IBM IDC explains why developing data literacy is imperative to better manage the challenges that unprecedented unstructured data growth has created. Learn how Komprise Intelligent Data Management and IBM Cloud Object Storage can boost storage efficiency and cut data storage costs with a more proactive approach to data management.     Download IDC Unstructured Data Management Report Thanks for reaching out. We'll be in touch ### Quit Your Addiction to Storage with Intelligent Data Management Do you own your data, or does your data own you? You were listening when they told you that data was the new oil, so you spend your working lives buying, tending and managing storage. But we don’t spend enough time talking about your data. What is it? How much is anyone actually using? And is there a more efficient way of managing it than traditional storage tiering? Analytics-Drive Unstructured Data Management Watch this webinar to learn more about the data storage challenge and the opportunity to reduce data storage costs and get greater value from your existing enterprise data storage infrastructure without buying more hardware. Move to the cloud faster, get greater data value with Komprise Intelligent Data Management. ### Komprise Deep Analytics Finding just the right data across billions of files can be challenging. Komprise Deep Analytics enables you to search and find data that fits your specific criteria across storage. Use the search results as a dynamic data lake to both plan your data management and to enable new uses like Big Data Analytics. Read White Paper ### The Register - Business Fit Assessment In this Business Fit Assessment from The Register, learn how to avoid getting burnt by keeping “cold” data” on expensive “hot” storage systems. Learn how to stop wasting capacity, costs and effort with a smarter approach to data management. Manage your data, not just your storage In this Business Fit Assessment from The Register, learn how to avoid getting burned by keeping “cold data” on expensive “hot” storage systems. Learn how to stop wasting capacity, data storage costs and effort with a smarter approach to unstructured data management. Download This Unstructured Data Assessment Report Thanks for reaching out. We'll be in touch ### Komprise Cloud Data Management Chalk Talk Learn how Komprise helps companies get visibility and analytics into their cloud data so they can assess data growth across their clouds and help move cold data to optimize costs. Mike Peercy and Mohit Dhawan are joined by Krishna Subramanian, COO, and Kumar Goswami, CEO, for this cloud data management discussion. ### Get 2020 Vision Into Your Cold Data: Know Before You Act By 2025, 175 ZB of data will be created, up from just 33 ZB in 2018, including structured, semi-structured and unstructured data. The result? Costs to store and transmit data , while always a concern, has taken on increased importance in this era of explosive data growth and pressure to become data-driven. ### Build a Virtual Data Lake in Minutes—Komprise Deep Analytics Finding just the right data across billions of files is like the proverbial needle in the haystack. Komprise Deep Analytics uses powerful data search and indexing technology to automate the process of finding unstructured data across disparate storage platforms based on specific criteria. Use the search results as a dynamic data lake to both plan your data management and enable Big Data applications. ### Build a Virtual Data Lake in Minutes—Komprise Deep Analytics Finding just the right data across billions of files is like the proverbial needle in the haystack. Komprise Deep Analytics uses powerful data search and indexing technology to automate the process of finding unstructured data across disparate storage platforms based on specific criteria. Use the search results as a dynamic data lake to both plan your data management and enable Big Data applications. ### Block-Level vs. File-Level Tiering – What’s the Difference? As data grows exponentially, your storage costs continue to escalate. While it’s easy to think the solution is more efficient storage, the real cause is poor data management. Over 70% of data is cold and has not been accessed in months, yet it sits on expensive storage and consumes the same backup resources as hot data. ### Seamlessly Archive Cold Data to AWS Glacier Using Komprise In dit overzicht wordt uitgelegd hoe organisaties kosten kunnen besparen, primaire opslagcapaciteit kunnen vrijmaken en de gegevensbescherming kunnen verbeteren door hun koude gegevens in alle opslag te identificeren en transparant te archiveren naar AWS Glacier. In deze korte beschrijving wordt uitgelegd hoe organisaties kosten kunnen besparen, primaire opslagcapaciteit kunnen vrijmaken en de gegevensbescherming kunnen versterken door hun cold data in alle opslag te identificeren en transparant te archiveren naar AWS Glacier. Met Komprise ziet u de geschatte vrijgemaakte opslagcapaciteit, de back-up die wordt verminderd en de verwachte kostenbesparingen van het verplaatsen van data naar verschillende AWS-opslagklassen, waaronder S3, S3-IA en Glacier. Leer meer over voordelen zoals geautomatiseerd AWS ILM, snel ophalen van gearchiveerde data en geen impact op gebruikers en apps, met identieke toegang tot verplaatste data zoals voorheen. Lees de oplossingsbrief ### Seamlessly Tier Cold Data to Amazon S3 Glacier Using Komprise This brief explains how organizations can cut costs, free up primary storage capacity, and strengthen data protection by identifying their cold data across all storage and transparently archiving it to AWS Glacier. This brief explains how organizations can cut costs, free up primary storage capacity, and strengthen data protection by identifying their cold data across all storage and transparently tiering data to Amazon S3 Glacier. With Komprise, you can see the estimated data storage capacity that will be freed, the backup that will be reduced, and the projected data storage cost savings of moving data to different Amazon S3 storage classes, including S3 Standard, S3 Standard-IA, S3 Glacier and AWS Snowball. Read Solution Brief Learn more about Komprise and AWS data storage. ### How to Stop Paying the 400% Data Tax on Storage In 2025, it’s estimated that 90% of data will be unstructured, which will continue to escalate storage costs. Too many enterprises replicate and backup all the data sitting on their NAS in the same way, regardless of how often it’s used. In 2025, it’s estimated that 90% of data will be unstructured data, which will continue to escalate data storage costs. Too many enterprises replicate and backup all the data sitting on their NAS in the same way, regardless of how often it’s used. But cold data doesn’t need to be on the highest performing storage and add to costs of active management. This Solution Brief explains why cold data and “the tax” you pay for active management should be a fraction of hot data’s cost and what you can do to stop paying a 400% “data-tax.” Read Solution Brief ### Maximize Your Pure Storage FlashBlade Investment with Komprise Migrations needn’t be expensive, error-prone, and labor-intensive. This brief explains how to easily find and migrate the right data from any NAS to Pure FlashBlade. Migrations needn’t be expensive, error-prone, and labor-intensive. This brief explains how to easily find and migrate the right data from any NAS to Pure Storage FlashBlade. Learn how Komprise quickly analyzes data across your NAS, so you can choose the right data to be migrated and then migrate it, using the same software—with a click of a button. You can also maximize your Flash investment and performance by using Komprise to continually offload cold data. Read Solution Brief Learn more about Pure Storage Data Management from Komprise. ### IDC Infobrief: So verwalten Sie Ihr Datenwachstum mit Datenkompetenz intelligenter IDC erklärt, warum die Entwicklung der Datenkompetenz unerlässlich ist, um die Herausforderungen, die durch ein beispielloses Datenwachstum entstanden sind, besser bewältigen zu können. Erfahren Sie, wie wichtig ein proaktiverer Ansatz für das Datenmanagement ist: Verstehen Sie Ihre Datentypen und Zugriffsmuster und platzieren Sie Daten strategisch in der richtigen Speicherinfrastruktur, um erhebliche Kosten zu sparen und mehr Wert aus Ihren Daten zu ziehen. Bericht herunterladen Thanks for reaching out. We'll be in touch ### Why Data Growth is Not a Storage Problem Data growth is skyrocketing. Storage capacity is running out, backups are taking longer, and budgets can’t keep up with the unstructured data deluge. The answer isn’t so much a storage issue as it is how the data in your storage is managed. Because treating all your data as if it’s the same will cost you plenty. When it comes to modern, hybrid data storage today, enterprises are dealing with: More data More choices The fact that tiered data is live, not offline, and can be used in apps, BI, analytics, AI, machine learning, etc. The need to “right-place” data continuously across silos, edge, and hybrid cloud storage infrastructure. Komprise Intelligent Data Management puts you in control of your data. Our analytics-driven approach works across all storage vendors and backup architectures giving you a single data management pane. Get instant insight into all your data—wherever it resides. See patterns, make decisions, make moves, and save money—all without affecting user access. There are three key areas that set Komprise apart, offering a unique data management solution that puts data control where it belongs—with data owners: 1. Dynamic Data Analytics 2. Transparent Move Technology (TMT™) 3. Direct Data Access Learn more about the benefits of storage-agnostic unstructured data management. There are three key areas that set Komprise apart, offering a unique unstructured data management solution that puts data control where it belongs—with data owners. Read White Paper ### Cloud-Datenwachstumsanalyse Komprise Intelligent Data Management bietet jetzt Transparenz und Analyse Ihrer Cloud-Daten, sodass Sie das Datenwachstum in Ihren Clouds verstehen können. Komprise Intelligent Data Management bietet jetzt Transparenz und Analyse Ihrer Cloud-Daten, sodass Sie das Datenwachstum in Ihren Clouds verstehen können. Es hilft Ihnen dabei, kalte Daten zu verschieben, um die Kosten zu optimieren, und bietet Ihnen eine einzige durchsuchbare Ansicht der Daten in Ihrer Cloud und im lokalen Speicher. Datenblatt lesen ### Cloud Data Growth Analytics Komprise Intelligent Data Management now provides visibility and analytics into your cloud data, so you can understand data growth across your clouds. Komprise Intelligent Data Management now provides visibility and analytics into your cloud data, so you can understand data growth across your clouds. It helps you move cold data to optimize cloud costs and gives you a single searchable view into data across both your cloud and on-premises storage. Read Datasheet Learn more about Komprise cloud cost optimization. ### Know Before You Act: Analyze Data, Not Storage By 2025, 175 ZB of data will be created, up from just 33 ZB in 2018, including structured, semi-structured and unstructured data. The result? Costs to store and transmit data , while always a concern, has taken on increased importance in this era of explosive data growth and pressure to become data-driven. How can you keep up with today’s growth while identifying the “who, when and what” of your unstructured data? It starts with knowing your data, improving your organization’s competencies to manage and capitalize data, helping you to better-understand data types, access patterns, and place them strategically in the right infrastructure. ### Block-Level vs. File-Level Tiering – What’s the Difference? As data grows exponentially, your storage costs continue to escalate. While it’s easy to think the solution is more efficient storage, the real cause is poor data management. Over 70% of data is cold and has not been accessed in months, yet it sits on expensive storage and consumes the same backup resources as hot data. ### Block-Level vs. File-Level Tiering – What’s the Difference? As data grows exponentially, your storage costs continue to escalate. While it’s easy to think the solution is more efficient storage, the real cause is poor data management. Over 70% of data is cold and has not been accessed in months, yet it sits on expensive storage and consumes the same backup resources as hot data. ### Block-level Tiering vs File-level Tiering Finding and tiering your cold data can save substantial costs by offloading it from expensive storage and backups. Tiering has been a solution for years, but the way it’s done can significantly change your actual savings and affect your options to access your cold data. Learn the difference between block-level tiering, which moves blocks that can no longer be directly accessed from their new location without vendor software, and file-level tiering, which is what Komprise uses to fully preserve file access at each tier by keeping the metadata and file attributes with the file—no matter where it lives. Know the difference to make the right choice for your moves. Download Report Thanks for reaching out. We'll be in touch ### Komprise Deep Analytics: Discover the Value in Data Traditional approaches to managing your organizations' data struggle to keep up with today's scale and will prove to be entirely inadequate over the next few years. Join Komprise COO, Krishna Subramanian, and Cloudian CMO, Jon Toor, as they discuss data management and protection strategies that you can implement today to enable your organization to scale with tomorrow's exponential data growth. ### Feature-Highlight: Deep Analytics Finding just the right data across billions of files can be challenging. Komprise Deep Analytics enables you to search and find data that fits your specific criteria across storage and use this dynamic data lake to plan your data management and for new applications like Big Data Analytics. Finding just the right data across billions of files can be challenging. Komprise Deep Analytics enables you to search and find data that fits your specific criteria across storage and use this dynamic data lake to plan your data management and for new applications like Big Data Analytics. Read Datasheet ### Feature Highlight: Deep Analytics for Unstructured Data Finding just the right data across billions of files can be challenging. Komprise Deep Analytics enables you to search and find data that fits your specific criteria across storage and use this dynamic data lake to plan your data management and for new applications like Big Data Analytics. Finding just the right data across billions of files can be challenging. Komprise Deep Analytics enables you to search and find data that fits your specific criteria across data storage and use this dynamic data lake, known as the Global File Index, to plan your unstructured data management and for new applications like big data analytics. Read Datasheet Learn more about Komprise Deep Analytics. ### Stop Paying the 400% Data-Tax: How to Control Your Data Destiny Rising storage costs, long backup windows, unreliable recovery…Sound familiar? In the midst of exponential data growth, costs are staggering. While many perceive storage as the villain, the more costly problem is the 400% Data-Tax you pay on each file and keep paying, forever. Compounded with the statistic that Enterprises have over 80% of cold data…you end up being unnecessarily taxed 4x on data that has not been used in over a year. ### Understanding Komprise Transparent Move Technology™ (TMT) With data footprints growing fast, managing all of it with ease has become crucial. Komprise data management software analyzes and manages stored data across any storage. It does not use any storage agents or use any proprietary HW, SW or static pointers. Komprise identifies and copies or moves cold... ### Unleash the Full Potential of Your Data Hewlett Packard Enterprise and Komprise are partnering to transform data management. Komprise analyzes data across all storage, provides visibility into data growth, and transparently moves infrequently accessed data to cost-efficient HPE Scalable Storage. Join HPE and Komprise to learn how to modernize your infrastructure and unleash the full potential of your data. ### Unleash the Full Potential of Your Data Hewlett Packard Enterprise and Komprise are partnering to transform data management. Komprise analyzes data across all storage, provides visibility into data growth, and transparently moves infrequently accessed data to cost-efficient HPE Scalable Storage. Join HPE and Komprise to learn how to modernize your infrastructure and unleash the full potential of your data. ### New Approach to Managing Unstructured Data Today's data management encompasses features that help organizations better understand their data, its purpose, its location, and more. As data management becomes more sophisticated, additional capabilities are being introduced. Advances in storage systems, data protection, secondary storage, and database management platforms give IT organizations with data management challenges a plethora of solutions to choose from. ### New Approach to Managing Unstructured Data Today's data management encompasses features that help organizations better understand their data, its purpose, its location, and more. As data management becomes more sophisticated, additional capabilities are being introduced. Advances in storage systems, data protection, secondary storage, and database management platforms give IT organizations with data management challenges a plethora of solutions to choose from. ### 2019 Data Deluge Prediction: Prepare Now The sheer volume of data produced today is staggering, doubling approximately every two years. But this is nothing compared to how data is set to grow over the next decade. Traditional approaches to managing your organizations' data struggle to keep up with today's scale and will prove to be entirely inadequate over the next few years. ### 2019 Data Deluge Prediction: Prepare Now The sheer volume of data produced today is staggering, doubling approximately every two years. But this is nothing compared to how data is set to grow over the next decade. Traditional approaches to managing your organizations' data struggle to keep up with today's scale and will prove to be entirely inadequate over the next few years. ### Komprise Product Deep Dive with Mike Peercy and Kumar Goswami  Mike Peercy, Founder and CTO, and Kumar Goswami, Founder and CEO, provide a product deep dive to explain the technical foundation of the Komprise Software solution. This includes a whiteboard map of the general architecture. ### HPE Referenzkonfigurationsleitfaden Dieses Referenzhandbuch bietet einen Überblick und Best Practices für die Datenverwaltung mit Komprise Intelligent Data Management und HPE Speicherservern für Datei und Objekt. Dieses Referenzhandbuch bietet einen Überblick und Best Practices für die Datenverwaltung mit Komprise Intelligent Data Management und HPE Speicherservern für Datei und Objekt. Read Guide ### Western Digital & Komprise: A Wiser Approach to Data Management  By combining Western Digital ActiveScale™ with Komprise Data Management, you can take advantage of the scalability, performance, and economics of private cloud object storage for more of your data. Komprise transparently moves your data from primary NAS to object storage, while preserving the file-based access on which your file hierarchies, applications, and users rely. ### Western Digital & Komprise: A Wiser Approach to Data Management By combining Western Digital ActiveScale™ with Komprise Data Management, you can take advantage of the scalability, performance, and economics of private cloud object storage for more of your data. Komprise transparently moves your data from primary NAS to object storage, while preserving the file-based access on which your file hierarchies, applications, and users rely. ### IDG-Bericht: Bedenken hinsichtlich des Datenwachstums Mit intelligentem Datenmanagement intelligenter mit dem Datenwachstum umgehen Das Datenvolumen wächst rasant, alle zwei Jahre mehr als verdoppelt, und die alten Ansätze zur Speicherverwaltung versagen. Dieses IDG-Whitepaper beleuchtet die wachsenden Datenprobleme mit einem Blick auf eine intelligentere Herangehensweise an das Problem. Wir nennen es Komprise. Bericht herunterladen Thanks for reaching out. We'll be in touch ### Get off Your Lazy NAS by Transforming Your Data Management Strategy It‘s not uncommon for primary NAS systems to contain mostly cold or infrequently accessed data, which takes up expensive capacity and slows down performance.Take 10 minutes to learn about a breakout solution that is quick, affordable and transparent. ### Get off Your Lazy NAS by Transforming Your Data Management Strategy It‘s not uncommon for primary NAS systems to contain mostly cold or infrequently accessed data, which takes up expensive capacity and slows down performance.Take 10 minutes to learn about a breakout solution that is quick, affordable and transparent. ### New Year, New NAS: Komprise for NAS Migration Migrating NAS file data can be a nightmare. Join VP of Engineering, Mohit Dhawan, as he walks through how Komprise eliminates the errors and the guesswork by automating the migration with a reliable solution that is resilient and handles network and storage glitches. ### Stop NAS Storage Growth with IBM and Komprise Data is growing fast—nearly 90 percent of the world’s data was created in the last two years, and enterprise data is doubling every two years. Join Komprise & IBM to learn how modern enterprises are transforming the way they manage their unstructured data management utilizing the joint solution. ### Migrate Unstructured Data to AWS with Zero Disruption Using Komprise How do you gracefully move file data from your current storage to the cloud without any headaches or downtime for your users or applications? How can you go beyond just copying data to the cloud to preserving full file-based access to data in S3? Download Slides ### Intelligent Data Management Watch how Komprise enables organizations like yours to gain visibility into their data, manage capacity growth, and cut costs — all without disrupting users or applications. Watch how Komprise enables organizations like yours to gain visibility into their data, manage capacity growth, and cut costs — all without disrupting users or applications. ### Intelligent Data Management Watch how Komprise enables organizations like yours to gain visibility into their data, manage capacity growth, and cut costs — all without disrupting users or applications. Watch how Komprise enables organizations like yours to gain visibility into their data, manage capacity growth, and cut costs — all without disrupting users or applications. ### Komprise for Media & Entertainment Komprise and IBM provide a comprehensive solution for managing data that provides media & entertainment organizations crucial analytics and insight into their data as well as the tools they need to lower storage costs by 70%+ without disruption to employees or mission-critical applications. Komprise and IBM provide a comprehensive solution for managing data that provides media & entertainment organizations crucial analytics and insight into their data as well as the tools they need to lower storage costs by 70%+ without disruption to employees or mission-critical applications. Learn more about Komprise for Media and Entertainment ### Komprise for Manufacturing Engineering and Design Komprise and IBM provide a comprehensive solution for managing data that provides manufacturing, engineering and design organizations crucial analytics and insight into their data as well as the tools they need to lower storage costs by 70%+ without disruption to employees or mission-critical applications. Komprise and IBM provide a comprehensive solution for managing data that provides manufacturing, engineering and design organizations crucial analytics and insight into their data as well as the tools they need to lower storage costs by 70%+ without disruption to employees or mission-critical applications. ### Komprise for Public Sector Komprise and IBM provide a comprehensive solution for managing data that provides public sector organizations crucial analytics and insight into their data as well as the tools they need to lower storage costs by 70%+ without disruption to employees or mission-critical applications. Komprise and IBM provide a comprehensive solution for managing data that provides public sector organizations crucial analytics and insight into their data as well as the tools they need to lower storage costs by 70%+ without disruption to employees or mission-critical applications. Learn more about Komprise for the Public Sector ### Komprise for Genomics & Life Sciences Komprise and IBM provide a comprehensive solution for managing data that provides genomics and life sciences organizations crucial analytics and insight into their data as well as the tools they need to lower storage costs by 70%+ without disruption to employees or mission-critical applications. Komprise and IBM provide a comprehensive solution for managing data that provides genomics and life sciences organizations crucial analytics and insight into their data as well as the tools they need to lower storage costs by 70%+ without disruption to employees or mission-critical applications. Learn more about Komprise for Genomics and Life Sciences How Komprise helped Pfizer create a cold data migration strategy migrate to the cloud ### Komprise For Automotive Komprise and IBM provide a comprehensive solution for managing data that provides automotive organizations crucial analytics and insight into their data as well as the tools they need to lower storage costs by 70%+ without disruption to employees or mission-critical application Komprise and IBM provide a comprehensive solution for managing data that provides automotive organizations crucial analytics and insight into their data as well as the tools they need to lower storage costs by 70%+ without disruption to employees or mission-critical application ### Komprise & IBM Storage Komprise and IBM provide a comprehensive solution for managing data that provides organizations crucial analytics and insight into their data as well as the tools they need to lower storage costs by 70%+ Join Krishna Subramanian, Komprise COO, and Eric Herzog, IBM Chief Marketing Officer and VP of Worldwide Storage Channels, as they discuss how Komprise & IBM provide a comprehensive solution for managing data that provides organizations crucial analytics and insight into their data as well as the tools they need to lower storage costs by 70%+ without disruption to employees or mission-critical applications. ### Komprise & IBM Storage Komprise and IBM provide a comprehensive solution for managing data that provides organizations crucial analytics and insight into their data as well as the tools they need to lower storage costs by 70%+ Join Krishna Subramanian, Komprise COO, and Eric Herzog, IBM Chief Marketing Officer and VP of Worldwide Storage Channels, as they discuss how Komprise & IBM provide a comprehensive solution for managing data that provides organizations crucial analytics and insight into their data as well as the tools they need to lower storage costs by 70%+ without disruption to employees or mission-critical applications. ### Komprise for Finance Komprise and IBM provide a comprehensive solution for managing data that provides Financial Services organizations crucial analytics and insight into their data as well as the tools they need to lower storage costs by 70%+ Komprise and IBM provide a comprehensive solution for managing data that provides Financial Services organizations crucial analytics and insight into their data as well as the tools they need to lower storage costs by 70%+ without disruption to employees or mission-critical applications. ### Genomics Firm Keeps Testing Affordable by Lowering Their Data Storage Costs By partnering with Komprise, a major genomics company is making high-quality genetic testing affordable by lowering the cost of preserving test data using Komprise Analytics Driven Automation and the cloud. A major genomics company lowered the cost of preserving test data using Komprise Analytics Driven Automation and the cloud, making genetics testing more affordable for more people. Read Case Study ### Genomics Firm Keeps Testing Affordable by Lowering Their Data Storage Costs By partnering with Komprise, a major genomics company is making high-quality genetic testing affordable by lowering the cost of preserving test data using Komprise Analytics Driven Automation and the cloud. A major genomics company lowered the cost of preserving test data using Komprise Intelligent Data Management for analytics-driven automation and the cloud data management, making genetics testing more affordable for more people. Read Case Study ### Komprise & Wasabi Komprise and Wasabi have partnered to help businesses slash storage costs and improve data protection. Komprise and Wasabi have partnered to help businesses slash storage costs and improve data protection. The integrated solution lets you seamlessly move infrequently accessed data to Wasabi to free up primary storage capacity, shrink backups, and better align storage costs with data value—all with no disruption to file-based users or applications. Read Datasheet ### Komprise & Wasabi Data Management Komprise and Wasabi have partnered to help businesses slash storage costs and improve data protection. Komprise and Wasabi have partnered to help businesses slash data storage costs and improve data protection. The integrated solution lets you seamlessly move infrequently accessed data (cold data) to Wasabi to free up primary storage capacity, shrink backups, and better align storage costs with data value—all with no disruption to file-based users or applications. Read Datasheet Learn more about Komprise Intelligent Data Management for Wasabi. ### Komprise 2.7 Announcement Migrating NAS file data can be a nightmare – Komprise eliminates the errors and the guesswork by automating the migration with a reliable solution that is resilient and handles network and storage glitches. Komprise Announces version 2.7 Key Features: Restricted Retrieval & Data Migration Migrating NAS file data can be a nightmare... Komprise eliminates the errors and the guesswork by automating the migration, using a reliable solution that is resilient and can handle both network and storage glitches. ### Realizing the HSM Promise Komprise is a powerful and flexible solution that provides all the features that were the goal of early HSM solutions. Analyst Review: Komprise Intelligent Data Management Komprise is a powerful and flexible solution that provides all the features that were the goal of early Hierarchical Storage Management (HSM) solutions. It provides automated and effective management of data throughout its entire lifecycle. With an analytics engine providing extensive metrics on all data along with the ability for the user to run “what if” simulations to gauge the effects of various data policies before they are implemented as well as recommendations on data placement. Read Report ### Komprise Intelligent Data Management Komprise CEO Kumar Goswami talks about how Komprise Intelligent Data Management helps transparently archive, replicate, and migrate the right data across NAS and Cloud. Komprise CEO Kumar Goswami talks about how Komprise Intelligent Data Management helps transparently archive, replicate, and migrate the right data across NAS and Cloud. ### Quantifying the Value of Intelligent Data Management How can you leverage cost-efficient secondary storage, such as cloud and object storage, without disrupting users or applications? How can Intelligent Data Management enable you to leverage cost-efficient secondary storage, such as cloud and object storage, without disrupting users or applications? The TCO metrics in this report are a culled aggregation of data from 10 organizations using Komprise to manage data across Network Attached Storage (NAS), object storage and cloud. The data footprint in the organizations varied from 100 terabytes of NAS data to 10 petabytes of NAS data. Typical NAS used were NetApp, EMC Isilon, and Windows File Servers. Average 3-Year Savings assed in this Komprise Business Value report. Download Komprise Unstructured Data Management TCO and ROI Report Thanks for reaching out. We'll be in touch ### Komprise for Migration Komprise Intelligent Data Management now includes NAS migrations as a data management capability, so you can eliminate the guesswork and sunk costs of migration. NAS Migrations are a reality and they happen every few years. Yet, they are time-consuming, error-prone, labor intensive, and disruptive. You are faced with either using free tools that are unreliable and require a lot of manual effort, or using expensive migration point products that are a sunk cost. Komprise Intelligent Data Management now includes NAS migrations as a data management capability, so you can eliminate the guesswork and sunk costs of migration. Read DataSheet ### Komprise & Scality Ring Scality and Komprise work together seamlessly to keep all data available and ensure that organizations and users get the most from their data and their data storage budgets. Data growth is a reality, and managing storage to optimize access and TCO can sometimes seem like opposing goals. They’re not. Scality and Komprise work together seamlessly to keep all data available and ensure that organizations and users get the most from their data and their data storage budgets. Read Case Study ### Komprise & Scality Ring Scality and Komprise work together seamlessly to keep all data available and ensure that organizations and users get the most from their data and their data storage budgets. Data growth is a reality, and managing storage to optimize access and TCO can sometimes seem like opposing goals. They’re not. Scality and Komprise work together seamlessly to keep all data available and ensure that organizations and users get the most from their unstructured data and their data storage budgets. Read Solution Brief ### Large Media & Entertainment Company Cuts Storage Costs with “Always-On” Live Media Archives Komprise identifies and transparently archives cold data to tape or cloud while keeping the data fully accessible in the same name space as the original files. Old media assets can no longer be offline – in today’s world of on-demand, how can you manage growing volumes of media at lower costs yet keep the archives readily available? Komprise identifies and transparently archives cold data to tape or cloud while keeping the data fully accessible in the same namespace as the original files. Read Case Study ### Transparently Extend Capacity with Komprise & Western Digital ActiveScale Komprise seamlessly extends NAS with the affordable capacity, scale and fast retrieval of Western Digital ActiveScale Komprise Intelligent Data Management seamlessly extends existing NAS with the affordable capacity, scale and fast retrieval of Western Digital ActiveScale while keeping all data available without changes to user or application access. Read Solution Brief ### Komprise Data Management und EMC Isilon, ECS Da unstrukturierte Daten weiterhin exponentiell anwachsen, müssen IT-Organisationen die Speicherkapazität und das Datenmanagement sowohl im On-Premise- als auch im Cloud-Speicher effizient skalieren. Da unstrukturierte Daten weiterhin exponentiell anwachsen, müssen IT-Organisationen die Speicherkapazität und das Datenmanagement sowohl im On-Premise- als auch im Cloud-Speicher effizient skalieren. Scale-out-Speicher wie EMC Isilon und ECS helfen Unternehmen dabei, mit weniger mehr zu erreichen – wie können Sie diese nahtlos nutzen, ohne kostspielige und komplexe Migrationen oder Benutzerunterbrechungen? Datenblatt lesen ### Komprise Data Management and Dell EMC Isilon, ECS As unstructured data continues to grow exponentially, IT organizations need to efficiently scale storage capacity and data management across both on-premise and cloud storage. As unstructured data continues to grow exponentially, IT organizations need to efficiently scale storage capacity and data management across both on-premises and cloud storage. Scale-out storage such as Dell EMC Isilon and ECS help businesses do more with less - how can you seamlessly leverage these without costly and complex data migrations or user disruption? Whether it's smart data migrations, transparent data tiering or AI-ready data to power your analytics initiatives, with Komprise Intelligent Data Management you get a no lock-in path to the cloud for your file and object data. With Komprise for Dell EMC Isilon / PowerScale, Unity, ECS you are able to: Get a single view of how NAS & object data is growing and being used across your storage silos. Identify what data is hot vs. what’s inactive and gone cold to be tiered, archived, replicated or moved to EMC targets (Dell EMC Isilon, Unity, ECS). Set policies for Isilon data migration projects, transparently tier and archive, or replicate to EMC targets and see your projected savings and ROI. With Komprise data migration and Transparent Move Technology, moved data is still accessible as files from your NAS or as files or objects in Dell storage environments without vendor lock-in. Native data access with no disruption to your users or applications. Migrate petabytes of data from any other NAS or object storage to Dell EMC Isilon or migrate or tier from Isilon to the cloud or other targets with full data fidelity, reliable, fast data transfer, and with Intelligent Data Management and Elastic Data Migration. Learn more about Komprise for Dell EMC data storage, including Isilon. Smart Data Migration from Isilon. Read Datasheet ### Cut NAS Costs & Streamline Cloud Operations With AWS & Komprise Komprise Intelligent Data Management identifies the cold data and based on your business objectives, transparently archives the data to AWS Komprise Intelligent Data Management identifies the cold data and based on your business objectives, transparently archives the data to more appropriate and cost-effective solutions, such as Amazon Web Services (AWS)—all without disruption to file-based access for users or applications. Read Solution Brief ### Cut NAS Costs & Streamline Cloud Operations With AWS & Komprise Komprise Intelligent Data Management identifies the cold data and based on your business objectives, transparently archives the data to AWS Komprise Intelligent Data Management identifies the cold data and based on your business objectives, transparently archives the data to more appropriate and cost-effective solutions, such as Amazon Web Services (AWS)—all without disruption to file-based access for users or applications. Read Solution Brief ### Curb NAS Expansion with Komprise & IBM Cloud IBM and Komprise, have teamed to help IT organizations extend Network File System NFS and SMB/CIFS storage. IBM and Komprise, Inc. have teamed to help IT organizations extend Network File System NFS and SMB/CIFS storage. Read Datasheet ### Manage Data Growth without Disruption: NetApp & Komprise Seamlessly migrate, archive, and replicate data, while shrinking backup costs, with a joint NetApp and Komprise solution. Seamlessly migrate, archive, and replicate data, while shrinking backup costs, with a joint NetApp and Komprise solution. Learn more about NetApp Cloud Migration solutions with Komprise. ### Intelligent Data Management: Staying Out-of-Band A data management solution should never get in the way of hot data access on your storage. It should not be in the network path, metadata path, or the data path itself. Join Komprise COO, Krishna Subramanian, for this informational series on the state of data management and the benefits of Intelligent Data Management. ### Intelligent Data Management: Scale-Out Architecture With today's rapid growth of data, it is important that your data management solution can scale as demands change. Join Komprise COO, Krishna Subramanian, for this informational series on the state of data management and the benefits of Intelligent Data Management. ### Intelligent Data Management: Adaptivity Data management should run in the background, adapting to your environment, without causing disruption to users or applications. Join Komprise COO, Krishna Subramanian, for this informational series on the state of data management and the benefits of Intelligent Data Management. ### Intelligent Data Management: Analytics & Data Visibility Gain visibility across your storage, into how your data is growing, how it is being used, and who is using it. Join Komprise COO, Krishna Subramanian, for this informational series on the state of data management and the benefits of Intelligent Data Management. ### Intelligent Data Management: Policy-Based Automation Policy-based automation helps you align with your business goals and makes managing data simple. Join Komprise COO, Krishna Subramanian, for this informational series on the state of data management and the benefits of Intelligent Data Management. ### Why You Need Intelligent Data Management Intelligent data management makes our lives simpler and makers managing data at scale possible. Join Komprise COO, Krishna Subramanian, for this informational series on the state of data management and the benefits of Intelligent Data Management. ### What's Wrong with Data Management Today? Most enterprises are managing all of their data in the same place where users put that data. Considering that 60-90% of data is cold within months of creation, this approach is highly inefficient and here's why... Join Komprise COO, Krishna Subramanian, for this informational series on the state of data management and the benefits of Intelligent Data Management. What is Data Management? Why Komprise? Smarter, Faster Data Management ### Komprise & NetApp StorageGrid NetApp and Komprise have partnered to enable businesses to seamlessly extend NFS and CIFS storage NetApp and Komprise have partnered to enable businesses to seamlessly extend NFS and CIFS storage with the scale and capacity of NetApp StorageGRID Webscale object storage without any disruption to users or applications Read Datasheet ### Komprise and NetApp StorageGrid Learn how to manage data growth efficiently without disrupting users in the hybrid cloud era. Learn how to manage data growth efficiently without disrupting users in the hybrid cloud era. This solution brief shows how Komprise and NetApp make it easy to know your data, manage it more effectively, cut cold data costs, and migrate without headaches. Read Solution Brief ### Genomics-Unternehmen spart 60% bei der Verwaltung seines Wachstums bei Sequenzierungsdaten Pacific BioSciences nutzt Komprise, um Einblicke in die Datennutzung und das Wachstum zu erhalten, und spart Kosten bei der Umstellung auf Speicher niedrigerer Ebenen. Pacific BioSciences nutzt Komprise, um Einblicke in die Datennutzung und das Wachstum zu erhalten, und spart Kosten bei der Umstellung auf Speicher niedrigerer Ebenen. Fallstudie lesen ### Cloudian & Komprise-Lösung Cloudian and Komprise let you move that data to on-premises archival storage and immediately reclaim 60% of your Tier 1 NAS capacity. Cloudian and Komprise let you move that data to on-premises archival storage and immediately reclaim 60% of your Tier 1 NAS capacity. Read Datasheet ### Cloudian & Komprise Solution Cloudian and Komprise let you move that data to on-premises archival storage and immediately reclaim 60% of your Tier 1 NAS capacity. Cloudian and Komprise let you move that data to on-premises archival storage and immediately reclaim 60% of your Tier 1 NAS capacity. Read Datasheet ### Financial Services Firm Cuts Zombie Data Costs Across NAS and Cloud A multinational finance and insurance firm realized they had a “zombie data” problem - data created by ex-employees who were no longer in the company, yet was being stored, replicated and protected the same way as hot data. A multinational finance and insurance firm realized they had a “zombie data” problem - data created by ex-employees who were no longer in the company, yet was being stored, replicated and protected the same way as hot data. Using Komprise, they identified and realized millions of dollars in savings. Read Case Study ## Research, Reports, and Guides > Original Komprise research and curated guides for enterprise IT leaders on AI data readiness, storage cost management, migration, and governance. ### Komprise 2025 AI Survey: AI, Data & Enterprise Risk Report 2025 Komprise IT Survey: AI, Data & Enterprise Risk   AI Puts a Shadow on Enterprise AI as Risks Get Real Komprise surveyed 200 IT directors and executives at U.S. enterprise organizations of 1000 employees and larger. The purpose of the survey was to discover how IT teams are preparing their unstructured data for AI and the challenges they are facing. The Komprise IT Survey: AI, Data & Enterprise Risk showed that: Nearly 80% of organizations have experienced negative data incidences with generative AI - with 13% resulting in financial, customer or reputational damage. The vast majority (90%) are concerned about shadow AI from a privacy and security standpoint, with 46% reporting that they are “extremely worried. The greatest challenge in preparing unstructured data for AI is finding and moving the right data to locations for AI ingestion (54%) followed by a lack of visibility into data. Download the report get all of the details along with the 5 key takeaways.      Download White Paper First Name* Last Name* Work Email* Company Name* Job Title* Phone* Country* --United StatesCanadaAfghanistanÅland IslandsAlbaniaAlgeriaAmerican SamoaAndorraAngolaAnguillaAntarcticaAntigua and BarbudaArgentinaArmeniaArubaAustraliaAustriaAzerbaijanBahamasBahrainBangladeshBarbadosBelarusBelgiumBelizeBeninBermudaBhutanBolivia, Plurinational State ofBosnia and HerzegovinaBotswanaBouvet IslandBrazilBritish Indian Ocean TerritoryBrunei DarussalamBulgariaBurkina FasoBurundiCambodiaCameroonCape VerdeCayman IslandsCentral African RepublicChadChileChinaChristmas IslandCocos (Keeling) IslandsColombiaComorosCongoCongo, The Democratic Republic of theCook IslandsCosta RicaCôte dIvoireCroatiaCubaCuraçaoCyprusCzechiaDenmarkDjiboutiDominicaDominican RepublicEcuadorEgyptEl SalvadorEquatorial GuineaEritreaEstoniaEthiopiaFalkland IslandsFaroe IslandsFijiFinlandFranceFrench GuianaFrench PolynesiaFrench Southern TerritoriesGabonGambiaGeorgiaGermanyGhanaGibraltarGreeceGreenlandGrenadaGuadeloupeGuamGuatemalaGuineaGuinea-BissauGuyanaHaitiHeard Island and McDonald IslandsHondurasHong KongHungaryIcelandIndiaIndonesiaIranIraqIrelandIsle of ManIsraelItalyJamaicaJapanJerseyJordanKazakhstanKenyaKiribatiKorea, Republic ofKuwaitKyrgyzstanLao People's Democratic RepublicLatviaLebanonLesothoLiberiaLibyan Arab JamahiriyaLiechtensteinLithuaniaLuxembourgMacaoMadagascarMalawiMalaysiaMaldivesMaliMaltaMarshall IslandsMartiniqueMauritaniaMauritiusMayotteMexicoMicronesia, Federated States ofMoldova, Republic ofMonacoMongoliaMontenegroMontserratMoroccoMozambiqueMyanmarNamibiaNauruNepalNetherlandsNetherlands AntillesNew CaledoniaNew ZealandNicaraguaNigerNigeriaNiueNorfolk IslandNorth MacedoniaNorthern Mariana IslandsNorwayOmanPakistanPalauPalestinian Territory, OccupiedPanamaPapua New GuineaParaguayPeruPhilippinesPitcairnPolandPortugalPuerto RicoQatarRéunionRomaniaRussiaRwandaSaint BarthélemySaint HelenaSaint Kitts and NevisSaint MartinSaint LuciaSaint Pierre and MiquelonSaint Vincent and the GrenadinesSamoaSan MarinoSão Tomé and PríncipeSaudi ArabiaSenegalSerbiaSeychellesSierra LeoneSingaporeSlovakiaSloveniaSolomon IslandsSomaliaSouth AfricaSouth SudanSpainSri LankaSudanSurinameSvalbard and Jan MayenSwazilandSwedenSwitzerlandSyrian Arab RepublicTaiwanTajikistanTanzania, United Republic ofThailandTimor-LesteTogoTokelauTongaTrinidad and TobagoTunisiaTurkeyTurkmenistanTurks and Caicos IslandsTuvaluUgandaUkraineUnited Arab EmiratesUnited KingdomUnited States Minor Outlying IslandsUruguayUzbekistanVanuatuVatican CityVenezuela, Bolivarian Republic ofVietnamVirgin Islands, BritishVirgin Islands, USWallis and FutunaWestern SaharaYemenZambiaZimbabwe State —Please choose an option— How did you hear about Komprise?*Search EngineSocial MediaMarketing EventBlog or PublicationRecommended by Friend or ColleagueRecommended by Industry AnalystRecommended by a PartnerOther Other By submitting this form, I confirm that I have read and agree to the Privacy Statement. Δ Read the 2025 Komprise IT Survey: AI, Data & Enterprise Risk. ### Komprise Reports for Unstructured Data Management To easily share metrics with stakeholders and gain buy-in for data storage and data management plans, Komprise provides a set of customizable report templates that make it easy to build and share insights across your organization. Always evolving, report templates are available for: Orphaned Data Potential Duplicates Showback Migrations Access Time Breakdown ...and the list continues to grow Read Solution Brief ### State of Unstructured Data Management Report 2024 The State of Unstructured Data Management 2024 Enterprise IT Builds AI Infrastructure on a Budget The fourth annual survey finds that most (70%) of enterprises are still experimenting with AI and “preparing for AI” remains a top data storage and data management priority for IT leaders. Yet leaders said that cost optimization is an even higher priority this year and they are trying to fit AI into existing IT budgets. Only 30% say they will increase their IT budgets to support AI projects. Read the press release. This report summarizes the responses of 300 global enterprise storage IT directors, VPs and C-level executives at companies with more than 1,000 employees in the United States. UNSTRUCTURED DATA MANAGEMENT 2024 REPORT COVERAGE IT Ops Times Intelligent CIO Diginomica datanami CFO Dive Storage Newsletter ...and more   Receive a Free Copy First Name* Last Name* Work Email* Company Name* Job Title* Phone* Country* --United StatesCanadaAfghanistanÅland IslandsAlbaniaAlgeriaAmerican SamoaAndorraAngolaAnguillaAntarcticaAntigua and BarbudaArgentinaArmeniaArubaAustraliaAustriaAzerbaijanBahamasBahrainBangladeshBarbadosBelarusBelgiumBelizeBeninBermudaBhutanBolivia, Plurinational State ofBosnia and HerzegovinaBotswanaBouvet IslandBrazilBritish Indian Ocean TerritoryBrunei DarussalamBulgariaBurkina FasoBurundiCambodiaCameroonCape VerdeCayman IslandsCentral African RepublicChadChileChinaChristmas IslandCocos (Keeling) IslandsColombiaComorosCongoCongo, The Democratic Republic of theCook IslandsCosta RicaCôte dIvoireCroatiaCubaCuraçaoCyprusCzechiaDenmarkDjiboutiDominicaDominican RepublicEcuadorEgyptEl SalvadorEquatorial GuineaEritreaEstoniaEthiopiaFalkland IslandsFaroe IslandsFijiFinlandFranceFrench GuianaFrench PolynesiaFrench Southern TerritoriesGabonGambiaGeorgiaGermanyGhanaGibraltarGreeceGreenlandGrenadaGuadeloupeGuamGuatemalaGuineaGuinea-BissauGuyanaHaitiHeard Island and McDonald IslandsHondurasHong KongHungaryIcelandIndiaIndonesiaIranIraqIrelandIsle of ManIsraelItalyJamaicaJapanJerseyJordanKazakhstanKenyaKiribatiKorea, Republic ofKuwaitKyrgyzstanLao People's Democratic RepublicLatviaLebanonLesothoLiberiaLibyan Arab JamahiriyaLiechtensteinLithuaniaLuxembourgMacaoMadagascarMalawiMalaysiaMaldivesMaliMaltaMarshall IslandsMartiniqueMauritaniaMauritiusMayotteMexicoMicronesia, Federated States ofMoldova, Republic ofMonacoMongoliaMontenegroMontserratMoroccoMozambiqueMyanmarNamibiaNauruNepalNetherlandsNetherlands AntillesNew CaledoniaNew ZealandNicaraguaNigerNigeriaNiueNorfolk IslandNorth MacedoniaNorthern Mariana IslandsNorwayOmanPakistanPalauPalestinian Territory, OccupiedPanamaPapua New GuineaParaguayPeruPhilippinesPitcairnPolandPortugalPuerto RicoQatarRéunionRomaniaRussiaRwandaSaint BarthélemySaint HelenaSaint Kitts and NevisSaint MartinSaint LuciaSaint Pierre and MiquelonSaint Vincent and the GrenadinesSamoaSan MarinoSão Tomé and PríncipeSaudi ArabiaSenegalSerbiaSeychellesSierra LeoneSingaporeSlovakiaSloveniaSolomon IslandsSomaliaSouth AfricaSouth SudanSpainSri LankaSudanSurinameSvalbard and Jan MayenSwazilandSwedenSwitzerlandSyrian Arab RepublicTaiwanTajikistanTanzania, United Republic ofThailandTimor-LesteTogoTokelauTongaTrinidad and TobagoTunisiaTurkeyTurkmenistanTurks and Caicos IslandsTuvaluUgandaUkraineUnited Arab EmiratesUnited KingdomUnited States Minor Outlying IslandsUruguayUzbekistanVanuatuVatican CityVenezuela, Bolivarian Republic ofVietnamVirgin Islands, BritishVirgin Islands, USWallis and FutunaWestern SaharaYemenZambiaZimbabwe State —Please choose an option— How did you hear about Komprise?*Search EngineSocial MediaMarketing EventBlog or PublicationRecommended by Friend or ColleagueRecommended by Industry AnalystRecommended by a PartnerOther Other By submitting this form, I confirm that I have read and agree to the Privacy Statement. Δ Unstructured data management highlights:  Top data storage priority: Cost Optimization 57% say preparing for AI is the top business challenge for unstructured data management 44% are creating AI-ready infrastructure and 32% are building their own learning models 47% say AI data governance/security is the top future capability, up from 28% in 2023 Download the report today to understand the primary unstructured data management challenges and opportunities to deliver greater cost savings and data value. ### Transparent Data Tiering Between FlashBlade//S and FlashBlade//E with Komprise Transparent data tiering between FlashBlade//S  and FlashBlade//E  with Komprise Pure Storage technical white paper to manage data intelligently Komprise gives enterprises the ability to intelligently manage their data by identifying rarely accessed data from FlashBlade//S and transparently tiering it to FlashBlade//E without any changes to the user or application access. The combination of Komprise Intelligent Data Management with the FlashBlade line of high-performance, resilient storage ensures optimal cost/performance ROI. Read this Pure Storage FlashBlade white paper to further understand the need for transparent data tiering, suggested architecture, solution validations and the benefits. The paper reviews: Data Management Challenges Pure Storage FlashBlade Overview Komprise Intelligent Data Management Solution Validation and Benefits Learn more about Komprise for Pure Storage. Download White Paper First Name* Last Name* Work Email* Company Name* Job Title* Phone* Country* --United StatesCanadaAfghanistanÅland IslandsAlbaniaAlgeriaAmerican SamoaAndorraAngolaAnguillaAntarcticaAntigua and BarbudaArgentinaArmeniaArubaAustraliaAustriaAzerbaijanBahamasBahrainBangladeshBarbadosBelarusBelgiumBelizeBeninBermudaBhutanBolivia, Plurinational State ofBosnia and HerzegovinaBotswanaBouvet IslandBrazilBritish Indian Ocean TerritoryBrunei DarussalamBulgariaBurkina FasoBurundiCambodiaCameroonCape VerdeCayman IslandsCentral African RepublicChadChileChinaChristmas IslandCocos (Keeling) IslandsColombiaComorosCongoCongo, The Democratic Republic of theCook IslandsCosta RicaCôte dIvoireCroatiaCubaCuraçaoCyprusCzechiaDenmarkDjiboutiDominicaDominican RepublicEcuadorEgyptEl SalvadorEquatorial GuineaEritreaEstoniaEthiopiaFalkland IslandsFaroe IslandsFijiFinlandFranceFrench GuianaFrench PolynesiaFrench Southern TerritoriesGabonGambiaGeorgiaGermanyGhanaGibraltarGreeceGreenlandGrenadaGuadeloupeGuamGuatemalaGuineaGuinea-BissauGuyanaHaitiHeard Island and McDonald IslandsHondurasHong KongHungaryIcelandIndiaIndonesiaIranIraqIrelandIsle of ManIsraelItalyJamaicaJapanJerseyJordanKazakhstanKenyaKiribatiKorea, Republic ofKuwaitKyrgyzstanLao People's Democratic RepublicLatviaLebanonLesothoLiberiaLibyan Arab JamahiriyaLiechtensteinLithuaniaLuxembourgMacaoMadagascarMalawiMalaysiaMaldivesMaliMaltaMarshall IslandsMartiniqueMauritaniaMauritiusMayotteMexicoMicronesia, Federated States ofMoldova, Republic ofMonacoMongoliaMontenegroMontserratMoroccoMozambiqueMyanmarNamibiaNauruNepalNetherlandsNetherlands AntillesNew CaledoniaNew ZealandNicaraguaNigerNigeriaNiueNorfolk IslandNorth MacedoniaNorthern Mariana IslandsNorwayOmanPakistanPalauPalestinian Territory, OccupiedPanamaPapua New GuineaParaguayPeruPhilippinesPitcairnPolandPortugalPuerto RicoQatarRéunionRomaniaRussiaRwandaSaint BarthélemySaint HelenaSaint Kitts and NevisSaint MartinSaint LuciaSaint Pierre and MiquelonSaint Vincent and the GrenadinesSamoaSan MarinoSão Tomé and PríncipeSaudi ArabiaSenegalSerbiaSeychellesSierra LeoneSingaporeSlovakiaSloveniaSolomon IslandsSomaliaSouth AfricaSouth SudanSpainSri LankaSudanSurinameSvalbard and Jan MayenSwazilandSwedenSwitzerlandSyrian Arab RepublicTaiwanTajikistanTanzania, United Republic ofThailandTimor-LesteTogoTokelauTongaTrinidad and TobagoTunisiaTurkeyTurkmenistanTurks and Caicos IslandsTuvaluUgandaUkraineUnited Arab EmiratesUnited KingdomUnited States Minor Outlying IslandsUruguayUzbekistanVanuatuVatican CityVenezuela, Bolivarian Republic ofVietnamVirgin Islands, BritishVirgin Islands, USWallis and FutunaWestern SaharaYemenZambiaZimbabwe State —Please choose an option— How did you hear about Komprise?*Search EngineSocial MediaMarketing EventBlog or PublicationRecommended by Friend or ColleagueRecommended by Industry AnalystRecommended by a PartnerOther Other By submitting this form, I confirm that I have read and agree to the Privacy Statement. Δ Komprise along with both Pure Storage FlashBlade//S and FlashBlade//E offers a unique proposition for enterprises to tier their inactive out of high-performance, top tier FlashBlade//S to the capacity and cost optimized FlashBlade//E. Read this paper to understand the benefits of Komprise + Pure Storage. ### State of Unstructured Data Management Report 2023 The State of Unstructured Data Management 2023 ** Download the Latest Report ** The third annual survey finds that IT and business leaders are largely allowing employee use of generative AI but the majority (66%) are most concerned about the data governance risks from AI, including privacy, security and the lack of data source transparency in vendor solutions. This report summarizes the responses of 300 global enterprise storage IT directors, VPs and C-level executives at decision makers at companies with more than 1,000 employees in the United States and in the UK. UNSTRUCTURED DATA MANAGEMENT REPORT COVERAGE eWeek datanami Blocks & Files BetaNews SilverLinings …and more Receive a Free Copy First Name* Last Name* Work Email* Company Name* Job Title* Phone* Country* --United StatesCanadaAfghanistanÅland IslandsAlbaniaAlgeriaAmerican SamoaAndorraAngolaAnguillaAntarcticaAntigua and BarbudaArgentinaArmeniaArubaAustraliaAustriaAzerbaijanBahamasBahrainBangladeshBarbadosBelarusBelgiumBelizeBeninBermudaBhutanBolivia, Plurinational State ofBosnia and HerzegovinaBotswanaBouvet IslandBrazilBritish Indian Ocean TerritoryBrunei DarussalamBulgariaBurkina FasoBurundiCambodiaCameroonCape VerdeCayman IslandsCentral African RepublicChadChileChinaChristmas IslandCocos (Keeling) IslandsColombiaComorosCongoCongo, The Democratic Republic of theCook IslandsCosta RicaCôte dIvoireCroatiaCubaCuraçaoCyprusCzechiaDenmarkDjiboutiDominicaDominican RepublicEcuadorEgyptEl SalvadorEquatorial GuineaEritreaEstoniaEthiopiaFalkland IslandsFaroe IslandsFijiFinlandFranceFrench GuianaFrench PolynesiaFrench Southern TerritoriesGabonGambiaGeorgiaGermanyGhanaGibraltarGreeceGreenlandGrenadaGuadeloupeGuamGuatemalaGuineaGuinea-BissauGuyanaHaitiHeard Island and McDonald IslandsHondurasHong KongHungaryIcelandIndiaIndonesiaIranIraqIrelandIsle of ManIsraelItalyJamaicaJapanJerseyJordanKazakhstanKenyaKiribatiKorea, Republic ofKuwaitKyrgyzstanLao People's Democratic RepublicLatviaLebanonLesothoLiberiaLibyan Arab JamahiriyaLiechtensteinLithuaniaLuxembourgMacaoMadagascarMalawiMalaysiaMaldivesMaliMaltaMarshall IslandsMartiniqueMauritaniaMauritiusMayotteMexicoMicronesia, Federated States ofMoldova, Republic ofMonacoMongoliaMontenegroMontserratMoroccoMozambiqueMyanmarNamibiaNauruNepalNetherlandsNetherlands AntillesNew CaledoniaNew ZealandNicaraguaNigerNigeriaNiueNorfolk IslandNorth MacedoniaNorthern Mariana IslandsNorwayOmanPakistanPalauPalestinian Territory, OccupiedPanamaPapua New GuineaParaguayPeruPhilippinesPitcairnPolandPortugalPuerto RicoQatarRéunionRomaniaRussiaRwandaSaint BarthélemySaint HelenaSaint Kitts and NevisSaint MartinSaint LuciaSaint Pierre and MiquelonSaint Vincent and the GrenadinesSamoaSan MarinoSão Tomé and PríncipeSaudi ArabiaSenegalSerbiaSeychellesSierra LeoneSingaporeSlovakiaSloveniaSolomon IslandsSomaliaSouth AfricaSouth SudanSpainSri LankaSudanSurinameSvalbard and Jan MayenSwazilandSwedenSwitzerlandSyrian Arab RepublicTaiwanTajikistanTanzania, United Republic ofThailandTimor-LesteTogoTokelauTongaTrinidad and TobagoTunisiaTurkeyTurkmenistanTurks and Caicos IslandsTuvaluUgandaUkraineUnited Arab EmiratesUnited KingdomUnited States Minor Outlying IslandsUruguayUzbekistanVanuatuVatican CityVenezuela, Bolivarian Republic ofVietnamVirgin Islands, BritishVirgin Islands, USWallis and FutunaWestern SaharaYemenZambiaZimbabwe State —Please choose an option— How did you hear about Komprise?*Search EngineSocial MediaMarketing EventBlog or PublicationRecommended by Friend or ColleagueRecommended by Industry AnalystRecommended by a PartnerOther Other By submitting this form, I confirm that I have read and agree to the Privacy Statement. Δ Unstructured data management highlights:  Top data storage priority: Preparing for AI 66% say data governance is a top GenAI concern 32% manage more than 10PB of data 85% say non-IT users should help manage their data 73% spend 30% plus of their IT budget on data storage Download the latest report today to understand the primary unstructured data management challenges and opportunities to deliver greater cost savings and data value. Also read the paper: Unstructured Data Management in the Age of AI. ### State of Unstructured Data Management Report 2022 The State of Unstructured Data Management 2022 ** Read the Latest Report **   IT Leaders are Investing in Unstructured Data Analytics Unstructured data has reached a tipping point for cost and complexity. IT leaders indicate greater urgency to manage data efficiently for cost savings and help end users find new insights from growing unstructured data volumes. This report summarizes the responses of 300 global enterprise storage IT directors, VPs and C-level executives at decision makers at companies with more than 1,000 employees in the United States and in the UK. Receive a Free Copy First Name* Last Name* Work Email* Company Name* Job Title* Phone* Country* --United StatesCanadaAfghanistanÅland IslandsAlbaniaAlgeriaAmerican SamoaAndorraAngolaAnguillaAntarcticaAntigua and BarbudaArgentinaArmeniaArubaAustraliaAustriaAzerbaijanBahamasBahrainBangladeshBarbadosBelarusBelgiumBelizeBeninBermudaBhutanBolivia, Plurinational State ofBosnia and HerzegovinaBotswanaBouvet IslandBrazilBritish Indian Ocean TerritoryBrunei DarussalamBulgariaBurkina FasoBurundiCambodiaCameroonCape VerdeCayman IslandsCentral African RepublicChadChileChinaChristmas IslandCocos (Keeling) IslandsColombiaComorosCongoCongo, The Democratic Republic of theCook IslandsCosta RicaCôte dIvoireCroatiaCubaCuraçaoCyprusCzechiaDenmarkDjiboutiDominicaDominican RepublicEcuadorEgyptEl SalvadorEquatorial GuineaEritreaEstoniaEthiopiaFalkland IslandsFaroe IslandsFijiFinlandFranceFrench GuianaFrench PolynesiaFrench Southern TerritoriesGabonGambiaGeorgiaGermanyGhanaGibraltarGreeceGreenlandGrenadaGuadeloupeGuamGuatemalaGuineaGuinea-BissauGuyanaHaitiHeard Island and McDonald IslandsHondurasHong KongHungaryIcelandIndiaIndonesiaIranIraqIrelandIsle of ManIsraelItalyJamaicaJapanJerseyJordanKazakhstanKenyaKiribatiKorea, Republic ofKuwaitKyrgyzstanLao People's Democratic RepublicLatviaLebanonLesothoLiberiaLibyan Arab JamahiriyaLiechtensteinLithuaniaLuxembourgMacaoMadagascarMalawiMalaysiaMaldivesMaliMaltaMarshall IslandsMartiniqueMauritaniaMauritiusMayotteMexicoMicronesia, Federated States ofMoldova, Republic ofMonacoMongoliaMontenegroMontserratMoroccoMozambiqueMyanmarNamibiaNauruNepalNetherlandsNetherlands AntillesNew CaledoniaNew ZealandNicaraguaNigerNigeriaNiueNorfolk IslandNorth MacedoniaNorthern Mariana IslandsNorwayOmanPakistanPalauPalestinian Territory, OccupiedPanamaPapua New GuineaParaguayPeruPhilippinesPitcairnPolandPortugalPuerto RicoQatarRéunionRomaniaRussiaRwandaSaint BarthélemySaint HelenaSaint Kitts and NevisSaint MartinSaint LuciaSaint Pierre and MiquelonSaint Vincent and the GrenadinesSamoaSan MarinoSão Tomé and PríncipeSaudi ArabiaSenegalSerbiaSeychellesSierra LeoneSingaporeSlovakiaSloveniaSolomon IslandsSomaliaSouth AfricaSouth SudanSpainSri LankaSudanSurinameSvalbard and Jan MayenSwazilandSwedenSwitzerlandSyrian Arab RepublicTaiwanTajikistanTanzania, United Republic ofThailandTimor-LesteTogoTokelauTongaTrinidad and TobagoTunisiaTurkeyTurkmenistanTurks and Caicos IslandsTuvaluUgandaUkraineUnited Arab EmiratesUnited KingdomUnited States Minor Outlying IslandsUruguayUzbekistanVanuatuVatican CityVenezuela, Bolivarian Republic ofVietnamVirgin Islands, BritishVirgin Islands, USWallis and FutunaWestern SaharaYemenZambiaZimbabwe State —Please choose an option— How did you hear about Komprise?*Search EngineSocial MediaMarketing EventBlog or PublicationRecommended by Friend or ColleagueRecommended by Industry AnalystRecommended by a PartnerOther Other By submitting this form, I confirm that I have read and agree to the Privacy Statement. Δ Unstructured data management trends include: More than 50% of enterprise IT are managing at least 5 PB of data today. In 2022, 87% of IT leaders rate managing unstructured data growth as a top priority, up from 70% in 2021. A majority (65%) of organizations plan to or are already delivering unstructured data to big data platforms. Download the latest report today to understand the primary unstructured data management challenges and opportunities to deliver greater cost savings and data value. Unstructured Data Management Report Coverage VentureBeat TDWI SpiceWorks TechHQ ITProToday ...and more ### State of Unstructured Data Management Report 2021 Infographic Komprise hired a third-party to survey IT and storage directors at large enterprises in the U.S. and U.K., in June 2021. The survey covers unstructured data growth and storage trends plus goals and challenges for data management. As a preview to the survey report, this infographic reveals five of the top stats. View Infographic Be sure to review the latest Sate of Unstructured Data Management Report     Interested in jumping straight into the report instead? Unstructured Data Management Report 2022 ------------------- ### State of Unstructured Data Management Report | 2021 The State of Unstructured Data Management  Komprise Survey Finds IT Leaders Lack Insights for Hybrid Cloud Unstructured Data Management   Download the 2022 State of Unstructured Data Management Report Persistent data growth is straining IT budgets, causing more organizations to prioritize cloud data migrations — but data visibility, planning and management across hybrid clouds remains a key roadblock. The 2021 Komprise Unstructured Data Management Report examines the challenges and opportunities with unstructured data in the enterprise—from how much data enterprises are managing, to cloud data priorities and future approaches for data management. This report summarizes responses of 300 global enterprise storage IT decision makers at companies with more than 1,000 employees in the United States and in the UK. All respondents work at the IT manager level or above, across IT/technology operations teams. Highlights of the survey include: 65.5% of organizations spend +30% of IT budgets on data storage and management. 44.5% want better visibility for planning. Investing in analytics tools is the highest priority (45%) over buying more cloud or on-prem storage or modernizing backups. Download Report First Name* Last Name* Work Email* Company Name* Job Title* Phone* Country* --United StatesCanadaAfghanistanÅland IslandsAlbaniaAlgeriaAmerican SamoaAndorraAngolaAnguillaAntarcticaAntigua and BarbudaArgentinaArmeniaArubaAustraliaAustriaAzerbaijanBahamasBahrainBangladeshBarbadosBelarusBelgiumBelizeBeninBermudaBhutanBolivia, Plurinational State ofBosnia and HerzegovinaBotswanaBouvet IslandBrazilBritish Indian Ocean TerritoryBrunei DarussalamBulgariaBurkina FasoBurundiCambodiaCameroonCape VerdeCayman IslandsCentral African RepublicChadChileChinaChristmas IslandCocos (Keeling) IslandsColombiaComorosCongoCongo, The Democratic Republic of theCook IslandsCosta RicaCôte dIvoireCroatiaCubaCuraçaoCyprusCzechiaDenmarkDjiboutiDominicaDominican RepublicEcuadorEgyptEl SalvadorEquatorial GuineaEritreaEstoniaEthiopiaFalkland IslandsFaroe IslandsFijiFinlandFranceFrench GuianaFrench PolynesiaFrench Southern TerritoriesGabonGambiaGeorgiaGermanyGhanaGibraltarGreeceGreenlandGrenadaGuadeloupeGuamGuatemalaGuineaGuinea-BissauGuyanaHaitiHeard Island and McDonald IslandsHondurasHong KongHungaryIcelandIndiaIndonesiaIranIraqIrelandIsle of ManIsraelItalyJamaicaJapanJerseyJordanKazakhstanKenyaKiribatiKorea, Republic ofKuwaitKyrgyzstanLao People's Democratic RepublicLatviaLebanonLesothoLiberiaLibyan Arab JamahiriyaLiechtensteinLithuaniaLuxembourgMacaoMadagascarMalawiMalaysiaMaldivesMaliMaltaMarshall IslandsMartiniqueMauritaniaMauritiusMayotteMexicoMicronesia, Federated States ofMoldova, Republic ofMonacoMongoliaMontenegroMontserratMoroccoMozambiqueMyanmarNamibiaNauruNepalNetherlandsNetherlands AntillesNew CaledoniaNew ZealandNicaraguaNigerNigeriaNiueNorfolk IslandNorth MacedoniaNorthern Mariana IslandsNorwayOmanPakistanPalauPalestinian Territory, OccupiedPanamaPapua New GuineaParaguayPeruPhilippinesPitcairnPolandPortugalPuerto RicoQatarRéunionRomaniaRussiaRwandaSaint BarthélemySaint HelenaSaint Kitts and NevisSaint MartinSaint LuciaSaint Pierre and MiquelonSaint Vincent and the GrenadinesSamoaSan MarinoSão Tomé and PríncipeSaudi ArabiaSenegalSerbiaSeychellesSierra LeoneSingaporeSlovakiaSloveniaSolomon IslandsSomaliaSouth AfricaSouth SudanSpainSri LankaSudanSurinameSvalbard and Jan MayenSwazilandSwedenSwitzerlandSyrian Arab RepublicTaiwanTajikistanTanzania, United Republic ofThailandTimor-LesteTogoTokelauTongaTrinidad and TobagoTunisiaTurkeyTurkmenistanTurks and Caicos IslandsTuvaluUgandaUkraineUnited Arab EmiratesUnited KingdomUnited States Minor Outlying IslandsUruguayUzbekistanVanuatuVatican CityVenezuela, Bolivarian Republic ofVietnamVirgin Islands, BritishVirgin Islands, USWallis and FutunaWestern SaharaYemenZambiaZimbabwe State —Please choose an option— How did you hear about Komprise?*Search EngineSocial MediaMarketing EventBlog or PublicationRecommended by Friend or ColleagueRecommended by Industry AnalystRecommended by a PartnerOther Other By submitting this form, I confirm that I have read and agree to the Privacy Statement. Δ Download the 2022 State of Unstructured Data Management Report “The survey shows that enterprises want analytics and systematic data management to make the best decisions on cloud migrations and archiving. The end goal is to cut storage costs and create new value from unstructured data over time.” -Krishna Subramanian, President and COO of Komprise ### State of Unstructured Data Management Report The State of Unstructured Data Management  Komprise Survey Finds IT Leaders Lack Insights for Hybrid Cloud Unstructured Data Management Persistent data growth is straining IT budgets, causing more organizations to prioritize cloud data migrations — but data visibility, planning and management across hybrid clouds remains a key roadblock. The 2021 Komprise Unstructured Data Management Report examines the challenges and opportunities with unstructured data in the enterprise—from how much data enterprises are managing, to cloud data priorities and future approaches for data management. This report summarizes responses of 300 global enterprise storage IT decision makers at companies with more than 1,000 employees in the United States and in the UK. All respondents work at the IT manager level or above, across IT/technology operations teams. Highlights of the survey include: 65.5% of organizations spend +30% of IT budgets on data storage and management. 44.5% want better visibility for planning. Investing in analytics tools is the highest priority (45%) over buying more cloud or on-prem storage or modernizing backups. Download Report First Name* Last Name* Work Email* Company Name* Job Title* Phone* Country* --United StatesCanadaAfghanistanÅland IslandsAlbaniaAlgeriaAmerican SamoaAndorraAngolaAnguillaAntarcticaAntigua and BarbudaArgentinaArmeniaArubaAustraliaAustriaAzerbaijanBahamasBahrainBangladeshBarbadosBelarusBelgiumBelizeBeninBermudaBhutanBolivia, Plurinational State ofBosnia and HerzegovinaBotswanaBouvet IslandBrazilBritish Indian Ocean TerritoryBrunei DarussalamBulgariaBurkina FasoBurundiCambodiaCameroonCape VerdeCayman IslandsCentral African RepublicChadChileChinaChristmas IslandCocos (Keeling) IslandsColombiaComorosCongoCongo, The Democratic Republic of theCook IslandsCosta RicaCôte dIvoireCroatiaCubaCuraçaoCyprusCzechiaDenmarkDjiboutiDominicaDominican RepublicEcuadorEgyptEl SalvadorEquatorial GuineaEritreaEstoniaEthiopiaFalkland IslandsFaroe IslandsFijiFinlandFranceFrench GuianaFrench PolynesiaFrench Southern TerritoriesGabonGambiaGeorgiaGermanyGhanaGibraltarGreeceGreenlandGrenadaGuadeloupeGuamGuatemalaGuineaGuinea-BissauGuyanaHaitiHeard Island and McDonald IslandsHondurasHong KongHungaryIcelandIndiaIndonesiaIranIraqIrelandIsle of ManIsraelItalyJamaicaJapanJerseyJordanKazakhstanKenyaKiribatiKorea, Republic ofKuwaitKyrgyzstanLao People's Democratic RepublicLatviaLebanonLesothoLiberiaLibyan Arab JamahiriyaLiechtensteinLithuaniaLuxembourgMacaoMadagascarMalawiMalaysiaMaldivesMaliMaltaMarshall IslandsMartiniqueMauritaniaMauritiusMayotteMexicoMicronesia, Federated States ofMoldova, Republic ofMonacoMongoliaMontenegroMontserratMoroccoMozambiqueMyanmarNamibiaNauruNepalNetherlandsNetherlands AntillesNew CaledoniaNew ZealandNicaraguaNigerNigeriaNiueNorfolk IslandNorth MacedoniaNorthern Mariana IslandsNorwayOmanPakistanPalauPalestinian Territory, OccupiedPanamaPapua New GuineaParaguayPeruPhilippinesPitcairnPolandPortugalPuerto RicoQatarRéunionRomaniaRussiaRwandaSaint BarthélemySaint HelenaSaint Kitts and NevisSaint MartinSaint LuciaSaint Pierre and MiquelonSaint Vincent and the GrenadinesSamoaSan MarinoSão Tomé and PríncipeSaudi ArabiaSenegalSerbiaSeychellesSierra LeoneSingaporeSlovakiaSloveniaSolomon IslandsSomaliaSouth AfricaSouth SudanSpainSri LankaSudanSurinameSvalbard and Jan MayenSwazilandSwedenSwitzerlandSyrian Arab RepublicTaiwanTajikistanTanzania, United Republic ofThailandTimor-LesteTogoTokelauTongaTrinidad and TobagoTunisiaTurkeyTurkmenistanTurks and Caicos IslandsTuvaluUgandaUkraineUnited Arab EmiratesUnited KingdomUnited States Minor Outlying IslandsUruguayUzbekistanVanuatuVatican CityVenezuela, Bolivarian Republic ofVietnamVirgin Islands, BritishVirgin Islands, USWallis and FutunaWestern SaharaYemenZambiaZimbabwe State —Please choose an option— How did you hear about Komprise?*Search EngineSocial MediaMarketing EventBlog or PublicationRecommended by Friend or ColleagueRecommended by Industry AnalystRecommended by a PartnerOther Other By submitting this form, I confirm that I have read and agree to the Privacy Statement. Δ “The survey shows that enterprises want analytics and systematic data management to make the best decisions on cloud migrations and archiving. The end goal is to cut storage costs and create new value from unstructured data over time.” -Krishna Subramanian, President and COO of Komprise ### Cloud Data Migration Checklist Make sure your solution has what it takes to take the dread out of data migrations. Cloud migration of file data can be complex, labor-intensive, costly, and time-consuming. Understanding your data migration options can help. Free Tools: Require a lot of babysitting and do not reliably migrate the data. Point Data Migration Solutions: Have complex legacy architectures and create sunk costs. Komprise Elastic Data Migration: Makes cloud data migrations simple, fast, reliable and eliminates sunk costs since you continue to use Komprise after the migration. Know your cloud data migration choices. Komprise is the only solution that gives you the option to cut 70%+ cloud storage costs by placing cold data in object classes while maintaining file metadata so it can be promoted in the cloud as files when needed. Learn more about Smart Data Migration. Make sure your cloud file migration solution has what it takes to take the dread out of data migrations. View Checklist ### How to Manage Cloud Costs and Accelerate Cloud Data Migration With cloud adoption skyrocketing, managing cloud costs and accelerating cloud migrations are top priorities. This white paper shows how an analytics-driven approach to data management can save 50% of cloud data storage costs while simplifying cloud data migrations.The following topics are covered: The challenges of managing cloud data Multicloud data management and cloud cost optimization How to cut cloud storage costs in half A cost comparison managing data in AWS with Komprise Read White Paper Learn more about optimizing cloud data and cloud costs ### The Register - IT Buyer’s Guide for Storage Management An evaluation and decision guide for IT leaders and professionals In this IT Buyer’s Guide from The Register, learn why traditional storage management won’t cut it with today’s massive data growth. Learn what to look for in a solution with a more granular and analytical approach, and find out how to assess which one is best for you to save the most. Analytics-driven Storage Management An evaluation and decision guide for IT leaders and professionals In this IT Buyer’s Guide from The Register, learn why traditional storage management won’t cut it with today’s massive unstructured data growth. Learn what to look for in a solution with a more granular and analytical approach, and find out how to assess which one is best for you to save the most.   Download The Register Storage Report Thanks for reaching out. We'll be in touch ### Extending NAS to Google Cloud Storage with Komprise Classic NAS has become an expensive tier of storage for seldom-accessed data. Learn how to use the Google Cloud Platform (GCP) service Cloud Storage and Komprise to actively archive and replicate data to the Google Cloud without disrupting users and applications. Read White Paper ### Extending NAS to Google Cloud Storage with Komprise Classic NAS has become an expensive tier of storage for seldom-accessed data. Learn how to use the Google Cloud Platform (GCP) service Cloud Storage and Komprise to actively archive and replicate data to the Google Cloud without disrupting users and applications. Read White Paper ### GigaOm: Market Landscape Report: Unstructured Data Management for the Cloud Era MARKET LANDSCAPE REPORT: Unstructured Data Management for the Cloud Era. Unstructured data growth is hardly news anymore. In fact, the challenge is no longer exponential growth, which we are now accustom to and have solutions for, but it is all about keeping data safe while giving access to users, applications, and devices distributed globally, as well as having control over it. . Unstructured data growth is hardly news anymore. In fact, the challenge is no longer exponential growth, which we are now accustom to and have solutions for, but it is all about keeping data safe while giving access to users, applications, and devices distributed globally, as well as having control it. The right strategy and modern tools can help take back control of data and exploit its value, transforming it from a liability to an asset and contributing towards increasing competitiveness. Download Report Thanks for reaching out. We'll be in touch ### HPE Reference Configuration Guide This reference guide provides an overview and best practices for data management with Komprise Intelligent Data Management and HPE storage servers for file and object. This reference guide provides an overview and best practices for data management with Komprise Intelligent Data Management and HPE storage servers for file and object. Read Guide ### White Paper: Stubs and Symbolic Links The “Information Big Bang” explosion happened a few years ago and we are now in a rapid digital expansion similar to the first few years after the original one that formed our universe. Thanks to lightning-fast adoption of applications like 4k (soon 8K) video, medical imaging, genomics, ADAS (Advanced Driver Automotive Systems), IoT and AI-based analytics, our data universe, much like the physical universe, is also rapidly expanding. Read White Paper ### IDG Report: Data Growth Concerns Getting Smart about Data Growth with Intelligent Data Management Data is rapidly growing, more than doubling every two years, and the legacy approaches addressing storage are failing. This IDG white paper highlights the growing unstructured data problems with a look at a smarter way to approach the issue. We call it, Komprise. Download IDG Unstructured Data Management Report First Name* Last Name* Work Email* Company Name* Job Title* Phone* Country* --United StatesCanadaAfghanistanÅland IslandsAlbaniaAlgeriaAmerican SamoaAndorraAngolaAnguillaAntarcticaAntigua and BarbudaArgentinaArmeniaArubaAustraliaAustriaAzerbaijanBahamasBahrainBangladeshBarbadosBelarusBelgiumBelizeBeninBermudaBhutanBolivia, Plurinational State ofBosnia and HerzegovinaBotswanaBouvet IslandBrazilBritish Indian Ocean TerritoryBrunei DarussalamBulgariaBurkina FasoBurundiCambodiaCameroonCape VerdeCayman IslandsCentral African RepublicChadChileChinaChristmas IslandCocos (Keeling) IslandsColombiaComorosCongoCongo, The Democratic Republic of theCook IslandsCosta RicaCôte dIvoireCroatiaCubaCuraçaoCyprusCzechiaDenmarkDjiboutiDominicaDominican RepublicEcuadorEgyptEl SalvadorEquatorial GuineaEritreaEstoniaEthiopiaFalkland IslandsFaroe IslandsFijiFinlandFranceFrench GuianaFrench PolynesiaFrench Southern TerritoriesGabonGambiaGeorgiaGermanyGhanaGibraltarGreeceGreenlandGrenadaGuadeloupeGuamGuatemalaGuineaGuinea-BissauGuyanaHaitiHeard Island and McDonald IslandsHondurasHong KongHungaryIcelandIndiaIndonesiaIranIraqIrelandIsle of ManIsraelItalyJamaicaJapanJerseyJordanKazakhstanKenyaKiribatiKorea, Republic ofKuwaitKyrgyzstanLao People's Democratic RepublicLatviaLebanonLesothoLiberiaLibyan Arab JamahiriyaLiechtensteinLithuaniaLuxembourgMacaoMadagascarMalawiMalaysiaMaldivesMaliMaltaMarshall IslandsMartiniqueMauritaniaMauritiusMayotteMexicoMicronesia, Federated States ofMoldova, Republic ofMonacoMongoliaMontenegroMontserratMoroccoMozambiqueMyanmarNamibiaNauruNepalNetherlandsNetherlands AntillesNew CaledoniaNew ZealandNicaraguaNigerNigeriaNiueNorfolk IslandNorth MacedoniaNorthern Mariana IslandsNorwayOmanPakistanPalauPalestinian Territory, OccupiedPanamaPapua New GuineaParaguayPeruPhilippinesPitcairnPolandPortugalPuerto RicoQatarRéunionRomaniaRussiaRwandaSaint BarthélemySaint HelenaSaint Kitts and NevisSaint MartinSaint LuciaSaint Pierre and MiquelonSaint Vincent and the GrenadinesSamoaSan MarinoSão Tomé and PríncipeSaudi ArabiaSenegalSerbiaSeychellesSierra LeoneSingaporeSlovakiaSloveniaSolomon IslandsSomaliaSouth AfricaSouth SudanSpainSri LankaSudanSurinameSvalbard and Jan MayenSwazilandSwedenSwitzerlandSyrian Arab RepublicTaiwanTajikistanTanzania, United Republic ofThailandTimor-LesteTogoTokelauTongaTrinidad and TobagoTunisiaTurkeyTurkmenistanTurks and Caicos IslandsTuvaluUgandaUkraineUnited Arab EmiratesUnited KingdomUnited States Minor Outlying IslandsUruguayUzbekistanVanuatuVatican CityVenezuela, Bolivarian Republic ofVietnamVirgin Islands, BritishVirgin Islands, USWallis and FutunaWestern SaharaYemenZambiaZimbabwe State —Please choose an option— How did you hear about Komprise?*Search EngineSocial MediaMarketing EventBlog or PublicationRecommended by Friend or ColleagueRecommended by Industry AnalystRecommended by a PartnerOther Other By submitting this form, I confirm that I have read and agree to the Privacy Statement. Δ ### White Paper: Komprise Scale-Out Data Management The Komprise Architecture Designed for today's massive scale of data, the Komprise architecture delivers unmatched simplicity, scale, and efficiency. This white paper highlights the keys elements of the Komprise Intelligent Data Management architecture and how it differs from legacy approaches to data management. Download Report First Name* Last Name* Work Email* Company Name* Job Title* Phone* Country* --United StatesCanadaAfghanistanÅland IslandsAlbaniaAlgeriaAmerican SamoaAndorraAngolaAnguillaAntarcticaAntigua and BarbudaArgentinaArmeniaArubaAustraliaAustriaAzerbaijanBahamasBahrainBangladeshBarbadosBelarusBelgiumBelizeBeninBermudaBhutanBolivia, Plurinational State ofBosnia and HerzegovinaBotswanaBouvet IslandBrazilBritish Indian Ocean TerritoryBrunei DarussalamBulgariaBurkina FasoBurundiCambodiaCameroonCape VerdeCayman IslandsCentral African RepublicChadChileChinaChristmas IslandCocos (Keeling) IslandsColombiaComorosCongoCongo, The Democratic Republic of theCook IslandsCosta RicaCôte dIvoireCroatiaCubaCuraçaoCyprusCzechiaDenmarkDjiboutiDominicaDominican RepublicEcuadorEgyptEl SalvadorEquatorial GuineaEritreaEstoniaEthiopiaFalkland IslandsFaroe IslandsFijiFinlandFranceFrench GuianaFrench PolynesiaFrench Southern TerritoriesGabonGambiaGeorgiaGermanyGhanaGibraltarGreeceGreenlandGrenadaGuadeloupeGuamGuatemalaGuineaGuinea-BissauGuyanaHaitiHeard Island and McDonald IslandsHondurasHong KongHungaryIcelandIndiaIndonesiaIranIraqIrelandIsle of ManIsraelItalyJamaicaJapanJerseyJordanKazakhstanKenyaKiribatiKorea, Republic ofKuwaitKyrgyzstanLao People's Democratic RepublicLatviaLebanonLesothoLiberiaLibyan Arab JamahiriyaLiechtensteinLithuaniaLuxembourgMacaoMadagascarMalawiMalaysiaMaldivesMaliMaltaMarshall IslandsMartiniqueMauritaniaMauritiusMayotteMexicoMicronesia, Federated States ofMoldova, Republic ofMonacoMongoliaMontenegroMontserratMoroccoMozambiqueMyanmarNamibiaNauruNepalNetherlandsNetherlands AntillesNew CaledoniaNew ZealandNicaraguaNigerNigeriaNiueNorfolk IslandNorth MacedoniaNorthern Mariana IslandsNorwayOmanPakistanPalauPalestinian Territory, OccupiedPanamaPapua New GuineaParaguayPeruPhilippinesPitcairnPolandPortugalPuerto RicoQatarRéunionRomaniaRussiaRwandaSaint BarthélemySaint HelenaSaint Kitts and NevisSaint MartinSaint LuciaSaint Pierre and MiquelonSaint Vincent and the GrenadinesSamoaSan MarinoSão Tomé and PríncipeSaudi ArabiaSenegalSerbiaSeychellesSierra LeoneSingaporeSlovakiaSloveniaSolomon IslandsSomaliaSouth AfricaSouth SudanSpainSri LankaSudanSurinameSvalbard and Jan MayenSwazilandSwedenSwitzerlandSyrian Arab RepublicTaiwanTajikistanTanzania, United Republic ofThailandTimor-LesteTogoTokelauTongaTrinidad and TobagoTunisiaTurkeyTurkmenistanTurks and Caicos IslandsTuvaluUgandaUkraineUnited Arab EmiratesUnited KingdomUnited States Minor Outlying IslandsUruguayUzbekistanVanuatuVatican CityVenezuela, Bolivarian Republic ofVietnamVirgin Islands, BritishVirgin Islands, USWallis and FutunaWestern SaharaYemenZambiaZimbabwe State —Please choose an option— How did you hear about Komprise?*Search EngineSocial MediaMarketing EventBlog or PublicationRecommended by Friend or ColleagueRecommended by Industry AnalystRecommended by a PartnerOther Other By submitting this form, I confirm that I have read and agree to the Privacy Statement. Δ ## Webinars, Podcasts, and Video > On-demand conversations, demonstrations, and expert interviews on unstructured data management, AI data pipelines, and storage strategy. ### Data on the Move: AI Unstructured Data Ingestion In this Data on the Move discussion, Darren and Krishna discuss AI data ingestion of unstructured data, challenges and the role of Komprise.  _______________________ Data on the Move: Unstructured Data Ingestion for AI In this episode of Data on the Move, Komprise Co-founder and COO Krishna Subramanian discuss AI data ingestion and explore why feeding all your unstructured data into AI is costly, inefficient, and risky. She outlines a smarter approach, curating, filtering, and classifying unstructured file and object data across NAS and object storage, so organizations can deliver just the right data to AI, improving outcomes while saving time and money. Watch the video to learn how Komprise helps simplify AI ingestion and AI data preparation through Intelligent Data Management. _______________________ ### Data on the Move: Agentic AI and Unstructured Data Preparation In this Data on the Move discussion, Darren and Krishna discuss agentic AI, AI data governance and the importance of brining the right data to AI with Komprise.  _______________________ Data on the Move: Agentic AI and Unstructured Data Management With so much hype about agentic AI, what is the the role of unstructured data and when will we see more AI pilots move to production in the enterprise. In this Komprise Data on the Move discussion Darren Cunningham talks to Krishna Subramanian about market trends and the importance of unstructured data preparation to AI success. What are AI agents? What are the common unstructured data preparation challenges? Why Komprise for AI data pipelines? _______________________ ### Demonstrations: Elastic Data Migration SID Mapping In this demonstration, Komprise product manager Neha Das reviews how to set up and run SID mapping in Komprise Elastic Data Migration. Komprise Elastic Data Migration SID Mapping In this demonstration, Komprise product manager Neha Das review the Komprise Elastic Data Migration SID mapping feature, which provides automated user and permission mapping. Define mappings for handling orphaned files during a migration. Define mappings to update permissions on target storage. With SID mapping, you'll save time and minimize potential manual errors that could lead to compliance issues. . Watch on our YouTube channel.  _______________________ Learn more about Komprise Elastic Data Migration Review the Unstructured Data Migration Guide _______________________ ### Data on the Move: Lower Cost Ransomware Data Protection In this Data on the Move discussion, Darren and Krishna discuss CIO priorities in the era of tariffs, cost avoidance and AI data workflows.  _______________________ Data on the Move: Lower Cost Ransomware Data Protection In this Data on the Move discussion, Darren and Kumar discuss the importance of shrinking the potential attack surface with analytics-driven unstructured data management from Komprise. Lower the cost of your overall ransomware data protection solution. Reduce risk by ensuring the data is protected. Shrink your backup window and eliminate wasted, unnecessary costs with Komprise Intelligent Data Management. Read: Tier your unstructured data to lower storage costs and cyber risk. _______________________ ### Data on the Move: Cost Avoidance and IT Priorities In this Data on the Move discussion, Darren and Krishna discuss CIO priorities in the era of tariffs, cost avoidance and AI data workflows.  _______________________ Data on the Move: Cost Avoidance & IT Priorities In this Data on the Move discussion, Darren and Krishna discuss cost avoidance and how to future proof your data for AI. We also review how Komprise Intelligent Data Management can drive even greater value during uncertain economic times: Know your data across all storage - 80% of data is cold. Do you have the right unstructured data management plan in place? Tier data without disruption and right place data to lower cost storage. Avoid the data rehydration penalty - don't get locked into the wrong data storage platform. Read the Blog: 6 Ideas for IT Leaders Amid Tariff Wars _______________________ ### The French Storage Podcast: Interview with Komprise CTO Mike Peercy In this podcast, Mike Peercy sits down with Philippe Nicolas to discuss what's new at Komprise, the Komprise architecture and what's next for our Intelligent Data Management platform. The French Storage Podcast is referenced on 13+ platforms, so if you search "The French Storage Podcast" on Amazon Music & Audible, Apple Podcast, Google Youtube Music, Spotify, Deezer, TuneIn, Stitcher, Feedly. You can also use these links for direct listening: The French Storage Podcast Talk with Mike Peercy ### Demonstrations: Elastic Data Migration Spring 2025 In this demonstration, Komprise product manager Jeremy Etsey reviews how to customize and schedule Komprise reports. Komprise Automatic Creation and Mapping of Destination Shares Komprise now automatically creates and maps destination shares, preserving source hierarchies for full transparency and compliance. This automation eliminates tedious setup work while reducing the risk of misconfigurations. In this demonstration Komprise product manager Neha Das shows how Komprise administrators can create multiple migrations simultaneously and customize them to run at a specific time. Watch on our YouTube channel.  _______________________ Enhanced Chain of Custody Reporting Regulated industries and enterprises with stringent compliance requirements need complete visibility into data mobility. Komprise automatically creates chain-of-custody reports which consolidate and track every file migration, computing checksums at both the source and destination and logs timestamps for full transparency. In this demonstration Komprise product manager Neha Das shows how to create these detailed reports and how Komprise administrators can easily provide evidence of successful migrations to auditors, compliance officers, and business stakeholders. Chain of custody reports can be generated and stored in a central location, such as a NAS file server or S3 buckets. Watch on our YouTube channel.  SID Mapping The Komprise Elastic Data Migration SID mapping feature provides automated user and permission mapping. Define mappings for handling orphaned files during a migration. Define mappings to update permissions on target storage. With SID mapping, you'll save time and minimize potential manual errors that could lead to compliance issues. Watch on our YouTube channel.  _______________________ Learn more about Komprise Elastic Data Migration Review the Unstructured Data Migration Guide _______________________ ### Demonstration: Custom Report Scheduling In this demonstration, Komprise product manager Jeremy Etsey reviews how to customize and schedule Komprise reports.  _______________________ Komprise Reporting: Schedule Custom Reports In this demonstration, Komprise product manager Jeremy Etsey reviews how to customize a Komprise report templates and how to schedule and send reports to individuals and groups in your organization. Learn more about Komprise reports Komprise TechKrunch videos _______________________ ### Data on the Move: Sensitive Data Management In this Data on the Move discussion, Kumar and Polly discuss Sensitive Data Management for AI governance and cybersecurity.  _______________________ Data on the Move: Sensitive Data Management In this Data on the Move interview, Polly and Kumar discuss Smart Data Workflow Manager and sensitive data management for AI data governance and cybersecurity. IT teams dread the risks of sensitive data lurking where it shouldn’t be -- risks that are compounding as unstructured data grows explosively across all industries. Komprise sensitive data detection and mitigation capabilities help organizations prevent the leakage of PII and other sensitive data to AI and reduce the risk of potentially ruinous data breaches. Komprise Smart Data Workflow Manager now includes detection for PII, regular expressions and keywords to simplify and automate the process of finding and tagging sensitive data and moving it to protected locations. Read the Press Release Read the Blog _______________________ ### Sensitive Data Management Demonstrations Watch demonstration of the PII detection and Regex search features included with Smart Data Workflows.  _______________________ Komprise Sensitive Data Management In this discussion and demonstration, we introduce the sensitive data management capabilities of Komprise Smart Data Workflow Manager and demonstrate the PII detection scanner. Find Sensitive Data Across Hybrid Storage Silos Scan for PII and custom sources (regex) Process data locally Move and Remediate Set policies to act on what you find Automate ongoing workflows Classify and Exclude Tag and bring structure to unstructured data Pre-process for AI ingest Custom Sensitive Data Management Demonstration In this demonstration, Paul demonstrates how you can find any text patterns in your data via both keyword and regular expressions (regex) search to identify specific data formats like employee IDs, machine or instrument IDs, product or project codes, or even PHI data like healthcare-system specific patient record IDs.  More Komprise Videos. ### Gartner IOCS 2024: Is Your Storage Team Ready for AI? In this Data on the Move video, Krishna reviews the highlights of Gartner IOCS 2024 and discuss how unstructured management fuels AI. Why Your Storage Team Should Care About AI In this Data on the Move video, Krishna reviews the highlights of the Gartner Infrastructure, Operations and Cloud Strategies (IOCS) conference in Las Vegas, December 2024. Learn more about Komprise Smart Data Workflows Listen to the discussion: Is your data ready for AI inferencing? _______________________ ### TechKrunch: Accessing Tiered Files Transparently In this TechKrunch video, Randy demonstrates how Komprise provides transparent access to tiered files. TechKrunch: Accessing Tiered Files Transparently In this TechKrunch video, Randy demonstrates the power and simplicity of Komprise Transparent Move Technology. Komprise Intelligent Data Management is never in the hot data path. Komprise Guide to Unstructured Data Tiering Watch More TechKrunch Webinars and Videos _______________________ ### TechKrunch: Elastic Data Migration Warm Cutover In this TechKrunch video, Randy demonstrates the warm cutover advanced feature of Komprise Elastic Data Migration. TechKrunch: Elastic Data Migration Warm Cutover In this TechKrunch video, Randy demonstrates the how you can set up a Warm Cutover or "zero-downtime" unstructured data migration with Komprise Elastic Data Migration. Komprise Guide to Unstructured Data Migration Watch More TechKrunch Webinars and Videos _______________________ ### TechKrunch: Deep Analytics + Actions In this TechKrunch video, Randy demonstrates the simplicity and power of Komprise Deep Analytics. TechKrunch: Deep Analytics + Actions In this TechKrunch video, Randy demonstrates the simplicity and power of Komprise Deep Analytics and the ability to add a Deep Analytics query to a Plan to set up an ongoing unstructured data management policy. Watch More TechKrunch Webinars and Videos How Data Storage Teams Use Deep Analytics _______________________ ### Data on the Move: NetApp INSIGHT 2024 In this Data on the Move discussion, Krishna and Darren discuss NetApp INSIGHT 2024.  Data on the Move: NetApp INSIGHT 2024 In this Data on the Move interview, Darren and Krishna discuss NetApp INSIGHT 2024. Komprise was a Gold Sponsor at the event and announced new data mobility and unstructured data management updates for NetApp customers. Read the announcement. Learn more about Komprise for NetApp. _______________________ ### Storage Insights Directory Explorer Watch a demonstration of the Directory Explorer, now available with Storage Insights.  _______________________ Komprise Storage Insights Directory Explorer In this demonstration, Komprise product manager Jeremy Estey reviews the Directory Explorer functionality that is now available to browse both directories and files within a single navigation pane. With the Directory Explorer you can easily analyze size, content, and coldness of directories and their subtrees. Also be sure to check out the webinar for a deeper dive overview. More Komprise Videos. Watch More TechKrunch Webinars and Videos. Learn more about Storage Insights. ### Data on the Move: 2024 Unstructured Data Management Report In this Data on the Move discussion, Polly and Krishna discuss the 2024 State of Unstructured Data Management report.  _______________________ Data on the Move: 2024 Unstructured Data Management Report Discussion In this Data on the Move interview, Polly and Krishna discuss the latest State of Unstructured Data Management report. Why is cost optimization a higher priority this year? What's next for AI? How are enterprise IT teams preparing for AI? Read the press release. Download the report. ### Komprise Elastic Data Migration for VAST Data Watch a demonstration of Komprise Elastic Data Migration for VAST Data. Know First. Move Smart. Take Control.  _______________________ Fast, Easy, Elastic Data Migration to VAST Data In this video, we walk through how to set up an analytics-driven file data migration to VAST Data with Komprise Elastic Data Migration. Learn more about Komprise for VAST Data. ### Komprise Recognized as an IDC Innovator ESG performed a detailed evaluation of the Komprise Intelligent Data Management solution by participating in a solution briefing and an in-depth, hands-on demo. Report: Komprise Recognized as an IDC Innovator for Unstructured Data Analytics and Intelligent Data Management Over 90% of the data we produce is unstructured (source: IDC's Global Datasphere 2023) and it is a key asset of enterprise intelligence as well as a big part of data storage costs. Komprise reduces the complexities with managing unstructured data growth with location-agnostic file analysis and indexing. That analysis is purpose-built to not "get in the way" (i.e., it will not disrupt data movement and operations). “Komprise's Intelligent Data Management helps the enterprise do two valuable things: unlock the value hidden in unstructured data and reduce storage costs. Proper metadata tagging and access ensures AI solutions can extract and present the right data, at the right time, and to the right person. Intelligent Data Management prepares unstructured data for AI consumers such as data lakes that feed into enterprise applications, including knowledge management platforms. The platform enables multiple use cases: migration, data tiering, replication, workflows, and AI preparedness.” Read 1 Page Summary Download Full Report ### Data on the Move: Introducing Smart Data Workflow Manager for AI In this Data on the Move discussion, Komprise cofounder and CEO Kumar Goswami introduces the Komprise Smart Data Workflow Manager and discusses how Komprise is at the intersection of AI and unstructured data.  _______________________ Introducing Komprise Smart Data Workflow Manager Komprise Smart Data Workflow Manager builds upon our Smart Data Workflow technology by delivering a point and click method to search for the right data set, configure a third-party AI service, define tags, set the schedule for how frequently the workflow should run and monitor dozens of workloads at once. Use the Smart Data Workflow Manager to simplify integrating your unstructured data securely with any AI service. Two major issues related to AI success are: Efficiently discovering and feeding the right data to an AI platform and Enriching data sets for AI. Both processes are highly manual, laborious tasks that are error-prone and require meticulous data governance. Read the blog. Watch a demo. ### Smart Data Workflow Manager In this demonstration, Komprise Sr. Director of Product Management demonstrates Smart Data Workflow Manager.  _______________________ Introducing Komprise Smart Data Workflow Manager Komprise has introduced a rapid no-code AI workflow builder addressing use cases such as sensitive data identification, chatbot augmentation and image recognition. The Komprise Smart Data Workflow Manager simplifies integrating an organization’s data securely with any AI service. What's in the Komprise Smart Data Workflow Manager? Easy Data Workflow Wizard Automated Workflows Pre-built Integrations with AI Services Intuitive Monitoring Tags to Retain Context Data Governance and Auditing Attend the webinar. Read the blog. Read the press release. ### Data on the Move: Introducing Komprise Elastic Replication In this Data on the Move discussion, Komprise cofounder and CEO Kumar Goswami discusses why he's so excited about Elastic Replication and saving on DR costs.  _______________________ Introducing Komprise Elastic Replication Komprise Elastic Replication drastically cuts the cost of replicating unstructured data. With more frequent and devastating natural disasters, cybersecurity and ransomware attacks, data protection and disaster recovery (DR) strategies are essential in the enterprise. Learn how Komprise Elastic Replication saves 70% on DR costs for unstructured data Read the press release. ### Komprise Elastic Data Migration Fall 2023 Watch a demonstration of Komprise Elastic Data Migration 5, now available in Komprise Intelligent Data Management.  _______________________ Komprise Elastic Data Migration Fall 2023 Komprise Elastic Data Migration Fall 2023 expands to address specific use cases for large-scale file data migrations. The latest release includes new options for customers that go beyond traditional enterprise migrations: Zero downtime or “warm cutover” migrations for real-time and IoT data; Pre-loaded migrations supporting non-empty destinations; Consolidation migrations for IT teams to combine multiple data storage shares in a single migration job. Learn more about what's new in the latest release. Register for the webinar. ### Webinar: Komprise Elastic Data Migration 5.0 See What's New in Komprise Elastic Data Migration Komprise Elastic Data Migration 5.0 is here. In this webinar Paul Chen, Sr. Director of Product Management at Komprise, demonstrates what's new in this major update. Komprise Field CTO Benjamin Henry also joins to discuss best practices for successful file and object migrations. Speakers: Paul Chen, Komprise Product Management Benjamin Henry, Komprise Customer Success Darren Cunningham, Komprise Marketing Next Steps: Read the blog post: Expanding File Data Migration Use Cases with Komprise Find out what's new in Komprise Intelligent Data Management ### Komprise Storage Insights Demonstration Watch a demonstration of Storage Insights, now available in Komprise Intelligent Data Management 5.0.  _______________________ Unify Data Management and Storage Management Introduced with Komprise Intelligent Data Management 5.0, Storage Insights gives administrators the ability to drill down into file shares and object stores across locations and sites, including relevant metrics by department, division or business unit, such as: Which shares have the greatest amount of cold data? Which shares have the highest recent growth in new data? Which shares have the highest recent growth overall? Which file servers have the least free space available? Which shares have tiered the most data? Storage Insights includes over 25 columns that users can customize and filter to understand the current state of enterprise storage assets across sites. Users can see details on capacity, percentage of modified or new data and can filter by shares, status, data transfer roles, and more. Easily sort your shares and view by largest, most cold data, highest recent modified data, least free space, most and least data archived or tiered by Komprise and more; and dig into specific file servers such as NetApp, Dell EMC Isilon (PowerScale), Pure Storage, AWS, Azure, Windows, etc. to analyze system health and ensure maximum ROI and cost savings. Learn more about what's new in the latest release. Register for the webinar. ### Potential Duplicates Report Watch a demonstration of the Potential Duplicate Report, now available in Komprise Intelligent Data Management 5.0.  _______________________ Find Potential Duplicates and Take Action Deleting data that is not needed naturally lowers your data storage footprint and energy usage. Often, especially in research organizations, data sets are replicated for different experiments and tests but never deleted. Excess duplicate data raises data backup and data storage costs needlessly, increases data sprawl and potentially grows compliance and security risk. Now with Komprise Intelligent Data Management 5.0, you have a report that allows you to: See how much potential duplicate data you have and how it breaks down by size; Download a detailed CSV with details and locations of each copy; Understand the cost savings you can achieve by deleting duplicates. Learn more about what's new in the latest release. Watch the webinar. Learn more about Komprise prebuilt reports. ### Data on the Move: Komprise Intelligent Data Management 5.0 In this Data on the Move discussion, Komprise cofounder and CEO Kumar Goswami discusses why he's so excited about Storage Insights and the latest release of Komprise Intelligent Data Management 5.0.  _______________________ Introducing Komprise Intelligent Data Management 5.0 In this Data on the Move interview, Darren Cunningham, VP Marketing at Komprise, talks to cofounder and CEO Kumar Goswami about what's new (and what's so exciting) in Komprise Intelligent Data Management 5.0. Learn more about what's new in the latest release. Watch the webinar. Expanding file data migration use cases with Komprise Elastic Data Migration Fall 2023. ### Webinar: Komprise Intelligent Data Management 5.0 See What's New in Komprise Intelligent Data Management Komprise Intelligent Data Management 5.0 is here. In this on-demand webinar Paul Chen, Sr. Director of Product Management at Komprise, demonstrates what's new in this major new release. Speakers: Paul Chen, Komprise Product Management Darren Cunningham, Komprise Marketing Next Steps: Read the latest State of Unstructured Data Report Find out what's new in Komprise Intelligent Data Management ### Data on the Move: 2023 State of Unstructured Data Management Report In this discussion, Komprise Krishna Subramanian and Darren Cunningham discuss the 2023 State of Unstructured Data Management report.  _______________________ The State of Unstructured Data Management The third annual State of Unstructured Data Management survey finds that IT and business leaders are largely allowing employee use of generative AI but the majority (66%) are most concerned about the data governance risks from AI, including privacy, security and the lack of data source transparency in vendor solutions. From Komprise cofounder and CEO, Kumar Goswami: “This year’s survey shows that in the blink of an eye, IT leaders are shifting focus to leverage generative AI solutions, yet they want to do this with guardrails. Data governance for AI will require the right unstructured data management strategy, which includes visibility across data storage silos, transparency into data sources, high-performance data mobility and secure data access.” ### Deep Analytics Directory Explorer In this demo, Komprise Product Management walks through the how to use the Deep Analytics Directory Explorer, which was introduced in the Komprise Intelligent Data Management Spring 2023 release.  _______________________ Find What You Need Faster The Directory Explorer is a file browser-like interface that gives users the ability to drill down into individual directories for more granular control. For example, if projects are organized by directories, a project manager can quickly locate them and request that the files go into a cloud tiering plan after a certain date. A departmental user could also select files from specific directories to be copied to another share or moved to the cloud for analytics or archiving per industry regulations. This gives users another way to discover files other than searching by metadata tags through Deep Analytics. Learn more about Komprise prebuilt reports. ### Data on the Move: File Storage and Backup Cost Optimization The pivotal role of data management and data governance to the future of artificial intelligence technology. Watch the interview with Krishna Subramanian, co-founder and COO of Komprise.  Do you know how much data you have and how much it is costing you? In this Data on the Move discussion, Krishna and Darren discuss the ebook: 8 Ways to Save on File Storage and Backup Costs. ### Cloud Data Management Overview Introducing the Komprise Directory Explorer. Watch the demonstration from Komprise product management. Komprise Cloud Data Management Set Up and Add Buckets This tutorial walks you through setting up Komprise Cloud Data Management for object to object migration and cloud cost optimization. Komprise Cloud Data Management Analysis This tutorial walks you through the analysis capabilities of Komprise Cloud Data Management for object to object migration and cloud cost optimization. Komprise Cloud Data Management: Create Virtual Data Lakes This tutorial walks you through the Deep Analytics capabilities of Komprise Cloud Data Management for object to object migration and cloud cost optimization. Create a Global File Index, which is essentially a virtual data lake to know first, move smart and take control of cloud data growth and costs. Komprise Cloud Data Management: Transparent Data Archive This tutorial walks you through the transparent archive capabilities of Komprise Cloud Data Management for object to object migration and cloud cost optimization. Know first with analysis, move smart with intelligent tiering, archive, copy, cloud migration and take control of cloud data growth and cloud costs with Komprise Intelligent Data Management. Komprise Cloud Data Management: Migration and Replication This tutorial walks you through the object data migration and replication capabilities of Komprise Cloud Data Management for object to object migration and cloud cost optimization. Know first with analysis, move smart with intelligent tiering, archive, copy, cloud migration and take control of cloud data growth and cloud costs with Komprise Intelligent Data Management. Learn more about how to optimize your cloud data with Komprise Cloud Data Management. ### Komprise Directory Explorer Introducing the Komprise Directory Explorer. Watch the demonstration from Komprise product management.  Deep Analytics with a Familiar Browser Interface The new Directory Explorer is a file browser-like interface that gives users the ability to drill down into individual directories for more granular control. For example, if projects are organized by directories, a project manager can quickly locate them and request that the files go into a cloud tiering plan after a certain date. A departmental user could also select files from specific directories to be copied to another share or moved to the cloud for analytics or archiving per industry regulations. This gives users another way to discover files other than searching by metadata tags through Deep Analytics. Learn more about what's new here. ### Webinar: How to Save 70% on File Storage Costs with Komprise and Azure Komprise Intelligent Tiering for Azure analyzes data across your file and object storage to identify cold data, and tiers cold files based on the policies you set to the appropriate Azure Blob tier. This file tiering approach maximizes savings on storage, backup and DR costs and can be used in two scenarios: Hybrid Cloud Tiering from On-Premises File Storage to the Cloud Cloud File Data Lifecycle Management: Most enterprise IT organizatio Join the experts from Komprise and Azure to discuss the true cost of file storage, strategies for better data management, storage cost savings and cloud cost optimization and see a demo of the new Azure Marketplace solution. Speakers: Tim Kresler, Microsoft Azure Product Management Steve Moore, Komprise Information Architect Darren Cunningham, Komprise Marketing ### Data on the Move: Komprise Intelligent Tiering for Azure The pivotal role of data management and data governance to the future of artificial intelligence technology. Watch the interview with Krishna Subramanian, co-founder and COO of Komprise.  Introducing Komprise Intelligent Tiering for Azure In this Data on the Move interview, Kumar Goswami, co-founder and CEO of Komprise, introduces Komprise Intelligent Tiering for Azure. The solution is now available on the Azure Marketplace. Read the series of blog posts on the Azure Storage Blog. The True Cost of Traditional File Storage. Read the press release: New Komprise Intelligent Tiering for Azure Slashes High File Storage Costs with Exclusive Azure Marketplace Offer. Read the blog post: Introducing Intelligent Tiering for Azure. _______________________ ### Data On The Move: The Role of Data Management and Data Governance in AI The pivotal role of data management and data governance to the future of artificial intelligence technology. Watch the interview with Krishna Subramanian, co-founder and COO of Komprise.  The Pivotal Role of Data Management in the Future of Artificial Intelligence In this Data on the Move interview, Krishna Subramanian, co-founder and COO of Komprise, discusses the need for a framework for artificial intelligence innovation and the pivotal role of data management and data governance to the future of AI. There is a missing piece in the discussion and hype around ChatGTP and generative AI technologies. Krishna outlines 3 areas that require data management for AI: Transparency into the training data to ensure the sources are trustworthy - verifiability, regulation is needed. Domain-centric governance into the training data - data privacy and governance is needed. Output governance - security and governance is needed. Do we need a pause on innovation to sort the data management and data governance requirements? What are the opportunities and threats? Watch the interview to get Krishna's take on this important topic. Learn more about Smart Data Workflows and the the role of delivering the right unstructured data to AI and analytics engines. _______________________ ### Data On The Move with Randy Hopkins: Maximize Data Storage Savings How to get cost savings on your Data Management. Watch the interview with Randy Hopkins, VP of Global Systems Engineering. https://www.komprise.com/wp-content/uploads/data-on-the-move-with-randy.mp4 _______________________ Maximize Data Storage Cost Savings How to reduce your data storage costs with the right Unstructured Data Management strategy. In this Data on the Move video, we interview with Randy Hopkins, VP of Global Systems Engineering at Komprise, talks Smart Data Migration, Intelligent Data Management and the importance of an analysis-first approach to file and object data management. Randy walks through the factors that can have an impact on the cost of your data - from volume, to age, type, classification and where it is being stored. Then he outlines some of the initial Komprise Analysis findings, such as: 50-70% of enterprise file and object data is typically cold Unstructured data volumes are doubling annually (as least) The amount of savings customers typically achieving per petabyte Komprise Intelligent Management is used to analyze then intelligently move (migrate, tier, replicate, confine, etc.) data to the right storage platform and tier and ensure data is reaching the right teams, applications, analytics or AI infrastructure at the right time through automated data management (also known as data services) policies. Infographic: Hot Tips for Managing Cold Data One of the key points from Randy is: KNOW BEFORE YOU GO. This infographic is a useful way as you think about managing and moving cold data. _______________________ ### Data on the Move: Komprise Fall 2022 Product Release Komprise VP of Marketing interviews Komprise COO, Krishna Subramanian, about the highlights of our Fall Product Release, focusing on new departmental self-service capabilities for data management.  _______________________ Komprise VP of Marketing interviews Komprise COO, Krishna Subramanian, about the highlights of the Komprise Fall 2022 Product Release, focusing on new departmental self-service unstructured data management capabilities. Learn More About the Fall 2022 Product Release: Read Press Release     Read Blog Post _______________________ ### Data on the Move: 2022 Unstructured Data Management Survey Highlights Komprise Director of Marketing Communications interviews Komprise COO, Krishna Subramanian, about the top findings of the Komprise 2022 State of Unstructured Data Management Report.  _______________________ 2022 Unstructured Data Management Survey Highlights with Krishna Subramanian Komprise Director of Marketing Communications Polly Traylor interviews Komprise COO, Krishna Subramanian, about the top findings of the Komprise 2022 State of Unstructured Data Management Report. This post  summarizes the top 5 trends from the report: Trend #1: Unstructured Data Management User Self-Service Trend #2: Moving Unstructured Data to Analytics Platforms Trend #3: Cloud File Storage Gains Favor Trend #4: Unstructured Data User Expectations Beg Attention Trend #5: IT and Storage Directors want Unstructured Data Flexibility Unstructured Data Management _______________________ ### Giving Unstructured Data Insights to LOB Partners In this demo, Komprise Senior Director of Product Management Paul Chen walks through the user self-service features of the Komprise 2022 Fall Release.  _______________________ Self-Service Access to Unstructured Data In this demo, Komprise Senior Director of Product Management Paul Chen walks through the user self-service features of the Komprise 2022 Fall Release. Komprise admin users can now create a new Deep Analytics user role for line of business IT managers who can then view and drill down into high level storage metrics. This in turn facilitates easier collaboration between central IT and departments on data management decisions. Learn More Watch a Demo of the Komprise Data Stores Directory Explorer. _______________________ ### Smart Data Workflows with Komprise An introduction to Smart Data Workflows, including a use case demonstration of this new functionality. Automate data discovery, deliver the right cloud native data. _______________________ Automate Data Discovery and Deliver the Right Cloud Native Data AI, ML and all the variants including deep learning and natural language processing are tools of the trade now in our data-driven society. Yet the one thing that machine learning requires for uncovering new insights and patterns is unstructured data — a lot of it. Cloud Data Lakes need the right data and they need it now. With Komprise, IT users can now create automated unstructured data workflows for all the steps required to find the right data across storage silos, tag and enrich the data, and send it to external tools for analysis. In this session, Mike Peercy, CTO & Co-Founder of Komprise, along with Krishna Subramanian COO & Co-Founder, will introduce Smart Data Workflows and provide a use case demonstration of this new Komprise Intelligent Data Management Platform functionality. Recorded in Santa Clara, CA on June 24, 2022 as part of Cloud Field Day 14. Watch Next: Smart Data Workflows Chalk Talk _______________________ ### Unstructured Data Management Interview with Enrico Signoretti In this interview with Komprise, an Intelligent Data Management Platform for Unstructured data, Enrico reviews his latest data management research, the industry and the potential to bring the right unstructured data to artificially intelligence (AI) and machine learning (ML) tools and technologies. ### Data on the Move: The Microsoft Azure File Migration Program Karl Rautenstrauch, Principal Program Manager for Storage Partners at Microsoft Azure, introduces us to the Microsoft Azure File Migration Program. _______________________ In this Data on the Move discussion, Karl Rautenstrauch, Principal Program Manager for Storage Partners at Microsoft Azure, introduces us to the Microsoft Azure File Migration Program. We discuss why file data is moving to the cloud, Azure partnerships including NetApp and the benefits of Komprise Intelligent Data Management for Azure customers. Be sure to also check out our blog interview with Karl: Azure’s Storage Guru on Customer Adoption and File Data Migration. Komprise for Azure     Free Azure Migration _______________________ ### Interview with GigaOm Research Analyst Enrico Signoretti Enrico Signoretti discusses the potential for AI and ML with unstructured data. _______________________ Enrico Signoretti discusses the potential for AI and ML with unstructured data. Read the Blog _______________________ ### Cloud Native Access – What is it and Why Does it Matter? The Importance of Cloud Native Access to File Data Migrations This webinar reviews the need to ensure unstructured data is accessible and delivering value to the enterprise. Why limit the potential of your file and object data by locking unstructured data into a proprietary format? Cloud native data access is essential to unlock the potential of cloud services while also removing the risk of storage vendor lock in. This means your data is no longer tied to the file system from which it was originally served. ### Preparing for a File and Object Data Migration: Know Before You Go Are you ready to migrate file data workloads to the cloud? Read the write of this webinar here: Tips for a clean file migration. We review these 5 points that should be on your cloud migration checklist: Define Data Storage Sources and Targets Establish Your Data Migration Rules & Regulations Data Discovery: Know Your Unstructured Data Know Your Topology: Smart Data Migration Before You Migrate Data: Test, Test, Test ### Smart Data Workflows for Autonomous Vehicles In this demo we show you how Komprise can enrich data by allowing the execution of external functions or cloud services either at the edge, datacenter or cloud and then tagging data with metadata. _______________________ Komprise Smart Data Workflows allow you to define and execute automated processes, which are often industry and domain specific, to visualize, mobilize and get greater value from unstructured data. In this demo we show you how Komprise can enrich data by allowing the execution of external functions or cloud services either at the edge, datacenter or cloud and then tagging data with metadata. Watch the on-demand webinar with AWS: Building a Modern Data Strategy for the Automotive Industry. Learn More _______________________ ### Deep Analytics Actions for Simplified Data Workflows In this demo you'll see how to create granular queries to automate unstructured data movement from multiple NAS sources to the cloud. _______________________ Analyze and Mobilize Unstructured Data In this demonstration Steve Pruchniewski, Director of Product Marketing, shows you how to create granular queries to automate unstructured data management and mobility from multiple NAS sources to the cloud with Komprise Deep Analytics. Move from storage administration to intelligent data management to get value from your data in the cloud. Learn more about Komprise Smart Data Workflows. _______________________ ### Migration from Object Storage to Microsoft Azure Blob In this demo you'll see how how fast and easy it is to migrate object data to Microsoft Azure Blob using Komprise Elastic Data Migration. _______________________ Migrate from Object Storage to Azure Blob In this demonstration we show how fast and easy it is to migrate data from object storage to Microsoft Azure Blob storage using Komprise Elastic Data Migration. Learn more about how Komprise works with Microsoft Azure. Quick Start Guide Learn more about Azure file migration Komprise Smart Data Migration. _______________________ ### Komprise Intelligent Tiering for Azure In this demo you'll see how how easy and non-disruptive it is to tier data to Microsoft Azure using Komprise Transparent Move Technology™ (TMT). _______________________ Tier to Azure Storage without Disruption In this demonstration we show how easy and non-disruptive it is to tier data to Microsoft Azure storage using Komprise Transparent Move Technology™ (TMT). Introducing Komprise Intelligent Tiering for Azure. Learn more about how Komprise works with Microsoft Azure. Check it out on the Azure Marketplace. Learn More Learn more about Azure file migration Komprise Smart Data Migration. _______________________ ### Microsoft Azure SMB Migration Using Komprise In this demo you'll see how fast and easy it is to deliver Microsoft Azure SMB data migrations. _______________________ Microsoft Azure SMB Migration with Komprise Elastic Data Migration In this demonstration we show how fast and easy it is to deliver Microsoft Azure SMB data migrations using Komprise Elastic Data Migration. Learn more about how Komprise works with Microsoft Azure. Quick Start Guide _______________________ ### ESG First Look: Komprise Intelligent Data Management ESG performed a detailed evaluation of the Komprise Intelligent Data Management solution by participating in a solution briefing and an in-depth, hands-on demo. Report: Data Management Challenges, Komprise Solution Overview, Demo Highlights ESG performed a detailed evaluation of the Komprise Intelligent Data Management solution by participating in a solution briefing and an in-depth, hands-on demo hosted by Komprise subject matter experts. The evaluation focused on highlighting the solution’s data management capabilities, including analytics, global indexing, and Transparent Move Technology™ (TMT) as a SaaS-managed service designed to maximize data’s value. "If you are looking for a way to truly capture the value of your data while driving costs down, we suggest taking a closer look at Komprise." Read Report ### Komprise What's New Fall 2021: Deep Analytics Actions Komprise Deep Analytics Actions Interview with Mohit Dhawan  _______________________ In this interview, Polly Traylor, Director of Communications at Komprise, discusses Komprise Deep Analytics Actions with Mohit Dhawan, SVP of Engineering and Cloud Operations. Komprise Deep Analytics delivers granular, flexible search and indexes data in-place across file, object and cloud data storage to build a comprehensive Global File Index spanning petabytes of unstructured data. Komprise Deep Analytics Actions leverages these virtual datasets for systematic, policy-driven data management actions, delivering the following benefits: Users only move the data they need, with the ability to create queries on countless file attributes and tags Eliminates the manual effort of finding custom data sets and moving them separately from different storage silos since Komprise can create a virtual data set based on the query and systematically and continuously move data from multiple file and object silos to the target location. Improves IT and business collaboration around data, as data owners/users can participate in data tiering decision-making by tagging files and creating their own queries from any combination of tags and metadata. Next Steps: Learn More about Komprise Deep Analytics Watch a Deep Analytics Actions Demonstration Read the Deep Analytics Actions Press Release _______________________ ### Komprise Deep Analytics Actions Demonstration Introducing Komprise Deep Analytics Actions, a new feature of Komprise Intelligent Data Management that creates a Global File Index Service . _______________________ Introducing Komprise Deep Analytics Actions for Unstructured Data A new feature of Komprise Intelligent Data Management that creates a Global File Index Service: Maintains indices of all files analyzed One place to search and create curated, virtual data sets across silos In this demo we show you how to take action on file and object unstructured data sets to right place data for cloud data analytics, compliance, and data tiering. Read the blog post: Introducing Deep Analytics Actions _______________________ ### Komprise Intelligent Data Management Demonstration Komprise Intelligent Data Management Demonstration In this Storage Field Day 22 session we demonstrate: 1.Visibility, assessment, and planning across multisite with Komprise 2. Deep Analytics and fine-grained data management policies with a preview of new Deep Analytics Actions 3. Transparent Move Technology (TMT) and the benefits of the easy to use Komprise end user experience Read the blog post: Why Data Management Must Be Independent from Storage ### Wie Pfizer Analytics zur Beschleunigung der Cloud-Datenmigration einsetzte Webinar Präsentiert von AWS und Komprise. Nehmen Sie an diesem Webinar teil, um zu erfahren, wie Komprise Pfizer geholfen hat, 20 Jahre lang steigende Speicherkosten zu stoppen und die an AWS gestuften Daten für die Forschung zu nutzen, ohne den Zugriff von Benutzern und Anwendungen auf ihre Dateien zu ändern. ### How Pfizer Used Analytics to Accelerate Cloud File Data Migration Webinar Presented by AWS and Komprise. Attend this webinar to learn how Komprise helped Pfizer stop 20 years of increasing storage costs and leverage the data tiered to AWS for research, all without changing how users and applications access their files. Learn more about Pfizer's Cloud File Migration and Unstructured Data Management Read the blog post: Pfizer's Cloud Data Gambit Read the white paper: Smart File Data Migration for AWS / Amazon Storage Download the eBook: 5 Ways to Use Analytics for Cloud Data Migrations Learn more about Komprise for AWS ### Komprise Intelligent Data Management for Nutanix Demo Komprise and Nutanix together enable enterprises to manage massive data growth while cutting costs. Komprise makes it easy to analyze file and object data, migrate the right data to Nutanix to leverage its simplicity and efficiency, and manage the data lifecycle to cut ongoing costs. This demo shows how Komprise works with Nutanix. _______________________ Komprise Intelligent Data Management for Nutanix Komprise and Nutanix together enable enterprises to manage massive data growth while cutting costs. Komprise makes it easy to analyze file and object data, migrate the right data to Nutanix to leverage its simplicity and efficiency, and manage the data lifecycle to cut ongoing data storage costs. This demo shows how Komprise works with Nutanix. _______________________ ### TechKrunch: Komprise Intelligent Data Management for Nutanix In this TechKrunch session, our experts will discuss and demonstrate how Komprise makes it easy to migrate file data to Nutanix and cut ongoing costs by analyzing and transparently archiving/tiering cold data to the cloud without any disruption. ### Komprise Asynchronous Replication for Pure FlashArray Files Demo This demo introduces Komprise Asynchronous Replication for Pure FlashArray Files, which provides FlashArray users with fast recovery in the event of a disaster. Asynchronous Replication is a feature of the Komprise Intelligent Data Management platform, enabling FlashArray users to protect their data by copying snapshots from their source FlashArray Files. Komprise Data Management for Pure Storage This demo introduces Komprise Asynchronous Replication for Pure FlashArray Files, which provides FlashArray users with fast recovery in the event of a disaster. Asynchronous Replication is a feature of the Komprise Intelligent Data Management platform, enabling FlashArray users to protect their data by copying snapshots from their source FlashArray Files. Besides copying all the data, Komprise also handles Pure Storage managed directories, ensuring that the copies provide point-in-time replicas that are copied on periodic schedules that you configure. ### Komprise and AWS: Driving Intelligent Data Management in Health and Life Sciences Industry experts from Komprise, AWS and Regeneron will be discussing the most common unstructured data management challenges and demonstrating ways to analyze, move and manage file and object data at any scale. ### TechKrunch: Transparent Tiering for Microsoft Azure Files and Azure BLOB Learn how and watch a demonstration of how Komprise uses Transparent Move Technology (TMT) to tier cold data to Microsoft Azure Files and Microsoft Azure BLOB. Learn more about Komprise for Microsoft Azure ### TechKrunch: Komprise “Confine Function” – What the heck is it? Learn how to automatically go out and clean those cold and dormant files that aren’t needed. This technical session(demonstration) will show how to build an automated plan for storage management leveraging Komprise “confine”, and why you might want to do it. ### Fast, Reliable Data Migration to NetApp As businesses evolve to faster, flash-based NAS and cloud storage, migrating data into these environments can be tough. The goal is to migrate large production data sets quickly, without errors, and without user disruption. This demo video shows how Komprise Elastic Data Migration makes that possible with a fast, reliable, and cost-efficient solution for your next NAS and cloud migration to NetApp. See why Komprise is 27 times faster than other solutions. Data Migration for NetApp As businesses evolve to faster, flash-based NAS and cloud storage, migrating data into these environments can be tough. The goal is to migrate large production data sets quickly, without errors, and without user disruption. This demo video shows how Komprise Elastic Data Migration makes that possible with a fast, reliable, and cost-efficient solution for your next NAS and cloud migration to NetApp. Learn more about Komprise for NetApp data management and NetApp data migration. See why Komprise is 27 times faster than other solutions. ### Quick Demo on Saving Costs in a Multicloud Environment Diese Demo zeigt, wie Komprise Kunden hilft, Kosten zu senken und ihre Daten besser zu verwalten, indem es ihnen ermöglicht, ihre Daten zu kennen und zu verstehen, ihr Multi-Cloud-Datenmanagement besser zu planen und Daten transparent über alle Silos hinweg ohne Unterbrechung der Benutzer zu verschieben und zu verwalten. Diese Demo zeigt, wie Komprise Kunden hilft, Kosten zu senken und ihre Daten besser zu verwalten, indem es ihnen ermöglicht, ihre Daten zu kennen und zu verstehen, ihr Multi-Cloud-Datenmanagement besser zu planen und Daten transparent über alle Silos hinweg ohne Unterbrechung der Benutzer zu verschieben und zu verwalten. ### Intelligent Data Management for MultiCloud In this demo, you’ll see how Komprise Intelligent Data Management for Multicloud provides companies the following...  Schedule a Demo View  Infographic The Fast, No Lock-in Path to the Cloud for File Data Workloads In this demo, you’ll see how Komprise Intelligent Data Management for multi-cloud, hybrid and on-premises data storage and IT infrastructure provides organizations with the following: Visibility across clouds into data and costs Data storage cost savings forecasts based on archiving policies Automated data lifecycle management Fast data migration across clouds All from one unstructured data management pane of glass--across multiple clouds. Learn more about the Komprise smarter, faster, no lock-in path to the cloud for unstructured data. To learn more about how to optimize cloud data and manage object data migrations, you can also watch a series of Komprise Cloud Data Management tutorial videos here. ### Session 1: Komprise & Cloudian Got the Memo, But Data Didn’t: Contain Rising Storage Costs In these unprecedented times, one thing is a constant: the continued growth of unstructured data and the costs to manage it. This webinar looks at a cost-effective strategy to quickly lower storage costs with an analytics-driven approach to data management. By knowing your data first, you can move your cold data to less expensive storage, which significantly lowers the cost of both storage, back-up and DR replication. ### Session 2: Komprise & Cloudian Got the Memo, But Data Didn’t: Contain Rising Storage Costs In these unprecedented times, one thing is a constant: the continued growth of unstructured data and the costs to manage it. This webinar looks at a cost-effective strategy to quickly lower storage costs with an analytics-driven approach to data management. By knowing your data first, you can move your cold data to less expensive storage, which significantly lowers the cost of both storage, back-up and DR replication. ### Data Didn’t Get the Memo: Contain Spiraling Storage Costs with Komprise In these unprecedented times, one thing is a constant: the continued growth of unstructured data and the costs to manage it. This webinar looks at a cost-effective strategy to quickly lower storage costs with an analytics-driven approach to data management. ### Komprise Transparent Move Technology Chalk Talk Take a deep dive into how the Komprise Transparent Move Technology works to natively move data with zero disruption. Mike Peercy, CTO and Mohit Dhawan, VP of Engineering also field delegate questions and conduct an interactive demo. ### Komprise Dynamic Data Analytics Chalk Talk & Demo Mike Peercy, CTO and Mohit Dhawan, VP of Engineering, take to the white board to present the Komprise architecture and explain how it provides analytics for data insights and what-if scenarios for cost savings. ### Komprise Cloud Data Management Demo Komprise Cloud Data Management: Growth Analytics, Cloud Cost Optimization Get visibility and analytics into your cloud data, so you can understand data growth across your clouds and help move cold data to optimize cloud costs. Schedule Demo View Infographic ### Komprise Deep Analytics Demo Deep Analytics addresses the biggest concern with big data analytics – searching across multiple storage platforms to identify the right data sets to analyze. In this demo, Paul Chen, Sr. Director, Product Management demonstrates the power of Deep Analytics. Learn More About Deep Analytics Read White Paper ### Komprise Deep Analytics Demo Deep Analytics addresses the biggest concern with big data analytics – searching across multiple storage platforms to identify the right data sets to analyze. In this demo, Paul Chen, Sr. Director, Product Management demonstrates the power of Deep Analytics. Learn More About Deep Analytics Learn More ### Analyst Webinar: Unstructured Data Management: A New Way of Fighting Data The fight against data growth and consolidation was lost a long time ago. Several factors contribute to the increasing amount of data we store in storage systems such as users desire to keep everything they create, organizational policies, new types of rich documents, new applications, and demanding regulations are just some of the culprits. ### Analyst Webinar: Unstructured Data Management: A New Way of Fighting Data The fight against data growth and consolidation was lost a long time ago. Several factors contribute to the increasing amount of data we store in storage systems such as users desire to keep everything they create, organizational policies, new types of rich documents, new applications, and demanding regulations are just some of the culprits. ### Department Of Energy: The Smarter Way to Handle Data Growth Join Komprise to learn how to easily identify, classify, analyze and cost-efficiently archive and manage hot and cold data, then transparently and securely move it to economic storage tiers, without disruption to users or applications in this webinar. We will discuss data management strategies that your organization can implement today to enable your organization to scale with exponential data growth. ### Komprise Company Introduction with Kumar Goswami and Mike Peercy  Kumar Goswami, Founder and CEO, and Mike Peercy, Founder and CTO, provides an overview of Komprise. This session also provides a high-level overview demo of the Komprise Software solution. Recorded at Storage Field Day 17 in Silicon Valley on September 20, 2018. For more information, visit http://techfieldday.com/event/sfd17 ### Save up to 70% & Extend NAS Capacity With IBM & Komprise Data growth is exploding, and you are being asked to address growing capacity needs within tight budgets. In this webinar, we explore to top challenges that organizations face when managing capacity growth and how to overcome them while also saving up to 70% on NAS Storage. Download Slides ### Save up to 70% & Extend NAS Capacity With IBM & Komprise Data growth is exploding, and you are being asked to address growing capacity needs within tight budgets. In this webinar, we explore to top challenges that organizations face when managing capacity growth and how to overcome them while also saving up to 70% on NAS Storage. Download Slides ### Seamlessly Cut NAS Costs Without User Disruption Data is growing fast, but IT budgets are staying flat. Join CANCOM, Qumulo, and Komprise for an informational webinar to learn how you can seamlessly cut NAS costs with a joint Qumulo and Komprise solution. Download Slides ### Webinar On-Demand: IBM Cloud Object Storage & Komprise Analyze data usage and growth across your storage and transparently move data to IBM Cloud Object Storage. Two Steps to Cut Nas Costs Streamline NAS with a joint IBM and Komprise solution. Analyze data usage and growth across your storage and transparently move infrequently accessed data to more cost-efficient IBM Cloud Object Storage. Download Slides ### Webinar On-Demand: IBM Cloud Object Storage & Komprise Analyze data usage and growth across your storage and transparently move data to IBM Cloud Object Storage. Two Steps to Cut Nas Costs Streamline NAS with a joint IBM and Komprise solution. Analyze data usage and growth across your storage and transparently move infrequently accessed data to more cost-efficient IBM Cloud Object Storage. Download Slides ## Additional Blog Posts and News > Press releases, announcements, industry commentary, and editorial content from Komprise. ### Komprise Welcomes New Global Support Executive Suresh Babu recently joined Komprise as the Global Head of Customer Support. He brings 20+ years of progressive experience in leading and scaling customer support operations, including at Citrix, CA Technologies and Computer Sciences Corporation (CSC). “I've had the privilege of learning and growing while building and leading global teams focused on delivering exceptional customer experiences,” he says. “I am passionate about leveraging the power of AI and automation to enhance support efficiency and drive operational excellence.” What intrigued you about joining Komprise? First, I was intrigued by the product and its innovative approach to unstructured data management. The market need for intelligent data management is significant and growing, and Komprise is clearly a leader in providing solutions that help organizations gain valuable insights and control over their data. Second, the leadership team is very passionate about what they do. Their commitment to customer success aligns perfectly with my own values. Building and leading a global support organization offers exciting challenges, especially considering my foundation in tech support. What are your near-term and long-term goals in this role? Currently, my focus will be on understanding the existing support infrastructure, our team and processes to identify immediate opportunities for improvement. I plan to connect with our customers to understand their support needs and expectations. I am currently building strong relationships with our Product Management, Engineering and Sales groups to streamline our efforts for customer success. My long-term plan is to build a world-class global support organization that makes Komprise stand out. My focus is to understand our customers’ needs using data-driven insights, delivering robust self-help resources and helping our highly skilled, global support team be as productive as possible. Ultimately, we will leverage customer feedback to drive product and business improvements and implement seamless, personalized support experiences. How do you define the ideal relationship between customer support and customer success? The distinction was much sharper a few years ago, but the lines have become increasingly blurred over time. As two sides of the same coin working in tandem across the entire customer lifecycle, our combined skills and perspectives ensure customers receive the support they need while achieving their desired business outcomes with Komprise. The goal is to foster long-term partnership and growth. How do you look at churn today? Customer churn is a critical indicator of customer health and a significant growth opportunity. Keeping customers happy and loyal requires a data-driven approach, proactive engagement and a thorough knowledge of the customer journey. My goal is to create positive customer experiences which address root causes and build a more resilient, customer-centric organization. We’re continually focused on driving successful adoption of the product with our customers so that they achieve the expected value and return. You have worked in technical support roles for the balance of your career. What are a few lessons or tactics that you’ve learned along the way? First and foremost, empathy is paramount. It’s critical to attain a thorough understanding of the customer perspective and the business impact of their issues. Secondly, being transparent and keeping customers informed at every stage of the support process manages their expectations and builds trust. Furthermore, support cannot function in silos; a strong collaboration with engineering, product management and sales is essential to ensure faster issue resolution and a more seamless customer experience. Finally, given the ever-evolving technology landscape, it is vital that we create an organization that prioritizes continuous learning and improvement, knowledge sharing and process optimization. How has enterprise tech support evolved in recent years and how do you see that continuing to evolve – such as from the impact of AI or other business trends? Enterprise tech support has undergone a significant transformation in recent years. We’ve moved from primarily fixing things after they break to a more proactive and customer-focused approach. Nowadays, customers expect self-service tools like online help centers and communities. There's a greater emphasis on understanding the entire customer journey to provide a seamless support experience. Looking ahead, AI will play a larger role by handling routine questions and proactively identifying potential problems. Analyzing support data will be crucial to better understand customer needs and improve how we help them. Is this automation hard for customers to accept? AI and automation are not inherently hard for customers to accept. As leaders in customer support, our responsibility is to implement AI and automation thoughtfully and strategically. Automation should enhance the human element, rather than replace it. The future of customer support is not about choosing between AI and human interaction, but about creating a powerful combination that best serves our customers' needs. How do you motivate your team? Keeping our global support team motivated comes down to genuine connection and care. We focus on making sure their work feels important by highlighting positive feedback and customer testimonials. Hearing how their efforts resolved a critical issue or delighted a customer is incredibly powerful. We are committed to providing opportunities for our team members to learn, grow, and advance, celebrating their wins, and building a positive and inclusive work environment. Their passion, empathy, and commitment make a big difference in how customers feel about us. There's a unique satisfaction in witnessing a customer overcome a challenge, streamline their operations, and ultimately succeed with our team's support. What’s motivating to you about leading customer support in SaaS? In the world of software as a service (SaaS) customer support, we're more than just fixers; we're active partners in our customers' journeys, directly enabling them to achieve their business goals through our product. There's a unique satisfaction in witnessing a customer overcome a challenge, streamline their operations, and ultimately succeed with our team's support. It's moments like these that reinforce the value of our work and keeps me motivated as a leader. What do you enjoy doing in your free time? I enjoy reading books and sometimes I cook on weekends. I love traveling and experiencing new culture and cuisines. In fact, one of my long-term personal goals is to visit all Seven Wonders of the World. So far, I have been to the Eiffel Tower, the Taj Mahal, the Empire State Building, the Leaning Tower of Pisa and the Colosseum in Rome. With so much of the world still to explore, I'm always looking forward to my next adventure. ### Komprise Recognized on 2025 CRN® Storage 100 List Komprise Intelligent Data Management is a three-time recipient of the annual CRN honor. Campbell, CA — April 14, 2025 — Komprise, the leader in analytics-driven unstructured data management, announced that CRN®, a brand of The Channel Company, has included Komprise on its prestigious annual Storage 100 list in the Data Recovery, Observability and Resiliency category. The CRN Storage 100 spotlights storage vendors advancing innovation, delivering cutting-edge technology, and supporting high-impact strategic partnerships. Selected by the CRN editorial team, each company on the list was chosen for its dedication to bringing best-in-class storage offerings to the channel. Komprise Intelligent Data Management achieves its third recognition on the CRN Storage 100, as enterprises seek an independent solution to manage large unstructured data estates across on-premises and cloud storage. Komprise customers, across multiple verticals including healthcare, life sciences, public sector, financial services and energy, are in the crosshairs of cost avoidance during a tariffs-impacted economy and the increasingly urgent need to prepare data for AI. As unstructured data volumes continue to grow exponentially across most verticals, customers often struggle to understand data growth and costs overall and by department, how to optimize it and how data might be stored out of compliance. Komprise, with its Global File Index, delivers granular analysis into all an organization’s unstructured data across any storage so that IT can make the best decisions to right place it for cost-effectiveness and to classify and secure it for AI pipelines. Earlier this year, Komprise released new capabilities for sensitive data management, leveraging Komprise Smart Data Workflows. Komprise now includes detection for PII, regular expressions and keywords to simplify and automate the process of finding and tagging sensitive data and moving it to protected locations. Komprise also delivers a platform for ransomware protection by tiering cold data to immutable object storage where it cannot be changed, which also can reduce the on-premises attack surface by 80 percent. In 2024, Komprise received several industry honors, including: Top Unstructured Data Management Supplier (Coldago Research) Inc. 5000 (third consecutive year) A Gold Stevie award for Data Tools & Platforms CRN Cloud 100 Data Management Platform of the Year (Data Breakthrough Awards) Cloud Computing Product of the Year Award (Cloud Computing Magazine) To learn more, visit the Awards and Recognitions page. “There is considerable pressure on enterprise IT organizations today, given uncertainty in the global economy, tariff and trade disputes and the need to adopt AI for competitive advantage,” said Mike Munoz, CRO at Komprise. “In this environment, unstructured data management is a game-changer because it helps organizations right-place their data and reduce unnecessary storage spending while also preparing large data estates for AI. We’re excited to be in the position to help our customers navigate these waters and honored to receive this recognition from CRN.” About Komprise Komprise powers the connection between unstructured data management and AI. Komprise Intelligent Data Management delivers a single platform to easily analyze, migrate, transparently tier and manage the lifecycle of petabytes of file and object data across hybrid environments. With Komprise, enterprise IT gains full visibility across silos to optimize storage, backup, ransomware and cloud costs. Komprise Smart Data Workflows and the Komprise Global File Index unlock unstructured data insights and access for AI. www.komprise.com About The Channel Company The Channel Company (TCC) is the global leader in channel growth for the world’s top technology brands. We accelerate success across strategic channels for tech vendors, solution providers, and end users with premier media brands, integrated marketing and event services, strategic consulting, and exclusive market and audience insights. TCC is a portfolio company of investment funds managed by EagleTree Capital, a New York City-based private equity firm. For more information, visit thechannelco.com. ### The Rise of Unstructured Data Observability This blog was adapted from the original article on Beta News. Data observability is one of those hot and trendy terms which means different things to different people. While there are many definitions, this one from IBM is easy to understand: “Data observability refers to the practice of monitoring, managing and maintaining data in a way that ensures its quality, availability and reliability across various processes, systems and pipelines within an organization.” Easy to understand of course is not the same as easy to adopt and implement. The end goal is important enough to make data observability a commendable goal for IT organizations: How can we observe our environment and then proactively and even automatically make fixes to things that aren't working, are anomalous, suspicious and/or could potentially cause a disastrous outcome? Such outcomes could include a network failure, a security breach, a server reaching capacity, or in the unstructured data management world -- something else entirely. Komprise COO Krishna Subramanian talks about what data observability means in unstructured data management and how you can start to generate this intelligence. What do we mean by unstructured data observability? KS: People managing unstructured data don't often think about observability; they're simply trying to maintain high performance file data storage systems and control costs. They prefer not to hear from users (much less executives and department heads) that access time is sluggish or their data seems to be somehow 'missing.' A data observability practice can aid those goals and protect data for long-term needs. Data observability is more than monitoring and alerts when it comes to unstructured data. It can provide a complete view of the files in an organization, regardless of where they are being stored, how they are being used and by whom, how fast data is growing, and any out-of-ordinary data storage and access patterns. How does unstructured data observability benefit IT teams? KS: This visibility gives organizations the means to reactively solve problems and hopefully, proactively prevent future problems from occurring. Unstructured data observability with analytics and reporting can also help increase collaboration across teams, improve planning, and help troubleshoot issues faster and more efficiently. Unstructured data observability gives insights into issues such as sensitive data being stored where it should not be. It may be useful to integrate data observability tools with IT service management software such as ServiceNow and Splunk. Which data observability metrics and findings that IT should be tracking? KS: This list is bound to grow with AI requirements, but here are a few points you can track on your unstructured data: • Data growth rates • Top file types and if they change or grow suddenly • Top data owners and if they change or grow suddenly • Zombie data amounts and changes • Orphaned data amounts and changes • Storage capacity metrics • Data access speeds • Percent hot data • Percent cold data • Percent data on source • Percent data modified, moved, new, free and full, on storage • Sensitive data What emerging technology trends do you see shaping the future of unstructured data observability, and how can organizations enhance their data monitoring and troubleshooting capabilities? KS: AI and data governance are two major areas that can aid unstructured data observability. AI can help by spotting anomalies and trends faster and by enriching the contextual and other information about your file and object data across hybrid cloud storage. Richer metadata can improve unstructured data observability by providing more dimensions for analytics, making it easier to spot trends, anomalies and issues. For instance, metadata tags for PII or IP can show IT if and where sensitive data is stored out of compliance. Unstructured data observability goes hand-in-hand with data governance programs because security vulnerabilities, unusual activity such as a large amount of deletes or a user's personal folders spiking quickly in size can indicate a security or compliance incident. Unstructured data management solutions allow IT users to drill down into directories and file shares to investigate alerts by IT monitoring and cybersecurity systems. Does data observability help with ransomware defense? KS: Absolutely. With more insights on data in storage, IT teams can make better decisions for its management, and this includes reducing the on-premises footprint of attack. For instance, if you can see that the organization has 65 percent of data that is 'cold' and hasn't been touched in more than two years, then it’s an easy decision to move that data to object storage in the cloud. That way, it's out of the data center attack surface and if stored in immutable object storage, it's further protected as ransomware actors cannot modify or delete it.  Learn more about how to circumvent file data risks for ransomware. What are some best practices for implementing a data observability strategy within an organization, particularly in complex environments with hybrid data pipelines and diverse data sources? KS: Many organizations say data observability is a modern moniker for data monitoring. If instead, they viewed unstructured data observability as a broader topic encompassing data analytics and reporting that feed into actionable unstructured data management, then observability becomes a much richer function that can not only help resolve issues faster but also proactively enrich the value of the data that is growing the fastest and costing you the most. Since data is retained for long periods of time, it makes sense that data observability goes beyond alerting and near-term reporting to focus on longer term trends which help IT proactively manage and assess the data estate. ### Komprise Interns Class of 2025 Software engineering internships remain a strategic way to launch your career in a development or QA role. Komprise has been hiring interns for the past several years in our Bangalore office, and we find that they quickly become influential contributors to our overall development efforts. We hire from top engineering colleges across India, and candidates must take a coding test and participate in technical interviews with our senior engineers and hiring managers. But once they are in the door, our interns are doing important work which contributes to our product development roadmap. There’s no better way to learn than while doing, right? At the end of their six-month internship and after they have received their degree, the interns start with a full-time role moving forward their learning. In this blog, we introduce you to our latest team of Komprise interns with a little bit of flavor about who they are and what motivates them at work and off the job.   Arooshi Jain I learned about Komprise through the on-campus hiring process. I was curious about how Komprise helped organizations solve the unstructured data management problems and to what extent. I am hoping to learn a lot during this internship, grow professionally as well as personally and contribute something meaningful. I am just at the beginning of my career, trying to absorb as much as I can and eagerly looking forward to this new phase of my life. For fun? I love reading, mostly mysteries and fantasy novels. It has been my go-to since childhood. Deepak Parmar What intrigued me most was Komprise's focus on data management and analytics. I interned at Vaco Binary Semantics LLP, where I developed new AI tools for different real-world applications. Collaborating with talented professionals and learning from their expertise while contributing to meaningful projects is something I deeply value. I'm also eager to refine my technical and problem-solving skills in a dynamic environment. Long-term, I aspire to take on leadership roles where I can drive innovation and mentor others, while contributing to solving complex challenges in technology. For fun? Outside of work, I enjoy listening to music, playing guitar, taking long walks, and just casually overthinking. These activities allow me to relax, learn, and approach challenges with a fresh perspective. Sarang Kumar Interning at Komprise attracted my attention because it would allow me to work on worthwhile projects, gain knowledge in a variety of fields, and watch my efforts directly affect the final product. In particular, I was thrilled about the opportunity to work with different groups and contribute to the creation of features that would be used, work on end-to-end features, engage with skilled engineers and have practical experience with tools and technologies to aid my professional development. My primary career goal is to gain significant experience in the corporate world, particularly in product development. For fun? When I am not working, I love watching anime, series, or movies. It helps me relax and take a break from work while enjoying engaging in stories and creative visuals. Raima Mahato I found the idea about handling large volumes of data very interesting as I never thought much about the amount of data unused in companies. I had previously interned at Excelsoft Technologies, making a proctoring tool for examinations. My career goal is to keep learning through from my colleagues and working on the product as well as improve my skill set. My longer-term goal is to take on more responsibility in my organization. For fun? I love singing and listening to music when I'm not working. I used to learn Carnatic music as a child and it helps me relax. Samar Pratap I found out about interning at Komprise through campus placements and the product immediately piqued my interest. I remember tons of questions popping into my head, mostly about how Komprise did data management differently. I’m looking forward to being a part of something important while learning how my coding skills translate to the real world. I’d like to explore as much of the product as possible and hopefully make some significant contributions while doing it. It’s safe to say I’m pumped about this opportunity! For fun? I absolutely adore reading. The thing I love most about reading is that I’ll always come across the odd article or excerpt from the musings of a recluse that will challenge my entire worldview and give me something to think about for the rest of the week.   Pranav Kumar Kamatham What really drew me to Komprise was the idea company is working on and the amazing work culture here, which makes me excited to contribute and be a part of it. Previously, I’ve interned at Bharat Electronics Limited as a software developer and at Qlik as a business analytics intern. I’m looking forward to gaining hands-on experience and working on impactful projects, learn new technologies and gain knowledge from the brilliant minds at Komprise. For fun? I love exploring new things, let it be technology or food. In my free time, I enjoy watching TV shows, spending time with friends and having some fun. Pradyota Kirtikar What intrigued me the most about interning at Komprise was the impactful work the company does and the rotation program I heard about. However, the most exciting aspect for me was the company culture I had heard so much about: being part of a company that offers such learning and growth opportunities, while fostering a dynamic and supportive environment. This internship will be a chance for me to learn how things actually work in the industry. I’m looking forward to picking up new technical and soft skills, stepping out of my comfort zone, trying new things, and growing as a more well-rounded person. My career goal is to keep learning and eventually find my niche. For fun? Singing and writing are two things I’ve always loved, and they’ve been a part of my life since childhood. When I’m not doing that, I also enjoy diving into the stock market, personal finance and investment strategies, which I picked up from my dad. Ashish Sharma What intrigued me about Komprise was the problem it is solving—helping organizations manage and analyze unstructured data more efficiently. It’s an exciting and impactful challenge, and I wanted to be part of that journey. I’m most looking forward to learning about the product and its architecture. It’s been a fantastic experience so far; the work is interesting, the people are supportive, and the environment is great for learning and growth. Expanding my technical expertise, especially in product architecture and development, is a top priority. For fun? I enjoy listening to music and reading books in my free time. ### Komprise: Market Momentum for Storage-Agnostic Unstructured Data Management It’s already been an exciting and tumultuous year in the IT sector. The latest news that rocked the globe is, of course, about AI and more specifically: DeepSeek and its potentially monumental impact on model training and AI infrastructure. As the AI market evolves towards the 95% use case of inferencing, preparing corporate unstructured data for AI data pipelines is becoming more crucial. Innovations across IT to address AI opportunities and threats, along with ever present cybersecurity needs, are coming fast and furious. Here at Komprise, we are evolving to address these needs for customers, but our perspective is and has always been squarely on the unstructured data which is at the heart of so many challenges and opportunities today. In this post, we’ll do a quick review of some 2024 highlights and summarize how we are moving forward this year and beyond. First though, a little context for those readers who don’t know us well. Most enterprise customers begin their journey with Komprise to optimize costs in their multi-petabyte data storage environment or to make the right decision for what data to migrate to what platform. Visibility Across Silos: We first help customers get immediate visibility into their unstructured data across storage silos so they can make the best decisions on how to right place it and how to reduce the annual storage bill. From there, there are opportunities to do much more. Learn more about Komprise Analysis. Global Search with Deep Analytics: Komprise is helping enterprise IT organizations answer ad hoc questions about their file and object data using our Deep Analytics search functionality. Are there legacy system application files that can be purged? Are noncompliant activities taking place – such as individuals storing large quantities of personal videos and photos? Which departments and individuals are consuming the most storage capacity? Can we consolidate all research files for a department by searching on file extensions and tags while also deleting duplicate data sets that were copied for projects and no longer needed? Learn more about Deep Analytics. Smart Data Workflows: The ability to efficiently find, tag, and move data—even to an external application for analysis—and back again is a game changer today when unstructured data is so large, unwieldy and diverse. Use cases include search and confine for deletion, AI data workflows, keyword and regex search, and sensitive data detection/mitigation. Every time we discuss Smart Data Workflows with customers we identify new potential use cases. This is an exciting area of innovation and opportunity to unlock greater value from unstructured data in the enterprise. AI Data Governance: Ensure that sensitive data doesn’t leak to GenAI tools. Our cofounder and COO Krishna Subramanian discussed this topic on a recent AWS podcast: AI Data Pipelines with Komprise and in a Blocks & Files interview. Automated, Transparent Tiering: Create policies to tier cold data to lower-cost storage without making users search for it nor break applications that depend upon it and give people and applications native access to data at the target. Read about Transparent Move Technology. Rapid Data Migration: Komprise migrates large volumes of unstructured data 25X faster than common tools—NFS , SMB or S3/object —and with a proven program called Assess Customer Environment (ACE) to avoid surprises that delay or break migrations. Read about Elastic Data Migration. Ransomware Defense: Shrink the ransomware attack surface by 80% when tiering cold data to immutable object storage. This is a growing use case as security and storage teams look to de-risk what is often the most vulnerable data in the enterprise and reduce overall ransomware data protection costs. Read the solution brief. 2024 Highlights It was great to see growing recognition that data management is not data storage. Data management is an independent layer you need above your storage, but not in front of it. Late in the year Gartner published two reports that highlight the benefits of storage-agnostic unstructured data management: Use Data Storage Management Services to Address Exponential Growth of Unstructured Data Modernize File Storage Data Services With Hybrid Cloud (Gartner subscription required.) In 2024, Komprise was included in three Gartner Hype Cycles and a Market Guide: Market Guide for Hybrid Cloud Storage Hype Cycle for Storage Technologies, 2024 Hype Cycle for Backup and Data Protection Technologies, 2024 Hype Cycle for Digital Sovereignty, 2024 We also received several industry honors during the year, including: Top Unstructured Data Management Supplier (Coldago Research) Inc. 5000 (third consecutive year) A Gold Stevie award for Data Tools & Platforms CRN Cloud 100 Data Management Platform of the Year (Data Breakthrough Awards) Cloud Computing Product of the Year Award (Cloud Computing Magazine) See our Awards & Recognition page for details. Additionally, we released the 4th annual Komprise State of Unstructured Data Management report, highlighting IT priorities and plans for data storage, data management, hiring and AI. The Power of the Platform and Partnerships In the last year, Komprise made further investments in Smart Data Workflows, with the release of Smart Data Workflow Manager, a simplified UI to configure and monitor workflows for search and tagging, data movement, data governance and AI data pipelines. Most recently, Komprise announced new PII, regex and keyword detection capabilities for Sensitive Data Management. Komprise is focused on growing our relationships with technology and channel partners to expand the value we provide to customers. Building on the success of the Azure File Data Migration Program and through the exclusive Komprise Intelligent Tiering for Azure offering, Komprise and Azure have helped customers like Katten Law shrink their ransomware attack surface by 80% while cutting 70%+ costs. Komprise Smart Data Workflows gives customers like Duquesne University the ability to automate the use of cloud AI services like Amazon Rekognition for search and tagging images across their entire campus, saving months of recurring manual effort. We were a Diamond Sponsor at Pure Accelerate and a Gold Sponsor at NetApp Insight and during the year hundreds of customers and partners became Komprise Technology Professional (KTP) certified. So what’s next for Komprise? Late last year, Komprise CEO and cofounder Kumar Goswami addressed the agenda for 2025 in this blog post. These three trends will dictate IT strategies for data management: The mass availability of AI. As AI expands in maturity and applications, many organizations are finding that they are unprepared to safely and ethically launch these tools to the workforce. Unstructured data growth, which is a blessing and a curse. The blessing is that we have the volume and diversity of data to generate new intelligence like we’ve never seen before. The curse is that this data is stretching the limits of IT budgets to store, backup and corral for re-use and to protect from cyber-attacks. The rising importance of the right infrastructure at the right time. AI needs specialized GPU compute and storage which are scarce. Data centers are running out of power and infrastructure costs are rising, so cost optimization remains a top CIO priority. Climate disasters and cyberattacks are becoming more deadly – calling for more data protection. Analyzing and right-placing data across silos of infrastructure will be a proactive strategy for the foreseeable future. We’re lining up trade shows for the year, including Bio-IT World in April. We’re also planning a series of Komprise Days events with a focus on AI and unstructured data management in many cities this year. Komprise Events Komprise YouTube Channel Komprise LinkedIn Newsletter Thanks to all our customers, partners and employees for a fantastic 2024. I couldn’t be more excited about what’s next for Komprise and storage-agnostic unstructured data management. ### Is Your Data Ready for AI Inferencing? Recently, eWeek Senior Editor James Maguire interviewed our COO Krishna Subramanian about her latest thinking on AI and data management. Watch the full video here and read our highlights below!   https://www.youtube.com/watch?v=cOLkb8qbP88 JM: It’s been a fast two years since ChatGPT came on the scene. There’s already been so much evolution with many companies scrambling to make the most of AI with some success or limited success in some cases. What do you see now in terms of businesses and where they're focusing their AI strategy? KS: A lot of companies have invested in training their own models. That's where we are seeing innovation right now. As these models are stabilizing, enterprises are starting to figure out how to use them with their own corporate data. That's what we mean by inferencing. Let's say you have a model that can recognize images, so it knows if James is in an image or if the eWeek logo is in an image. Now you want to give it all of eWeek’s images to find those that have James doing a podcast with eWeek's logo in it. That’s an example where the model was first trained to recognize images using a different data set, but now you're using the pre-trained model with your own corporate data. Inferencing is nine times larger than the training market. JM: People are trying to figure out how to use AI with their corporate data and there are two big challenges. The first one is, anybody in the company should be able to use AI, so the chance of data leakage or improperly sharing sensitive data increases exponentially. The second problem is, how do you help people find and feed the right data to AI and how do you do it with control? You don't want a shadow AI movement, right? KS: Correct. IT needs to deliver systematic management of the AI data workflow with oversight. We do an annual survey to find out the top issues for unstructured data and for the last two years, the feedback we received is: we're exploring AI but not really using it. Most companies are kind of paralyzed by AI; they don't know how to use it with their data. The good news is that there is a place where you can start right now. The first step is just indexing the data. Then you at least know what data you have and you can create systematic data workflows around it with automation. There are data management solutions that are starting to address this problem. The first step is just indexing the data. Then you at least know what data you have and you can create systematic data workflows around it with automation. Read more: AI Inferencing: What Your Data Platform Needs a Makeover JM: What are some future areas where you see AI and data management evolving?? KS: A fascinating area where we'll see more work is in the human AI interaction. Right now, people are thinking. oh AI is going to take over and then people will get cut out. The reality is that there are going to be some AI helpers to assist a human and there's going to be human oversight over everything the AI does. So how do you make that interaction easier and how do you enable iteration over it? There will be a human-enabled AI workflow evolving to address this. I also hear so much these days about agentic AI. The AI agent doesn’t give an answer like ChatGPT but it does a series of tasks and becomes a digital worker. JM: How can Komprise help customers with this journey, in a nutshell? KS: Komprise analyzes, indexes, moves and manages unstructured data--meaning any data that's not in a database. If you want to understand and classify your unstructured data or you're wondering how you can build data workflows for it, those are all the things that Komprise can do. Learn about the Komprise Global File Index. Learn about Komprise Smart Data Workflows. Blog: Why AI Data Workflows Can Boost AI Plans ### Top 5 Priorities for Unstructured Data Management in 2025 As a CEO and cofounder, I really enjoy this time of year to reflect upon the past 12 months and do some thinking about the year ahead. Malcolm Gladwell’s famous tome, The Tipping Point, comes to mind. Gladwell defines a tipping point as "the moment of critical mass, the threshold, the boiling point." Today I am seeing three tipping points that are irrevocably changing enterprise IT and our world as we know it: The mass availability of AI. The tipping point for that was, of course, ChatGPT’s launch in the fall of 2022. As AI expands in maturity and applications, many organizations are finding that they are unprepared to safely and ethically launch these tools to the workforce. Unstructured data growth, which is a blessing and a curse. The blessing is that we have the volume and diversity of data to generate new intelligence like we’ve never seen before. The curse is that this data is stretching the limits of IT budgets to store, backup and corral for re-use and to protect from cyber-attacks. The rising importance of the right infrastructure at the right time. AI needs specialized GPU compute and storage which are scarce. Datacenters are running out of power and infrastructure costs are rising, so cost optimization remains a top CIO priority. Climate disasters and cyberattacks are becoming more deadly - calling for more data protection. Analyzing and right-placing data across silos of infrastructure will be a proactive strategy for the foreseeable future. These three tipping points are also linked. AI depends upon easy access to unstructured data – large quantities of it. Unstructured data growth can be monetized to help offset the costs of storing and protecting it, if we determine how to use it safely and efficiently in AI. A flexible, hybrid cloud infrastructure with systems to intelligently move, manage and monitor data as needs change is the foundation for both unstructured data and AI. Komprise was founded in 2014 to address the challenges of uncontrolled unstructured data with a storage-agnostic, unstructured data management SaaS. As our company has evolved and customer needs have evolved with it, we are now seeing many more use cases for managing unstructured data strategically and with intelligence. Recently, Gartner published a report on the need for data storage management services (DSMS) to optimize storage, improve unstructured data governance, and reduce unstructured data risks. The Gartner report notes that by 2028, large enterprises will triple their unstructured data capacity across their on-premises, edge and public cloud locations. Independent unstructured data management solutions are also imperative for developing and managing successful, sustainable enterprise AI initiatives. Here are five top priorities for managing unstructured data that I believe CIOs, CTOs, VPs of infrastructure and storage directors should consider in the coming year.   1. Prepare your Data for the AI Tsunami. Like it or not, AI is now part of every IT organization’s strategic plan. Even if your organization is not planning to launch any AI applications internally or externally for customers, chances are, the tools you’re using to run your business are enhanced with AI. Your employees are using GenAI tools to get work done. Therefore, you need to be prepared. That means developing an AI data governance plan. Get started by gathering deep intelligence on your unstructured data across storage silos. Consider an unstructured data management platform that indexes all your data so that you always know how much you have and how fast it is growing, what is valuable and what is not (based on access patterns), what data needs to be protected from AI and what data is obscure and can be tagged for additional classification. These efforts will make your data more usable, more searchable, and protect against unnecessary risks. Read more on metadata tagging with Komprise. 2. Ingest Data to AI with Automated Workflows. We have entered the next phase of AI: from model training to retrieval augmented generation (RAG) and inferencing. Users need easy ways to search across corporate data stores, find the right data sets (which in turn requires data classification, per above), exclude sensitive data and move the right data to AI tools efficiently and without losing data or incurring lengthy delays. It’s critical to monitor and audit AI outcomes for accuracy and sensitive data leakage. Komprise Elastic Data Migration moves data 25 faster than common data migration tools with built-in risk assessment and troubleshooting capabilities. And Komprise Smart Data Workflows is a simple technology for setting up and auditing automated AI data pipelines. 3. Protect Against Ransomware and Sensitive Data Leakage. IT managers in charge of data storage know that they must work closely with security teams to protect data for the organization. Increasingly, security is integrated into the entire infrastructure stack including data storage technologies. Storage managers can do more by reducing the large attack surface of their file data, which makes it highly vulnerable to ransomware attacks. By moving cold, inactive data to immutable object storage where it cannot be modified, you reduce the ransomware attack surface by 80% or more – while saving that much or more on your overall storage and anti-ransomware budget. Read the Katten Law case study. Furthermore, you can use Komprise Smart Data Workflows to connect an AI data classification tool which can quickly locate and tag sensitive data and segregate it from AI workflows. Too often, IT leaders discover hidden, sensitive data sets residing on noncompliant storage where it is not adequately protected from cyber-attack. Read the blog for more tips on protecting unstructured data from ransomware. 4. Optimize Data Management for Costs and Sustainability.  Since the early days, Komprise has delivered a strong valuable proposition for our customers across all industries: you can use our solution to save 70% or more on your annual data storage and backup spending. Within minutes of installing Komprise in your environment, our familiar “data donut” dashboard shows you all kinds of useful insights on your data estate – such as how much data you have, common file types, top owners, departments with the highest spend, and so on. See metrics such as last access time to determine how much data is more than a year old (or whatever parameter you set) and then set a policy to continuously and transparently tier data as it ages to lower-cost secondary storage. You can also use Komprise to dig deeper into your data: finding duplicate data, orphaned data, zombie data, and any other data which can be archived to the cloud or even deleted. These tactics cut storage and backup costs while reducing your carbon footprint. Read more about our non-disruptive approach to cold data tiering using our patented Transparent Move Technology. 5. Deliver Governed Self-Service to Non-IT Users. In the age of self-service, IT and departmental users alike benefit from tools which allow them to search and access data, reports and analytics without filing a help desk ticket. For instance, researchers, scientists, and engineers may want to tag their own data (such as by project name or keyword) so they can easily find it later. IT can still be in control by deciding where the data should live for cost and performance needs while the departments dictate how the data is classified and who has access to which data sets. It is a wonderful collaboration between IT and business units – and Komprise can make this happen. Komprise also has useful reports such as Showback, so that department heads can see how they are being billed in chargeback environments. Learn more about our shareable reports.   As I look toward 2025 and what’s next in our industry, I’d like to thank Komprise customers, partners and employees for a fantastic 2024. It is clear to me that unstructured data management is at the intersection of enterprise IT infrastructure optimization, data governance and AI innovation. ### Unstructured Data Management for HPC Gains Traction High-performance computing (HPC) is once again in the spotlight at SuperCompute 2024 (SC24) in Atlanta this month. AI and supporting digital business initiatives are paramount in enterprise IT. HPC is maturing and while it creates many opportunities for innovation and productivity, it also creates large quantities of unstructured data. Komprise will be at the conference (booth #414) showcasing unstructured data management for HPC solutions. We see the intersection between enterprise IT organizations using HPC and the need to manage and protect that unstructured data in a new way. Unstructured data management for HPC is growing. Here's why:   Unstructured data is growing at a remarkable pace, and it’s not slowing down anytime soon. This data is costing enterprise IT organizations millions in storage, backup and data protection. Most organizations spend at least 30% of their IT budget on data storage, according to the Komprise 2024 State of Unstructured Data Management. These costs are growing and becoming unsustainable. Data is living in silos from the data center to edge and cloud and across multiple technologies and vendors, and therefore introduces risks: from compliance, to ransomware, to the missed opportunity of data analytics and AI and ML initiatives. You can’t manage nor analyze what you can’t see or don’t understand. Managing data using your storage vendor’s technology alone does not deliver the best ROI and limits your ability to see all data across the organization to be more proactive and sustainable. IT departments are beginning to get pressure from above to operate “greener.” That means not storing all your data on the highest-performing, energy hogging storage device. It also means cleaning up your data mess: data hoarding and duplicate data is a common problem. AI needs a different unstructured data strategy. Your organization might not be doing much with AI yet-- other than experimenting with generative AI for writing and research support. Yet the time will come when you’ll need to act fast. That means having the right infrastructure. It’s not just the storage, security and networks but also having the right unstructured data management platform to govern and classify your data and set up automated AI data workflows. Komprise can help HPC organizations in the following ways: Reduce 70-80% of annual storage costs: The Komprise Global File Index delivers a dashboard where you can see data growth rates, amount of data in storage, and time of last access so you can model plans to save. For instance, you can see potential savings of moving “cold” data that is one year or older and rarely accessed into secondary storage. Komprise patented Transparent Move Technology (TMT)™ tiers data across hybrid storage while maintaining native access to the tiered data both from the original location and from the cloud. Self-service access for research teams: Data stakeholders such as research directors need easier access to data and the ability to easily search for files and request workflows. They also need to understand department data usage so they can collaborate with storage teams on archiving strategies to free up space and/or help identify other areas for efficiency. Tag data for improved classification and segmentation: Metadata enrichment is increasingly valuable as unstructured data volumes grow into multiple petabytes in organizations. By adding tags to data, indicating file contents, location or project, data becomes more searchable. You can quickly identify sensitive data types, such as those containing PII, or curate specific data sets for use in AI and ML projects. Migrate faster and with lower risk: Large-scale data migrations are often painful, complex and may not deliver the expected ROI. Komprise has a proven process to analyze your environment and data prior to migration to ensure that you are moving just the right data to the right storage. Komprise Elastic Data Migration is significantly faster than many common tools and has built-in features for reliability and ease of use, such as by retaining all file permissions after a migration. Manage data across its lifecycle: One-size-fits-all storage is no longer viable in today’s world because of the size of data. Komprise unstructured data management analyzes HPC environments and executes data movement as it ages. You can automate policies to tier data from hot to warm to cold data tiers according to parameters that you set. Because of our patented TMT technology, you can access your data at any tier later, without expensive rehydration to the original storage. Expand your ransomware protection: By reducing the data stored on your expensive NAS though cold data tiering to immutable object storage in the cloud, you reduce your attack surface for ransomware actors. Unlike storage tiering solutions that lock the data into their file format and are incompatible with ransomware protection solutions like tamperproof snapshots, Komprise technology is transparent and fully compatible with ransomware protection and backup solutions. Prepare data for AI: Getting unstructured data ready to safely use in AI tools is one of the largest challenge for AI. Komprise offers a Google-like search across disparate data silos. You can tag your data via UI and API to enrich the metadata, making it more useable in AI. Komprise Smart Data Workflow Manager is the foundation for creating automated AI data workflows that enrich data and curate the right data sets for the right tools. Komprise also delivers systematic data workflow execution for RAG and inferencing. Read more about the data management requirements for AI inferencing. Govern data for AI workflows: When employees share organizational data with AI, IT needs a way to audit what was shared, ensure that sensitive information is restricted from AI and create data governance mechanisms. Komprise provides the framework for data orchestration and data governance with AI to protect sensitive data and avoid harmful outcomes. If you have a lot of unstructured data, you need a solution like Komprise. Our flagship solution, Komprise Intelligent Data Management, is best positioned to help enterprises manage all their unstructured data across any storage –whether it’s in your data center, at the edge or in the cloud.  Book a meeting with us at the show! ### Komprise Achieves Inc. 5000 Ranking for Third Year Running Unstructured data management SaaS provider doubles new subscriptions for a third consecutive year, as enterprises expand AI plans.   Campbell, CA, August 13, 2024 – Komprise, the leader in analytics-driven unstructured data management and mobility, announces that the company has been named for the third year in a row to the annual Inc. 5000 list, the most prestigious ranking of the fastest-growing private companies in America. Komprise was selected based on its revenue growth from 2020 to 2023. Komprise Intelligent Data Management helps enterprises address the exponential growth of unstructured data, which comprises at least 80% of all data created today. Komprise delivers advanced analytics on file and object data across storage silos to help enterprise IT teams save significantly on storage and backup costs and improve compliance and data visibility. Komprise provides the fastest, most transparent platform for data tiering, data migration and storage cost optimization so that organizations can achieve the best ROI from hybrid cloud storage. The Komprise Global File Index also delivers a foundation for AI data workflows, so that IT can create custom workflows to easily search, find, and tag the exact files departments need across all storage and automatically move it to a data lake or AI tool. Komprise 2024 Highlights: Komprise announced that new subscriptions doubled again in 2023, driven by strong growth in new logos and record expansion from existing customers. The company also grew average annual contract value (ACV) by 60% in 2023, with multiple seven-figure deals throughout the year, indicating growing enterprise adoption of Komprise as a platform to analyze, mobilize and extract value from unstructured data. Komprise hired finance and tech veteran Craig Gomulka as CFO. Komprise has received multiple industry awards and recognitions so far this year, including IDC Innovators, Data Breakthrough Awards for Data Management Platform of the Year, a Gold Stevie award for Data Tools & Platforms, 2024 Cloud Computing Product of the Year, and CRN Cloud 100 2024. View all here. In May 2024, the company announced Komprise Smart Data Workflow Manager to radically simplify integrating an organization’s data securely with any AI service. In August 2024, Komprise released the fourth-annual Komprise State of Unstructured Data Management Report, a survey on key trends in the world of data, storage and AI. “Making the Inc. 5000 for the third year in a row is testament to the value our customers are seeing by investing in an analytics-first unstructured data management solution,” says Mike Munoz, CRO at Komprise. “Komprise Intelligent Data Management is a proven solution to help enterprises right-place data into the most cost-efficient storage, delivering insights and visibility on file and object data across storage and automating data workflows for AI and other needs.” For complete results of the Inc. 5000, including company profiles and an interactive database that can be sorted by industry, location, and other criteria, go to www.inc.com/inc5000. All 5000 companies are featured on Inc.com starting Tuesday, August 13, and the top 500 appear in the new issue of Inc. magazine, available on newsstands beginning Tuesday, August 20. About Komprise Komprise is a provider of unstructured data management and data mobility software that frees enterprises to easily analyze, mobilize, and monetize the right file and object data across clouds without shackling data to any vendor. With Komprise Intelligent Data Management, you can cut 70% of enterprise storage, backup and cloud costs while making data easily available to cloud-based data lakes, analytics and AL/ML tools. www.komprise.com. ### Andy Kau: Investing in the Age of AI Andy Kau is a partner and managing director at venture fund Walden International. A member of the Komprise Board of Directors, we caught up with Andy to get his take on key trends in the tech startup world today. How did you get into venture capital and how have your investing priorities changed in the last 2 to 3 years? I have been at Walden International for the past 31 years. I started my career as an engineer and management consultant on the East Coast and moved to California when my wife started at Stanford. I interviewed with several VCs when we got here and ended up at Walden. In those days it was a relatively small industry. Over the last few years our focus has been in three areas, starting with semiconductors, which has been our bread and butter for decades. Secondly, is ML and AI and we’ve made several investments in the sector. That speaks to the impact of Komprise. And big data is the third sector: how it’s being used, how it has changed the industry and how it is tied to ML and AI. How do you see the latest announcements by the big companies (Nvidia, Amazon, Microsoft, Open AI, HPE, IBM) affecting the startup community right now? LLMs are still new in the big scheme of things. People are still searching for profitable use cases. Aside from code writing, which is clear cut, companies are still determining how to apply this to broader sectors like travel or financial services. As a VC we’re trying to understand this on the fly. We also do a lot on the infrastructure layer and Komprise fits in well in that space. This is analogous to our investing in the semiconductor space, whether it’s making more efficient chips, better packaging or memory architectures to support these high-powered, GPU-based data center racks that are being deployed like crazy. If you can now use the cloud for all your AI needs, though, why wouldn’t a company do that instead of building their own infrastructure? Companies often like to keep the important data and processing on premises. There is a huge push to do confidential computing which will allow you to shield for example, PII data. In those cases, a hybrid cloud environment will work. I think that most companies will manage AI on premises or in a hybrid environment. Komprise can address all three use cases for AI: on-premises, hybrid and cloud. Komprise brings the ability to take data from anywhere and via a single pane, look at all the data and take actions on it. This is a super important capability. Any predictions on the evolution of AI tech in the next 12-18 months? The AI wave is unstoppable. People need to get used to evaluating outputs from AI and understanding the risks of using information from these different sources. That’s a given. The questions really come in per what specific applications are possible. Medicine is one of the areas where we will see profound change, such as with AI-assisted drug discovery, helping make diagnoses and figuring out optimal treatments. AI can do a huge job of assisting doctors all the way down to the individual patient level. But how do you reduce costs enough so that AI gets pervasive? That comes down to infrastructure---and a lot of that has to do with power. The data centers are sucking up power like crazy. I saw an estimate that 20% of all incremental new power usage in Europe is going into data centers. Therefore, we’ll need efficient GPUs and rack systems and high-speed networks. In some ways these are the picks and shovels. Moving into data management, Komprise is innovating to build more bridges between unstructured data and AI. How do you see the challenges and opportunities here? Komprise is like two pieces. First, it’s dealing with the explosion of unstructured data. Companies have data all over the place and they want to more intelligently manage, group and store their data. Komprise has the migration and tiering technology to do this very well. Komprise secondly offers a way to tag data and put it into the right workflow. The ability of Komprise to take vast amounts of unstructured data and establish metatags and put the data into places where it is useful is going to be more important. What’s holding up AI progress in enterprises the most right now in your view: funding, governance ethical and security concerns, outdated systems infrastructure – or something else? When you say AI, it’s a broad area from GenAI to traditional ML. In the GenAI space, corporate customers are super cautious with what they put out there. The hallucination issue is real and is one of the biggest barriers to widespread adoption. Executives don’t want to create falsehoods and then deal with a huge PR backlash. On the ML side, it’s about using all the advances in algorithms to discern patterns in the data—anything from motor reliability to sales numbers. We are seeing this area really take off. What do you like to do in your free time? I enjoy doing a lot of things, but they have to fit in around the edges of my work-related stuff. I have a dozen investments spread across the world, so there's also a lot of travel. I enjoy being immersed in the outdoors. We joined the Yosemite Conservancy Council, so we try to get up to the park several times a year. We do a lot of hiking, often with our golden retriever. We recently did hikes in Tanzania, New Zealand and soon the Dolomites. I think it's important to have a deep appreciation and relationship with nature. It grounds me. ### Cracking the Code for Unstructured Data Classification This blog was adapted from the original article on Built In. Every enterprise, no matter the sector or size, is dealing with the same issue: unstructured data chaos. There’s too much of it, it’s growing too quickly and it’s becoming unaffordable. It’s also one of the most valuable assets that companies possess. Yet its sheer size--multiple petabytes in midsize to large organizations--and distribution across on-premises, edge and cloud storage, makes it tough to leverage. According to 2023 research by IDC, organizations analyze less than half of their unstructured data to extract value, and they also reuse less than half of said data. Why Unstructured Data Classification Matters Unstructured data classification is important because it adds structure to unstructured data – which makes it more findable and usable across the organization. Classification starts with the metadata that’s automatically generated by data storage technology. System-generated metadata includes information about when the data was created, who created it, its type, its size, when it was last accessed and when it was last modified. This helps IT managers classify data by the department it belongs to and identify rarely accessed data as ready for archiving and tiering to lower-cost storage destinations. IT professionals can also search based on data types, such as video or medical imaging files, which may be consuming too much storage (and budget) and require action such as migration. What Capabilities Do Your Tools Need? For additional unstructured data classification, it’s important to enrich metadata using tools that can crack open file contents to search for keywords or data types. This includes searching sensitive personal identifiable information, particular items in an image or videos with specific content. These tools may incorporate AI or machine learning technology to rapidly scan across file shares and directories to identify matches, but they usually can’t store this information. Unstructured data management solutions, however, can fill this critical gap by feeding the right data to the AI/ML indexers and tagging the outcomes of those AI scans. This delivers more metadata that can be readily searched and brings value in many ways. Given the sheer size of unstructured data and its siloed nature, automation is imperative to enrich the metadata needed for classification. Use Cases for Data Classification Security and Privacy: Data classification is critical to discover personally identifiable information, IP and other sensitive data that may be hidden or has been copied and stored in noncompliant locations. An organization can apply levels of security classification too, such as low, medium or high risk. Audits and E-discovery Some organizations have regular audits, such as for proper management of financial or personal health information data, which requires IT to work with auditors and demonstrate compliance. Without classification and segmentation of audited data, an organization may face heavy manual work to locate audited data. For e-discovery, which happens out of the blue, a company may need to quickly locate and copy security video footage to facilitate an investigation, for instance. Data Retention Industry or corporate rules may dictate the retention of files for a period. Searching metadata for file type, such as medical images, and time of creation, IT can find files that are prime for deletion. This also saves money by avoiding the endless storage of data that is no longer needed or required. Komprise Smart Data Workflows can allow IT to create workflows that discover and confine or delete files by policy. Cost Savings Data classification by age and time of last access is a smart way to find data that is rarely accessed, or “cold,” and move it to archival storage where it can be retained for as long as necessary — at a fraction of the cost. Metadata indicating file type, such as instrument or research data, further informs long-term storage strategies. Learn more about Komprise Analysis here. Search and AI Deep classification of unstructured data sets, such as by keyword or project name, helps employees can find what they need without bugging IT. They can then feed it to analytics tools or other applications as needed. For instance, healthcare analysts may want to run a study of breast cancer images from a certain demographic and with a particular diagnosis code. Enriching metadata with these tags in a policy-driven, automated way means that the required data sets are always updated and easy to locate by researchers. Data Governance for AI IT and security teams can tag and segment proprietary data sets which are banned from ingestion by AI tools, as well. This is an important consideration when using GenAI tools in the public domain, since sensitive and protected data can be easily and unwittingly leaked into training models. Unstructured data classification is no longer a nice-to-have capability: it is a requirement to manage the risks of uncontrolled, distributed data. It allows storage managers to deliver more services to the broader organization — whether that is to supplement data security and privacy needs, lower storage costs or deliver a Google-like search experience to find, tag and move precise data sets to data lakes and AI tools for analysis. ### IDC Innovators for Knowledge Management Technologies 2024 Komprise was selected as one of a few vendors in the IDC Innovators report for knowledge management, featuring Intelligent Data Management for excellence in global search, analytics UI and Smart Data Workflows. ### Komprise Brings Point-and-Click Simplicity to AI with New Smart Data Workflow Manager Rapid no-code AI workflow builder addresses use cases such as sensitive data identification, chatbot augmentation and image recognition. Campbell, CA – May 21, 2024 – Komprise, the leader in analytics-driven unstructured data management and mobility, today released the Komprise Smart Data Workflow Manager to radically simplify integrating an organization’s data securely with any AI service. Two major issues related to AI success are efficiently discovering and feeding the right data to an AI platform and enriching data sets for AI. Both processes are highly manual, laborious tasks that are error-prone and require meticulous data governance. A 2024 study by IBM revealed that nearly half (45%) of companies report that advances in AI tools that make them more accessible are driving AI adoption. The research also found that only 34% are currently training or reskilling employees to work together with new automation and AI tools. Automation without specialized skills in coding or AI tools is essential to achieve time-to-value with AI. Komprise Smart Data Workflow Manager radically simplifies AI use in enterprises with: Easy Data Workflow Wizard: Intuitive point-and-click UI wizard helps you set up an AI data workflow – from searching for the right data set, to configuring and tuning the AI service, to defining the tags and how frequently the workflow should run. Global Search and Analytics: Use Komprise Deep Analytics to search across your entire data estate, on-premises and in the cloud, and define the precise data set you wish to use. Since AI is compute-intensive, time consuming and expensive, you want to feed your AI application the exact data set required. Automated Workflows: Komprise automatically runs your workflow and repeats the process as new data becomes available, saving you time and effort and ensuring the continuous enrichment of your data. Pre-built Integrations with AI Services for sensitive PII data detection, chatbot augmentation and image detection: Create a variety of AI workflows and common use cases by leveraging a catalog of pre-built integrations to popular AI services from Azure, AWS and others. Intuitive Monitoring: Monitor hundreds of workflows from a single interface, even when leveraging AI services from different clouds. See the status of each workflow, how many files were processed, the runtime, the next scheduled run and any actionable errors. Tags to Retain Context: Enrich data with tags in the Komprise Global File Index. The tags then become file characteristics you can query and take actions on, so you do not have to re-run the AI service on the same data repeatedly, saving time and money. Tags are stored in the Global File Index and therefore don’t change the file attributes in any way. Data Governance and Auditing: Komprise delivers data governance with auditing by maintaining logs of details such as what data is fed, when, and to which service. "With Komprise, our librarians can process more images than ever and at faster speeds by leveraging AI to systematically tag all our digital collections," says Rob Behary, Head of Systems and Scholarly Communications, Gumberg Library at Duquesne University. “Our mission at Komprise is to help customers maximize the value of their data, and leveraging AI responsibly and efficiently is a priority,” says Kumar Goswami, co-founder and CEO, Komprise. “We are targeting common use cases that many of our customers have brought to us as a first step and we will continue to expand the Smart Data Workflow ecosystem to encompass any AI service.” Availability Komprise Smart Data Workflow Manager is available today as an early access program to customers. It is included in the Komprise Intelligent Data Management platform. Learn more at www.komprise.com/whatsnew. About Komprise Komprise is the leading provider of unstructured data management and mobility software that frees enterprises to easily analyze, mobilize, and monetize file and object data across hybrid cloud data storage without shackling data to any one vendor. With Komprise Intelligent Data Management, enterprise IT teams optimize enterprise storage, backup and cloud costs while making the right data available to analytics and AI tools. www.komprise.com ### Create & Manage Unstructured Data Workflows with Komprise In the age of AI, managing and preparing file and object data for use in an evolving array of analytics platforms is a major undertaking for most enterprises. Discovering, understanding and classifying this data is one massive barrier—and one which Komprise has been delivering upon for years. Yet empowering staff with the right skills also follows suit. Enterprises need tools to manage and monitor AI data workflows. According to a 2024 study by IBM, nearly half (45%) of companies report that advances in AI tools that make them more accessible are driving AI adoption. The research also found that only 34% are currently training or reskilling employees to work together with new automation and AI tools. There is a clear gap in skills needed to drive results from AI tools, which leads to our release of Komprise Smart Data Workflow Manager. Here are some of the high notes: An intuitive point-and-click user interface wizard that helps users easily set up an AI data workflow without the need for specialized coding or AI skills. Quickly search for the right unstructured data set, configure and tune the AI service that is part of the workflow, monitor and audit workflows. Pre-built integrations to popular AI services, such as Amazon Rekognition, which we will expand over time. Komprise automatically runs your workflow and repeats the process as new data becomes available, saving you time and effort and ensuring the continuous enrichment of your data. Monitor hundreds of workflows from a single interface, even when leveraging AI services from different clouds. Enrich data with tags in the Komprise Global File Index. The tags then become file characteristics you can query and take actions on, so you do not have to re-run the AI service on the same data repeatedly, saving time and money. Komprise delivers data governance with auditing by maintaining logs of details such as what data is fed, when, and to which service. Read the press release. The Intersection of Unstructured Data and AI There are two critical intersections between unstructured data and AI. AI needs large amounts of unstructured data to run its analysis and make results more accurate: this not simple when many enterprises have petabytes of data spread across hybrid cloud environments. AI can help businesses deliver more context to data through metadata enrichment and tagging, which speeds up search for end users looking for the right data sets. Data scientists and analysts often spend 80% of their time looking for the right data sets rather than doing the analysis. These processes are today highly manual and require specialized AI and coding skills, even when using an automated data workflow technology. Smart Data Workflow Manager builds upon our Smart Data Workflow technology by delivering a point and click method to search for the right data set, configure a third-party AI service, define tags, set the schedule for how frequently the workflow should run and monitor dozens of workloads at once. Now, any authorized IT or business user can create a workflow that speeds up getting the right data to AI and then enriching new data sets from the AI analysis with additional tags. Top Benefits of Komprise Smart Data Workflow Manager No-code functionality makes it easy for anyone to set up workflows; Faster time to value and less manual work to leverage unstructured data in AI platforms and services; Komprise automatically executes the workflow on the prescribed schedule and spins up the AI service only when new data needs to be processed which eliminates unnecessary AI costs; Komprise automatically creates audit logs of all the data sent to an AI process so you have an audit trail for data governance. Easy to monitor the progress of dozens or even hundreds of workflows at once from a single dashboard. Smart Data Workflow Manager Use Cases There are several potential use cases for Smart Data Workflows. For this release of the Smart Data Workflow Manager, we have focused on three use cases that we’ve prioritized based on customer and partner feedback: Image Recognition: Search across billions of files to find specific people or objects is no longer laborious and painstaking. Komprise automates the workflow of curating data by feeding Amazon Rekognition using both standard and custom LLMs. Komprise then tags the results in its Global File Index to cut hundreds of hours of manual effort for departmental teams. Sensitive Data Detection: Find Personal Identifiable Information (PII) across all your data using third-party PII services. You can define the types of PII and direct Komprise to act on it--such as moving it to a different location or confining it for deletion. Augmented Retrieval with Chatbots: Many organizations want to build chat services using AI like Azure CoPilot. Yet to provide tailored responses specific to your company, the AI needs access to corporate data. Komprise can find and feed the relevant corporate documents to augment retrieval prompts while ensuring corporate data remains private. Watch the demo! https://vimeo.com/948464849 Watch the webinar to dive deeper and learn more about what’s new in the latest Komprise Intelligent Data Management release. Watch Webinar On Demand ### Five Requirements of a Unified Control Plane for Unstructured Data Management Services Unstructured Data Management is Becoming Increasingly Important GigaOm notes in their 2024 Unstructured Data Management Radar Report: “As data ecosystems flourish, sophisticated unstructured data management (UDM) tools are emerging, poised to unlock the vast potential of dormant data and propel organizations into a data-driven future.” The report also advises: “Strategic deployment of UDM solutions grants organizations full visibility into their data, informing the development of cost-effective roadmaps that maximize ROI on data storage.” Unstructured Data Management is Not Storage Management Unstructured data management has emerged as a new category that encompasses elements of data classification, mobility, governance, and cost optimization. While data storage and backup vendors have some data management capabilities, these solutions are focused on optimizing their own devices and deployments, not providing comprehensive data visibility, mobility and value across heterogenous environments. Furthermore, they address the problem from a storage-centric perspective, leveraging their storage file system, which leads to inefficiencies and lock-in that can be paralyzing to customers. Five Requirements for an Unstructured Data Control Plane Here are five requirements for a modern approach to unstructured data visibility, mobility and management which achieves maximum data storage price/performance optimization and data value: Ease of Set Up and Administration. Are you administering a unified product or multiple piece parts? How many admin guides are there? What does it take to deploy and administer the solution? Agentless Architecture. Can the solution scale across environments without complexity or are you stuck managing many brittle connectors? What are the connectivity requirements and what is required for upgrades and ongoing management? Visibility + Mobility. Can the solution provide actionable data and storage insights? Can it help you define and execute data movement at scale with policies suited to your data and not to the storage cluster? Storage Insights demo. Native Data Access without Vendor Lock-In. As you move your data (e.g. tier data to the cloud) are you able to access that data in the new location natively or do you need to go through the source storage system? When it's time to upgrade your storage system, do you need to rehydrate all the data you tiered to migrate to the new system and then tier the data again from the new system? Unlock Data Value. Does the solution provide easy mechanisms to find specific data sets and feed AI/ML engines and other processors? Does it allow you to tag your files based on its content and create custom workflows to address data governance and compliance, which are core data management functions? Why Storage Agnostic Matters in a Data Control Plane Unstructured data management requires a cohesive product vision, product architecture and long-term commitment from a vendor to deliver enterprise scale. This is exactly what Komprise has done since its inception, and why we are successfully managing an exabyte of data across customers. At Komprise, we believe that data management functionality is a layer independent of storage. By considering data to be separate from the storage in which it resides, it is possible to manage data holistically across vendors, be they on-premises storage arrays or cloud providers--and across technologies, be they files or objects. This approach has allowed Komprise to create an unstructured data management solution that is vendor agnostic and integrates tightly with on-premises and cloud storage to create a hybrid data management platform. Komprise has also achieved multiple industry honors for our Komprise Intelligent Data Management platform. Check out the Awards page here. Next steps? Schedule a demonstration to find out how Komprise does against these five requirements for delivering a unified control plane for what Gartner now calls Data Storage Management Services (DSMS). ### And the Winner for Unstructured Data Management Is…Komprise! It’s been a superb month of recognition for Komprise and we couldn’t be more thankful to our customers, partners and employees for this industry recognition. Last week we announced that Komprise won the Gold Stevie® Award in the Data Tools & Platforms, New Product & Services category of the 22nd Annual American Business Awards® (ABA). Also in April, Komprise Intelligent Data Management was recognized for two other industry awards: “Data Management Platform of the Year” by the 2024 Data Breakthrough Awards “2024 Cloud Computing Product of the Year Award” from Cloud Computing Magazine and TMC These awards build on the momentum in the market for storage-agnostic unstructured data management and mobility as enterprises modernize data center infrastructures and prepare for AI workloads. In a recent Harvard Business Review survey of data leaders, only 37% agreed — and only 11% agreed strongly — that their organizations have the right data foundation for gen AI. Komprise Intelligent Data Management is delivering upon three core objectives for IT and data leaders: Supporting enterprise-wide goals for cost optimization, in an environment where data storage and backup costs constitute 30% or more of IT budgets in most organizations. Read more about our approach to analysis-based unstructured data management for cost savings here. Delivering a Deep Analytics platform to support granular search and automated policies for security, compliance and AI projects--such as finding sensitive data (read the AWS blog post), discovering anomalous activity on shares or out-of-policy usage by individuals and departments and segmenting data sets to be included OR excluded from GenAI usage. Speeding time to market for AI initiatives with our Smart Data Workflows functionality. Komprise Market Results In 2023, Komprise new subscriptions doubled (again), driven by strong growth in new logos and record expansion from existing customers. The company grew average annual contract value (ACV) by 60% with multiple seven-figure deals throughout the year. Further, we’re now managing unstructured data in the exabyte range across our customers, which span enterprises in healthcare, life sciences, public sector, legal, energy, financial services, higher education and media & entertainment industries. Check out the Komprise awards page for other honors and read our 2023 momentum press release. Unified Control Plane for Unstructured Data Management As a Stevie Gold Award winner for 2024, Komprise was recognized for Storage Insights, a unified console for data-centric and storage-centric metrics. Storage Insights gives storage administrators the ability to drill down into file shares and object stores across locations and sites, including relevant metrics by department, division or business unit, such as: Which shares have the greatest amount of cold data? Which shares have the highest recent growth in new data? Which shares have the highest recent growth overall? Which file servers have the least free space available? Which shares have tiered the most data? Read: 5 requirements for a unified control plane for unstructured data management Commentary from Stevie Awards judges Here's what the judges had to say about Komprise Intelligent Data Management: “Great innovation of self-discovery of unstructured data across the organization and efficient management of it. It is every companies’ growing pain and concern about siloed data storage." "Their advanced analytics solutions empower businesses to gain valuable insights from their unstructured data, facilitating data-driven decision-making and cost savings. Komprise’s commitment to data analysis and mobility has earned them recognition as a leader in business analytics and data management solutions.” “Robust solution for managing large volumes of unstructured data. Strong customer testimonials with tangible benefits in terms of cloud cost savings." “The new product, Storage Insights appears to address an important need in enterprise data management by providing IT managers with deep visibility and actionable insights into their data across storage environments. Storage Insights brings good value to enterprises by providing unified metrics, informed decision-making capabilities, efficiency and productivity gains, enhanced visibility into data, and support for analytics initiatives. It empowers IT managers to optimize storage resources, reduce costs, and unlock the full potential of their data assets." Thank you to the Stevie Awards judges for recognizing Komprise Intelligent Data Management. Be sure to watch a demonstration here and sign up for a custom Komprise demonstration to see Intelligent Data Management in action. ### Komprise Receives Prestigious Stevie Gold Award for Data Platforms Campbell, CA, April 25, 2024-- Komprise, the leader in analytics-driven unstructured data management and mobility, today announced that it has been selected as the winner of a Gold Stevie® Award in the Data Tools & Platforms, New Product & Services category of The 22nd Annual American Business Awards® (ABA) today. Komprise was recognized for Storage Insights, a unified console for data-centric and storage-centric metrics. The Stevie Award is the fourth industry award for Komprise in 2024, as the SaaS company continues its trajectory of rapid growth to meet the needs of enterprises managing petabyte-scale file and object data environments. See this page for a list of all Komprise awards and honors. Komprise Intelligent Data Management brings visibility across data silos through granular search, data classification, data tagging, dashboards and reporting. The simple UI allows users to analyze, migrate, transparently tier, replicate and manage data at scale simply and reliably—across all storage from the data center to the cloud. Storage Insights gives IT users a simpler, more efficient view of storage capacity and trends by vendor and key data insights such as percentage of modified or new data. Users can sort shares and view by largest, most cold data, highest recent modified data, least free space, most and least data archived or tiered by Komprise and more--and execute actions directly from the console. Komprise also delivers Smart Data Workflows to streamline and automate the search, tagging and movement of unstructured data to AI tools and storage platforms. "Komprise's advanced analytics solutions empower businesses to gain valuable insights from their unstructured data, facilitating data-driven decision-making and cost savings," remarked an ABA judge. "Their commitment to data analysis and mobility has earned them recognition as a leader in business analytics and data management solutions." "AI can be a competitive differentiator but feeding it the right unstructured data is key to success,” says Krishna Subramanian, COO and cofounder of Komprise. “We are grateful for the recognition we are receiving from our customers who rely on Komprise to harness the value of unstructured data with proper data governance while optimizing its costs, regardless of where the data lives.” The American Business Awards are the U.S.A.’s premier business awards program. All organizations operating in the U.S.A. are eligible to submit nominations – public and private, for-profit and non-profit, large and small. Over 3,700 nominations from organizations of all sizes and in nearly every industry were submitted this year for consideration in many categories. Details about The American Business Awards and the list of 2024 Stevie winners are available at www.StevieAwards.com/ABA. About Komprise Komprise is a provider of unstructured data management and mobility software that frees enterprises to easily analyze, mobilize, and monetize file and object data across hybrid cloud data storage without shackling data to any one vendor. With Komprise Intelligent Data Management, enterprise IT teams optimize enterprise storage, backup and cloud costs while making the right data available to analytics and AI tools. About the Stevie Awards Stevie Awards are conferred in nine programs: the Asia-Pacific Stevie Awards, the German Stevie Awards, the Middle East & North Africa Stevie Awards, The American Business Awards®, The International Business Awards®, the Stevie Awards for Women in Business, the Stevie Awards for Great Employers, the Stevie Awards for Sales & Customer Service, and the new Stevie Awards for Technology Excellence. Stevie Awards competitions receive more than 12,000 entries each year from organizations in more than 70 nations. Honoring organizations of all types and sizes and the people behind them, the Stevies recognize outstanding performances in the workplace worldwide. Learn more about the Stevie Awards at http://www.StevieAwards.com. ### Neha Das: From QA to Product Management at Komprise Neha Das joined Komprise in 2018 as a Senior QA engineer, based in the Bengalaru office. We asked Neha a few questions about her experience working at Komprise and her career journey so far in the unstructured data management and data storage sectors. What did you do before joining Komprise and how did you decide that it was a good place for you? Neha: Before joining Komprise I was a QA engineer at NetApp for five years. I joined NetApp right out of college as an intern. At NetApp, I was working with various data management initiatives, and when the opportunity came from Komprise, it was a natural leap. What convinced me that Komprise would be a good fit was the people I interviewed with; the overall culture and feeling from the team was warm. I felt it would be a great place to work and I was right! Can you describe your career journey in terms of responsibilities and where you are today? Neha: As a QA engineer, the focus was on making sure that the delivered features meet the requirements and during testing, finding out if there were any scenarios that were missed in the initial requirements and design. When the opportunity to apply for a product management role came up, I was slightly apprehensive; I wasn’t certain how my experience could help me excel at this new work. After talking to a few people, I decided to go for it and so far, have not regretted the decision. My testing perspective has been useful as I work on new features. I can estimate the design complexity that would be needed, or if there are similar designs that are already in play. There are multiple facets of this new role that are completely new to me including talking to stakeholders, making key decisions for a feature and understanding how to view the product from a customer perspective, but having experience on the engineering side of the product has been beneficial. Tell me about your day-to-day responsibilities – such as a typical day or week? Neha: I follow a hybrid model by working from the office two days a week. Since most of the PM team (including UX designers) are in the U.S., my hours are a little irregular. Meetings with the PM team happen late night or early morning whereas the stand-ups with engineering team happen during core workday hours in India. As a product manager, working on a new feature entails talking with developers and designers to come up with requirements and then reviewing and working on the new features and UI mock-ups. I attend the SCRUM standups with the engineering team and if there are any open questions or queries I help resolve them. What is the best part of your day or in general your job? What really gets you excited and motivated? Neha: It’s been exciting to build new things: seeing a feature go from the requirements phase on paper to actual customers using it and providing feedback. What are some challenges of the job? Neha: We’ve been working with strict deadlines this year for customer deliverables. This means we have multiple back and forth sessions between the teams to make sure we meet the deadlines without missing any requirements and customer use cases. For instance, we had started working on one feature, fleshing out the UX and the requirements and then due to changes in the prioritization we had to leave that and move on to a new project with shorter timelines. There were many last-minute discoveries and questions while testing the feature. While there were moments of stress, we released the feature on time. This experience gave me a quick jump into the responsibilities of being a product manager, which is one of the best parts of working in a startup like Komprise. You get to learn on the job in the best way possible. What are your career goals and how is Komprise helping you achieve them—or providing a supportive environment for career development/growth? Komprise really helped me make a career leap from QA to product management. I had great counsel from a lot of peers and managers before I made that switch. After I joined the PM team, the PM leader helped with the knowledge transfer by taking me through the different aspects of the job. He was always available to answer any queries (big or small) about the role and our objectives. Other PM team members were helpful by providing feedback and inputs on things that I may have not considered when working on a new feature. Finally, what do you enjoy doing in your time off from work? I enjoy reading, mostly fiction, and cooking for friends and family. I have an 18-month old child and we have been enjoying a lot of story books and nursery rhymes this year. ### Komprise Receives Two "Product of the Year” Awards Amid Expanding Enterprise Demand for Independent Unstructured Data Management Campbell, California – April 11, 2024 – Komprise, the leader in analytics-driven unstructured data management and mobility, announces two top technology industry honors. Komprise Intelligent Data Management was selected as “Data Management Platform of the Year” by the 2024 Data Breakthrough Awards. The company also garnered a “2024 Cloud Computing Product of the Year Award” from Cloud Computing Magazine and TMC. Check out the Komprise award page for other honors. The Data Breakthrough Award goes to the best products in data innovation and Komprise was selected from over 2,250 nominations. The Cloud Computing Product of the Year Award honors vendors with the most innovative, useful and beneficial cloud products and services that have been available to deploy within the past year. Komprise is a market-leader in unstructured data management, designed for the modern scale of data with an analysis-based approach. Komprise helps enterprises optimize the management of petabytes of file and object data stored on multi-vendor storage and clouds. Komprise gives data storage teams and the departments they serve visibility across data silos though granular search, data classification, tagging, dashboards and customizable reporting. This analysis drives automated policies and workflows to support business needs for data mobility, data lifecycle management and delivering the right data to AI tools. Komprise announced in February that new subscriptions doubled again in 2023 and that the company grew average annual contract value (ACV) by 60% with multiple seven-figure deals throughout the year. Komprise also this year announced Elastic Replication, which makes disaster recovery more affordable for growing volumes of unstructured data in the enterprise by right-sizing and right-placing DR copies and offering a more holistic approach to ransomware and data protection. “Growing customer usage with market validation is the best reward we can get as tech founders,” says Kumar K. Goswami, CEO. “We built Komprise to address the toughest problems with unstructured data. We are thrilled to achieve industry recognition for a storage-independent data management platform." About Komprise Komprise is a provider of unstructured data management and mobility software that frees enterprises to easily analyze, mobilize, and monetize file and object data across hybrid cloud data storage without shackling data to any one vendor. With Komprise Intelligent Data Management, enterprise IT teams optimize enterprise storage, backup and cloud costs while making the right data available to analytics and AI tools. www.komprise.com. ### Scott Slomba: How to Succeed in Customer Success Roles Scott Slomba is Director of Customer Success at Komprise. He has more than 25 years of experience selling and supporting innovative software products and services with a strong background in storage solutions, edge computing, and secure IoT solutions. Prior to Komprise, he spent 23 years as Director of Sales Engineering at Wind River. We chatted with Scott about his career in Customer Success. You started your career in engineering but quickly pivoted to the customer success/sales engineer roles. How did that transition happen? SS: I am not wired to sit in front of a screen all day coding. When I learned that there are these jobs where you can hop in a car or plane and go meet with people at other companies and talk about technology, I was all over it. Of course, this has now changed with people traveling less for work and working remotely more often. SS: Yes, and I was often working remotely back then when it was hard to be a remote employee dialing into a conference call in headquarters. You didn't know how many people were in the conference room and what side conversations were going on and what’s on the whiteboard. Since COVID, there was a huge shift of people working remotely online and that persists in most areas. This has now leveled the playing field for me. However, in a customer-facing role it’s more challenging, as you lose the small talk and the body language--even on video. What did you learn early on about the key challenges for a customer success professional? It’s all about the first 90 days. The sooner the customer realizes value the more beneficial for everyone. By nailing the onboarding process, the customer will realize value sooner, customer satisfaction will be higher, and the odds of renewals and expansion downstream will be much higher. Adoption is also less expensive than making diving catches during the last 90 days of a subscription. An excellent onboarding outcome builds trust early on, which makes the customer journey much smoother and enjoyable for both the customer and the customer success professional. But it’s hard to get the attention of enterprise IT customers as they are often spread so thin. It is. I come from the embedded computing space. It was a big change for me to move into SaaS because in embedded, most of our customers were working on a single project that had critical requirements, fixed budgets, and hard deadlines. Ask anybody in aerospace how catastrophic it is to miss a launch date. Customers were focused on the project. In the enterprise IT space, it’s about being more efficient, saving money, saving time or being compliant. That compelling event isn't always there. Instead, there are often fire drills randomly popping up all over the enterprise. Regular cadence calls help ensure that things are moving forward and issues are being addressed in a timely manner. In parallel, you have to check in with the businessperson regularly to make sure they are realizing value. It doesn’t matter how well things are going on the technical front, if the business needs are not being met you will lose the customer. For somebody wishing to go into this profession, what are the key traits/skill sets required? Common traits I’ve seen in strong CSMs are people who enjoy helping others, who like to learn new technologies, and who are inquisitive of how people do things. Key skills include consultative selling and project management skills. You need to be able to understand the customer’s pain, map out a plan to remove that pain, and execute. Do you need an engineering background? It depends on the customer success model in place. I’ve seen some customer success models where the focus is on quoting, forecasting, and obtaining the renewal PO. I’ve seen other models where the focus is on providing white glove support and managed services. Komprise has a trusted advisor model where we act as the customer’s advocate and provide oversite through the onboarding journey into adopting new use cases. Having an engineering background in this role helps to better understand the challenges the customer is facing, provide guidance on how to address those challenges, and speak the same language while doing so. Tell me about your experiences at Komprise. What are some of the rewarding parts of your job? I like helping people solve problems whether they are technical, financial, or organizational. I get to do this with customers directly and by working with colleagues inside the company. I like working for a small company where people can make decisions fast and quickly execute. I also like working in an environment where everyone has an impact and we each contribute to the company in a tangible way, every day. What is most interesting or exciting to you about the area of unstructured data management? I come from the embedded computing space where most applications require limited compute and memory resources because things need to fit in tiny spaces like an anti-lock braking system, missile, or a surgical robot. I don't even think I knew what a petabyte was five years ago. I find it mind boggling how much data is out there and how fast it is growing. IT organizations can no longer just keep buying more and bigger filers to address their data storage needs. Instead, they must strategically manage their ever-growing data footprints. Komprise has a real solution that addresses this problem that I believe was ahead of its time. What do our users—storage architects and engineers—struggle with daily? How do you view their overall challenges and role in IT? Companies are well past the point where they can just keep buying more data storage. They need to be more strategic about how they manage their explosive data footprint. The storage professionals need to deal with much more complex heterogenous hybrid environments both on prem and increasingly in the cloud as well as multiple clouds. At the same time IT budgets continue to tighten, constraining resources to manage these environments which are growing in complexity. Then throw security into the mix as cyberattacks are getting more prevalent. Factoring all these things together, these people are spread thin and have to be a jack of all trades. You’re lucky to have lived in San Diego for the past 30 years. What are your favorite things to do in that beautiful town? It is a treat to live here. I am originally from New England where the weather heavily dictates what you can do on a given day. In San Diego, it’s always a nice day where you can do anything. In my free time you will find me doing ocean swims, surfing and sailing, hiking, biking, and on winter weekends, skiing which is close. Then we have a small wine country that is very close. It’s a nice, relaxing way to spend the afternoon. That does not sound dull. Thanks Scott! ### Interview: AI Infrastructure and Independent Data Management Are On Trend Steve McDowell is Principal Analyst and Founding Partner at NAND Research and a contributor to Forbes. He has deep industry experience in engineering, strategic marketing, and strategy roles to help top-tier technology providers deliver the right products and solutions for the next-generation datacenter. He is an expert in IT architectures and infrastructure, storage, data management, hybrid multi-cloud, HCI/CI, edge computing, and AI/ML/DL. He shares his views on the market below, with AI infrastructure solutions top of mind. What were the most influential stories in your world in 2023? SM: Well, you have to say generative AI. But I don't care as much about all the cool applications for generative AI, because I cover infrastructure and this very much changes the way we think about the infrastructure that supports data, right? And AI, I think it has surprised people-- including the OEMs-- how cloud centric it is already. If you look at the earnings for Dell, HPE, Lenovo, and other big tech vendors, they're like, we didn't see the tailwinds we expected from generative AI. But then you look at Nvidia's earnings and the cloud guys and that's where all the money is; AI has turned into a cloud-first play. The year 2023 was the year we pretty much stopped saying digital transformation and started saying data transformation--and that’s due to AI. I think 2023 is also a big year for cyber security, coupled with governance and compliance. All these trends have the same set of implications on your data, which is: I need to know what my data is, where it's at, how it's being used, and who's talking to it. "AI has turned into a cloud-first play." How do you see things shaping up for enterprise tech in 2024? Are you bullish or bearish? SM: I am bullish: layoffs are a fact of life in tech. I don’t see that as a fundamental problem in the industry; there has been some rebalancing. But also when tech companies were planning for 2023, which usually starts in Q3, the plans didn’t factor in the impact of GenAI. That resulted in a lot of scrambling and reallocating of resources. But it is starting to stabilize a bit now. We're moving from phase one of generative AI, which is let's train it, let's figure out what these LLC's can do, to we're going to enable all these applications with generative AI and that requires a lot of horsepower to put it to work, to do the inference. And again it all comes down to the data. For generative AI to be useful in an enterprise, I can't take an off the shelf ChatGPT. I need to supplement the model with my data, which means I need to understand my data. What are the implications for storage vendors? SM: The demands on the whole storage infrastructure continue to change and I don't think we fully understand yet what that means. It's all unstructured data that's interesting to AI and analytics. I can't remember the last time a vendor said I'm selling a storage array. Now everyone is selling “data infrastructure platforms.” How is the edge and AI market playing out? SM: The 5G guys promised that 5G is going to explode the edge. We didn't see that happen, but man moving AI to the edge will. I was at a national retail show a couple of weeks ago. They're using AI to do things like inventory control and loss prevention. Everybody hates self-checkout, right? Now I can have a virtual checkout person there to help me. Qualcomm showed at CES their car of the future with a generative AI interface that helps with things like, how do I change my tire or check my oil. You can ask your car and it will give you step to step directions. That’s going to be on the market in 18 months. In the storage industry, we hear lots of talk about all flash environments, StaaS, and AI-ready storage. Is this where customers are right now? SM: I don't think I've seen a single storage vendor who said their storage is not AI ready. What we learned in 2023 is the number of companies doing training at scale is very small. AI as a service is going to solve the training problem. What companies need is storage that keeps up with my analytics needs, and that’s flash. The storage vendors are figuring that out. Flash price for capacity is very competitive with nearline hard drives. Sustainability is also a factor. Flash has a much smaller footprint. If it’s not reading and writing. I am not drawing on power. Where flash is still failing is in archival storage. If I'm doing big volumes of low performance storage, the hard drive guys are still gonna win. If you go to an Amazon data center, you’ll see a lot of spinning drives. Storage as a service is also strong; it’s already a healthy percentage of revenues for the vendors now. This comes out of what cloud did, which was reset expectations of how we buy and consume infrastructure. Finance guys love “as a service” and especially when it’s a managed service. Moving to cloud, do you think AI is going to fuel a resurgence of enterprise cloud spending and cloud data migrations in 2024? SM: AI is a cloud-first story because AI infrastructure Is expensive and complex. NVIDIA is maximizing its position. If I want to train a large language model, that’s a $30,000 card. The accelerators are expensive and they don't have the life of a server. You may need to swap them out in eight months. If you look at recent cloud earnings, AI is changing the dynamics. Amazon was a little late to the party with AI infrastructure for a variety of reasons. Looking at the growth last quarter, Amazon's is the lowest of any cloud provider. AI is helping cloud, but people are going to where there's availability and there's still scarcity of the GPUs. What else do you hear from customers or users on their infrastructure priorities this year? Security is surely a top one. SM: Enterprises are not buying and building infrastructure for AI. But they do care about security. That market is booming right now, and it's such a fragmented space in terms of the vendors who service it so it’s confusing and hard to navigate. Observability is another big one. Five years ago, observability meant reading log files but observability now is a core capability for IT. It's Black Friday and I have to rebalance all my workloads to meet the escalating demand. And again, it comes back to what is the data that supports that! How does unstructured data management fit into the above trends that we’ve been discussing? SM: I think it's central to everything. AI operates almost exclusively on unstructured data. Doing analysis on data in silos and understanding the data you have is business critical. The “data is the new oil” phrase that came out years ago is finally true. But building intelligence into my unstructured data management doesn't just feed all these business processes that are data heavy and data dependent right now; it also plays into cost optimization, certainly in a cloud. There is a very real economic impact to how I manage this data. There’s also a security component. How many data breaches are there going to be which are driven by somebody's exposed S3 key? Everything I'm doing in an enterprise now is governed by what's living in my unstructured data. Do CIOs and IT executives see the need to manage data differently—independently of storage vendors-- and be more efficient with storage spending? SM: Storage infrastructure moves more slowly than everything in your data center, but to do all these things we are talking about, you have to manage data independently of the media. One aspect is getting a grasp of my data spread across at least three if not five vendors. That gets in the way of the unified view. I need a data infrastructure independent of wherever the bits live. It also gives me a common set of controls. I'm a fan of object storage because it gives more flexibility than a traditional NAS. IT buyers want choice and independent data management improves the negotiating power of the IT buyer. The IT buyers want this both for reducing complexity and cost control. If you can cross cloud boundaries with an unstructured data management solution, it opens up all this flexibility where I can get the promise of cloud native and I can deploy new workloads wherever I have resources. Storage vendors are pushing back against it, but I think unified data management is the future. ### Komprise Doubles Subscriptions Again in 2023 As Enterprises Seek to Leverage Unstructured Data for AI Komprise business bolstered by new customer growth and expanded adoption of unstructured data management platform. Campbell, CA—February 14, 2024 – Komprise, the leader in analytics-driven unstructured data management and mobility, today announced a doubling of new subscriptions again in 2023. This growth comes at a time when organizations are facing enormous pressure to manage and optimize data growth and IT spending in challenging economic times. Organizations are not just focusing on cost efficiency but also seeking insights into unstructured data to feed and govern AI workflows in the wake of burgeoning interest in Generative AI. Komprise Intelligent Data Management analyzes, mobilizes and manages unstructured data workflows that sit at the nexus of these trends. Komprise Highlights from 2023 Komprise new subscriptions doubled again, driven by strong growth in new logos and record expansion from existing customers. The company grew average annual contract value (ACV) by 60% with multiple seven-figure deals throughout the year, indicating growing enterprise adoption of Komprise as a platform to analyze, mobilize and extract value from unstructured data. Komprise is now managing unstructured data in the exabyte range across its customers, which span enterprises in healthcare, life sciences, public sector, legal, energy, financial services, higher education and media/entertainment industries. Komprise released several major product updates including: Komprise Analysis standalone subscription, Komprise Intelligent Tiering for Azure, new Data Governance and Self-Service features, Storage Insights for a unified view of data-centric and storage-centric metrics, and Elastic Data Migration enhancements to support a broadening set of use cases. More than half of Komprise customers are heavy users of Komprise Deep Analytics, which included a new Directory Explorer in 2023. Research and departmental IT teams are growing use of Deep Analytics to gather more granular information on data assets to support cost management, security, compliance and AI initiatives. Komprise was recognized for several industry honors including the Inc. 5000 List, Deloitte Technology Fast 500, CRN Tech Innovators, and others listed here. Published the second annual Komprise State of Unstructured Data Management report, which found that preparing for AI was the leading data storage priority in 2023. “Komprise is on a mission to change the way the world manages unstructured data, which is growing exponentially in the enterprise,” said Kumar Goswami, Komprise cofounder and CEO. “In the AI era, customers want to turn data volumes into data value and Komprise Intelligent Data Management leverages AI to look inside files and provide another level of insight and value. I’m excited about our momentum and look forward to driving even greater customer success this year.” About Komprise Komprise is a provider of unstructured data management and mobility software that frees enterprises to easily analyze, mobilize, and monetize file and object data across hybrid cloud data storage without shackling data to any one vendor. With Komprise Intelligent Data Management, enterprise IT teams optimize enterprise storage, backup and cloud costs while making the right data available to analytics and AI tools. www.komprise.com. ### Global Namespace or Global File System? Half of organizations in the U.S. and UK with over 1000 employees are managing 5PB or more of data and those managing more than 10PB of data grew from 27% to 32% this year, a 19% increase, according to the Komprise 2023 State of Unstructured Data Management. This blog discusses how a global namespace is critical to managing petabytes of data. This rate of data growth is a concern—from ensuring data protection and compliance, to managing data storage and backup costs, and responding to business needs for simple, fast data access. In fact, 72% of organizations report that data volumes are growing faster than their ability to manage them, according to Foundry. One Plane to Manage and Optimize All Your Data This is where unstructured data management comes into play—and a global namespace. Imagine having one place to get visibility into data across all your silos, identify hot and cold data, and plan and execute data migrations and data tiering across all your storage and cloud locations? And what if this same system allowed your users to search for relevant data across storage silos and feed AI/ML pipelines and create automated data workflows? These are the many advantages of a global namespace for enterprise data storage. As unstructured data volumes continue to expand exponentially, data silos proliferate and IT budgets remain relatively flat, many organizations are interested in simplifying data visibility and managing data across various silos. A global namespace can offer this. However, it’s important to note that a global namespace does not require a global file system (GFS), despite vendors often claiming this to be the case. A GFS sits in front of the data and serves the appropriate files, thus acting as a controller. A GFS is useful in certain collaboration scenarios where simultaneous editing of large files is needed across geographically disparate locations that can share data without violating data privacy issues. Yet for the broader use case of visibility across data silos, a global namespace that is not in the hot data path is a better solution for unstructured data management, data tiering and feeding data to AI/ML applications with the best performance. Our latest paper looks at the issue in detail, explaining the differences between a global namespace and a GFS. Download it here. Top things to know about a global namespace A GFS delivers one siloed view of your data—but not the entire view unless you are only using one storage vendor, or you are willing to control all data access through a single vendor. Unstructured data management should sit outside the hot data path and bring visibility to all data, not just data that it fronts. It should right-place data while putting users in full control of their data. Managing data growth requires an unstructured data management solution that delivers a global namespace, not a global file system. It is important to understand your use case and pick the best solution that fits your needs. In a world overflowing with unstructured data of many diverse types and across many different silos from on-premises to the cloud, Komprise delivers the primary benefits of a global namespace that can cut costs, support departmental data services and help extract greater value from your unstructured data. Read the white paper to learn more! ### Komprise Hires Finance and Tech Veteran as CFO Amid Accelerating Demand for Unstructured Data Management Campbell, CA, January 9, 2024-- Komprise, the leader in analytics-driven unstructured data management and mobility, today announced that Craig Gomulka has joined the company as CFO. Gomulka has more than 20 years of experience spanning finance, operations, business development, partnerships, and investment transactions in the healthcare, technology and financial sectors. Komprise is working with Fortune 500 companies across critical sectors including healthcare, life sciences, manufacturing, services and public sector, helping customers save upwards of 70% on annual storage and backup costs and create streamlined processes for finding and providing unstructured data to AI and cloud services. Gomulka’s hire comes at a time of increased enterprise demand for storage-agnostic unstructured data management amid rising costs and the need for AI-ready infrastructure. Most recently, Gomulka served as CFO at AI-powered recruiting software company, Visage. Prior to Visage, Gomulka was CFO at both Flowhub and Health Fidelity, a healthcare-focused natural language processing (NLP) company now owned by Edifecs. He has also held senior finance roles at large healthcare systems and was a partner for 12 years in venture capital. “As Komprise continues its strong growth and execution, Craig’s rich experience at large enterprises, in SaaS finance as well as in AI-focused businesses, is a great fit for Komprise,” says Kumar Goswami, CEO and co-founder of Komprise. “Komprise was a compelling opportunity for me because of the company’s impressive top-line growth combined with its focus in data and AI,” Gomulka says. “Enterprises need to tap into their unstructured data and manage it better to take advantage of AI and Komprise is right at the intersection of these trends.” About Komprise Komprise is a provider of unstructured data management and mobility software that frees enterprises to easily analyze, mobilize, and monetize file and object data across hybrid cloud data storage without shackling data to any one vendor. With Komprise Intelligent Data Management, enterprise IT teams optimize enterprise storage, backup and cloud costs while making the right data available to analytics and AI tools. ### Getting to the Heart of your Unstructured Data AI is a pervasive and controversial topic today. It dominates the headlines but has also given ample cause for concern. In my view, we are on the cusp of a transformational stage of existence with AI. However, in addition to developing AI strategies enterprise IT execs have bottom-line pressures to face in a still-difficult economy, security threats that are becoming harder to thwart and increased pressure to contain costs. Even so, business leaders don’t want to be left behind in the AI age. AI initiatives require large volumes of unstructured data. If you can’t find it and move it safely and efficiently to the right cloud services and tools, you’re missing out. That’s why getting your arms around your growing volumes of unstructured data and harnessing this data to deliver value will be a top priority in 2024. There’s a lot at stake, and it points to the need to understand your unstructured data at a deeper level so you can do things you could never do before. A core feature of the Komprise Intelligent Data Management platform is Deep Analytics. As Komprise analyzes your data across data silos, all the metadata is stored in a single, highly distributed Global File Index (GFI). Deep Analytics provides a simple UI that leverages the GFI so users can search across silos with a single global search. With Smart Data Workflows (SDW), we take it one giant step further. SDW allows you to create custom workflows that can use your AI application or any third-party AI service to scan your files and store the findings in the GFI. You can find protected data quickly and create governance and compliance applications with Komprise, all using our simple UI. This ability to understand your unstructured data at a granular level, regardless of which storage or cloud it lives in, and the ability to then move and manage it based on those insights is a game changer. Below I’ve laid out three core areas where Komprise Deep Analytics is helping our customers answer questions they never could before and deliver meaningful benefits to their organizations. 1. Cost savings with unstructured data analysis Komprise delivers core metrics to understand costs and create plans to save money, such as: rate of data growth, storage usage per department, cost savings opportunities by tiering cold data to archival storage and information such as top file types, top file sizes and top data owners. Here are two examples: Searching across shares for cold data tiering opportunities. A multi-billion-dollar biomedical company generates huge amounts of instrument data every day. In the past, IT deleted some of the data over time. But due to the cost of regenerating the data if needed and regulations which require data retention for seven to 25 years, the company ceased deleting data. This resulted in hefty on-premises storage purchases, so IT decided to shift to the cloud. Using Komprise Deep Analytics to analyze access patterns and cold data by share and department, they were able to tier petabytes of cold data to cheaper tiers in the cloud-- saving money and reducing the need to request approvals to buy more storage. They also love that Komprise leaves behind links so users can still access the data if needed and users can access the data in the cloud natively without requiring Komprise--supporting several AI efforts underway. Identifying and deleting duplicates is another great use case for Deep Analytics. For instance, the marketing department copies 50,000 images for use in a global marketing campaign. Once the program ends, the images are no longer needed. You can run a query to search for duplicate image files and then move them off your expensive storage quickly; you’re cutting costs and freeing essential space on your primary storage. 2. Answering questions about unstructured data on the fly When you have questions about your data, it can be difficult or even impossible to get the answers when managing petabytes of data across many different silos. Yet Komprise Deep Analytics allows you to ask all sorts of interesting questions, some of which are critical to daily operations. For security compliance, Deep Analytics allows you to investigate your data estate to ensure there are no files that should be removed or if certain files are not located in secure locations where they belong. Deep Analytics is a simple way to ensure your entire data estate is compliant. Here are some examples: Discovering and correcting compliance issues. A data center manager at an oil and gas company who was asked to clean up several data silos leveraged the global search provided by Deep Analytics. Instead of repeating the effort for each silo, he leveraged Deep Analytics and found many old .PST files spread across the organization. He showed the report to the department manager to get buy-in to delete the files. In another case, after an acquisition, Deep Analytics found a large volume of files from an unsupported productivity application sitting on a file server. They posed a security risk and wasted precious storage space, so IT promptly deleted them. Finding critical data across multiple data silos. A large European engineering construction company which has grown by acquisition had to find critical soil data regarding a project which was affected by an earthquake. An IT manager located the data across the silos of its many small engineering firms in minutes, using Komprise Deep Analytics. Without it, they used to make calls, send emails and wait for responses from the various firms. 3. Leveraging AI to find the precise data sets. AI is punitively time-consuming and expensive if you must copy massive buckets of files to an AI application. For AI to be viable, it’s important to find and send the precise data sets to AI. In some cases, such as when you have lots of data at the edges, it is better to bring the AI application to the data. Komprise makes this possible. After processing the data with AI, you want to save the results so that you can use them anytime without having to repeat the analysis. Here are a few real-life examples: A university is deploying Komprise for AI-aided image recognition and tagging to support marketing and fundraising projects. With millions of files, the marketing team surmised it would take several months just to find the images they needed for a big campaign. Rather than an arduous manual process of looking through thousands of images in storage, they used Komprise SDW to feed only image files to Amazon Rekognition. The image analysis tool tagged the files in the GFI with the required metadata ( e.g. tag images containing university buildings with GPS coordinates). The process took under two hours to complete and allowed the marketing team to get their campaign launched in time. Finding and segmenting sensitive data based on PII. Smart Data Workflows (SDW) can create a continuous process to scan all new files through a personal identifiable information (PII) scanner. This could be a third-party scanner or a cloud service like Amazon Macie, which then tags files containing PII information in the Komprise GFI.  You could use Komprise to tier cold data with PII to AWS Gov Cloud and those without PII to the cheaper regular AWS cloud. You can set this up as something that happens all the time - ensuring continuous cost savings that comply with regulations. You can bet your security team will love this capability! I’m very excited about our approach of providing one global search across data silos and the ability to enrich the metadata it contains through SDW and AI. It opens a host of data-driven management activities which were never possible until now. That’s the power of Komprise Intelligent Data Management and Deep Analytics. I look forward to hearing about your requirements, sharing more use cases with you and helping you bring structure to your unstructured data in 2024. Happy Holidays! ### Trends in Unstructured Data Management for 2024 This blog was adapted from its original version on VMblog.com We launched our company in 2014 to create a solution for the new category of unstructured data management. While some of the early tenets that we created the company around – regaining control over unstructured data and getting deep visibility into data to make better decisions and maximize cost savings – are still paramount for customers, there is so much more today. Today we see a host of new requirements for AI, self-service, departmental collaboration, cloud migrations and compliance. I’ve put together some predictions for next year in that light here. Storage Teams Advance User Self-Service The trends towards IT-as-a-Service coupled with increased interest in AI are causing enterprise storage teams to look for ways to manage data across storage vendors and deliver new and improved data services to business users. Most (85%) of IT leaders in the Komprise 2023 State of Unstructured Data Management say that non-IT users should have a role in managing their own data and 62% already have attained some level of user self-service for unstructured data management. Data storage professionals will need to focus on tighter collaboration with departments, such as through showback reporting, to cut costs by finding and tiering cold data and eliminating unnecessary duplicates. End users should be able to quickly search for the types of files they need and inform IT about their intentions so that IT can set policies for data movement - such as to a cloud AI service. IT Will Create GenAI Guardrails for Data The Komprise survey shows that organizations are largely allowing employee use of GenAI and most have outlined some restrictions on data or applications. Yet there are limitations on guardrails due to the early, amorphous nature of the technology and a lack of understanding in how the tools work behind the scenes and what vendors are doing (or not) to protect organizations and their data. It's hard to fully control employee use, as with shadow IT. The best place to start is to create and enforce a comprehensive data governance framework that manages the Security, Privacy, Lineage, Ownership, and Governance (SPLOG) of data interactions with AI. Read our blog post here. AI Data Governance Will be Layered Given the multifarious threats from generative AI, it's hard to imagine a single AI data governance solution that will fit the bill. Instead, there will be layers of AI security tools, starting at the network layer to prevent the access of blocked data by an AI tool or prevent users from sending corporate data to unauthorized AI services. There would be another level of protection at the data layer which audits which data was moved, where, when and by whom and alerts if PII or sensitive data is being shared. Finally, there could be a security mechanism at the user layer that may warn users when they are engineering prompts with corporate or sensitive data or provides feedback when prompts may be giving away too much corporate context. Cloud Migrations Will Require Specific Cost Optimization Strategies Common tactics to avoid cloud waste include leveraging cost savings plans and other pricing promotions offered by the cloud vendors, using commercial spend monitoring tools, deleting duplicate and orphaned data and reducing cloud sprawl through automated discovery and corporate policies. An independent unstructured data management solution informs cloud migrations by giving storage and IT managers a means to view and analyze data assets across all storage and establish automated movement of data to the most cost-effective storage solution for current needs. This avoids data sitting endlessly on high-priced storage when it's no longer active. Continuous data lifecycle management through automated policies can also ensure that data moves to the optimal location as it ages or its business value changes. IT Will Seek Unified Storage and Data Metrics Storage managers do not have a single console to see detailed usage and capacity data on both storage and data assets. This is important now because unstructured data growth has exploded in recent years, creating massive strain on IT budgets and complexity plus increased security and compliance risks. Plus, storage managers are increasingly procuring storage from many different vendors. That's making it difficult to see trends to save money or manage capacity, performance and security more effectively for end users. Komprise introduced Storage insights this fall to help customers work more effectively and productively. As industry analyst Steve McDowell remarked recently in Forbes: "Storage Insights is unique in the market in providing a holistic view of an enterprise's unstructured data across cloud boundaries, including data stored on-prem on nearly every storage vendor's solution. That's powerful." ### Dan de Gruchy: Customer Support Guru You have to enjoy puzzles and be curious. Why did something break and can we prevent it from happening again?--Dan de Gruchy, Head of Customer Support, Komprise A common theme in our Gartner Peer Insights reviews is the responsiveness of the Komprise Customer Support organization. Here are a few recent remarks: “Komprise overall is a great tool, with exceptional product support.” “The support and guidance by the Komprise team has been better than expected.” Recently a customer wrote to us about the troubleshooting abilities of our head of customer support saying, “I actually joke with him all of the time that if an alien spaceship crashed in my back yard, he would be the first person I would call.” With that kind of feedback, we caught up with Dan de Gruchy, Komprise head of customer support, to get his perspective on working with our enterprise customers. How did you get into customer support? Dan: I wanted to work in computers and video games. I started out in sales but that wasn’t very exciting for me so I became a sales engineer and the support side of the role blossomed for me. I always enjoyed puzzles. I don’t have a technical degree, so it has been 100% learning on the go. Over time I learned that there are core bits that are the same in most products. What is the most rewarding thing about this career? Dan: It's really when you solve the difficult problem, the one that many people have been focused on and it’s like, OK, cool, we conquered Everest and customers are elated and over the moon with the help that you've provided. That's a big deal plus making friends with some of the customers is also rewarding. Recently, with one of our pharmaceutical customers, we were having to bulk recall data to move from Azure to AWS. Their environment came from many mergers. Users had multiple SIDs (security identifiers) and it was very complex. Day after day, working with them for weeks on end and getting hundreds of terabytes of data back, it was a lot of fun and pretty rewarding. The customer wrote a book of a thank you. What is a day in the life? Dan: The beauty I guess is that every day is different. You don't have time to get bored and you're always learning something new. I have been here for six years and I am still learning new things about the product and the operating systems. One day it's performance, the next day it's domain issues. Things do come in waves. The only weird truth about support is that regardless of the company, you’ll sometimes see the same issue hit several customers at once and then you won’t see it again for months to years afterwards. How have tools and tactics changed over the years? Dan: The base toolbox is the same: you look at the logs and focus on the issue and research the errors. In terms of communicating with customers, I have noticed death of the phone. Remote meetings are predominant. I miss the phone in a way. But it’s also kind of peaceful. The nice thing is, with a remote session you have more control because it’s a scheduled interaction. Thankfully we don’t get too many angry people. I have noticed death of the phone. Remote meetings are predominant. I miss the phone in a way. But it’s also kind of peaceful. What is the most difficult part of the job? Dan: The evolution of the product. With every company, the product must keep growing and that is particularly true with SaaS. But I would love to fix all the bugs before adding new features. Stability in any product is a challenge. You need to find ways around the bugs when you can. If it’s an integrated product, you don’t have a lot of control. Also, everyone’s environment is different so you have to learn their environment with all the subtle security bits and quirks and nuances. Another challenge is dark sites where you can’t see the logs-- which is typical in government. That is a real struggle to support and you just have to trust that the customer is giving you the best information. What are the main skills and characteristics that someone needs to enter this field? Dan: You have to enjoy puzzles and be curious. Why did something break and can we prevent it from happening again? Is anything else broken? You need to be calm and be able to distract customers when they’re stressed. It’s important to listen to the venting but also course correct the venting. Let's fix it. You have to extrapolate from what the customer says and what they don’t say. You need the ability to coordinate resources to solve the issue if you can’t. What is interesting to you about unstructured data management? Dan: Coming from the data storage sector (I worked at Data Domain, Nimble and Rubrik prior to joining Komprise in 2017), I love getting out of hardware. Working in software lifts half of the problems away. Also, because data is one of the major currencies in the world, it’s not going away: it’s only growing. Working with data is lifelong security. I used to say that Sarbanes Oxley was the best thing that happened to storage because you couldn’t get rid of the data. That's still true with unstructured data management as you have to take the data and do something with it, put it somewhere or move it to low-cost storage and still be able to touch it. The unstructured data management space won’t ever go away, and it will morph especially with AI. What do you do for fun? Dan: I play and collect guitars, love retro video games, and lately have been doing a lot of hiking. ### Komprise Intelligent Data Management 5.0 is Here: Storage Insights Unstructured data continues to dominate the enterprise IT landscape. “In 2022, 90% of the data generated by organizations was unstructured, and only 10% was structured, according to a recent IDC report. “That year, organizations globally generated 57,280 exabytes of unstructured data — a volume that is expected to grow by 28% to over 73,000 exabytes in 2023.” The analyst firm also noted: “Half (50%) of our survey participants told us their company's unstructured data is mostly or completely siloed.” Similarly, in the 2023 State of Unstructured Data Management report, organizations managing more than 10PB of data grew from 27% to 32% this year, a 19% increase. Nearly three-quarters (73%) are spending 30% or more of their IT budget on data storage and protection. Massive data growth and data storage costs, increasing silos and the need to effectively and efficiently manage unstructured data are primary reasons why Komprise was founded in 2014. Intelligent Data Management 5.0  Today we’re happy to announce the general availability of Komprise Intelligent Data Management 5.0. This release includes some exciting updates developed to ease the burden on storage professionals managing complex hybrid cloud environments, including a new user interface that unifies data and storage management, called Storage Insights. Watch the Webinar Now, Komprise users can drill down into file shares and object stores across locations and sites in a single console. Storage managers can spot trends and track and manage custom metrics across storage environments in one place, and they can take action right from the console.   Those actions include things like: Tier cold data transparently from the shares that have the highest amount of cold data to cheaper storage; Customize the display to show storage owned by each department or division, then download a CSV report to “showback” to the data owners their impact on the corporate storage budget; Identify cloud migration opportunities such as moving least modified shares or copying project data to data lakes; Spot potential security threats and ransomware attacks on data stores with anomalous activity, such as high volume of modifications; Storage Insights includes over 25 columns that allow users to choose what information to display, and in what order to understand the current state of enterprise storage assets across sites.   Early Customer Feedback Over the last few months, I was able to listen in as our product management team previewed the new user interface to customers and the feedback has been amazing: “This is something we can present to our VP. He likes to know and view the holistic app view of our storage.” “I would find this incredibly useful myself. It would save me from asking our storage admin questions all day long.” “Storage by department – that will be huge!”   "The Storage Insights functionality will give us the ability to see our storage footprint across our hybrid cloud. It’s a single interface that will show us important metrics like capacity usage in every storage location, which will save us a lot of time and ensure we make the right decisions for our departments and users." -- Matt Madill, storage systems administrator at Duquesne University.  New Reports and Other Platform Updates Komprise customers should spend time reviewing the release notes and documentation as there are enhancements across the platform—from Komprise Analysis to Elastic Data Migration to numerous performance improvements, new support for Azure Lifecycle Management and more. Komprise Intelligent Data Management 5.0 also includes new easy-to-share reports now available in the Reports tab, including Potential Duplicates Report, Orphaned Data Report, Users Report, and Migrations Report. Here’s a short demonstration of how customers can quickly determine new cost savings opportunities by filtering and finding potential duplicates across storage silos:    Attend the Webinar Watch the on-demand webinar with Komprise product management to review what’s new in the latest release. Watch Now “As unstructured data continues to grow explosively, enterprise storage is becoming more distributed across on-premises, multi-cloud and edge environments, and often across multiple vendor systems. This latest release gives customers an easier, faster way to proactively manage and deliver data services across this complex hybrid IT environment while optimizing their data storage investments.” -- Komprise cofounder and CEO Kumar Goswami What's new in Komprise Elastic Data Migration 5x? In October, we announced expanded support for new unstructured data migration use cases with Komprise Elastic Data Migration. Read the blog and watch the on-demand webinar to learn more. ### Paul Malkon: Leading the Channel at Komprise Paul Malkon joined Komprise this year to lead the company’s channel programs, as the VP of Global Partner Sales. With an extensive career in data storage, backup and business continuity, Paul is working closely with Komprise partners to bring modern unstructured data management solutions to enterprise customers around the globe. We asked Paul a few questions about his journey in the industry and how he sees the customer challenge today. Connect with Paul on LinkedIn. Komprise: You have been working in enterprise sales for storage and infrastructure companies for many years. How has the data storage industry changed most in recent times? PM: In the last two decades, the hard drives have become faster, smaller in size and with greater capacity and now it is all Flash or moving to all Flash. Storage has evolved to where you can work from anywhere and access your data. Customers have data optimization strategies and options like before. At the senior level there, there is a top line message of efficiency, cost avoidance and doing the right thing. But as data grows so does storage consumption. Gartner references: “Bad data management leads to spiraling storage costs. The IT industry doesn’t have a storage problem; it has a data management problem." Komprise provides insights into data which allows customers to make intelligent decisions and generate revenue from those decisions or deploy cost avoidance strategies. Komprise: What trends do you foresee coming up next in the sector, especially as pertains to AI (Artificial Intelligence) and unstructured data management? PM: AI gives us the ability to identify data patterns and hidden insights to make intelligent, informed decisions with data without compromising strategy. AI can deliver new insight to help you choose what is most important around customers’ data optimization strategy. AI is also going to be instrumental in optimizing workflows, so we can move from manual efforts to automated actions. Komprise: Describe the opportunity for partners with Komprise and your goals for working with the channel? PM: Most (80% or more) of data in companies is now unstructured. It is growing at north of 40% year over year. The opportunity is for our channel partners to bring value to their customers in this space. Data optimization conversations are happening daily. The challenge: helping their customers evolve and shift from managing storage to managing data and delivering critical data services that will drive business innovation and efficiencies. Komprise: What barriers do you see for enterprises customers adopting an unstructured data management solution? PM: The barriers are the legacy incumbents with integrated, mature solutions that create vendor lock-in. The other challenge is that enterprise customers have silos and sometimes business units make decisions on their own. Consensus to get a global decision on how to manage unstructured data causes inaction. Komprise: How can Komprise and its partners help overcome those challenges so that customers can regain control of their data and achieve the kind of ROI that we often see—which is 60-70% savings and a pathway to deliver long-term value from data? PM: There are a few things. First, Komprise has a net dollar retention of 120% which I think speaks highly of our ability to take care of our customers. Then we partner with great companies like Pure Storage, AWS and Microsoft, which brings solutions with a lot of benefits and value to our market. What we can do is provide analytics and insight into the unstructured data footprint, across all storage and silos. This visibility allows partners to plan, position and leverage our offering so that we can help with their customers' initiatives. Komprise: What do you do outside of work? PM: I love being with my family and friends. My kids are older now, but we still go on trips together. I love animals and have three dogs who keep us busy. ### Top 7 Requirements for Unstructured Data Mobility This article has been adapted from its original version on InsideBigData. Unstructured data is growing everywhere and is the future of AI and ML success. To manage it well, it needs a lifecycle management strategy. File and object data should move to less expensive storage and backup technologies as it ages or declines in value. Unstructured data also needs to be available to move to data lakes and analytics applications. IT leaders need a strategy to manage unstructured data mobility. Here’s why: Cost management: Data is growing too fast, straining the ability of IT to adequately store and protect it for near and long-term needs. Most enterprises are spending at least 30% of their IT budget on data storage, according to the Komprise 2022 State of Unstructured Data Management Report. Aging data: Organizations need a nuanced approach to data rather than treating it all the same. It’s not sustainable, it’s too expensive, and it’s wasteful. Ensuring easy mobility for the data as it ages and understanding the best options for different data segments is paramount. Data reuse: Another reason why unstructured data mobility is imperative is due to growing AI and machine learning adoption. Once data is no longer in active use, it has the potential for another life in big data analytics programs; AI depends upon large quantities of unstructured data. Technology refresh: Storage architectures typically become obsolete every three to five years. A flexible unstructured data management architecture can meet new business requirements as they come up so you can find, segment and move data to new locations without undue hassle or cost. Seven new requirements for ongoing unstructured data mobility Ad hoc strategies to address data mobility no longer work in this complex data environment when needs are in constant flux. IT leaders need a systematic way to manage data movement and meet new requirements, cut costs, be sustainable and support new projects for unstructured data analytics. Here’s what’s involved: Visibility of data: The ability to look at data across storage silos for trends, patterns, anomalies and to do cost modeling is critical to make smart decisions. Similarly having a unified way to search for data across silos is important to find specific data sets and move them to new locations as needed. Analysis on data: IT organizations need to understand data across various characteristics to make the right decisions for its management. Age of data and time of last access, file size and type, top data owners, costs, volume of data and data growth rates are some of the top metrics to track. Cold data tiering: Segment and tier inactive or cold data to low-cost object storage such as AWS Glacier or Azure Blob before you migrate. Too often, organizations will send large data sets to the cloud to save money.  However, they miss out on significant savings because they are lifting and shifting data from one expensive storage location to another. Understand cloud storage classes: Cloud storage options are always changing and maturing for customers. Choice is great but can be overwhelming. Partner with a cloud data storage expert to help guide these decisions so you can efficiently map the right data sets to the right cloud storage service and create a plan for cloud data management. Departmental collaboration: Working directly with data owners on strategies is essential to avoid conflicts and to ensure that decisions for data mobility and management are sound. Policy automation: In large scale data environments with many different stakeholders, shares and directories, you can’t support data lifecycle management manually. Use an unstructured data management solution that allows you to easily create and automate policies to copy, tier, migrate and confine/delete distinct data sets. This will result in more savings, better compliance and the assurance that data is always living in the right place at the right time. Native access to data: The notion of native access to data simply means that if you move data to a new storage location, such as object storage in the cloud, you can access it there and move it somewhere else without needing to go through your file storage layer. This avoids unnecessary licensing fees and the need to maintain primary storage capacity. Cloud native access is essential for using cloud-based AI and ML services. Unstructured data is both a liability and an asset. Managing it properly with a plan for long-term data mobility should be one of the top initiatives for enterprise IT today. By doing so, you can get more value from massive unstructured data volumes, be as cost-effective as possible and enable new ways of finding and using data to better serve the broader organization. Watch this short demo to learn about the different use cases for unstructured data mobility with Komprise. https://www.youtube.com/watch?v=weDdEKfRGcc&t=1s ### Generative AI & Data Management: Two Models This is part 2 of a two-part series on AI and unstructured data management. Read part 1 here. ChatGPT and Google Bard - AI Chatbot technology In many sectors, the latest generation of AI tools is creating excitement about their potential to change and improve many facets of work. In banking, generative AI technology could deliver value equal to an additional $200 billion to $340 billion annually and in retail and consumer packaged goods, the potential impact is $400 billion to $660 billion a year, according to new research by McKinsey. Work will become more efficient and less mundane. New innovations will get to market faster and help solve real societal problems. Yet as with most new transformative technologies, there are downsides. In the case of generative AI, these downsides range from the leakage of sensitive, private and proprietary data to the rampant spreading of false or biased information, the production of faulty products that harm others and more sinister outcomes still: imagine an AI bot that can manipulate data to start a war or a deadly pandemic. AI industry leaders from companies including OpenAI and Google DeepMind have warned that AI could one day kill us all. Existential threats are extreme: but the risks from unmanaged AI have already begun to appear as companies like Samsung experimented too early without proper guardrails. In the previous blog, I reviewed five key areas of AI data governance to consider when using generative AI solutions. We call this SPLOG, for security, privacy, lineage, ownership and governance of unstructured data. It’s crucial to understand these risks and create a plan for managing these different areas before you implement a generative AI solution in your organization. Next, how do you go about safely and efficiently using these new tools for competitive advantage? Today, we see two core approaches: Customize a LLM with corporate data using Curate Audit and Move (CAM): A custom approach which manages feeding of corporate or domain-specific unstructured data to a pre-trained Large Learning Model (LLM); Prompt a pre-trained LLM with corporate data using SPLOG: Use a pretrained LLM with prompt-based augmentation that you feed data to and manage across the SPLOG principles. CAM: Curate, Audit and Move The Curate, Audit and Move (CAM) approach entails creating a custom language learning model (LLM) which affords enterprises the ultimate control over their data and its protection while mitigating the risks of using public data sets. This involves selecting a third-party pretrained model, such as GPT 4, BERT, T5 or RoBERTa, and training it with your own data to create a proprietary LLM. Building a custom LLM is a complex, resource-intensive task requiring specialized data science expertise and a robust computing infrastructure. The AI computing stack typically consists of high computing capacity (CPUs and GPUs), efficient flash storage, and appropriate security systems to protect any sensitive IP data used in the LLM. Your team will also need to develop an unstructured data management workflow to identify, copy and move the right data to your LLM, provide an audit record of this so data scientists can later review it to investigate any issues or errors in the outcomes, and then delete or archive the data from high-performance storage upon project completion. Due to the cost and time required to create and manage your own custom LLM, cloud providers are developing platforms to ease the process. Two of these include Azure Open AI and Amazon Sagemaker. Adapt a Pretrained Third Party LLM Most IT organizations will use a pre-trained model and SaaS application (such as ChatGPT) with their own data. This doesn’t require that you build an internal computing platform and you don’t need a team of data scientists to run it. As covered in my earlier blog about the SPLOG process, this approach requires a heavy lift on the data governance and data management side of things. It’s critical to mitigate the risks of proprietary data leakage and security and privacy issues, while also navigating data ownership, transparency and data lineage factors. A Data Management Framework for AI IT leaders in concert with security, legal and data science experts should develop a data management framework for AI. Here are some top considerations for a framework and associated guidelines: Create employee guidelines for sending data to AI systems. Which data is sanctioned and for what kinds of research and use cases? Which data sets are off limits and secured so that individuals cannot access them to feed AI tools? What documentation and assurances can you obtain from AI vendors for handling of your data? What tools does the vendor offer to help mitigate data risk and have they been tested well enough for broad use? For instance, ChatGPT now allows users to disable chat history so that chats won’t be used to train its models. Can you segregate sensitive and proprietary data into a private, secure domain which restricts sharing with commercial applications? Maintain an audit trail of all corporate data that has fed AI applications. Track who commissioned derivative works from generative AI tools and how those works are used internally and externally, to protect against any lawsuits for copyright infringement. What additional tools and capabilities are needed to protect, manage and monitor unstructured data in AI applications? Moving forward In these early days of generative AI, it’s best to proceed cautiously with projects. It will be months before industry standards and regulations catch up. Start by doing an assessment of your data assets and understand any potential liability issues as pertains to your data’s inclusion in an AI prompt. Spend time researching the vulnerabilities, limitations and any protections offered by an AI tool before implementing it. Discover the needs and top use cases for AI as the goals will determine the best possible AI solution. Not all projects are suited for generative AI, which is designed to create new content rather than do predictive analysis. Keep an eye out for new software tools that can help filter outcomes for objectionable or inaccurate data sources, monitor security and privacy risks of your data to avoid leakage or privacy violations, or offer private sandbox environments for experimentation. An unstructured data management solution can also help with tracking if and how employees are using internal data in an AI system and provide holistic visibility into data assets and where they are stored. ### Komprise Expands Pure Storage Support to New FlashBlade//E Pure Storage is making some noise. Storage Newsletter published a detailed summary of their most recent earnings. Highlights included: subscription annual recurring revenue (ARR) $1.2 billion, up 29% YoY. A few weeks back, a Pure Storage executive predicted an end to hard drives in favor of the kind of efficient Flash products that Pure sells, as reported in Blocks&Files: “Our CEO in many recent events has quoted that 3 percent of the world’s power is in datacenters. Roughly a third of that is storage. Almost all of that is spinning disk. So if I can eliminate the spinning disk, and I can move to flash, and I can in essence reduce the power consumption by 80 or 90 percent while moving density by orders of magnitude in an environment where NAND pricing continues to fall, it’s all becoming evident that hard drives go away.” This week Pure Storage is going big in Vegas at their annual Pure // Accelerate conference, with Shaquille O’Neil as the featured speaker. The show will have a focus on AI and sustainability, according to ITProToday. Komprise has partnered with Pure Storage for several years to deliver unstructured data management and mobility. Today Komprise is excited to announce support for Pure’s new FlashBlade//E and Purity 4.1, complementing our support for FlashBlade//S and FlashArray Files. Komprise for Pure Storage Here is an overview of the primary use cases we see for Pure customers using Komprise: File Migration for NFS, SMB, Object workloads: Pure Storage already resells Komprise Elastic Data Migration to deliver unstructured data migrations into Pure FlashBlade and FlashArray environments. Komprise is used by Pure Storage Professional Services teams for data migrations. This blog post covers five complex use cases for Komprise Elastic Data Migration. One customer migrated 2.5 PB and 4.6 billion files from NetApp to Pure and tiered 4 PB to Wasabi, resulting in massive data storage cost savings. In late 2022, Komprise released Hypertransfer, a significant update to Elastic Data Migration, which delivers 25x faster migrations compared to other common tools. Hypertransfer solves problematic migration scenarios such as SMB workloads, high counts of small files, and WAN-based migrations. Komprise Analysis for Storage Assessments: Recently Komprise announced a new subscription offering, Komprise Analysis, for enterprises that want visibility first and are not yet ready to move data. It includes a new set of pre-built reports along with dynamic interactive analysis. Enterprise IT teams can see across all their hybrid storage environments and make the best decisions for maximum cost savings and value. File and Object Tiering to FlashBlade//E: With an analytics-first approach, Komprise has proven to save customers 70% on file and object storage with a policy–based approach to unstructured data management. Customers can use Komprise to transparently tier from FlashBlade//S to FlashBlade//E and the cloud to tackle unstructured data growth while cutting cold data costs. And moving more data to Flash also means customers use less energy and can operate more sustainably. Deep Analytics: With our Global File Index, we’re excited to work with the Pure Storage team to develop Smart Data Workflows into Nvidia-based AIRI systems and power their AI-ready data storage infrastructure. Here’s to a great partnership helping customers sustainably manage unstructured data growth while maximizing data value. Learn more about Komprise for Pure Storage. ### Komprise Automates Data Governance for IT, While Simplifying Unstructured Data Access for End Users New Directory Explorer and expanded share-based access control deliver self-service administration and access for researchers and departments. Campbell, CA – May 18, 2023 – Komprise, the leader in analytics-driven unstructured data management as a service, today announced new governance and self-service capabilities that simplify departmental use of Deep Analytics, a query-based way to find and tag file and object data across hybrid cloud storage silos. IT organizations need to maintain data governance and data security while also making it easier for users to find, use and manage data. Often, these goals are in conflict and require significant IT overhead. The Komprise Intelligent Data Management Spring 2023 release minimizes administrative effort and improves unstructured data governance with new capabilities: Share-Based Access for Groups: A recent Informatica survey revealed that data governance is the top priority among chief data officers and that 68% of data leaders will increase data management investments in 2023. But managing access control while enabling self-service unstructured data management for users often requires IT to spend considerable time provisioning each user’s role-based file and object storage access. Komprise simplifies this task by giving administrators the ability to assign group access to shares using Active Directory which automatically provisions data management access only to users in those groups. Directory Explorer: A new Directory Explorer gives authorized line-of-business teams and departmental researchers the ability to augment the global search capabilities of Deep Analytics with a familiar browser interface. This means users can drill down into individual directories. Users now have multiple ways to find what they need: either by searching for it using queries on metadata and tags through Deep Analytics or if they know exactly where the data is, using the Directory Explorer. Exclusion Query Filters: The Global File Index search capabilities of Komprise Deep Analytics now includes the ability to filter data using exclusions (e.g., "all data except .log files" or "all data except in .dat directories") and then use these queries to create data management policies. This makes it easy to specify data management policies in situations where outliers can prevent data movement. “Komprise is on a mission to change how enterprises manage unstructured data to deliver maximum cost savings and value,” says Kumar Goswami, Komprise co-founder and CEO. “Increasingly, line of business and research teams rely upon data that has been historically locked away in disparate storage systems to run analytics, AI and ML. Our latest release makes it dramatically easier for teams to find and manage their own data, while simplifying governance for IT.” Availability Komprise Intelligent Data Management Spring 2023 is available today. Deep Analytics is included with the full software-as-a-service (SaaS) platform. Learn more at komprise.com/what’s new. About Komprise Komprise is a provider of unstructured data management and mobility software that frees enterprises to easily analyze, mobilize, and monetize the right file and object data across clouds without shackling data to any vendor. With Komprise Intelligent Data Management, you can cut 70% of enterprise storage, backup and cloud costs while making data easily available to cloud-based data lakes and analytics tools. www.komprise.com ### Expanding Unstructured Data Access with Governance Spring is finally here (in the Northern Hemisphere). And that means it’s time to introduce Komprise Intelligent Data Management Spring 2023. Building on themes of our last few releases, we’re continuing to advance our Deep Analytics capabilities, delivering greater data access and unstructured data governance for groups, more powerful search capabilities and a new Directory Explorer that makes it easier for researchers and storage administrators to navigate down a familiar hierarchy to find what they need. As always, customers should visit our Support Portal to review release notes and guides. Be sure to also read the blog post on the new reporting capabilities, including a new Reports Tab and check out one of our recent Customer Success webinars. Unstructured Data Governance and Access: What Gives? There is always a tricky balance between protecting data for security, privacy and compliance reasons while also making sure that authorized users can get to the data they need quickly without disruption and a lot of IT overhead. It’s even more critical to fulfill these goals today, with so much riding on data to meet business goals. And now, with generative AI creeping into many aspects of technology, IT leaders must stay focused on managing data access appropriately without jeopardizing business innovation. Here are some data points to support these priorities: In a recent DataOps Survey by 451 Research, data privacy, compliance, and data access and preparation are top priorities for data-driven organizations. According to a survey from Informatica, data governance is the top priority among chief data officers (CDOs). With these goals in mind, here are the new updates to Komprise Intelligent Data Management: Deep Analytics in Focus Komprise Deep Analytics builds a comprehensive Global File Index that can span petabytes of unstructured data across storage silos. IT users can create custom data sets through tagging and deliver policy-driven data management and mobility to meet organizational needs which can vary by department and team. Komprise Deep Analytics updates in the Spring 2023 release include: Share-Based Access for Groups Komprise administrators now can assign group access to shares using Active Directory, which automatically provisions data management access only to users in those groups. Share-based access control (SBAC) for Groups saves IT administrators time because they no longer need to manually configure access to individuals. This also means that users, such as an R&D IT director, can get access faster to see all their data and understand her group’s usage patterns. She can tag data for policy-driven management; for example, the Komprise administrator can execute a plan to move research files to archival storage once the project has finished. Directory Explorer The new Directory Explorer is a file browser-like interface that gives users the ability to drill down into individual directories for more granular control. For example, if projects are organized by directories, a project manager can quickly locate them and request that the files go into a cloud tiering plan after a certain date. A departmental user could also select files from specific directories to be copied to another share or moved to the cloud for analytics or archiving per industry regulations. This gives users another way to discover files other than searching by metadata tags through Deep Analytics. Exclusion Query Filters Komprise Deep Analytics search now includes the ability to filter data using exclusions and then use these queries to create data management policies. For example, “find all data except .log files or find all data except in .dat directories.” This gives users one more way to find precisely the data they need—and nothing they don’t. That way, you don’t end up moving more data than you need to a different location (such as expensive cloud file storage) or the wrong data to a location where it won’t meet compliance requirements (such as PII data that must be stored on a specific NAS on-premises). Watch the Directory Explorer demo:  Other platform updates with the Spring 2023 release: New Reports UI tab – read the blog and check out our recent Customer Success webinar. New support for Komprise Intelligent Tiering for Azure – read the blog. Tiering and migration for Pure FlashBlade SMB – learn more about Komprise for Pure Storage. SMB migration with Elastic Shares – learn more about Komprise Hypertransfer. The Intersection of AI and Data Management The implications of unstructured data growth are ever-expanding as IT organizations see the need to manage it for cost savings and protect it for future use. AI will increasingly be center to these conversations, and properly managing unstructured data governance and data access is becoming core to every storage manager’s job. For more on the intersection between AI and data management, watch this short video with Komprise COO Krishna Subramanian.  ### Maha Ibrahim on Venture Capital and Startups in 2023 Maha Ibrahim is a General Partner with Canaan Partners, an early-stage venture capital firm and an investor of Komprise. Canaan just closed on $850 million across two new funds. We caught up with Maha to get her take on working in VC in 2023 and which sectors in tech are rising to the top. How and why did you get into venture capital? I joined Canaan Venture 23 years ago. I originally wanted to be an academic after getting my PhD in Economics, but I decided that academics was too isolating for me. In early 1998 I joined a startup telecommunications company Qwest Communications, which is now CenturyLink, and they gave me a big job. I was only 26 then, so the job was probably a little much. My role was to interface with the startups in Silicon Valley, bringing their venture-backed technologies to our network. I was interfacing a lot with the VCs and I started talking to them about moving over and decided that Canaan was the right place for me. How has the job of being a VC investor and board member changed over the years in the tech sector? The job hasn’t changed once we’re invested in a company, but all the work leading up to that is changing. The VC industry used to be much smaller and more collaborative. There were often multiple VCs in a series A board meeting and many more generalists. These days everything is more competitive, so to win a deal and add value, you need to be more of a subject matter expert. The generalist VC is aging out. We see many more people concentrated in areas like cybersecurity and AI. How has your focus changed? My focus has been more of a generalist. Over the last 23 years, I've amassed expertise in a variety of areas so I can bop back and forth. When I joined, because I came from a startup telecommunications company, the bulk of what I was doing was infrastructure, software and some hardware related deals. About five or six years in, I was getting a lot of inbound consumer deals. I stiff armed them because if I start doing consumer deals, I'm never going to stop because I'm a woman and that's all I'll see. I didn't want to be labeled with that expertise and let go of the infrastructure software piece that I loved. About five years later, I decided to bite the bullet and do one because I really liked this one deal that we were evaluating. From then on, I decided that I'm going be a generalist and there you go! Which sectors in tech will be in a strong position by the end of this year? Roughly 15 years ago, the VC industry was buoyed by the wave of mobile, social and cloud all converging at the same time, which spawned a ton of companies. Many of these are public companies that we all know and love today. Over the last few years we've been saying, gosh, I don't know what is the next wave. And then, lo and behold, a year ago, all this conversation around ChatGPT and AI springs up; now we are seeing that left and right. Most of the companies that we're seeing on the enterprise side are using AI to better support the decisions that the software solutions are making. AI technologies are gaining a ton of steam in certain use cases like document review and speech to text. Those companies are getting funded. Cybersecurity is very hot, along with digital health and robotics. Why did AI take off now, since AI and ML technologies have been around for decades? The technology has evolved to be mainstream. ChatGPT and OpenAI transform processing capabilities in such a way that we can find answers based on a much larger set of data and therefore get better answers than ever before. The bearishness that we've seen in the venture market over the last six months will turn around as people get excited about this new wave of AI. On the other hand, next-gen cloud cost management companies are  popping up. How that converges or conflicts with the AI conversation is going to be fascinating in the next couple of years because we are seeing budgets contract. We want to take advantage of these new technologies, but then many companies are holding tight on making big bets right now. There are sectors that are doing super well and there are companies within flat sectors that are doing super well. It’s a tricky market to navigate. Understanding your customers and how well they're doing has become an increasingly important component of the sales process. In retail, you have large brands like JCPenney and Kohl's that are struggling and they might not have dollars to spend. On the other hand, there's Lululemon and Vuori, which are hitting the ball out of the park. You would love to be able to sell in to them. In this market, it feels like there's a bigger delta between the haves and the have nots. How do you see the challenges and opportunities for the space that Komprise lives in – unstructured data management and storage? NetApp is doing well because of all the software solutions which they are putting around their hardware. Some of the big legacy storage companies haven’t transitioned. The winners will need to augment their solutions with more intelligence and offer cost rationalization. Consolidation is a when not if question in this space as companies need to present broader solutions to their customer base. It's just a matter of who has the broadest customer base, who has the most capability in their platform and when the timing is right. If there’s one thing I have learned in this business, it’s that timing is almost everything. If you didn’t go into this career, what would’ve been your next choice? I am on the board of one public company and a few non-profits, but I like the private side. I can go fluidly between all these roles, so it’s hard to think about this job as not being the best job I could have. I really love what I do. Working in venture is a tougher job than it seems from the outside: it is so much more than just investing. How do you recharge? I play tennis, fly fish and I love word games. I am religious about crossword puzzles. ### File Data Metrics to Live By Are you measuring the right things for unstructured data management and file storage? This article has been adapted from its original version on VentureBeat. The explosion of unstructured data and the diversity in data types today is bringing a host of new challenges for enterprise IT departments and data storage professionals. These include escalating file storage and backup costs, management complexity, security risks, and an opportunity gap from hindered visibility. It’s not enough to shoot in the dark anymore. IT leaders need new, smart analytics and metrics which go beyond legacy storage indicators to understand data which leads to cost savings and better compliance. These metrics should also include measures to track and improve energy consumption to meet broader sustainability goals, which are becoming critical in this age of cyclical energy shortages and climate change. First, let’s review what storage metrics IT departments have traditionally tracked: Legacy storage IT metrics Over the last 20-plus years, IT professionals in charge of data storage tracked a few key metrics primarily related to hardware performance. These include: Latency, IOPS and network throughput Uptime and downtime per year RTO: Recovery point objective (time-based measurement of the maximum amount of data loss that is tolerable to an organization) RPO: Recovery time objective (time to restore services after downtime) Backup window: Average time to perform a backup The new metrics: Data-centric versus storage-centric In today’s world, where data is the center of decisions, there are a host of new data-centric measures to understand and report beyond traditional IT infrastructure metrics. IT leaders need insights to inform cloud data management strategies. Departments and business unit leaders are increasingly responsible for monitoring their own data usage — and often paying for it. Discussions with IT organizations can be contentious when, while IT is trying to conserve spend and free up capacity, business leaders are uneasy about archiving or deleting their own data. These metrics, which are standard in Komprise, help bridge the gap: Storage costs for chargeback or showback: Even if a department doesn’t participate in a chargeback model, stakeholders should understand costs and be able to drill down into metrics. They can identify areas where tiering to cold data storage can be applied to reduce spend.           Data growth rates: Overall trending information keeps IT and business heads on the same page so they can collaborate on new ways to manage explosive data volumes. Stakeholders can drill down into which groups and projects are growing data the fastest and ensure that data creation/storage is appropriate according to its overall business priority. Age of data and access patterns. Most organizations have a large percentage of “cold data” which hasn’t been accessed in a year or more. Metrics showing percentage of cold versus warm versus hot data are critical to ensure that data is living in the right place at the right time according to its business value and to meet savings goals.   Top data owners/users: This can show trends in usage and indicate any policy violations, such as individual users storing excessive video files or PII files being stored in the wrong directory. Surveys show that compliance and data governance are becoming a top priority in data management. Common file types: A research team collecting data from certain applications or instruments may not know how much they have or where it’s all stored. The ability to see data by file extension can inform future research initiatives. This could be as simple as finding all the log files, trace files or extracts from a given application or instrument and moving them to an analytics tool. Getting this data requires a way to find and index data across vendor boundaries, including cloud providers, using a single pane of glass. Collating data between all your storage providers to get these metrics is possible yet manually intensive and error-prone. Independent data management solutions such as Komprise, with the help of our Global File Index, can help achieve these deeper and broader analytics goals. New metrics for sustainable data management Another core set of needed metrics for IT infrastructure teams relates to energy use, a growing mandate across all sectors. Managing data and IT responsibly is no small facet of sustainability programs. A report by Schneider Electric found that IT sector electricity demand will grow 50% by 2030. Most organizations have hundreds of terabytes of data which can be deleted but are hidden and/or not understood well enough to manage appropriately. Storing rarely used and zombie data on top-performing Tier 1 storage (whether on-premises or in the cloud) is not only expensive but consumes the most energy resources. The sustainability-related data management metrics below can help measure and reduce energy consumption as relates to data storage. Komprise delivers all of these metrics today! Last access time and creation time: Data access and age metrics can inform decisions about moving data to a lower-carbon storage location such as cloud object storage. Duplicate data reduced: Deleting data that is not needed naturally lowers the storage footprint and energy usage. Often, especially in research organizations, datasets are replicated for different experiments and tests but never deleted. Data stored by vendor: Legacy storage technology (RAID, SAN, tape) is more wasteful in general, which is why SSD and all-flash storage has been growing quickly. Newer storage technologies are much more efficient than spinning disks, reducing power consumption. Understanding the percentage of data stored on legacy solutions is a starting point toward defining how and when to upgrade to more modern technology, including cloud storage. ---------- Why New Metrics Matter Investing in new initiatives to expand metrics programs requires time, resources and money. Doing so can inform cost-effective and sustainable unstructured data management decisions — easily cutting spending and energy usage by 50% or more. Furthermore, data consumers gain detailed insights into their data and reduce the amount of time spent searching for data. An estimated 80% of the time spent conducting AI and data mining projects is spent finding the right data and moving it to the right place. Want to learn more about metrics you can see with Komprise? Check out this post on Komprise Analysis. Want to learn what's new with Komprise unstructured data reporting? Check out this post. Randy Hopkins is VP of global systems engineering and enablement at Komprise. ### Best Practices for Data Management During Mergers & Acquisitions This article has been adapted from its original version on DBTA. A hairy M&A and divestiture challenge which executives too often underestimate is migrating massive amounts data, most of it unstructured, between entities. When you create a new company through a merger, acquire an existing company or spin off a new business out of an older one, you typically need to move and restructure a number of data assets—ranging from business records, to documentation databases, software source code, and beyond. Here are the needs and risks: Large volumes of data across the entities requires a plan for data transfer as well as combining and purging data. This is difficult with petabytes of data distributed across multiple data centers, clouds and branch locations. Time for data management planning is not always accounted for in the reorganization, leading to a situation where data transfers happen quickly and haphazardly. Data can get lost, damaged, or mismanaged. Now somebody’s got to clean up that mess later and it won’t fall just on enterprise IT’s shoulders—but all departments, divisions and executives across the organization. A major business restructuring from a merger or divestiture carries with it legal risks—for privacy, security, auditing and more. These things can take a big financial bite later if issues with customers or regulators occur. Not all data needs to be migrated during a divestiture or an acquisition—figuring out what data should stay, what data should migrate, what data is obsolete and then taking the appropriate action can be cumbersome, laborious and error-prone. A data management plan for M&As should allow you to plan, identify and move data efficiently, and provide deep visibility into the data so that you can stay on top of any security, compliance or auditing challenges that may arise in the future. Here are a few tips to guide the way: Just say no to the data storage dump. It’s common to take all or most of the data from the original entity and dump it onto storage infrastructure at the new company. While this may seem like the simplest way to handle a data migration, it’s highly inefficient. You end up transferring lots of data that the new business may not actually need or records for which the mandatory retention period may have expired. This also increases the risk that you’ll run afoul of compliance or security requirements that apply to the new business entity but not the original one. For instance, the new business may be subject to GDPR data privacy mandates because of its location in Europe. The better approach: analytics-driven migration. Start by creating a global index of all of the data that exists at the time of the merger, acquisition or divestiture. Your index should allow you to determine which types of data you are dealing with–legal records, compliance databases, files containing PII, video, customer emails and chats, R&D documents, productivity documents and so on—and when they were created, who created and accessed them and so on. Then, using the data index, develop a plan for transferring the data as efficiently as possible and with minimal risk. Dispose of obsolete data and archive non-critical data: Data that is no longer needed can be deleted before the transfer to save time and reduce storage costs. In some cases, you may determine that you no longer need a given data set, but you may still want to keep the data on hand in case you decide to leverage it in the future. You can place this data in a cold storage solution like AWS Glacier, where it will remain available at very low cost. Move data granularly: Referencing your data index, determine where each data asset needs to go. If there are multiple new business entities in the picture, it’s likely that you’ll need to break up some data sets so that different parts end up at different businesses. The data index ensures you can make the right decisions about where to target each piece of data and that files are stored appropriately for specific requirements such as performance and security. Update access controls. The data index allows you to determine which security protections need to be in place for each data asset. Ensure that you configure the right access controls after the data transfer, even if access control tooling changes–which it typically will if, for example, you end up moving on-premises data into a public cloud. Maintain an audit trail: Keep meticulous records during the data transfer so that you can track which data moves where and which access controls are in place following the transfer. Enhance metadata: In some cases, you may wish to add extra context to your data by creating tags that identify the data’s original source. Use the data index for this purpose gives even more visibility into the data in case you need it down the road. It would be nice if data management following a merger, acquisition or divestiture deal were as simple as moving a bunch of files from one storage location to another. But it’s not. To minimize risk and maximize efficiency, you need a data transfer plan tailored to the requirements of the business entities that emerge from the deal. Although developing such a plan requires work, having a global index that catalogs all of the data at stake makes the process smooth and efficient. When you know which data you are dealing with and how it impacts the business, you can transfer it with confidence, even in the most complex M&A+D scenarios. Learn more about the Komprise Global File Index and Smart Data Migration approach. ### Komprise Sales Director Rob Kummer: Doing Business in 2023 Rob Kummer is the regional sales director at Komprise, based in Charlotte, NC. We caught with him to ask about selling in tech, and what customers and prospects are saying about unstructured data management ---------- Komprise: You started your sales career in medical devices/biotech. How would you describe selling in that sector versus IT infrastructure products? RK: It is very similar which is why I could make the transition. Both are a complex technical sale with long procurement and budget approval processes. Knowing how to handle a long complicated sales cycle is very important. The challenges are in getting to the right decision maker and helping them justify the costs. ---------- Komprise: How has your job changed since the pandemic? Do you think the days of in-person selling and customer visits are over? RK: I think it’s just different. There’s still a strong appetite for meeting people in person. I’ve become much more efficient at Zoom meetings and we now do a lot of technical discussions on Zoom. I used to have to drive all over the place. It’s a nice change because now I can sync up faster with customers. My in-person meetings are typically over lunch or coffee rather than in the prospect’s or customer’s office. When you can meet in person, you can really build the relationship and build a champion within accounts and get the information that people are not willing to share over a Zoom call. ---------- Komprise: Is selling these days harder than before the pandemic? RK: Selling has always been hard. There continues to be more and more new technology, so it's a crowded space. You’re not just competing for time against a competitor but against any project they are working. For me the best way to sell is to network in person. One of the great things about the IT industry is networking. The partner community is very important and in-person networking is still encouraged. You can meet people for coffee or happy hour and when you send them an e-mail or if you're able to text them, they remember you. They’re more inclined to take a meeting to learn a little bit more. A lot of my pipeline comes from the partner community by staying top of mind and having a technology that is interesting to them and can benefit them and their customers. ---------- Komprise: Are there any common themes right now when talking with prospects about challenges and pain points—and has this changed recently due to the state of the global economy? RK: The state of the economy makes Komprise more relevant by highlighting the cost savings we enable for customers. Maybe two years ago, customers would look at us for our data analytics alone; now they are seeing the cost prevention angle. They're not going to reduce their storage bill on-premises unless you're preventing them from expanding. If they're at capacity or near capacity, we can say, let's archive data into a cheaper cloud storage vendor versus expanding your current NAS footprint. ---------- Komprise: What kind of myths and objections do you encounter when selling? RK: First, they already have tools that can tier data within their ecosystem, but that gets greatly more complex when they try to go to the cloud. Some can't do it. The data is not available and in native format the way ours is after we tier it. They also think they already have good detail on their data but their storage tool doesn’t see all of their data. When we get into a POC environment we can scan just 100 terabytes and show a customer our base level analytics, and their eyes open. Sometimes they may guess that they have a bunch of cold data and that’s validated. ---------- Komprise: Can you share a recent story about a customer interaction or a new deal that motivated you? RK: I like to take victories in everyday things and knowing you’re in an opportunity that is the right one. I was just talking to a prospect at a large university and he invited me to present to his monthly architectural review board in April. Their CIO, CTO and departmental heads all attend this meeting and it was a very positive sign to get invited. That’s the kind of thing that excites me! ---------- Komprise: What do you enjoy about working in the unstructured data management space? RK: When I was at Dell EMC I liked the unstructured data management platform and it was growing fast. When I learned about Komprise, I saw that it was a great solution to solve a big problem: What are you going to do about all this data you are storing? We're in the very early stages where organizations are starting to extract value out of that type of data rather than just holding on to it for a long period of time. Most enterprises are just starting to tackle things like getting a data management plan in place. It’s a constant battle against all the legacy products and other tools that are already in place but I truly believe that Komprise is offering a different solution. ---------- Komprise: I need to ask about your swimming career. When did you start swimming and what made you decide to swim at the college level, at Clemson? RK: From the age of 10 I began swimming year-round. When I was in high school, I don't think there was ever a question that I wasn't going to swim in college. It was more of asking myself: can I find a college where I will fit in, where I can be successful and do my best. And that's what ultimately led me to Clemson. Swimming has given me so much in life. It got me out of Indiana and gave me lifelong friends whom I still see regularly. I met my wife because she also swam at Clemson, after I did. I spent my entire life in a very structured environment and it was easy to carry that forward. I'm extremely competitive, which is why I ended up in sales. ---------- Komprise: What do you do for fun or relaxation outside of work? RK: I have three boys who are 7, 5 and 4 and I spend most of my free time with them. My wife and I enjoy getting out for the occasional date night. We like trying new restaurants and good food and cocktails. That’s about it! ---------- ### Komprise Interns of 2023 Komprise interns are an active part of our workforce in Bangalore. We hire from top engineering colleges across India, and candidates must take a coding test and participate in technical interviews with our senior engineers and hiring managers. But once they join the team, they are full members of our team – doing real work and contributing to the development of our product. They get to move between different teams and experiment with new technologies while getting a taste of what it’s like to work for a fast-paced SaaS. Once their six-month internship is complete and they have received their degree, many of our interns join us in a full-time role. Meet our new group of interns! Sejal Priya College: PES University Hometown: Ranchi Interests: Listening to music puts me at ease. Exploring new places and trying out local cuisines makes me happy. Why Komprise? Through the interview process, I could gauge the plethora of opportunities available at Komprise to learn, grow and upskill myself. "If you are ready to put in the work, you'll see yourself grow" is something that was mentioned in one of my rounds. Hard work is something I swear by and I believe the work culture at Komprise will acknowledge it. My goals: Stepping in with the expectation of growing both personally and professionally while adding a ton to my knowledge bank during my time here.     Prabhjout Singh Arora College: MNNIT Allahabad, Electrical Engineering Hometown: Jhansi Interests: Cricket, Table tennis, Pubg, Coding. I am a Punjabi Munda but you can expect more of a UP vibes from me. Hoping to see myself being consistent in a gym once in a lifetime. Why Komprise? It’s an open work culture where everyone is approachable and always ready to help. It is always a good learning exposure in a startup and a startup that is working on data is like the cherry on the cake.   Akshat Chand College: MIT Manipal Hometown: Delhi Interests: Tennis, reading, FormulaOne, cooking. I am proficient at diffusing tense situations with lame jokes. I’m highly likely to trouble you with random facts and tidbits. On the weekends, you can find me making a fool of myself at the tennis court or buried in some book. Why Komprise? The problem statement that Komprise is working on is highly relevant and will become more so with the explosion in unstructured data. I wanted to start my career with a company like Komprise because I felt they provide the right opportunities to accelerate my learning and growth in comparison to larger organisations.     Pramatha Bhat College: PES University Hometown: Karwar Interests: In my free time I watch anime, read comics and check out memes while I stay in. When I go out, I go for a swim or just catch up with friends. Why Komprise? The objective of Komprise is extremely pertinent with the expanding data sector. The opportunities Komprise provides me best align with my interests in this and upcoming stages in my career.     Ankush Nath College: MIT Manipal Hometown: Kolkata Interests: Sports, fitness, music. I love playing team sports, and I think that has made me a good team player. I enjoy solving complex problems but I also enjoy a good nap. Why Komprise? With unstructured data growing at the rate it is, I feel like Komprise will play a vital part in the industry in the near future and I want to grow with Komprise.   Poorani R College: PES University Hometown: Madurai Interests: Singing and listening to songs. I love learning and exploring various aspects of coding. I have a keen interest not only in problem solving but also in in-depth understanding of the different perspectives of the issue. I have immense experience in Abacus due to my time invested in completing all the levels in it. I also acquired a skill in classical music and have performed in a few places. Why Komprise? I want to learn and grow positively every single day. I see myself as a very good problem solver and I wanted the place where I work to be challenging. The people at Komprise, unlike many other companies, not only encourage freshers to learn but are equally enthusiastic and hungry for knowledge. This makes the environment a positive place to not only learn but also grow.   Dyutish Bandyopadhyay College: MIT Manipal Hometown: Kolkata Interests: Gaming, music, cricket. I like to think that I am a quick and curious learner but I guess we'll figure that out soon. Whenever I'm not working, you'll find me gaming or hooked to the most random TV shows. Why Komprise? I have always strived to learn and grow fast in the software industry and Komprise aligned all of that in this fast-paced and ever-growing data industry.   Lohith T Srinivas College: PES University Hometown: Chitoor Interests: Playing badminton and football, watching football and cricket. I am a major football buff. I am someone who likes to socialise and up-skill myself. I have been a part of the Debate Club, Theatre Arts Club and the MUN club back in college. Why Komprise? I felt Komprise was the best platform for me to constantly challenge and up-skill myself since I learned that Komprise is one of the fastest growing startups in data management and hence was convinced that the journey would be challenging but very helpful for my career. ### Komprise Funding News and 2023 Outlook The year 2023 is well underway and this is a time of excitement and gratitude here at Komprise. Today I’m thrilled to announce another pivotal milestone in our journey to transform enterprise data management: a new funding raise of $37 million of growth capital from Canaan Partners, Celesta Capital, Multiplier Capital and Top Tier Ventures. We will use this investment to scale operations and extend market leadership in unstructured data management and mobility. Our mission is helping enterprises across many sectors save and make money on their massive stores of unstructured data. One of our investors, Kevin Sheehan, Founder and Managing General Partner, Multiplier Capital, shares his thoughts on the raise in today’s press release: “We invested in Komprise because of their impressive growth and path to profitability combined with the massive opportunity in edge data management and unstructured data for AI/ML in the cloud. We believe in the company’s market, vision, team and execution.” Unstructured Data Management Goes Mainstream In the 2022 Komprise State of Unstructured Data Management Survey, more than 50% of enterprise IT directors said they are managing at least 5 PB of data today. A majority (87%) rate managing unstructured data growth as a top priority. We have grown rapidly in recent years as the need to manage data more effectively is now paramount to meet operational and marketplace goals. Here are a few highlights of our industry momentum: Komprise grew 306% from 2018 to 2021, with new customer logos in 2022 accounting for 30% of the total and 2022 Net Dollar Retention of 120%. We were named one of the fastest-growing companies in the Bay Area and North America on the 2022 Deloitte Technology Fast 500™. The company achieved its first-ever 2022 Inc. 5000 ranking. Meeting customers’ Unstructured Data Management needs in 2022 Last year, we enriched the larger story of independent data management and all it offers. The goal is not just efficiently managing data for cost savings but to do so much more—from compliance to data lifecycle management to leveraging unstructured data analytics. To that end, we announced a new Deep Analytics user role so that departmental IT and power users can view insights on their data and work with central IT to make data management decisions. We released Smart Data Workflows so users can create business processes to find and tag data and automatically move it to third-party analysis engines or to target storage for archives, security or audit needs. Finally, Komprise Hypertransfer for Elastic Data Migration, announced in December, makes Komprise the fastest file migration software available today. Supporting industry mandates for data center consolidations and efficient data migrations to new or better storage solutions, now customers can transfer data across a WAN 25x faster than point tools. Everything we developed and released in 2022 was geared toward forging a tighter connection between storage IT professionals and the business. Addressing global economic pressures in 2023 Most of the people I talk to—whether they are colleagues, friends or family members—don’t feel terribly optimistic about the first half of 2023 from a macroeconomic perspective. Inflation remains high and supply chain issues persist. Tech layoffs are escalating and the Ukraine War rumbles on without a quick end in sight. In this market, fiscal conservatism is in vogue. IT and business leaders, while still committed to a hybrid IT infrastructure, are rethinking their cloud investments. Many have been burned with big bills over the past year. Amid all this, sustainability initiatives and mandates are becoming widespread, especially during an energy crisis which has hit Europe hard. To survive and thrive, companies will manage their assets and operations differently—and data is at the heart of this: Analytics plus action to cut costs and waste. Komprise comes to play here with our analytics-first approach, which we call Smart Data Management. Our solution delivers visibility across all storage, from on-premises to edge to cloud, so that you can understand your data and optimize costs. It’s one place to understand what data lives where, is used by whom and how often, and consumes how much storage. But we don’t stop there: you can design automated data management policies based on those analytics, executed by Komprise. Actionable analytics is how you move from insight to outcomes that matter. Prepare data for the AI age. As I discussed in a recent blog post, enterprises need to be ready for the onslaught of workplace shifts coming with AI and ML maturity. IT leaders need to get their unstructured data sorted and prepped, as this file and object data is the critical ingredient for AI/ML platforms. New data management strategies which create automated ways to index, segment, curate, tag and move unstructured data continuously to feed these tools will be imperative. How we’re different: analytics-first unstructured data management Komprise is a vendor-agnostic, policy-driven solution that can manage unstructured data at scale and ensure customers are not locked into any storage platform or service. We differ in the marketplace in three distinctive ways: Transparent Move Technology™ (TMT): With TMT, Komprise transparently moves data across storage platforms or services with zero disruption to users and applications while saving on average 70% of storage and backup costs. This means faster time to value and great experiences for users. Global File Index: Komprise automatically creates a global index of all your data across silos, providing one place to search, find and operate on the data. Komprise is designed to work in hybrid environments including edge data centers that collect massive amounts of data from distributed devices, manufacturing, autonomous vehicles and the like. Smart Data Migration: With the Komprise analytics-first approach to unstructured data management, you can see data insights before moving anything anywhere. You'll know which data can migrate, to which class and tier, and which data should stay on-premises in your hybrid cloud infrastructure. That means your company will always have the right data in the right place at the right time—which is important for both cost management and performance. This white paper provides a closer look at the analytics you can get with Komprise. Komprise was designed from the ground up to address the massive influx and generation of unstructured data. It’s delivering millions of dollars of savings for our customers and giving storage IT professionals and others the power to manage data in ways not possible before. The time to transform the way your organization manages data for present and future value is here. We hope to be with you on that journey, no matter where your data lives or what your unique data goals are for this year and beyond. Komprise raises $37M to help companies index, manage and transform data ---------- ### Interview: Ram Mantena on Komprise India Engineering Ram Kumar Mantena is the Senior Director of Engineering at Komprise India, based in Bangalore. He’s been with Komprise since 2017, beginning as a Senior Manager for Engineering. Today he leads a team of ~35 engineers responsible for ensuring on-time delivery and high product quality. “I enjoy working to ensure that customers get the features the way they want, while also making sure that our team gets opportunities to learn and grow on the job.” Ram spoke with us about his job role and keeping employees motivated during challenging times. What are some of your day-to-day challenges? We are a fast-paced organization with a small but highly skilled staff. We’re always working on new innovations that are a result of customer feedback aligned with our long-term vision for the platform. The continual focus is how we can accelerate delivery without compromising on quality. Like other organizations, we’ve had to work hard to help our people get through the past few years with the pandemic and remote work. We have come our stronger than before and the team grew significantly too. Today we’re in the office two or three days a week and we’re starting to have more personal events like lunches, poker and team bonding exercises. We make a point to have regular meetings that don’t focus just on work tasks but connecting with others. This was helpful in the difficult times. What are the hottest IT or software career tracks now in India? What do the new college graduates want to do? Full stack and software development engineer in test (SDET) are the most sought-after tracks these days. Our college hires have a taste-it-all program, which means they work in most of the teams during their first few months. This helps them and us discover their interests and where they are the best fit in the organization. We conduct regular boot camps for new employees so they can learn about the organization, product, processes, culture and values. Finally, we help employees who are new to the city get settled and get their personal needs met. We are a very people-friendly team which really stands out. Komprise is the kind of environment where you can say, hey I’d love to work on this project! We try to accommodate that. Are there any best practices you’ve learned over the years by working in leadership in India for a U.S.-based company? Yes, located seas away from the United States, visibility and communication are critical and the Komprise team does a great job of engaging us with the overall mission. We have a quick 30-minute all-hands meeting every week to discuss what’s going on at headquarters. We share and discuss recent achievements, business updates and priorities, customer wins and hot escalations. We are a very synchronous team; India experiments and learns from what’s working in the U.S. and vice-versa. We also create a matrix of mentors to new employees and that connection is important to help them get situated. Our people have great attitudes and they are go-getters. When we interview candidates, we look for people who are super collaborative and passionate about what they do. What keeps you motivated at work? I enjoy the challenges here which are unique every day. Even after five years, every day is like day one. I get a lot of energy from my team and I love helping solve problems so they can stay productive and are happy working here at Komprise. What's fun about working at Komprise? We are a little ahead of the market, which is an exciting place to be. Sometimes customers don’t know they even have the challenge that we are trying to solve. And that problem is something that everyone can relate to: managing all the data that is being created in the world every day. Data growth is exponential and we need to protect it and manage it well so that we can look back and analyze what happened and make better decisions. I believe that Komprise is a pioneer in unstructured data management and we are setting the future direction of this space. -------------------- ### Data Analytics in 2023 and Beyond James Maguire, eWeek's Editor-in-Chief, moderated a discussion in December on the future of analytics, which included some intriguing 2023 predictions. A good panel includes different and unique perspectives and this one didn’t disappoint. The panelists were: Radhika Krishnan, Chief Product Officer, Hitachi Vantara Torsten Grabs, Director of Product Management, Snowflake Krishna Subramanian, Chief Operating Officer, Komprise Barry McCardel, Chief Executive Officer, Hex Technologies In 2023, the panelists focused on a few key data analytics themes: Actionable data insights: There will be a greater ability to connect data and insights to actions and decisions. Data collaboration and sharing will extend to the logic that knows how to derive insights from the data. Tighter feedback loops will be created between decision makers and data teams. Data governance: Cybersecurity continues to be a top concern for IT organizations and CXOs as tactics become more complex, intelligent and destructive from foreign actors. As a result, panelists expect a heightened focus on data governance, data security and data privacy from enterprise data teams. Smart data workflows will efficiently enable data workflows across the edge, data center and cloud infrastructure and across organizations/departments to meet compliance and security needs for different data sets. Machine learning maturity: The panelists predict that we’ll see a rise in marketplaces for sharing ML models for re-use, which will help demonstrate the impact of data science projects/investments and require less expertise in the core ML technology. Reusable models should reduce the cost of compute and barriers to entry. The major cloud providers offer these already; expect more startups to compete this year. Edge data management: There will be a greater need to collect and analyze data at the edge, where there is exponential data growth from sensors/IoT and mobile apps.   Beyond 2023: Here’s what panelists predict for data and analytics strategies in the next 10 years: Machine learning everywhere: ML will become part of the decision-making process and daily workflows, as the models become easier to build and the tools become easier to use for the average information worker. Like what happened with self-service in the business intelligence (BI) market, imagine if you don’t have to be a data scientist to work with advanced analytics and ML applications? However, other challenges remain, including how to clean up, organize and contextualize data effectively across edge and hybrid infrastructure to enable smart cities, smart buildings, electric vehicles and more. Sustainability gets real: In the era of “data hoarding,” when you think about the edge alone, there’s too much data being collected and not enough space. Intelligent extraction and curation strategies to manage and keep only the data that are needed will emerge to streamline data management, cut costs and conserve energy. How to set, measure and track sustainability goals will become a priority. Ethics of data analytics: If we rely too much on ML, we might be missing ethical and social context. This remains a big unknown. Explainability in analytics and AI is gaining traction. Generative AI: Natural language AI technologies like GTP-3 --ChatGPT is the over-hyped face of this--will change creative workflows across the board with wide-ranging impacts, including upskilling and empowering people to do more creative work and democratizing data science. This trend is happening faster than we think and will need to be on the radar of anyone who works in a data management or data science/analysis role. I tried to capture the key points from the panel. It was only 30 minutes, but full of great insights. Credit to James McGuire for driving great engagement and keeping the conversation flowing. You can check out other podcasts and posts from James here: https://www.eweek.com/author/jmaguire --------------- ### Artificial Intelligence Needs Unstructured Data: Are You Ready? I’m sure I am not alone in saying that we often learn about the latest hot things going on from our kids. Just the other day, my 16-year-old son showed me an app that literally blew me away. The software, ChatGPT by OpenAI, responds to natural language requests to quickly create articles or answer complex questions. Other apps on the market today are generating art, writing code or troubleshooting software bugs—projects and challenges which can take hours, days or weeks yet are accomplished with astonishing accuracy and relevance in minutes. This kind of technological innovation, while obviously still nascent and experimental, is mind-boggling to say the least. There is much that we have yet to understand about the potential for AI and its impact on not only work and economic output but our personal lives. Below I’m going to share my observations on the future of unstructured data management in response to rapid AI progress. But first, let me summarize where we’ve been. Komprise Intelligent Data Management is an enterprise game changer because of the savings that we bring to customers. On average, customer save 70% on storage, backups and cloud spend from managing file and object data more efficiently across hybrid cloud storage; Komprise achieves this through analytics, smart data migration and cloud tiering. But this is only the beginning. The enormous opportunity at hand is to fully leverage unstructured data for use in AI and ML engines. Enterprises need to be ready for this wave of change and it starts by getting unstructured data prepped, as this data is the critical ingredient for AI/ML. This entails new data management strategies which create automated ways to index, segment, curate, tag and move unstructured data continuously to feed AI and ML tools. Unforeseen changes to society, fueled by AI, are coming soon and you don’t want to be caught flat-footed. Komprise is helping customers modernize their data management infrastructure and strategy to take advantage of the AI/ML innovation landscape. Here are some fundamental requirements for taking your unstructured data to the next level with AI: 1. If you aren’t indexing your unstructured data today, that’s a problem. A major barrier to data analytics is finding the precise data you need to mine. Most people in “data” jobs-- data analysts, data scientists, researchers, marketers—spend most of their time looking for the data that will fit a project’s requirements. One of our customers told us how their researchers from one location used to call those in another to find the data they needed for experiments. This doesn’t scale. Data indexing is a powerful way to categorize all your unstructured data across your enterprise and make it searchable by key metadata such as file size, file extension, date of file creation, date of last access, and custom (user-created) metadata such as experiment name or instrument ID. Komprise is unique in the unstructured data management sector because of our Global File Index, which is created as soon as you connect our solution to the file and object storage systems across your total data estate. This gives central IT, departmental IT teams and data researchers the equivalent of Google Search across your enterprise. 2. Make new uses of unstructured data while still being cost-efficient. Now that your data is indexed, users can find precisely the data sets they need and create policies to automate the movement of data in a query to the location of choice—such as a cloud data lake for AI analysis. Our May 2022 announcement of Smart Data Workflows demonstrated our commitment to automation and ease of use by delivering a simple way to connect the dots to deliver the right data to the right place (and to the right people or applications) for action. Imagine creating custom workflows that enrich and optimize your data. For example: Komprise can tag and automatically tier instrument data to low-cost cloud storage as it is created. Cloud AI and ML tools can then ingest the data for analysis. Once the analysis is complete, Komprise can automatically move the data to a colder, cheaper tier. Meanwhile all of this happens automatically and at significantly lower costs to IT. 3. Collaborate with departments on unstructured data needs. Another critical piece to the puzzle is giving users and departments more insight into their data assets so they can work with IT on creating the best data management policies that support ongoing and future analytics initiatives. In October 2022, we announced new self-service features whereby central IT can authorize departmental end users to interactively monitor usage metrics, data trends, tag and search data and identify datasets for analytics, tiering and deletion. Not only does this bridge the gap between IT and departments on data management decisions but both parties benefit: IT meets savings and governance goals while departments regain control over the data they need to protect and mine for future value. As the year 2022 reaches its end, predictions for 2023 show a need for caution and smart spending in a roller-coaster economic environment. IT organizations will need to institute further cost controls to stem wasteful spending and they will need to think more about sustainability in all their practices to cope with a global energy and supply chain crisis. They will need to do all of this while keeping their eyes on the prize: getting their data and data infrastructure ready for the AI age, which is just around the corner. -------------- ### Kay Zeren: Herding Cats as a Technical Program Manager Kay Zeren is Principal Technical Program Manager at Komprise. In this interview, we asked her about her daily work, tools of the trade, and if she’s ever experienced barriers working as a woman in Silicon Valley. What does a technical program manager do in software? KZ: My job is to work across engineering to make sure projects and releases are on time, work with managers on resourcing and requirements and with QA on testing to ensure that it’s on schedule. It’s about getting the right people in the room to shepherd the release according to any changes that come up. I work with customers as well, and customer issues always take priority so that finds me sometimes working with our support team to ensure a hotfix is delivered. As a program manager, I am often herding cats: nobody reports to me but everyone has to work with me. My job is to dislodge any log jams so that the project and feature can move forward. What are the biggest challenges for you in this role? KZ: When I came into this role at Komprise, we didn’t have all the processes in place. Over the last three years I’ve been working to do that. We want to be nimble, but priorities change and you have to shift quickly if management has a new direction. But that is life in a startup. For many of our young staff, this is their first job. As you grow, you need to make sure the knowledge transfer is happening. We are establishing the process as we go—which is like laying the rails down as the train is moving. What skills do you draw upon to succeed? KZ: People skills are extremely important and being cognizant that you are the hub of the communications. I spend a lot of my time getting the right information to the right people. I need a high-level view of all the channels—slack, email-- to make sure that people are sharing information when required. I don’t have an engineering background so when my team members get into details that go over my head I need to grasp at a high level what’s going on. That means understanding the right questions to ask. Ultimately, key decisions ride with engineering and product management, whereas I bring the facilitator mentality to the party. Being organized is important. I have a deep background in product management and have learned a lot of technologies at a non-technical level: those experiences have really been helpful. How do you counteract stress or negativity on the job, while straddling the line between different teams and viewpoints? KZ: My job is to facilitate discussions. There is never one right answer. When I hear about customer issues, I’m not the one on the call taking the heat so I can be more objective to come up with the best solution and then turn around and make sure we deliver it. When I hear that we fixed an issue quickly for a customer, that is a very satisfying part of my job. Your career began as a brand manager for Coca Cola in Japan. What was that like? KZ: I’m originally from Japan and this was my first job out of grad school. It was cool to work at a global company and have a multimillion budget. I learned about all the pieces that come together to build a brand and the marketing and processes behind it, such as packaging, messaging and sponsorships. Doing this work gave me key skills needed to work across different functions. I took that knowledge to Silicon Valley and applied it in the consumer tech sector. As my career has evolved, I’ve learned that what has been important is not so much what I did, but how I did it. Do you think opportunities for women in tech have improved in recent years and how? KZ: Fortunately, I have never felt that being a woman was a handicap. Your skill set and capabilities are what matter. I definitely felt a glass ceiling in Japan and that culture is still prevalent there. In Japan there are much more traditional expectations of women overall and long commutes made it harder for women trying to work and raise a family. But I also took a different career path so I could be mom first and have a career second. I haven’t necessarily sought title promotions, but my salary and responsibilities have absolutely grown over the years. If you could pursue any other career regardless of being qualified, what would it be? KZ: These days, I am more interested in giving back to the community or to Mother Earth. Working in tech has been great for us, and I enjoy living here in Silicon Valley. My next chapter might be quite different. -------------------- ### Komprise Named One of the Fastest-Growing Companies in the Bay Area and North America on the 2022 Deloitte Technology Fast 500™ Komprise achieved a three-year revenue growth of 306% to earn its place on the prestigious tech list. Campbell, CA, November 16, 2022— Komprise, the leader in analytics-driven unstructured data management and mobility, today announced its inclusion on the Deloitte Technology Fast 500™, a ranking of the 500 fastest-growing technology, media, telecommunications, life sciences, fintech, and energy tech companies in North America, now in its 28th year. Komprise grew 306% from 2018 to 2021, as enterprises grapple with explosive unstructured data growth. Unstructured data has been growing exponentially over the last few years, now consisting of at least 80% of the world’s data and is largely unmanaged. The Komprise Intelligent Data Management Platform helps enterprises save and make money on their unstructured data, such as log files, documents, audio and video files, IoT and research data, clinical images and genomics data. Komprise analyzes a customer’s data estate regardless of where the data lives to give customers unprecedented visibility. Komprise then intelligently mobilizes data through data migration, data tiering and data lifecycle management to lower-cost storage in the data center or cloud. Komprise customers save an average of 70% of data storage and backup costs. With the Komprise Global File Index, Deep Analytics and Smart Data Workflows, enterprises can dramatically cut the time needed to prepare unstructured data for big data analytics, machine learning and other cloud services initiatives. Komprise recently announced new self-service data management features for line of business teams, bridging the gap between IT’s focus on reducing storage costs and protecting data and users’ need to quickly access key files and data sets and drive data analytics. “This recognition in the 2022 Deloitte Technology Fast 500 is a wonderful testament to the rapid growth and technological innovation that Komprise has led in the IT infrastructure marketplace during the most challenging period of the 21st century,” remarked Kumar Goswami, CEO and co-founder of Komprise. "Our customers are leveraging Komprise to navigate the tough global economic turbulence by modernizing and finding new ways to harness their petabytes of unstructured data for competitive value while cutting costs." “This year’s Technology Fast 500 list is a true reflection of some of today’s most determined and inspiring pioneers who have prospered by anticipating what’s next, understanding what’s needed to succeed and driving creativity forward,” said Christie Simons, partner, Deloitte & Touche LLP and industry leader for technology, media and telecommunications within Deloitte’s audit and assurance practice. “Representing all facets of technology, the winners have shown they not only have the vision but can also expertly manage their companies through rapid growth. We congratulate each winner on their impressive achievements.” About Komprise Komprise is a provider of unstructured data management and mobility software that frees enterprises to easily analyze, mobilize, and monetize the right file and object data across clouds without shackling data to any vendor. With Komprise Intelligent Data Management, you can cut 70% of enterprise storage, backup and cloud costs while making data easily available to cloud-based data lakes and analytics tools. About the 2022 Deloitte Technology Fast 500™ Now in its 28th year, the Deloitte Technology Fast 500 provides a ranking of the fastest-growing technology, media, telecommunications, life sciences, fintech, and energy tech companies — both public and private — in North America. Technology Fast 500 award winners are selected based on percentage fiscal year revenue growth from 2018 to 2021. In order to be eligible for Technology Fast 500 recognition, companies must own proprietary intellectual property or technology that is sold to customers in products that contribute to a majority of the company’s operating revenues. Companies must have base-year operating revenues of at least US$50,000, and current-year operating revenues of at least US$5 million. Additionally, companies must be in business for a minimum of four years and be headquartered within North America. Media Contact: Kevin Wolf kevin@tgprllc.com _______________________ ### Crushing the Customer Success Architect Role In this interview, Benjamin Henry, Customer Success Architect at Komprise, discusses how he works with customers, sales and engineers to improve enterprise results with unstructured data management. Benjamin, tell me a bit about how you came to Komprise and what it's like to be a customer success architect? BH: I've spent the last 17-plus years working in life sciences in different functional areas—from application development to database to data protection and disaster recovery. I’ve even been through a few planned and unplanned situations—hurricanes and other natural disasters—putting those disaster recovery capabilities to good use. Then fast forward into today where my focus is on managing unstructured data. Migrations, archiving or tiering, mergers, acquisitions and divestitures provided me with a broad overview of how the world of life sciences moves and evolves at an extraordinary pace. What do you do generally speaking in your role? BH: It’s pretty cool because it's not just one specific area where I get involved. I can move or pivot between engineering to support to customers to presales and post sales—you name it. It's never a boring day. I enjoy the challenges and the complexities that come along with some of our enterprise customers. One of my goals in the customer success area is to ensure that customers are successful using our product in their environment.  _______________________ In this short video, Benjamin shares on-the-job advice about the customer success architect role.   It sounds like a multi-disciplinary role where you must gather input from a lot of different people. How do you work with customers? BH: There are different ways I typically get engaged. I like to be proactive where possible and determine what we can do to prevent speed bumps or hurdles during the deployment. Then, if we hit one of those hurdles, how do we overcome the situation or do we lean into an alternate route to achieve the goal? That's where I get to leverage the expertise over many, many years prior and ultimately resolve what we're facing. When you're troubleshooting issues, do customers give you feedback that you then can turn around and send to the product team? BH: Absolutely, I enjoy tracking the trends and providing direct feedback to Engineering and Product Management. Sometimes it affects change almost instantly or ends up in a future release. We’re also able to identify use cases for something that maybe we hadn't intended. I love connecting those dots. It improves our product and the customer experience. What are the key skills or experiences from your career that have been important to you in this role? BH: Networks are evolving at a rapid pace and they stitch the world together. I would say networking skills are the number-one. You don’t have to be the expert, but you should have the ability to interpret and understand the topology, how things are connected, how data gets from point A to point B, and every hop along the way. The second most important skill would be efficiently operating within your cloud provider(s). Knowing how to manage services and data, manage costs, and leveraging analytics and automation to manage both is a recipe for success. Somebody who's new to a storage architect role or an IT admin role, they may not know how to get that information. Do you think that there's still some work to do in connecting these silos in large companies? BH: Yes, and I’m seeing a trending resurgence in silos. Traditionally cloud accounts and their associated services were administered by one team. Today those teams are leveraging concepts such as tenants, subscriptions or child accounts to encourage self-service. They set the organizational standards and then enable you to manage your own services. It’s not uncommon to speak to different areas of the business where each has individualized access to create and manage their own cloud services. Just don’t be afraid to connect with your peers in other teams. Maybe they already have a process or are willing to partner with your team to achieve shared goals. Who knows, maybe you’ll end up saving money by splitting the associated costs or even negotiate a better rate for the additional resources or capacity. What do customers express as their top concerns, challenges or goals in unstructured data management? BH: Data migrations. That's where I'm seeing the largest challenge. There's been a lot of change in the industry within job roles themselves, from companies reducing or collapsing roles to resignations and retirements. Domain knowledge can be lost with each change. Data migrations are notorious for unearthing previously unknown or forgotten interdependencies, which can lead to a lively event. Communication and testing are critical to a successful migration event. The best recommendation I can provide is to always test before and after you move data: validate ports, firewalls, routes, bandwidth saturation and dependencies. Otherwise, there’s a high probability you're going to run into challenges that will either force rollbacks, cause service reductions or worst-case scenario: outages. Finally, always communicate. Provide timely updates to everyone involved including leadership and celebrate the accomplishments. Watch Benjamin's webinar: Preparing for a File and Object Data Migration ----------- ### 5 Tips to Optimize Your Unstructured Data This article was adapted from its original publication in TechBeacon. IT organizations have more unstructured data than ever before – data which could inform a number of critical decisions and initiatives across the company. Yet they also face many challenges in putting all their unstructured data to good use. Problems like figuring out how to move unstructured data without disrupting users, poor visibility into unstructured data, and legal constraints are all common barriers to optimizing unstructured data management, according to the Komprise 2022 State of Unstructured Data Management Report. To overcome those hurdles, IT organizations need ways of deriving value from unstructured data while simultaneously addressing priorities like securing the data, reining in data storage costs and future-proofing data against the business needs of tomorrow. It's possible to square this circle, but only with the right approach to unstructured data management. This article walks through five best practices for maximizing the value of unstructured data—meaning any type of data that doesn't originate in a database, spreadsheet, or other structured data format. 1. Don't Fly Blind with Unstructured Data Effective management of unstructured data starts with knowing your data and understanding the core metrics surrounding your data. To get started, you'll need visibility into such things as: How much data you have and its age; Where the data is stored; File types and file sizes; Data owners and access patterns and; What it costs to store the data. This visibility is critical because, in most cases, unstructured data is born inside silos. And unless you know which unstructured data you have, you can't make informed decisions about how best to manage it. Read the white paper: Komprise Analysis Overview 2. Plan for Ongoing Unstructured Data Mobility Businesses tend to treat data migration as an infrequent, periodic event. When they plan to migrate data from on-premises into the cloud, for example, they might assume that unstructured data migration ends once data has been moved to the cloud. The reality of data lifecycles is more complex. In many cases, unstructured data is constantly in motion. After you migrate data to the cloud, you're likely to move it to different storage tiers within the cloud, or from one type of cloud service (like object storage) to another (like a data analytics platform). It’s best to treat cloud data migrations as an ongoing process, supported with policy-based automation wherever possible. That's the only way to ensure that data is always living in the right place as it moves through the lifecycle from active use to cold data storage or archives—and then, sometimes, back again to active use. Read the blog post: 5 Tips to Optimize Your Unstructured Data 3. Continuously Add Value to Unstructured Data IT leaders are already thinking about how to add value to unstructured data. For instance, according to the Komprise survey, 65 percent of organizations seek to deliver unstructured data to big data analytics platforms to derive value from it. But smart IT leaders are also indexing unstructured data as part of their data migration and consolidation processes, so that the data becomes easier to find, search, and use. And they're using the cloud not just as a low-cost storage solution, but to build a data lake where they can easily leverage cloud compute services and drive analytics for their data. The point here is that IT leaders should be constantly on the lookout for ways to make unstructured data easier for everyone within the business to use. Big data analytics are part of that equation, but they're not the only component. Read the blog post: Quantifying the Business Value of Komprise Unstructured Data Management 4. Enable Secure Self-Service for Your Unstructured Data Along similar lines, empowering business users with self-service access to unstructured data should be a priority for IT leaders. Moving data into the cloud nor creating a data lake are enough on their own to guarantee that data has real business value. To achieve that, users must be able to find the data easily and integrate it into their workflows using seamless self-service processes. Systematically tagging unstructured data is pivotal. When data is well-labeled, users across the business can easily search for and find the documents, photos, videos, and other types of information they need—no matter how many data assets the business owns and no matter its organizational structure. Of course, the search and access mechanisms need to enforce security and access control so that each user only sees the data they are authorized to access. Read the blog post: Komprise Brings Data Storage Insights to Business Teams and Departments 5. Embrace Standards-Based Unstructured Data Management Your data is yours. Don't let vendors control where you can store it and what you can do with it. Instead, choose unstructured data management tools that are standards-based. This ensures that you can move data across any platform or use any type of data service that is also standards-based, without depending on a specific vendor to enable that functionality. As well, standards-based tooling ensures that you never end up stuck using a platform you no longer want because you can't migrate data otherwise. On top of this, standards-based tools help ensure that businesses can do whatever they need to do with their data without paying licensing penalties and costs, such as for a third-party cloud filesystem or unnecessary cloud-egress fees. By using data management solutions like Komprise that store data in native format in each tier, you can directly access the data and use all the cloud data services on your data without having to pay a data management or storage vendor. Avoiding these costs is a priority for 42 percent of IT leaders surveyed by Komprise. Read the blog post: Why Unstructured Data Management Must Be Independent from Storage Parting Thoughts on Unstructured Data Management The amount of unstructured data that businesses manage will grow steadily for the foreseeable future. Rather than viewing unstructured data as a liability or a challenge, IT leaders should look for ways to derive more value from it. Doing so starts by understanding the data and implementing automated unstructured data management. From there, businesses can build data enrichment into their management processes, offer self-service access to data, and embrace standards-based operations to get the most out of the unstructured data they generate. ---------------------------------- ### Komprise Gives Users Across the Enterprise New Tools for Managing Unstructured Data Self-service features allow line-of-business data owners and data specialists to view data usage, run queries and more, fostering collaboration with IT. Campbell, CA, October 4, 2022 – Komprise, the leader in analytics-driven unstructured data management and mobility, today announced the Fall 2022 release of Komprise Intelligent Data Management, which introduces self-service features for line of business (LOB) IT, analytics and research teams. Now, central IT can authorize departmental end users to interactively monitor usage metrics, data trends, tag and search data and identify datasets for analytics, tiering and deletion. These new capabilities bridge the longstanding gap between IT’s focus on optimizing storage infrastructure and costs and users’ focus on finding and operating on the right data sets. The Fall 2022 release builds upon Komprise Smart Data Workflows, a new capability which allows IT teams to automate the process of tagging and discovering relevant file and object data across hybrid data storage silos and feeding the right data to cloud services. The new Deep Analytics user profile gives authorized users read-only access to view their own data characteristics such as number, type and age of files and collaborate with IT on data management, delivering compliance and storage savings benefits while giving data owners greater control of their data. “I've been looking for years for a way to know where my data is and what type of unstructured data it is, all with the goal of getting it moved to the correct tier of storage,” says Brett Sayles, storage engineer at St. Luke’s Health. “Komprise does this for me and now I’m excited that I can offer a read-only role to end users. I can see how this would be handy for the business analytics crew and even for engineers/analysts during upgrades to see what data is out there and identify old data that could be purged.” Use Cases Include: Showback: Authorized departmental users can monitor and understand their data usage (examples: how many and what type of files, where stored and biggest consumers) in an interactive dashboard and create queries to get real-time visibility and manage costs, rather than ask IT to create static reports. For example: A new research initiative at a life-sciences organization requires 100TB of data storage and the project lead needs to work with IT to move older file shares to cloud storage and free up space. User-driven tagging: Users can enrich data with additional metadata tags to facilitate easier search and data management actions such as archival storage or compliance and legal hold. For example: the HR director needs to ensure that all files with employee PII data are tagged so that information is properly and securely stored for privacy requirements and searchable for deletion when the employee leaves. Read this post on a sample data workflow for handling PII using Komprise and Amazon Macie. User-driven tiering and data mobility: Authorized users can identify data sets with certain characteristics (such as project or age) to move to cloud storage or other secondary storage for more cost-effective data management or research initiatives. For example: All files related to a project need to be copied to the cloud for data analytics. The entire workflow between users and IT is automated via Komprise. Data deletion: IT can set up data movement and deletion as a workflow in Komprise and the entire process is automated without requiring user or IT intervention. For example: All marketing campaign files should be moved to archival storage 30 days after project completion and then confined for deletion after two years. The entire data workflow can be based on user queries and policies created by IT. User self-service is a growing trend to offload administrative tasks from central IT and give end users the ability to get the data and functionality they need faster,” said Todd Dorsey, a data storage analyst with DCIG. “By putting more control in the hands of departmental teams and data owners, Komprise is helping increase value from unstructured data in the enterprise.” “In most organizations, IT is becoming a shared service to its business units and building trust is paramount to its success, especially in the cloud,” says Kumar K. Goswami, CEO Komprise. “Komprise now allows IT to build trust and drive data-driven decision making in lock-step with their business users.” Komprise Intelligent Data Management Platform Updates Other Komprise Fall 2022 updates include improved SMB protocol for data migration performance and share management, bulk data recall performance improvements, native support for Amazon FSx for NetApp ONTAP, plug-and-play integration with AWS Snowball to automatically migrate PBs of data to the cloud and enhanced data indexing to support faster data and file search with Deep Analytics. About Komprise Komprise is a provider of unstructured data management and mobility software that frees enterprises to easily analyze, mobilize, and monetize the right file and object data across clouds without shackling data to any vendor. With Komprise Intelligent Data Management, you can cut 70% of enterprise storage, backup and cloud costs while making data easily available to cloud-based data lakes and analytics tools. www.komprise.com Media Contact: Kevin Wolf kevin@tgprllc.com _______________________ ### Interview: Komprise Senior Director of Product Management Paul Chen is the Senior Director of Product Management at Komprise. He has extensive experience in product management and technical marketing roles. We asked him about his career journey in product management and the skills necessary to succeed. How and why did you get into software product management? I loved to build things as a kid--even when I was a teen. Then I studied engineering and was a TA in graduate school and I found that I really enjoyed teaching. Through this process I realized that I didn’t want to be coding by myself all day, but I wanted a more people-oriented technical position. I started my career in customer support and then moved to product marketing where I was explaining and evangelizing the products and vision. Then I moved to product management and realized this was where I should have started. I love helping people and working at companies where products really help customers do what they need to do and be more efficient or build things or be more secure. How has the job changed over the years?  Product management hasn’t changed that much over the years. It’s about listening to customers and prospects, hearing their challenges, crafting the requirements to meet those needs and working with engineering to translate those requirements into new features and hopefully delighting customers with the new release. In the end it’s all about communications with customers. How do you work with customers as a product manager? When working with a prospect, sales is doing their due diligence, but the prospect may have deeper questions. We start by asking simple questions about their goals and often end up getting surprising answers. They may have legacy tech issues that they have struggled with for decades or have gone through a series of acquisitions and are sorting through a technology mess. Rule number-one with a prospect is to never contradict sales. The goal is to back up sales while also trying to help prospects get perspective on their needs and what the product can offer. If there is a box the product doesn’t check, how important is that right now? If there is a show-stopping feature that a prospect really wants, we will work behind the scenes to get it done within a few months after they sign. The ultimate goal is to sell what’s on the truck. With existing customers, communications may be related to a renewal or to help them with any issues and discuss new use cases. We are small and agile, and we do listen and try to give customers solutions to their most pressing problems in data management. How can product managers get closer to the customer and help the development team at the same time? Is this a conflict? Yes, product managers must listen to both. You need to listen to customers and their issues and what they think is a solution. But it's important to discern what the actual problem is and brainstorm with engineering on a solution. It’s critical to get engineering involved early in the process because they know the technology and what they can deliver. If you leave engineering in a vacuum and just say 'the customer wants this feature,' they may end up delivering something that doesn’t address the problem at all. What do you need to consider if you are looking into a product management career at a small company/startup?  Any startup or smaller company will have fewer resources, so your job will probably be wider than the job description. You end up doing a lot of related tasks--such as presales engineering to help a young or inexperienced sales team. Later after a customer signs, you may end up acting as a deployment engineer. And then there is the documentation--and product management is well situated to write about the features and how they work and the benefits. In some cases, you may even be pulled into other areas like product marketing, website design or messaging. What's powerful is that a startup environment gives a product manager the opportunity to see the whole spectrum of product and operations from lead generation to presales to sales and then deployment, customer success and ongoing maintenance. How often do your engineering skills come into play? Most product managers in software have an engineering background. The better ones can leave that behind so they can focus more on the “what”—what the product delivers and customer benefits—and leave the “how” to the engineering team. But it does help to be able to understand the “how” when you meet with engineers. The rubber always hits the road with the code. Sometimes engineers will show you the code and your ability to sift through the information quickly helps engineering too. How do you progress as a product manager—what is the career journey? Product managers are kind of like the quarterback of the software company. They can play a very central role and you get to work with and see every aspect of the business. Product managers are very fit to move up to a VP of Product role. Then you could go on to be the CEO of your own company. The career path could be as high as you want it to be. What have you enjoyed about your career at Komprise so far and what goals do you have in the coming months/year? I love helping build products that help customers do really great things. We are helping our customers not only reduce their storage costs but understand their unstructured data while improving outcomes for data tiering and data migration. Our users can evolve from being a traditional storage hardware manager to a business-centric data manager. And this allows them to make better decisions and gives an understanding of who needs to use the data and how to optimize it. It empowers our customers to move beyond being a cost center with an opportunity to add value to the entire business. Here at Komprise we have a fantastic group of smart, motivated people. We are continuing to innovate the product and are growing fast. It’s an exciting time in our company journey. ------------- ### Komprise Achieves 2022 Inc. 5000 Ranking Unstructured data management SaaS provider achieves three-year revenue growth of 300%, making it one of America’s fastest-growing private companies.   Campbell, CA, August 16, 2022 – Komprise, the leader in analytics-driven unstructured data management and mobility, announces that the company has been named to the annual Inc. 5000 list, the most prestigious ranking of the fastest-growing private companies in America. The list represents a one-of-a-kind look at the most successful companies within the economy’s most dynamic segment—its independent businesses. Facebook, Chobani, Under Armour, Microsoft, Patagonia, and many other well-known names gained their first national exposure as honorees on the Inc. 5000. Komprise was selected based on its revenue growth from 2018 to 2021. Komprise Intelligent Data Management allows organizations to save and make money on unstructured data. Komprise delivers advanced analytics to help enterprises with petabyte-scale data challenges save significantly on data storage, backup and cloud costs. With intelligent data tiering and smart data migration, Komprise customers move the right data across storage silos to the cloud faster without disrupting user access.  Komprise also helps organizations glean new value from massive unstructured data volumes. The Komprise Global File Index and new Smart Data Workflows allows IT users to easily curate, enrich and move unstructured data into cloud analytics and processing. So far in 2022, Komprise has won numerous industry awards, including most recently: Silver Winner for Big Data Solutions by the American Business Awards and Best Data Management Software by Storage Newsletter. Komprise was also selected as a launch partner for the Microsoft Azure File Migration Program.  “We are incredibly humbled to reach the pinnacle of private business recognition by making the Inc. 5000 list,” says Kumar Goswami, CEO and Co-founder of Komprise. “Our rapid growth over the past few years speaks to real enterprise pains in managing data growth and discovering a frictionless, safe path to the cloud. We’re excited to help our customers on the next stage of unstructured data management maturity by enabling automated workflows to move the right unstructured data into cloud AI/ML and other big data tools.” Complete results of the Inc. 5000, including company profiles and an interactive database that can be sorted by industry, region, and other criteria, can be found at www.inc.com/inc5000. The top 500 companies are featured in the September issue of Inc. magazine, which will be available on August 23.  “The accomplishment of building one of the fastest-growing companies in the U.S., in light of recent economic roadblocks, cannot be overstated,” says Scott Omelianuk, editor-in-chief of Inc. “Inc. is thrilled to honor the companies that have established themselves through innovation, hard work, and rising to the challenges of today.”  About Komprise Komprise is a provider of unstructured data management and mobility software that frees enterprises to easily analyze, mobilize, and monetize the right file and object data across clouds without shackling data to any vendor. With Komprise Intelligent Data Management, you can cut 70% of enterprise storage, backup and cloud costs while making data easily available to cloud-based data lakes and analytics tools. www.komprise.com. Media Contact: Kevin Wolf, TGPR www.tgprllc.com kevin@tgprllc.com ---------------- ### Top Tech & Storage Deals of 2022 Komprise July Update: Product, People and News Ah yes, it is summertime. Between the baseball games, backyard barbecues and boating, there’s plenty of back-office deal-making going on. Here’s what we know and also what’s going on at Komprise lately. Big Enterprise tech deals of 2022…so far. ComputerWorld reports on top tech company acquisitions so far this year. Hugely controversial and not yet approved by shareholders, Elon Musk’s $44 billion bid for Twitter is one of the largest. Microsoft’s massive cash deal of $68.7 billion for Activision Blizzard, a gaming content provider, is one that investors will be watching with a close eye. Other notables include Google’s purchase of cybersecurity provider Mandiant for $5.4 billion, Intel’s bid for Tower Semiconductor for the same price and Kaseya’s $6.2B snap up of security company Datto. Slower market for storage tech deals. Storage Newsletter covered the activity so far in 2022 for storage industry acquisitions, which it noted was cooler than previous years. The industry publication reported 19 acquisitions in the first 6 months of 2022, compared to 15 for the same period in 2021, 20 in 2020 and 24 in 2019. The record of 52 acquisitions was set in 2006. This year’s deal highlight was the jaw-dropping $61 billion paid by Broadcom to acquire VMware from Dell—which also makes it one of the tech industry’s biggest deals overall. Public cloud landscape: the majors and the minors. In related news, here’s the latest lineup of top public cloud storage providers globally with their respective market share, as reported in Dgtl Infra. The list also details leading small cloud service providers — by no means small companies — which include Baidu AI Cloud, Softbank, Fujitsu, Salesforce, SAP, Rackspace, HPE and VMWare among others. Tech industry hiring gets unpredictable. There’s been some volatility in the usually-resilient tech labor market. As reported on CNBC: “Companies including Uber, Meta, and Microsoft have announced that they’re slowing down hiring as inflation rages and talk of a recession intensifies. Redfin, Netflix and Klarna are among the companies that have announced layoffs. And in a complete reversal of the white-hot labor market of the past year, Twitter, Redfin, and Coinbase are rescinding job offers they’ve made, citing the turmoil in the economy.” Yet the unemployment rate for tech occupations is 1.3%, its lowest level since June 2019 and about one-third of the current national unemployment rate (3.6%), according to CompTIA. Keeping all this in perspective, it’s safe to say that the tech industry will remain one of the hottest sectors for recruiting top talent. Komprise In the News Cloud Field Day In June, Komprise was honored to be included in the Cloud Field Day tour, organized by Stephen Foskett’s Gestalt IT. Our video presentation on cloud tiering and smart data migration featured our CEO & Co-Founder Kumar Goswami. New Gartner Hype Cycle Includes Komprise Gartner includes Komprise in the Management Software-Defined Storage section of the new Gartner Hype Cycle for Storage and Data Protection Technologies 2022. (registration required) 2022 SaaS Awards Komprise was shortlisted in the 2022 SaaS Awards program for the category: Best Data-Driven SaaS Product. Now in its seventh year of celebrating software innovation, the Software Awards program accepts entries worldwide, including the US, Canada, Australasia, EMEA and UK. The organization will announce finalists and winners later this summer. eWeek Interview with Komprise COO eWeek Editor-in-chief, James McGuire, interviewed Komprise COO, Krishna Subramanian, on unstructured data management trends. You can listen to the podcast or watch the video interview! Unstructured Data Management Maturity devmio: Krishna shares a new model for Unstructured Data Management Maturity with 5 stages to plan for as organizations move from a storage-centric to a data-centric practice that focuses on right-placing data and attaining long-term value. Smart Data Migration Webinar Series Finally…in June we got busy talking to experts about smart cloud data migrations. Click directly below to access the on-demand webinars: Smart Data Migration for File and Object Data Preparing for a File and Object Data Migration Cloud Native Access: What is it and Why Does it Matter? ------------------ ### GigaOm’s Enrico Signoretti on Unstructured Data Management Trends Enrico Signoretti is an internationally renowned expert author, blogger, and speaker on data storage. He has tracked the evolution of the storage industry for years as a GigaOm Research Analyst, an independent analyst, and as a contributor to The Register. In the recent GigaOm Radar for Unstructured Data Management, you segmented the results into business vendors versus IT focused vendors. Can you explain the difference? The difference is that infrastructure-focused solutions look at aspects of the infrastructure that are more related to storage management than from the data perspective--so we are talking about tiering and about the TCO of the infrastructure. These solutions don't look inside the data itself but show customers how to save money and understand how much data they have and what people are doing with it. Business focused solutions, on the other hand, look into the data or at least go beyond the metadata. Think about, for example, the Chief Data Officer (CDO) which is a role that is now very common in Europe. The CDO doesn't really need to know where the data is physically stored but this individual does want to know if the organization is storing personal information or files that they shouldn't store anymore, because of policy or regulations or whatever. The business unstructured data management solutions are much more focused on delivering business insights and retaining compliance and governance information. Are you seeing in practice an evolution where organizations are looking at data management as a different discipline than storage management? It really depends on the size of the data under management and the type of company. The very large companies have regulations, they have more needs than in the past and they are creating huge amounts of data. They are recognizing that they really need to understand all this data. In many cases, smaller organizations just need to save money. There is, however, a general maturation in the market. Even a few years ago, large organizations weren’t that interested in managing unstructured data. Now with the rate of data growth, large organizations are saying that they need to manage data more strategically. They may learn that they have customer data stored in a remote location that’s in text files where anyone can see it. Before ransomware is detected, bad actors are looking at the data before encrypting it, they are copying it, and all this information is trickling away. As a result, ransomware has been another strong driver for organizations to invest in unstructured data management. Following on that, are you seeing greater collaboration between data professionals and storage professionals or is there still a wall between these parties? When it comes to business-focused infrastructure data management solutions, storage people must talk to other personas in the organization. CDOs in some companies have large budgets and they are craving solutions for unstructured data management. The environment is very complex in Europe with all our regulations, such as GDPR and privacy, and this reality makes collaboration with the CDO and data teams critical. In general, there are skilled engineers who have deep knowledge of storage systems, but with all the automation that many vendors have introduced this is less important somehow. Storage professionals today need to learn more about the data they're storing and about how the applications interact with the data. They need to understand how users are working with this data over how the single bits are stored in a repository. To gain this insight, it is becoming imperative for storage managers to work closely with departments and users. Do you see growing adoption of cloud data services such as AI/ML on data sets in the cloud? There are a lot of enterprise IT teams who are starting to think about using AI and ML more, but it's not as easy as some of the cloud vendors show us. To make it relevant for your business, you have to select the right amount of data and move it to the cloud. A client of mine has thousands of lawyers distributed across the world and they produce documents every day. They want to use AI and ML to scan these documents for any kind of mistakes or formatting errors which can create issues later, but how are they going to select only the relevant documents? If they want to run analysis on all files that include a contract of a certain type, you just can’t do it without automation using the proper data management tool. ---------------------- ### Komprise Smart Data Workflows: Automate Unstructured Data Discovery Artificial intelligence, machine learning and all the variants including deep learning and natural language processing are tools of the trade now in our data-driven society. Yet the one thing that machine learning requires for uncovering new insights and patterns is unstructured data — a lot of it. This is an issue because unstructured data is difficult to manage, not to mention mobilize. This data does not fit nicely into rows and columns. It comes in a variety of formats with no underlying semantic structure and is extremely difficult to ingest into traditional analytics platforms. Plus, since unstructured data makes up at least 80% of all data and the creation and replication of data is growing at a faster rate than storage capacity, it is too large to process and analyze in-house for most enterprises. But lo and behold, cloud data lakes and cloud machine learning tools are making the previously impossible now possible, by creating more affordable, scalable ways to do machine learning without requiring massive investments in skills and infrastructure. Yet you still must get the right unstructured data into them and avoid the situation of creating data swamps if too much irrelevant data ends up there. Most of this work in finding and categorizing unstructured data to feed machine learning pipelines has been manual. It doesn’t scale as data volumes grow and results in delays for timely research projects. Komprise Smart Data Workflow Leveraging External Functions to Cull and Extract Data into Data Lakes This Leads Us to Our Latest Announcement: Komprise Smart Data Workflows We’re excited to share that with Komprise Intelligent Data Management, IT users can now create automated workflows for all the steps required to find the right data across your storage assets, tag and enrich the data, and send it to external tools for analysis. The Komprise Global File Index and Smart Data Workflows together reduce the time it takes to find, enrich and move the right unstructured data by up to 80%. We know that data scientists spend most of their time finding and preparing data for analysis, rather than doing the actual analysis and refining tests and results. We think this next phase of unstructured data management will be a major force for organizations looking to not only migrate and tier data to cloud storage for cost savings but also to monetize unstructured data. “Komprise has delivered a rapid way to visualize our petabytes of instrument data and then automate processes such as tiering and deletion for optimal savings,” says Jay Smestad, Senior Director of Information Technology at PacBio. “Now, the ability to automate workflows so we can further define this data at a more granular level and then feed it into analytics tools to help meet our scientists’ needs is a game changer.” Komprise Smart Data Workflows Are Relevant Across Many Sectors Here’s an example from the pharmaceutical industry: Search: Define and execute a custom query across on-prem, edge and cloud data silos to find all data for Project X with Komprise Deep Analytics and the Komprise Global File Index. Execute & Enrich: Execute an external function on Project X data to look for a specific DNA sequence for a mutation and tag such data as "Mutation XYZ". Cull & Mobilize: Move only Project X data tagged with "Mutation XYZ" to the cloud using Komprise Deep Analytics Actions for central processing. Manage Data Lifecycle: Move the data to a lower storage tier for cost savings once the analysis is complete. Here’s an example of an edge-to-cloud workflow: Lab instruments often generate terabytes of data which are stored in a NAS file system. This file system can be used as a daily cache and Komprise can tag and automatically tier instrument data to low-cost cloud storage as it is created. Cloud AI and ML tools can ingest the data for analysis. This approach ensures lab data is tagged and available in the cloud. Users can natively access the data as objects, so they can import the right data for analysis at significantly lower costs. Powered by APIs and the Global File Index Updates to the Komprise Intelligent Data Management Platform, which make Smart Data Workflows possible, include: API to Execute External Functions: Komprise can enrich data by allowing the execution of external functions or cloud services either at the edge, datacenter or cloud and then tagging data with metadata. Examples include: Snowflake, Amazon Macie, Azure machine learning. Global File Index and Tags: The tags set by external functions are managed by the Komprise Global File Index and searchable no matter where the data moves. Expanded Deep Analytics Actions leveraging the Global File Index: Expanding the range of data mobility actions, Komprise can use Deep Analytics query results to not only tier data specified by a query but also copy and confine such data. Deep Analytics User Role: For better data governance and separation of duty, this role limits a user to only specify and save queries. An IT administrator with full privileges can then use the saved queries to manage the data using Komprise Deep Analytics Actions. Learn more about Smart Data Workflows. ### Getting Your (Unstructured) Data into Shape Komprise May Update: Product, People and News Unstructured data is the untapped reservoir of goodness for many sectors. We found some excellent tactical articles on how to manage and prepare unstructured data for machine learning and AI applications. Here’s our take on intriguing IT infrastructure and data trends reported in the trades, along with our own latest news. _______________________ What Else to Consider in Data Management Evolution We talk a lot about modern data management here at Komprise and there’s a lot to pack into that: better managing and leveraging unstructured data, analyzing data for cost-saving insights, and generating automated workflows for specific data sets. This article in MIT Technology Review adds to the narrative with a checklist that includes these tactics: federation of data into a fabric, not centralized or siloed; knowledge is organized by context and tagged by both publishers and subscribers; machine learning automates data engineering tasks and data is measured in economic terms, not accounting terms. _______________________ Wrangling Unstructured Data “Getting your data in great shape before you feed it to an algorithm will net you better results and let you get more from your tech,” writes Paul Barba in Datanami. We couldn’t agree more. Part of the advice discusses the importance of metadata in preparing unstructured data for analytics. It should include: document source, date created, author, locations, tags and more. “More metadata makes it easier to sort and filter your data.” _______________________ Unstructured Data Plus Machine Learning = the Future of Industry Fern Halper wrote on the topic of unstructured and semi-structured data in AI and ML a few years ago in TDWI. Lo and behold, unstructured data analytics is still relevant and nascent in terms of mainstream adoption. Her examples alone are worth the read: “Using deep learning, a system can be trained to recognize images and sounds. For instance, a computer can be trained to identify certain sounds that indicate that a motor is failing. Such technology is also being employed to classify business photos for online auto sales or for identifying other products. Image recognition is being put to work in medicine to classify mammograms as potentially cancerous and in genomics to understand disease markers.” _______________________ Are 3-2-1 Backups Worth It? Given the importance of data and protecting it from loss, theft or inadvertent damage from user error or system failure, IT organizations typically store three copies of it. Yet as this article in SearchDataBackup spells out, this comes at enormous expense to IT organizations. Backup software licensing is the biggest cost factor, the author explains. Because of this, it’s smart to consider different variations of the 3-2-1 method: “This might include moving an on-premises copy to the cloud to reduce cost or using a more robust 3-2-2 strategy with an additional remote copy.” _______________________ Komprise News Silver Stevie© Award Winner In April, Komprise was honored by the American Business Awards in the Big Data Solutions category. “Komprise is doing a great job helping customers solve for a big challenge – how to manage data at scale,” noted an ABA Stevie Award judge. _______________________ Komprise in the Media Tagging and Metadata Our director of product marketing writes about tagging in this eWeek article: “Tagging is the process of adding labels to categorize unstructured data, so users can easily search and find the data they need when they need it. Put simply, it’s adding and enriching the metadata on your data.” _______________________ Data Modernization Komprise CEO Kumar Goswami is interviewed by TDWI on modernization strategies for unstructured data: “The old adage -- if it ain’t broke don’t fix it -- may apply here but to be honest, data management is actually broken and people don’t know it.” _______________________ Hybrid Cloud Data Management “You can’t erase the siloed nature of data in a hybrid cloud. It just comes with the territory,” observed our VP of Global Systems Engineering Randy Hopkins in this New Stack article. “What you can do, however, is to take steps to simplify and streamline the way you work with data across the various silos that exist within a hybrid cloud.” _______________________ ### Komprise Named Silver Winner for Big Data Solutions by the 2022 American Business Awards® Komprise, a leader in analytics-driven unstructured data management and mobility, was named the winner of a Silver Stevie® Award in the Big Data Solutions category in The 20th Annual American Business Awards®. Learn more ### Komprise Honored as Silver Winner for Big Data Solutions by the 2022 American Business Awards® Campbell, CA – April 28, 2022 – Komprise, a leader in analytics-driven unstructured data management and mobility, was named the winner of a Silver Stevie® Award in the Big Data Solutions category in The 20th Annual American Business Awards® today.  More than 3,700 nominations from organizations of all sizes and in virtually every industry were submitted this year for consideration in a wide range of categories, including Startup of the Year, Executive of the Year, Best New Product or Service of the Year, Marketing Campaign of the Year, Thought Leader of the Year and App of the Year, among others. More than 230 professionals worldwide participated in the judging process to select this year’s Stevie Award winners. With Komprise Intelligent Data Management enterprise IT teams can easily analyze, search and use unstructured data across silos to deliver greater visibility, mobility and value. Komprise customers save over 70% on storage, backup and cloud costs with intelligent data tiering, move to the cloud 27x faster with smart data migration and see a dramatic reduction in time spent preparing data for analytics workflows with our Global File Index. Komprise customers span many sectors and include brand names such as Pfizer, Cadence Design Systems, Carhartt, Fossil and Pacific Biosciences. Read about Komprise 2021 results. “Komprise is doing a great job helping customers solve for a big challenge - how to manage data at scale,” noted an ABA Stevie Award judge. “Komprise helps customers extract business value from their data without disrupting access, which is key for most businesses. Pfizer and St Luke's are great references in this space, so kudos to the team!” “It clearly demonstrates the need for the solution and its patented TMT technology and features like Global File Index can help reduce storage costs by a massive margin for the organizations,” another judge wrote. “This could be a potential game-changer for enterprise organizations around the world.” “It is such a wonderful honor to be recognized by the American Business Awards for excellence in Big Data technology,” said Krishna Subramanian, COO and President of Komprise. “Our customers are storing petabytes of unstructured data and they are challenged with not only right-placing this data for optimal savings, access and security but also uncovering new value from it for operational and marketplace gain. Komprise is helping these enterprises rapidly search across all their data silos and move specific data sets efficiently to the cloud where it can be leveraged by cloud-native analytics tools.”  Learn more about the Komprise Global File Index for rapid search and data tagging capabilities.  Details about The American Business Awards and the list of 2022 Stevie winners are available at www.StevieAwards.com/ABA.   About Komprise Komprise is a provider of unstructured data management and mobility software that frees enterprises to easily analyze, mobilize, and monetize the right file and object data across clouds without shackling data to any vendor. With Komprise Intelligent Data Management, you can cut 70% of enterprise storage, backup and cloud costs while making data easily available to cloud-based data lakes and analytics tools. www.komprise.com. About the Stevie Awards Stevie Awards are conferred in eight programs: the Asia-Pacific Stevie Awards, the German Stevie Awards, the Middle East & North Africa Stevie Awards, The American Business Awards®, The International Business Awards®, the Stevie Awards for Women in Business, the Stevie Awards for Great Employers, and the Stevie Awards for Sales & Customer Service. Stevie Awards competitions receive more than 12,000 entries each year from organizations in more than 70 nations. Honoring organizations of all types and sizes and the people behind them, the Stevies recognize outstanding performances in the workplace worldwide. Learn more about the Stevie Awards at http://www.StevieAwards.com. ### The Need for Policies to Corral Your Unstructured Data This blog is adapted from the original article which appeared in VentureBeat. Unstructured data management policies ensures that data is always stored in the appropriate environment according to its usage, age, value and business priority. For instance, an electric car manufacturer wants to understand how its vehicles perform under different climate conditions. Therefore, they may want to create a data management policy to continually pull trace files from cars at regular intervals into data lakes and analyze them. Once the study has completed, that policy will retire and the moved data could be deleted or moved to deep archive storage. A hospital may have a policy to retain medical images for the life of the patient and the policy could dictate where and when those images move to cold storage. Managing policies manually is no longer a viable option given the scope of data stored in enterprises today. With data growing at an unprecedented rate, comprising 30% or more of the overall IT budget on its storage, now is the time to hunker down on the idea of unstructured data management policy automation. The benefits of adopting a systematic way to create, execute and manage policies for data include: Automated policies align data strategy with business goals; Simplifies data management by reducing manual effort and ad hoc decision-making; Deliver the means to maximize cost savings by continuously moving cold data tiering to less expensive storage; Ensure compliance with industry regulations; Add ransomware protection by copying data from primary storage into object lock storage where it cannot be compromised. Automatically feed data pipelines into data lakes and tools for analytics and AI programs. The notion of data management policies isn’t new, but historically, this activity took place within storage vendor technology. A storage vendor-centric approach was all well and good before data hit the petabyte and growing levels of today and before organizations were using multiple storage vendors and clouds to manage their data. But now, the storage-centric approach to policy management creates vendor lock-in and silos, making it onerous to cost-effectively manage data and move it expediently to different storage technologies and services as needed to support users, big data analytics initiatives and cost-saving mandates. Considerations for Unstructured Data Management Policies Access anywhere: Distributed workforces now require instant access to data—regardless of where it’s stored—with a transparent user experience. Automate as much as you can: Many organizations still employ IT managers and spreadsheets to create and track policies. The worst part of this bespoke manual effort is searching for files containing certain attributes and then moving or deleting them. These efforts are inefficient, incomplete and impede the goals of having policies—it’s so painful to maintain them and IT professionals have too many competing priorities. Plus, this approach limits the potential of using policies to continuously curate and move data to data lakes for strategic AI and ML projects. Instead, look for solution with an intuitive interface to build and execute on a schedule and which runs in the background without human intervention. Measure outcomes and refine: Any data management policy should be mapped to specific goals, such as cost savings on storage and backups. It should measure those outcomes and let you know status so that if those goals are not being met, you can change the plans accordingly. This is akin to a smoke detector which is always checking its own battery and then alerts you when it’s time to change it out. For instance, if you have a data management plan which tiers data after it reaches one year of age into object storage in the cloud, you’ll expect a certain percentage of savings. However, if this cold data ends up being frequently pulled back into local applications and storage, you face high egress fees which counteract those savings. At that point, you would want to consider a different tiering model. Better yet, a data management solution can recognize the trend and applies the declarative action to right-place it. Align staff roles: Data management policies should be managed by a team within the organization that identifies how policies are created and used and align with business units to ensure retention and protection considerations are consistent. The team is also responsible for managing, enforcing and refining policies and communicating them to employees with a need to know. Large enterprises should consider including top executives who contribute to discussions concerning data governance, protection and monetization. Metadata management: Another consideration is to simplify searches across all file metadata from a unified global file index but also enables actions to copy, move, archive, tier and report on unstructured data files. In closing, enterprise data is not owned by any individual or business unit; it is owned by the enterprise and needs to be managed holistically and strategically to meet stakeholder needs and broad organizational objectives. Data should be accessible to users no matter where it resides. Ultimately, a data management policy should guide your organization’s philosophy toward managing data as a valued enterprise asset. _______________________ ### The Komprise Internship Experience Komprise has an active intern program in our Bangalore office. We hire from the best and most reputed engineering colleges across India, and candidates undergo an intensive evaluation process that includes a coding test and technical interviews with our senior engineers and hiring managers. The offer we make is a six-month paid internship transitioning to a full-time position upon graduation. So, what’s it like to work as an intern at Komprise? The job is not doing low-level coding or administrative work. Our interns work in the trenches developing features and solving real issues like other engineers. Here’s the scoop from four interns who now work as full-time employees. _______________________ Sourav Agrawal – Software Engineer Komprise was Sourav’s first internship experience. He admits to being overwhelmed at the start, given the nature of a fast-paced startup environment. “It was more of a sink or swim deal, which worked very well for me, in retrospect. I was given real responsibilities from the beginning and was working on real stuff. I sensed and saw first-hand the effort everyone else put into getting us interns up to the expected mark.” He says he was motivated by how others encouraged him—not as a lowly intern but as someone who could handle the responsibilities. “I always knew I had the support I needed to finish whatever tasks were assigned to me.” _______________________ Kashish Oberoi – Software Engineer Working as a Komprise intern was not his first internship, but Kashish loved the inclusive approach which involved rotations on different teams and the ability to meet many individuals across the organization. He got to work on the live product during the internship and helped resolved issues as a matter of course. “I knew my contribution mattered,” he said. “I never got the impression that something was beyond my pay grade.” The experience has helping him succeed in his job as a full-time developer today: “Now I understand the code and I know whether it’s feasible for what I write to work for the other teams, which helps me make an informed decision. As a full-time employee I feel a responsibility towards my juniors, the incoming interns. The bar has been set really high and I try to inculcate in myself things I have seen others doing or have done for me during my internship.” _______________________ Aparna Mrityunjay – Software Test Engineer Aparna’s experience as an intern at Komprise delivered several benefits: exposure to the full testing process, a sense of belonging to the entire organization, time management and ambition: “I realized the value in having a hunger for knowledge because I saw that hunger in everyone whom I worked with, even experienced team members. They never hesitated to ask each other for an explanation on anything they were not sure of.” This culture of collaboration and feedback was positive as a young, untested intern; she says she felt appreciation from both her seniors and peers, however negligible the impact of the work. This gave her a strong foundation as she moved into a permanent role at Komprise: “I had the opportunity to be a mentor to a newcomer as soon as I started full-time and I was amazed at the confidence my manager showed in me. I now feel competent enough to contribute to someone else’s learning journey.” _______________________ Nishikanth CS – Software Test Engineer “Compared to friends’ experiences in larger companies, I feel like I am making a greater impact towards the customer experience,” says Nishikanth. “I realized that the work I’m doing impacts the business and that I am responsible for something that affects other people as well. Coming from a college set up it was an eye opener.” Nishikanth also remarked on how patient and approachable his fellow Komprisers were as he was adjusting to the learning curve: “They answered every little question and gave me direction.” As well, he found unexpected benefits of being an intern in a remote working situation: getting out of his comfort zone and gaining maturity. As a full-time employee, he says he’s motivated knowing that what he did during his internship is still in motion. _______________________ ### Storage: Supply Chain Pains and Sustainability Komprise February Update: Product, People and News Ben Franklin gets credit for the quote: “Nothing is certain in this world but death and taxes.” Were he around today he might have added “data growth” to that list. The data management space is at an interesting inflection point: Customers are weighing the current and perceived future value of data (supported by advances in analytics) against the burden and costs of storing, protecting and making that data available. Here’s our take on intriguing IT infrastructure and data trends reported in the trades, along with our own latest news. No one ever said data management was easy… Enterprise data management has never been an easy gig and Quantum has released a data management survey that sums up the issues at hand. Business leaders recognize that data is a core driver for growth but storing, protecting and extracting value from data is hampered by cyber threats, data sprawl and rising costs. There is a lot to unpack here but let’s dive into one point: When do you delete or retain data? More than half of the respondents were concerned they were deleting data that had value. The challenge of what to retain, for how long and at what cost is a struggle which won’t go away soon. _______________________ Storage vendor supply chain pains Nothing can slow down growth in storage except for maybe supply chain issues. IDC reports that new disruptions to the industry should be less severe than was seen in 2020 although still concerning in regard to impact on system shipments and pricing. _______________________ And now this happened… Chip shortages, supply chain, pandemic and now a massive manufacturing issue at Western Digital and its partner Kioxia is threating to drive up flash prices. A contamination issue in the manufacturing process will result in a staggering loss of 6.5 exabytes of flash, according to Tom's Hardware. Analyst firm TrendForce expects SSD prices to rise 5-10% as result. _______________________ Data growth fuels data management software It’s clear that IT leaders are grasping the need to effectively manage data to control storage costs and reduce the dependency on expensive flash storage, among other goals. The explosive growth of data is driving growth in enterprise data management software, which is expected to grow to $136B by 2026, according to Analytics Insight. _______________________ Sustainability needs data management Sustainability isn’t just good for the environment; it turns out that it’s also good for business. Protocol reports that in the first six months of 2021 alone, investors spent more than $570 million on environmental data startups. “Last year JPMorgan bought ESG startup OpenInvest and Blackstone acquired sustainability software Sphera for $1.4 billion.” This is giving rise to an environmental data market. (Sustainable data management) _______________________ Storage costs from data security Datanami reports that a major challenge to responsibly using data is dealing with sensitive information like PII. Maintaining different data sets with granular security is a huge cost for customers in terms of storage and complexity. A newer approach is to create multiple “views” of your data, where a given view has a specific access to data. While this is much lower cost from a storage point of view, the management of these “views” is also complex. IT must manage the security and access to these views over multiple analytics platforms. _______________________ The Latest from Komprise Azure File Data Migrations Migrating large volumes of file data to the cloud is fraught with hassles and risk. Microsoft launched a new program to help its Azure customers and Komprise is one of two launch partners! _______________________ IT Press Tour covers Komprise This past month Komprise hosted the IT Press Tour, a tech journalist event that covers visits to multiple vendors in the data management space. Chris Mellor from Blocks and Files explored the Komprise approach to data management versus others in the space and draws a distinction between the metadata and data control planes with the Komprise Global File Index. _______________________ Best in Data Management We are proud to announce Komprise has won the Storage Newsletter Best in Data Management Award. Komprise was also named a winner in TechTarget’s Enterprise Data Storage 2021 Products of the Year, in the storage system and application software category. _______________________ Extracting Value from Unstructured Data  “The boundary between hot and cold data is based on what can you index and search, and what the customer defines. The customers can set different policies for different data sets,” explained Krishna Subramanian, COO and co-founder, in an article for Information Age. _______________________ ### Komprise Channel Chief Recognized by CRN The honor comes on the heels of two product awards for Komprise as unstructured data management gains steam. Campbell, CA— February 10, 2022– Komprise, the leader in analytics-driven data management and mobility, today announced that CRN, a brand of The Channel Company, has named Larry Dabrow, director of North America channel sales at Komprise, to its 2022 Channel Chiefs list. CRN’s annual Channel Chiefs project identifies top IT channel vendor executives who continually demonstrate expertise, influence and innovation in channel leadership. In January, Komprise was named Best in Data Management Software by StorageNewsletter and a finalist in SearchStorage Products of the Year. Larry Dabrow has a 20-year record driving multi-million dollar growth in the enterprise IT and cloud sector. Prior to Komprise, Dabrow worked in senior channel and sales roles at Dell, EMC, FalconStor and Ingram Micro. His accomplishments at Komprise include the successfully launch in 2021 of the Komprise Konnect Channel Program which has thus far increased revenue opportunities by 20 percent.  Komprise partners—spanning regional resellers to large global IT integrators and VARs such as Technologent, Mainline, Computacenter and CDW —can address urgent enterprise demand for smart cloud data migrations, move beyond storage management to data management and help both IT and the business leverage unstructured data for new value through artificial intelligence and machine learning applications. Komprise had more than 400 customer and partner graduates from the Komprise Technical Professional training program in 2021, up 80% from the previous year.  “Larry’s focus and contributions have been instrumental in building a channel program that has helped us deliver multiple years of combined 100%+ growth in Annual Recurring Revenue (ARR), says Mike Munoz, chief revenue officer at Komprise. “We think 2022 will be a standout year for our global channel-driven business as enterprise customers are looking beyond storage cost savings to generating long-term value from unstructured data, which are the two main value propositions the Komprise platform is now delivering to the market.” “CRN’s 2022 Channel Chiefs recognition is given exclusively to the foremost channel executives who consistently design, promote, and execute effective partner programs and strategies,” said Blaine Raddon, CEO of The Channel Company. “We’re thrilled to recognize the tireless work and unwavering commitment these honorees put into fostering outstanding business innovation and building strong partner programs to drive channel engagement and success.” CRN’s 2022 Channel Chiefs list is featured in the February 2022 issue of CRN Magazine and online at www.CRN.com/ChannelChiefs. About Komprise Komprise is a provider of unstructured data management and mobility software that frees enterprises to easily analyze, mobilize, and monetize the right file and object data across clouds without shackling data to any vendor. With Komprise Intelligent Data Management, you can cut 70% of enterprise storage, backup and cloud costs while making data easily available to cloud-based data lakes and analytics tools. www.komprise.com.  Media Contact: Kevin Wolf, TGPR www.tgprllc.com  kevin@tgprllc.com ### IT Press Tour Uncovers Data Industry Disruption Komprise CEO Kumar Goswami at IT Press Tour In January, a cadre of journalists from across Europe and North America traveled to Silicon Valley to meet with some of the top companies in data and storage infrastructure. Komprise was lucky to be included in the tour and hosted the visiting reporters at our office in Campbell. The IT Press Tour, organized by Philippe Nicolas of StorageNewsletter.com, has held 41 IT Press Tour events, meeting with 249 (and counting) companies across Europe, the U.S., and Israel. It was refreshing to have an engaging, live conversation around the conference room (albeit with masks) to discuss the ever-changing data management sector. Komprise President Krishna Subramanian with reporters. We’ve compiled snippets of the reporters’ coverage of Komprise below. _______________________ Nick Ismail, Editor, Information Age Excerpt: The first problem Komprise solves is providing insight into their customers’ data – where it is and the level of importance. The hot and cold data is then defined, indexed and tagged across disparate data silos; and this can be moved between on-premise and cloud via smart migration. “The boundary between hot and cold data is based on what can you index and search, and what the customer defines. The customers can set different policies for different data sets,” explained Krishna Subramanian, COO and co-founder. The tiering of this data leads to dramatic costs savings, up to 60% for IT infrastructure and data storage, through data tiering, data replication, data migration and capacity planning. An additional feature is the onboarding of this unstructured data to a single view platform, allowing organisations to extract value through deep analytics. What they call, the Global File Index, for true cloud transformation that also enables legal compliance, governance and security. Chris Mellor, Editor, Blocks & Files Excerpt: We can see that Komprise is in the control plane, so to speak, for files its software has moved, but not for files which it has not moved – the so-called hot data. Komprise can do more. The index can be queried to show how much capacity is taken up by files of particular classes and a graphical display created to show this. How many files are stored on Qumulo filers? What age are they? How many files have been accessed in the last day, week, month, year and so on. Komprise says this is like a search plane as opposed to a control or data plane. Its software can also be used to add tags to files, to extend the metadata. Then you can answer questions such as “which image files contain the company logo or personal identity information (PII)” and do things with them. You can set up workflows to have public cloud services, such as AWS Lambda functions, act on subsets of data. Or delete emails by ex-employees that have not been read in 3 years. Such activities are called Deep Analytics Actions. There is API access to these actions and Python scripts can be used to connect data services. Tom Smith, IT & Marketing Analyst, Insights from Analytics Excerpt: [Komprise CEO] Kumar told us about a leak that caused water damage to his home. State Farm had Kumar send them videos of the leak, the damages, and the repairs. Data from Kumar, and thousands of other homeowners, are in State Farm data centers all over the country. Ultimately, Kumar received a check from State Farm without ever meeting with an adjuster in person. State Farm is using unstructured data to automate and reduce the cost of claims processing while enhancing the customer experience. A tremendous amount of unstructured data is being collected at the edge. It's impractical to move all this data to a central data store. Komprise helps extract the important data from all the data. Users need a consistent, systematic way to manage unstructured data to do something productive with it. Data management has to be an independent layer that works across servers and data schemes. It cannot be on the hot data path, it has to be beside it. Antony Savvas, Freelance Writer, IT Europa, IoT Now Excerpt: Unstructured data management vendor Komprise reported 115% year-on-year subscription growth in 2021, with new customer acquisition growing by 200%. The amount of data under Komprise's data management platform also doubled. VP of sales EMEA Ben Conneely told press and analysts on the Tour that the UK, Ireland, DACH, Benelux and the Nordics were set to continue to be growth regions this year, with big sales targets also set in France, Spain and Italy. The company recently hired an EMEA channel alliances director. Existing EMEA partners include CDW, Telefónica Tech, Nephos, Oriium, SVA, Dynamigs, DMP and NetNordic. _______________________ ### Komprise CTO Talks Architecture, TMT and the Global File Index Mike Peercy,CTO and co-founder, Komprise A successful software company depends upon the care that goes into developing the right technical foundation for near-term and long-term use cases. I had a chat with Mike Peercy, CTO and co-founder of Komprise, about the core elements of the Komprise architecture for unstructured data management and mobility and why it matters for our customers. Listen to Mike on the French Storage Podcast. _______________________ How would you describe the core components of the Komprise data management architecture? Mike: First, we have a scalable architecture. This is critical so that our customers can scale out each site with shares and data as needed and scale among sites to bring data from many places into the same view. The Komprise Global File Index is the second core piece of the architecture and it’s what holds the data for Deep Analytics and Deep Analytics Actions. Finally, we have our patented Transparent Move Technology™ (TMT), which drives the ability to save storage costs. On average, our customers are saving 70% on storage and backup costs by using Komprise to help identify data which can move to less expensive archival storage. _______________________ What makes the Komprise architecture scalable in particular? Mike: Our distributed, scale-out architecture means a few things. Firstly, it is stateless, which means that the grid of Komprise Observers and proxies (these are our data movers) do not share anything between them. This enhances the performance of the software as data volumes grow, along with reducing complexity and errors. Since Komprise runs as a service, there’s no central database for the customer to install or manage. All of this means that customers can scale deployment at any site to handle more shares and data, whenever they wish, by simply adding more Observers. _______________________ That seems to be quite advantageous in these times when unstructured data is growing exponentially. Can you tell us a bit more about the Global File Index and how it works? Mike: The Global File Index (GFI) is the basis for automated data management. Komprise Observers—whether they’re in your data center, at the edge or in the cloud—analyze data quickly and bring it to the GFI, a service we run in the cloud or that a customer can host on premises. Either way, the customer doesn’t have to manage it. The GFI continually indexes all files in place and analyzes all the metadata. Data and storage professionals can search, tag and create curated data sets across the silos and then copy and move those curated data sets. External scripts can also use the GFI. The Global File Index (GFI) is the basis for automated data management. _______________________ Awesome. Now can you give us more detail on TMT as well? Mike: Yes. TMT is fundamental because it makes things easy for users and eliminates conflicts between data owners and storage administrators. This technology transparently moves files from the source to the target and leaves a dynamic link behind. Users never need to hunt a file down after Komprise moves it. Storage managers never have to ask users again before tiering data, because there’s no change required for users or applications to access their files. The files are also now sitting at the target, typically object storage like AWS S3 or Azure Blob. Moved files are also in native form—which means that users can leverage them there for analysis or other purposes. Further, Komprise delivers file-object duality which means that when a file becomes an object, a user sees it as a file. _______________________ What have been the greatest technical challenges of building this architecture? Mike: A global indexing cloud service is very hard to build because it needs to index across files and objects and across silos including datacenters, clouds and vendors. It must have massive scaling capabilities so that it can index across billions of files and thousands of buckets and file shares. Finally, the GFI needs to be a scalable cloud service, analogous to a Google search on file metadata. Mobilizing unstructured data is also hard to get right. You need to preserve duality of file and object data across silos, enable native cloud services on the data so you can use it in the cloud, and ensure that hot data at the original source is handled by that storage vendor. Finally, we wanted to build a solution which didn’t require the use of proprietary agents or stubs to access files once moved. Interestingly, in developing TMT end-to-end, we solved the hardest mobility problem first. TMT delivered a foundation to build other mobility functionality, such as file migration or share replication, and provides a great advantage in developing new features. Read more about Komprise unstructured data management architecture in this white paper. _______________________ What’s exciting to you about the roadmap for Komprise? Mike: The data-driven organization today revolves around the liberation of data for broad use by different departments. In a secure way, Komprise aims to empower data end users with self-service and the storage IT and LOB IT teams that support them. Users can tag their own files and create policies to move their data to approved targets, run analysis on it and then further enrich it by adding additional tags. This process brings hidden or forgotten data into the light for new uses, while also giving individuals the power to delete datasets when they are no longer needed or when compliance requirements dictate actions. _______________________ ### Komprise Expands in India Prateek Kansal, Head of Engineering, India Operations at Komprise, shares his thoughts on Komprise’s team and strategy in India and the company’s big data analytics story. This interview was adapted from the original article in Analytics Insight. Tell us about the Komprise journey since inception? PK: Our Co-Founders Kumar Goswami, Mike Peercy, and Krishna Subramanian had worked on two previous startups before launching Komprise and brought their expertise in distributed fault-tolerant computing. In talks with CIOs, they learned a common theme: organizations were drowning in unstructured data, they don’t know how much data they have, they wished there was something to help them better understand and do something about it. The founders realized that we needed to do something vendor agnostic. Everybody had silos of data. But they didn’t want another solution that would create a middleman that would be needed to access their data. Komprise developed from there, and they knew that analytics was going to be a key part of the equation. But not just analytics. We had to allow customers to take action on the insights. We need to ensure we didn’t get in front of the data. All of these principles that came from early customer validation are still part of our DNA. How is Komprise contributing to AI and big data analytics? PK: Komprise uses ElasticSearch technology to create a Global File Index that can search across all data regardless of where it’s stored. This allows IT users to find precisely the data sets they are looking for (such as by project or file type) and then send it to a cloud data lake where it can be analyzed and leveraged. By making it easier to search across data sets, rather than trying to unify and move massive stores of data to a central location, Komprise is speeding time to analysis for customers wishing to monetize the petabytes of data they are storing on-premises or in the cloud. Our latest product update, Deep Analytics Actions takes this one step further by enabling policies on those queries. For example, if you want to tier files generated by certain instruments to the cloud, you can create a policy in Komprise in seconds so that as new files are generated, they are continuously and automatically moved by Komprise. This makes it easy to systematically leverage analytics to move and operate on data.   What are the most important trends that you see emerging across the globe concerning big data analytics and unstructured data? PK: With the growing interest in machine learning and artificial intelligence (AI), I think we will see more investment in unstructured data analytics and data management solutions that enable this. Since unstructured data is very large and unwieldy and much of it is growing outside the cloud at the edge, data management that spans edge-to-cloud and simplifies ingestion of unstructured data for cloud analytics will become a notable trend. Already we are seeing cloud data warehouses like Snowflake beginning to support unstructured data, but these are early days. Merging unstructured data into the world of structured and semi-structured analytics tools and practices is an area ripe for innovation. Yet there are other issues beyond toolsets. Big data is almost too big and is creating data swamps that are hard to leverage. Precisely finding the right data in place no matter where it was created and ingesting it for data analytics is a game-changer because it will save ample time and manual effort while delivering more relevant analysis. So, instead of big data, a new trend will be the development of so-called “right data” analytics. What’s your growth plans for the next 12 months in India? PK: The engineering team in India is responsible for building some of the most critical functions of our product and is involved in all the core new innovations we are building for customers. India staff has taken a lead on building and sustaining the Komprise Deep Analytics engine and helped deliver our cloud data management capabilities which are instrumental for customers adopting multi-cloud storage. We are looking to fill many crucial roles at our India office right now including several engineering roles in Bangalore and senior-level opportunities. Since we are growing our team, we are also looking to hire for product and operations leadership. We plan to expand the India operation by over 50% in the coming year. How do you see the company and the industry in the future? PK: Komprise is in an excellent position to help IT organizations solve critical issues with data management. Our analytics-first approach makes sense as it’s the only way to truly understand data to control costs and manage data properly for business needs, compliance needs, and long-term value. Our customers around the globe have attained notable cost-saving while simplifying their data management workloads. Additionally, as cloud-based services for ML, AI and data lakes mature and grow in popularity due to their scale, affordability and user-friendly features, now is the time to integrate unstructured data with these analytics tools. We plan to be a major contributor to cloud analytics in terms of enabling technology. We can help customers avoid vendor lock-in (including lock-in to our solution) so that they can move data where they wish, whenever they wish and without onerous fees or hassle. ### Komprise Named Best Data Management Software of 2022 The recognition from StorageNewsletter.com comes on the heels of 115 percent sales growth and 200% growth in new customers for Komprise in 2021. Campbell, CA—January 19, 2022– Komprise, a leader in analytics-driven data management and mobility, today announced that it has been recognized by StorageNewsletter.com as Best in Data Management Software and Best in Archive Software, in the 2022 Storage Products of the Year Awards. The awards come at a time of heightened interest in storage and data management technologies as midsize and large enterprise grapple with the rapid growth of unstructured data and the need to cut costs, better protect data assets from ransomware and other threats and ultimately derive more value from their growing volumes of unstructured data.  Komprise Intelligent Data Management gives IT and storage directors the means to regain control over unstructured data while enabling departments to use data for AI/ML and data analytics. Komprise indexes all file and object data in-place with the Global File Index and systematically moves the right data at the right time by policy to maximize savings and feed data pipes. The Komprise Elastic Grid architecture and patented Transparent Move Technology (TMT)™ ensures that once data is moved from NAS, file-object duality is preserved so users can always access files as before from the NAS while still accessing them as native cloud objects without lock-in. Komprise customers span many sectors and include brand names such as Pfizer, Northwestern University, Carhartt, Fossil and Pacific Biosciences. Read about Komprise 2021 Results. “The requirements of storage, backup and data management vendors are becoming increasingly nuanced, as enterprises need an agile, multi-vendor, multi-cloud strategy,” said Philippe Nicolas, editorial team at StorageNewsletter. “We’re looking forward to seeing how leaders in the data management space, such as Komprise, will advance solutions to meet these changing needs for data-driven innovation while also delivering upon widespread needs for cost savings and compliance.” “This is an exciting time to work in the data management space and it’s fantastic validation from StorageNewsletter about Komprise software, which gives an instant ROI to our customers,” says Kumar Goswami, CEO and co-founder of Komprise. “We’re seeing that enterprises are moving from adopting data management strictly as a cost-saving proposition to a broader strategy, where unstructured data is managed strategically through its lifecycle to meet the needs of different user groups and workflows including big data analytics and AI and machine learning projects in the cloud.” See the full list of StorageNewsletter Best Storage Products here. About Komprise Komprise is a provider of unstructured data management and mobility software that frees enterprises to easily analyze, mobilize, and monetize the right file and object data across clouds without shackling data to any vendor. With Komprise Intelligent Data Management, you can cut 70% of enterprise storage, backup and cloud costs while making data easily available to cloud-based data lakes and analytics tools. www.komprise.com.   Komprise Awards and Recognition Media Contact: Kevin Wolf, TGPR www.tgprllc.com  kevin@tgprllc.com ### Komprise Doubles Sales in 2021 as Unstructured Data Management Becomes an Enterprise IT Priority Komprise also doubled the rate of customer acquisition as the urgency grows to derive more business value from unstructured data. Campbell, CA—January 11, 2022– Komprise, a leader in analytics-driven data management and mobility, today announced robust growth for 2021 over 2020, with more than a doubling of its revenues and the amount of data under management. The past few years have seen explosive growth in data worldwide, leading to enterprise pains and opportunities alike, spanning: dramatic increases in storage costs, enterprise priorities for cost-optimized cloud migration and accelerated AI adoption plans as cloud-based AI and ML services mature. Amid these trends, Komprise has seen rampant market uptake of its value proposition for unstructured data management, enabling massive data storage savings while facilitating faster, more automated analytics projects in the cloud. Customer Growth Komprise revenues, which are subscriptions, grew by 115% year-over-year. New customer acquisition grew by 200% over the year before, particularly in life sciences, media and entertainment, and the public sector. Customers spent more on expansions than on their initial purchase, indicating increased adoption and satisfaction. Komprise had more than 400 graduates from the Komprise Technical Professional training program in 2021, up 80% from the previous year. Customers accounted for roughly half of the attendees. Intelligent Data Management Platform Innovation In October, Komprise announced Deep Analytics Actions, which delivers a systematic way to find specific data sets across hybrid cloud storage silos and move it to secondary storage and/or rapidly feed cloud analytics tools and services. Komprise was awarded a patent for asynchronous restoration of files from delayed recall storage such as AWS Glacier, building on the prior patent for Transparent Move Technology (patented in 2019). In June, Komprise announced new capabilities for global data management with multisite controls, giving IT directors a single consolidated view across multiple Komprise-managed sites while enabling local execution to meet site-specific policies and needs. Expanded Alliances and Industry Recognition Komprise and existing reseller partner Pure announced that Komprise Asynchronous Replication would deliver reliable data replication for Pure FlashArray™ file customers. The company made inroads with its key public cloud partners including achieving: AWS Migration & Modernization Competency, support for new AWS file storage and analytics services and support for Azure Files NFS. Komprise received several industry honors in 2021: NAB Show Product of the Year award (cloud computing & virtualization category), Best Enterprise Data Management Solution by New World Report and CRN Storage 100. Komprise President and COO Krishna Subramanian was honored as a Top Woman of Influence in Silicon Valley, by the Silicon Valley Business Journal. “Komprise sits in the sweet spot by enabling IT to save 70 percent or more on storage and backup costs through its analytics-first approach, while also extending the value of data through flexible search, tag and transparent movement to cloud-based data lakes, warehouses and AI/ML tools,” says Kumar Goswami, CEO and Co-founder of Komprise. “Our global file index brings visibility across all storage so that enterprise IT teams can make the best decisions on where data should live as needs change.”   Learn how Komprise Intelligent Data Management can help you save and make money on your unstructured data. About Komprise Komprise is a provider of unstructured data management and mobility software that frees enterprises to easily analyze, mobilize, and monetize the right file and object data across clouds without shackling data to any vendor. With Komprise Intelligent Data Management, you can cut 70% of enterprise storage, backup and cloud costs while making data easily available to cloud-based data lakes and analytics tools. www.komprise.com.  Media Contact: Kevin Wolf, TGPR www.tgprllc.com  kevin@tgprllc.com ### Driving Market Trends & Roadmap for Data Infrastructure Data infrastructure is on the brink of transformation This is a core message outlined by Bessemer Venture Partners in their Data Infrastructure: Roadmap, published earlier this year. The firm has an excellent thesis on how businesses are becoming data driven and the new startup ecosystem that is emerging to support this imperative. This thesis is interesting because Bessemer has recognized the need to look at data infrastructure as its own category due to its massive market opportunity potential. The missing piece in the thesis: The challenge presented by unstructured data in the enterprise The bulk of data being generated today is unstructured: files and objects such as medical images, video and audio files, IoT files, log files and so on. IDC predicts there will be 175 zetabytes stored worldwide by 2025, of which at least 80% will be unstructured. The trouble is, all this data doesn’t neatly collect right next to the data analytics compute platform. There is massive data sprawl with data collecting at the edges, in various data centers and across different cloud vendors. The process of searching across these disconnected environments is enervating at best. Therefore, a means to find unstructured data across disparate silos, curate that list and feed it to compute engines for specific analytics-driven use cases becomes a critical task ripe for automation. So while companies are making strides to become data-driven with structured and semi-structured data, what’s right around the corner is the need to manage and extract value from unstructured data. Here is the Komprise take on Bessemer's assessment of driving market trends for data infrastructure: 1. Growth in adoption of cloud software. Bessemer writes about the momentum behind cloud analytics and cloud data warehouses to support the rapid movement of data to the cloud. Beyond data warehouses, cloud data lakes are on the rise because organizations can put any data into a data lake in its native form without any preprocessing. But this flexibility has also led to data lakes becoming unwieldy data swamps, especially of unstructured data, because this data has no specific schema. The problem is: "How can you make it easy to collect and ingest data – to search, collect, find and extract relevant data from a completely unstructured data lake or several data lakes or silos?” Komprise addresses this problem by automatically indexing file and object data, thereby creating an actionable global file index to easily search, find and use data from data lakes. So, our statement would be: Growth in adoption of cloud software both for data ingestion and cloud native data services. _______________________ 2. Increase in volume of accessible unstructured data. Bessemer writes: “Enterprises now need flexible and seamless connections with various data sources such as databases, SaaS apps, and web applications, spinning up new sources as the number of systems they use to operate their businesses expands in the digital realm.” Data volumes are certainly growing in the cloud, but there is also the edge. We are just at the beginning of data at the edge. As edge data continues to grow, enterprises will no longer find it economical or technically feasible to stream all data to the cloud. The Komprise architecture brings flexible connections to cloud, data center and edge sources containing unstructured file and object data. With Komprise, you can view, search, tag and create a culled list of just the data you want to analyze and transfer it to the cloud. This is why our data management and data mobility vision includes making data easily accessible and searchable everywhere and also enabling local processing and culling of data when needed. You will not be able to centrally store and process all the data. So, our statement would be: Increase in volume of accessible unstructured data across edge, datacenter and clouds. _______________________ 3. Data becomes a differentiator. We agree--and machine learning is a critical underpinning capability. Machine learning relies on unstructured data, so the easier we make extracting the right unstructured data and ingesting it into ML, the faster we can deliver business outcomes. Also, this process is iterative: how can you use a cognitive service such as PII detection or audio sentiment analysis and preserve its outcome by inserting tags that represent that outcome? We are continually optimizing and enriching metadata through tags. The context is not trapped inside different cognitive services or data processing silos because the Komprise framework works across them. This is why global tag management in the Komprise Global File Index ensures that the learnings from any data processing are tagged, indexed and can be leveraged anywhere for future processing. Komprise continually optimizes and enriches data throughout its lifecycle. So, our statement would be: "Optimized unstructured data becomes a differentiator" because raw, dark, inaccessible data is not usable, especially for unstructured data. _______________________ 4. Demand for talent and sophistication in leveraging data. We agree that automation plays a significant role in addressing the growing labor and skills shortage and also supports “citizen science”. How can we evolve from needing specialized data engineers to every functional role being able to leverage data for better outcomes? How can you focus your data scientists on the skilled analysis and not on the blocking and tackling of finding and ingesting data? As one of our customers said, "We bought Komprise because we no longer want our data scientists to be data finders and data movers." Data growth should not have a deleterious effect on the skilled data professionals hired to model and analyze data. They should not be turned into data administrators. So, our statement would be: "Shortage of talent pool will lead to greater citizen science and automated data management." _______________________ The Bessemer thesis paints an elegant picture of the data journey from the source to data output. To reflect the journey of unstructured data, the graphic, under "Data Collection, Ingestion and Storage", might well include Unstructured Data Management software to cover "Search, Collection/Curation, and Mobilization" of unstructured data. As an example, in the figure, below S3 is shown as a data lake. Yet S3 as a data lake does not have any way to optimize the finding, curating, and ingesting of the unstructured data in it. Also, S3 is not monolithic. It can consist of hundreds of buckets across multiple storage tiers and multiple AWS accounts. It does not consider that data may be spread across Azure Blob, Google Cloud Storage or on-premises object storage. Also missing is cloud file storage which is gaining prominence as well as hybrid cloud file storage which is an untapped source of unstructured data. To summarize, the Bessemer thesis does a great job of capturing how businesses are becoming more data driven and the startup ecosystem that is developing to support it. Now, with the exponential growth of unstructured data in the enterprise, we will see the startup ecosystem expand to include the management, collection and mobilization of unstructured data and an increasing adoption of machine learning to process unstructured data for greater business value. _______________________ ### Why Unstructured Data Is the Future of Data Management This interview with Komprise President and CEO Krishna Subramanian was originally published in VentureBeat. Enterprises are increasingly relying on unstructured data for regulatory, analytic, and decision-making purposes. Unstructured data will power analytics, machine learning, and business intelligence. According to the latest figures from research firm ITC, the volume of unstructured data is set to grow from 33 zettabytes in 2018 to 175 zettabytes, or 175 billion terabytes, by 2025. There has to be some kind of data management so organizations have the right kind of data available at the right time. Krishna Subramanian, president and COO of Komprise, a data management software provider, sat down with VentureBeat to discuss the business benefits and challenges associated with unstructured data. VentureBeat: Does the average enterprise IT organization know how much unstructured data they have and how fast it is growing? Krishna Subramanian: Intuitively they know a lot is unstructured and it is growing in double digits, but they don’t know exactly how much they have and how fast it’s growing. We know that 80-90% of the world’s data is unstructured. VentureBeat: What’s the problem with this data growth — there is now endless cloud storage after all, right? Subramanian: The big issue is the cost. Over two-thirds of the cost of data is not in the storage, but in its active management. For every piece of data, companies typically keep a few backup copies and a replication copy for disaster recovery. If you think your data is growing at 30%, it’s more like 90-100% when you factor in all the copies of the data. It’s also wise to consider that cloud storage is not necessarily cheaper. For instance, AWS itself today offers over 16 tiers of unstructured file and object storage. If you don’t put your data in the right place and control egress costs, you may end up paying more than if you were storing it on premises because every time you even read the data you’ll be charged. The key here is that over 80% of data is not actually actively accessed and is cold. This cold data can be stored on cheaper storage and does not require the same level of backup and replication. Therefore, you need to manage hot data that is actively used and cold data that is rarely used differently. As just one example, Pfizer researchers generate between 8TB and 10TB a day, and they were running out of datacenter space. They were able to use a data management product to identify the cold data and eliminate it from their expensive storage, backups, and replication by moving it to lower cost-resilient storage in the cloud and taking it out of active management. The company wound up cutting 75% of their data storage and backup costs, all without users having to notice any change. [Read the blog on Pfizer’s cloud data migration] What’s hard about data growth is that a lot of organizations don’t like to delete data. You never know when you might need it. And when you do, you want to be able to find it easily. And users and applications should not have to change their behavior when you move data around. In the past, with archiving to tape, that wasn’t possible, but now it is with cloud storage and with data management software. VentureBeat: Why is it important to be strategic about how you manage it, store it — isn’t it just about making sure you can find it for the BI team? Subramanian: Today, data is a valuable corporate asset. You’ve got to be strategic with it because it’s not just for your BI teams, but for the R&D and customer success teams. They need historical data to build new products or to improve the ones they already have. This is super relevant in manufacturing, such as in the semiconductor chip industry, but also in other industries that are so important to our economy, such as pharmaceuticals. COVID researchers depended upon access to SARS data when developing vaccines and treatments. Data often becomes valuable again later, and what if you don’t know what you have or you can’t find it? We’ve had customers in the media and entertainment business, and in the past when they wanted to find an old show, they’d need access to a tape archive. Then, they needed an asset tag to locate the tape. That can be very difficult, and it’s why archiving is not popular. Live archive solutions that are available today make archived data instantly accessible and transparently tier data so users can easily locate files and access them anytime. VentureBeat: How will tools and practices evolve to help IT departments better leverage this unstructured data for the organization/business users? What’s needed, where are the gaps? Subramanian: You need a storage-independent way to look at data across all of your storage technologies, whether in your datacenter or in the cloud, to not only move data to the right place but also to help businesses extract value from the data. Gartner calls this category “data management software,” and it includes companies like Cirrus Data for block data and Komprise for file and object data. The ultimate goal is to help business users leverage historical data, and this requires data search, data analytics, and data intelligence. These are hot areas where a lot of innovation is happening. The cloud providers offer several data warehousing and data analytics solutions that can be leveraged in conjunction with data management software, such as AWS Redshift and QuickSight. For instance, we use distributed Elasticsearch in our software to rapidly search billions of files and find just the data relevant to a user, such as all the data for a particular project, and export this data to RedShift for further analysis. Why have all this data if you can’t detect significant trends, such as anomalies or ransomware? I believe we need more predictive analytics around data. VentureBeat: Will the data management challenge spur a whole new sector of startups in the coming year or two? Subramanian: Definitely. Analysts are beginning to recognize data management software as a new category. Beyond the use cases above, consider all the new types of data analytics companies getting funded, such as Snowflake, Databricks, and Apache Spark. So many companies are coming to light right now to solve data management and data analytics issues at scale. VentureBeat: How are the big cloud providers responding to problems and opportunities with unstructured data growth? Subramanian: They are all offering more services to store data at different performance and price points. Amazon Elastic File System (Amazon EFS) and Azure Files were born to address the need for file storage in the cloud. The major CSPs are investing in partners across many areas of unstructured data management, including migration and analytics. ### Komprise TMT Deep Dive Part Two: No Stubs, Dynamic Links In Part One of my interview with Komprise co-founder and CEO Kumar Goswami, we talked about the power of Komprise Transparent Move Technology (TMT) for right-placing data and the importance of direct data access and staying out of the data path. In Part Two, we dig deeper into the barriers with legacy unstructured data management approaches and how TMT delivers a cleaner, simpler approach. How is TMT different from stubs? KG: Traditionally, solutions that move data have relied on one of two approaches: They either move the data entirely out of the primary storage, which is undesirable in most scenarios because it creates user friction as users think their files have disappeared. If they try to move data transparently, they then leave behind a proprietary “stub” file that points to the moved file. Stubs are problematic for two reasons: A stub is proprietary and you need an agent installed on the file storage to detect when the stub is opened. This puts the data movement tool in the hot data path, which is undesirable, as it impacts performance and can become a bottleneck. Worse, since a stub is statically pointing to the new location of the file, if a stub is accidentally deleted, then data can get orphaned. Also, a stub can only go to another similar file system, so you cannot bridge file and object for example. The bottom line is stubs are hard to manage. They need to be backed up, they’re in the data path, they’re limiting, and they are risky because they are a single point of failure. Komprise eliminates these issues by using a patented mechanism called Dynamic Links that uses standard protocol constructs and therefore eliminates proprietary agents and doesn’t get in the hot data path. What are the advantages of Dynamic Links? Figure 1: A file Komprise tiered to the cloud opens like a normal file on the desktop. KG: TMT uses the standard, built-in feature of Windows, Linux, and Mac called symbolic links which replace a file with a tiny pointer to another location. By using our Dynamic Link inside the standard symbolic link, we can extend the file system to call these files from the cloud or other storage systems. Dynamic Links dynamically bind a request to the actual data so it can move a file from NFS or SMB to a native cloud object and still provide transparent access from the source. As a user if you want to see how this works, click a file that Komprise tiered to the cloud and you get it back instantly. Simply right click the file and you will see that the path points to the Komprise Dynamic Link instead of a file on your computer. Komprise TMT is a scalable, storage-agnostic data movement solution that maintains file and object duality to give you the best of both worlds – transparent data access from the source and native data access outside the data path.   Figure 2: Komprise preserves file-object duality, so moved files can be natively accessed as objects.   With our patented Dynamic Links, Komprise stays outside the hot data path to deliver a standards-based open data management solution that is resilient and avoids the pitfalls of static stubs or symlinks.   What is the response to TMT from non-technical users? KG: Asking users to change how they access data prevents adoption of tiering and requires significant IT involvement. Komprise developed TMT to ensure that data can be moved without disrupting users. Users don’t have to go to a separate access point or know where their data is after movement. They can see their moved data when they run the “ls” command in a Unix shell or use Explorer as if it is still on the source. This approach keeps  users happy and enables cost-effective data management. You like to say direct data access puts you in control of your data, not your storage vendor. Why is that so important today? White Paper: Komprise Transparent Move Technology™ KG: With the advent of affordable big data analytics, the ability to index, search, and operate on all your data is a game changer. It’s also a fundamental requirement for ever-increasing compliance and legal data hold requirements. Maintaining direct data access to all your data without going through the original source is critical to meet these objectives. Komprise makes this possible because it transfers your company’s NAS data to a cloud or secondary storage target as a file with its complete metadata, whether that target is on-premises NAS or object storage or cloud storage. The file is stored in the format native to the target storage device, and the metadata is stored in NFS or SMB format, depending on the protocol used to archive or copy the data. You can get more details on the benefits of TMT in the white paper. In summary, what are three takeaways about Komprise TMT? KG: I would emphasize the following: 1. Komprise Transparent Move Technology moves data without proprietary interfaces such as stubs or agents. Stubs and agents are proprietary interfaces that are problematic to manage and brittle. Stubs can be problematic: if deleted, they can leave data orphaned, creating myriad problems for users and applications. Instead, Komprise has improved upon industry-standard symbolic links and invented a superior system to point to the moved data using dynamic links that are resilient and work across multi-vendor storage, with no user impact. 2. Komprise also provides the ability to "lift and shift" data to the target. In this case, a dynamic link is not left behind. The files are completely moved from the source file system to the target in a format native to the target. This approach makes sense to move data-heavy projects that are completed. In this approach, the data is accessible either directly from the target or by mounting Komprise as an NFS / SMB drive to view, list, drag-and-drop and operate on just the data that has been moved. Users/admins will need to know to go to this mounted drive. Note that even if the data is moved to an object store or the cloud, it can be accessed as objects from the target but also still accessed as files (with all the proper permissions) from the Komprise mounted drive. Other solutions are unable to provide this critical feature that requires less training and less disruption to the users even when using a “lift and shift” approach. 3. TMT = insight + action. Komprise policies can be set up to take automatic actions on a set of data (e.g., data by age, or a custom query of owner, location, etc.). As new data fits the policy, Komprise will systematically find and move data based on the policy without manual intervention. With Komprise you don’t need to manually run scripts and manage the transfers, thus cutting IT labor and overhead. Komprise provides reports of transfer errors and it automatically retries to address transient errors. Komprise provides a powerful and simple interface to administer policies, and it makes APIs available for scripting where custom actions are required. I get most excited about Komprise customer success, so check out some of our great customer stories or set up a custom demo with our team to learn more. Be sure to also watch the Storage Field Day video below with our CTO and co-founder Mike Peercy to learn more about TMT and the Komprise architecture. Read Part One: Transparent Move Technology Deep Dive with Kumar Goswami _______ ### Komprise Transparent Move Technology: Digging Deeper with Kumar Goswami One of the strengths of Komprise is what we call Transparent Move Technology™ (TMT). We recently published a white paper overview of TMT and I wanted to dig into some of the common questions we hear when we introduce TMT. Who better to go to than Kumar Goswami, Komprise CEO and Co-founder. In this two-part interview, we’ll talk about the unique (and patented) approach of TMT and the benefits of not being in the hot data path. What gets you most excited about TMT? KG: We focused on making it simple for users to embrace the potential of data management because Komprise requires no change to users, application workflows or processes. With our patented Transparent Move Technology, users and applications can continue to access moved data exactly as before. Komprise provides two options. First, we can move data transparently so that users can still list, search and access moved data from the same location as they did before the files were moved. TMT allows IT to specify policies for tiering and archiving data to ensure cold data is continuously and systematically moved from the source without disruption to users, applications or IT data protection workflows. Komprise puts IT in control to operationalize the solution for maximum savings. TMT does this without any stubs or agents, using just open standards. Second, Komprise also provides a policy-driven way to “lift and shift” data completely off the source to file and object store targets. Nothing about the file is kept on the source. This approach does not provide user and application transparency, but it may be ideal for data-heavy projects where users will likely no longer access the data. For such cases, the moved data can be accessed directly from the target or by mounting Komprise as an NFS or SMB share. In the latter case, users can list and access the moved files or drag and drop these files into other sources easily and without requiring IT intermediation. Note that this is possible even if the files are moved to an object store or the cloud. You get both file and object access regardless of the target. Figure 1: Moving to the cloud using Komprise Transparent Move Technology™ (TMT). Learn More.   What are some of the common issues you see with proprietary tiering solutions provided by storage vendors? KG: Proprietary data tiering solutions are implemented at the block level and not at the file level. Here are the issues that result: They access and interact with cloud storage using the same complex block management mechanism and protocol designed for their internal storage platforms. While fetching and writing to small blocks within your own storage might work, it does not work with the cloud because the cloud has much higher latency and you must pay to read and write objects leading to unnecessarily high costs and poor performance. As a result, this block-based approach is highly inefficient, leads to greater cloud egress costs, and potentially slows down overall performance of the storage unit. With data stored as proprietary blocks you also can’t access the data from the cloud. You cannot take advantage of cloud-native AI/ML workflows nor advanced security features such as S3 Object Lock to protect your data from ransomware. Essentially, block-based storage pool tiering creates a digital landfill of proprietary blocks in the cloud. Proprietary solutions don’t reduce the backup and DR footprint, especially for third-party backup tools, leaving much of the savings from tiering cold data on the table. Finally, these solutions don’t provide any analytics so you cannot see all of your data, regardless of where it’s stored. This means you can’t holistically plan your data management strategy and approach. Komprise TMT, unlike storage array tiering and cloud storage gateway solutions, works at the file level and doesn’t have these issues. It works equally well with cloud storage as well as on-premises object stores. Figure 2: Moving cold data to the cloud with storage array tiering. Learn More.   Figure 3: Migrating data to the cloud using cloud storage gateways. Learn more. Does TMT work with backup solutions? KG: Yes. Komprise TMT works with all standard backup solutions and does not rehydrate the data, thereby reducing the backup footprint and ensuring significant cost savings. TMT simplifies the movement of cold data to less expensive storage. You can literally make your data moves with a click of a button, without any disruption to users, applications, data protection workflows, or access to mission-critical hot data. While some storage vendors provide proprietary tiering solutions for cold data, these don’t provide analytics or a granular policy that can be customized for each share. And if you use them with a third-party backup solution, all archived data may be rehydrated. You’ll need to maintain the full capacity of your existing data to rehydrate all archived data, thereby eliminating any cost savings! Why is not being in the hot data path such an advantage for TMT? KG: Many data management solutions sit in front of your primary storage and divert requests for the cold data to the tiered location. This affects the performance of hot data since it introduces a middleman. A “traffic cop” now directs data access, which is a tremendous risk for all your company’s data. A failure in this system creates an access nightmare and when access to all of your data is lost, you’ll hear far worse than honking horns. This approach also requires that scaling be based on hot rather than cold data access rates. Since hot data is 99.999% of your data access, this means that the so-called “man-in-the-middle” device must be able to handle the massive hot data traffic requests. As this traffic increases with your data growth, the “man in the middle” must scale accordingly and still handle access spikes. If you don’t plan accordingly, you’ll decrease performance on your new flash storage. These types of solutions—and their issues—have been around for years and have resulted in customer defections due to high cost, decreased performance, and risk. In part two of my interview with Kumar Goswami, we’ll talk about how Komprise TMT is different than stubs and the benefits of direct data access. ### Komprise Named Winner in the 2021 Software and Technology Awards Komprise recognized as Best Enterprise Data Management Solution by New World Report. Campbell, CA—November 10, 2021– Komprise, the leader in analytics-driven data management, announces that it has been recognized by New World Report in the sixth-annual Software and Technology Awards as winner of the Enterprise Data Management category. The Software and Technology Awards looks to acknowledge and reward the continued efforts of the pioneers and disruptors of modern technology, as well as those who have sustained excellence and exhibited long-term dedication to their commitment to the development and advancements in technology. Komprise is used by the world’s leading organizations to handle the two most pressing issues with unstructured data – managing its rampant growth and unlocking data value. Komprise enables customers to cut 70%+ storage and backup costs by providing visibility across data silos, both on-premises and cloud, and right placing data through transparent cloud tiering, cloud replication or cloud migration.  Komprise’s elastic grid architecture and patented Transparent Move Technology (TMT)™ ensures that once data is moved from NAS, users can always access files from the original location, in their original form and enabling native access in the destination storage. The Komprise Global File Index provides an easy way to search across silos of data, billions of files and objects to find and ingest specific data sets into AI/ML and data analytics engines.    “We work with enterprise customers across different sectors and their pains are common,” said Krishna Subramanian, president and co-founder of Komprise. “They’re managing petabytes of file data at a drastically increasing cost and with limited visibility. We are seeing a marketplace shift from managing storage to managing data and our mission is to help customers make that transition with an independent, analytics-driven data management solution. We’re delighted to receive this recognition from New World Report.”  “This year we have the honor to recognize more fantastic businesses in the software and technology industries,” said Kaven Cooper, awards coordinator with New World Report. “I offer my sincere congratulations to all of the winners, and we wish you luck for the future ahead.” About Komprise Komprise is a multi-cloud data management-as-a-service that frees you to easily analyze, mobilize, and access the right file and object data across clouds without shackling your data to any vendor. With Komprise Intelligent Data Management, you are able to know first, move smart, and take control of massive unstructured data growth while cutting 70% of enterprise storage, backup, and cloud costs. Media Contact: Kevin Wolf, TGPR www.tgprllc.com  kevin@tgprllc.com ### People of Komprise Komprise is a global software company, with employees dispersed across North America, Europe and India. Recently, we spoke with several members of the Komprise team to find out what they like about working at Komprise. If you’re looking for new opportunities in engineering, UX, product management, customer success and more in the exciting world of intelligent data management, check out our jobs page on LinkedIn.   Randy Hopkins VP Systems Engineering: "As a solution engineering leader, I like Komprise because we deliver a unique and different solution to an old but very difficult problem that customers just love. I wake up jazzed and energetic to make a huge difference by solving our customers approach the data challenges.”   Ratandeep Kaur Software Engineer: "At Komprise, I have the opportunity to work on different technologies as long as they are useful to the product. I’ve learned a lot by working on the full stack and I’m also expanding my knowledge in cloud storage."   John Johnson Director Inside Sales: "The executive and leadership teams truly care about their employees and their customers success. I feel like we've accomplished a decade's worth of progress within the one year I've been here."   Biju C P Staff Software Engineer: "The culture at Komprise is very approachable. People are great about helping each other and sharing knowledge. Frequent casual get-togethers as a team and playing sports are keeping us much closer. I am getting great opportunities to work in my areas of experience and solve interesting problems.”   Rohit Athikari Staff Software Test Engineer: "Komprise is a vendor agnostic product, so I get to work with our partners in the market such as AWS and Azure in terms of qualifying those products. I get to learn about all the products in our space so I am always upskilling.”   Michael Del Castillo Regional Sales Director: “Komprise gives me an opportunity to be part of something bigger than my daily work. I provide real time input into product direction to meet the changing needs of the Federal government. Everyone at Komprise is important and has an opinion that is valued and respected.”   Nisarga Madhav Backend Engineer: “There are different kinds of challenging problems to solve here at Komprise, which is what I really love to do. I get to implement core pieces of the product and work with people across the company – from UI, product management and the QA team.”   Somansh Reddy Satish Backend Engineer: “I started at Komprise as an intern, working daily with senior engineers who had a lot of knowledge to impart. Whenever I have questions, people take time to help me understand not only the feature but also why certain changes were made in order to develop that feature efficiently."   Kate Lapan Fox Software Architect: “There are lots of interesting problems to solve at Komprise and I get to wear a lot of different hats. I've also been able to advance from a principal engineer to an architect.”     Nahush Bhanage Principal Engineer: “I love solving challenging problems with file systems and network protocols. Komprise is a great place to work because we have innovative ideas for solving real problems for customers and I get to work with a group of really brilliant and fun people. Plus we’re a dog-friendly culture and I love dogs!”   Learn more about Careers at Komprise. ### St. Luke’s Health Gets Strategic about Data Management St. Luke’s is a nonprofit health system based in Idaho, operating clinics and medical centers across the state. The organization was honored by Fortune/IBM Watson Health for the eighth straight year as a Top 15 health system. St. Luke’s is a nonprofit health system based in Idaho, operating clinics and medical centers across the state. The organization was honored by Fortune/IBM Watson Health for the eighth straight year as a Top 15 health system. Like many healthcare providers, St. Luke’s has years of clinical data in storage – some of it critical to retain and much of it best suited for archives – but everything is stored in expensive on-premises NAS. “We have 20 years of files from various medical systems and we have been treating all data the same,” says Brett Sayles, storage engineer with St. Luke’s. We are nearing capacity on our NetApp and Pure Storage, and we know that a lot of these files can go to cheaper storage. The capacity issues have also strained the organization’s abilities to retain snapshots for disaster recovery purposes. Currently, St. Luke’s is only able to store three days worth of snapshots and with ransomware threats an urgent reality, this puts data at risk. St. Luke’s data ecosystem: Total amount of unstructured data: 1PB NAS solutions in use: NetApp, Qumulo Data Management: Komprise Object solutions in use: Azure, AWS Backup solutions in use: Veeam Snapshot solutions in use: Pure Storage, NetApp, Qumulo DR solutions in use: Zerto Cloud solutions in use: Azure / AWS Cloud Instead of continually buying more primary storage – a costly proposition in healthcare given the size and volume of clinical files and images – St. Luke’s began looking for a data management solution that could facilitate a granular approach to storing its one petabyte of unstructured data and free up the NAS systems for high-priority files and backups. White Paper: Komprise Transparent Move Technology™ (TMT) After evaluating several solutions, St. Luke’s chose Komprise as its unstructured data management platform. In the first phase of the project, the storage team will use Komprise to archive data from an 80TB share on Pure Storage Flash to secondary storage on Qumulo spinning disk. Data will be archived according to policies set for specific applications and file types. Next, St. Luke’s will clean up IT department shares and delete files or archive them to Qumulo, using Komprise. Sayles says that Komprise’s transparent move technology™ (TMT) which uses industry-standard symlinks to move data so that users don’t experience any change of access is a great advantage: “Komprise does what it says by being simple.” The benefits of a data management system for St. Luke's: Up to 80% savings on storage by moving cold data from Pure to lower-cost Qumolo storage. Increase snapshot storage from freeing up Pure storage capacity, to protect against undetected ransomware. Obtain deeper insight into data by age, owners and usage to better inform data management policies and practices. Leverage Deep Analytics capabilities in Komprise to understand the unique requirements of different clinical file types so that IT can create the optimal storage environment for short and long-term access needs. Komprise is part of St. Luke’s broader data management evolution to be more strategic, cost-efficient and intelligent with data in order to deliver excellent patient care and meet regulatory requirements. “You can’t do the right the right thing with data if you don’t know what you have,” Sayles says. Read the TechTarget article: St. Luke's triages healthcare data management for hospitals More Komprise customer success stories. ### Rob Gordon: What Storage Architects Love and Hate Rob Gordon is a seasoned architect with a primary focus on storage architectures. Rob has worked closely with all levels within an organization resulting in high-level and deep technical discussions to provide understanding of complex storage systems. He shares his thoughts here on the storage industry and data management industry. You’ve been in the storage industry for several years now. What did you do before IT and why did you decide to make the leap into storage? RG: Early in my career I was a tubular heater designer and piping designer within the manufacturing industry. I also taught college algebra in a bar. I have so many questions…go on. RG: In 1999, I leapt into IT and I loved it because people didn’t know much about it then. Over the past 20 years I have run global data centers, been a director of storage at an ISP and sold storage to enterprise customers. In the end, I wound up being a Field CTO at NetApp where I focused on the Department of Defense, intelligence community and government agencies. In my view, storage is the most critical part of IT and it’s also the most expensive component. Why did you join Komprise and what do you see as the greatest challenges of our customers? RG: I was looking to do something that was a fresh approach for the industry, as well as a solution I really believed I could evangelize, and Komprise was it. As a result of my experiences, I know the pain of managing storage. The storage professional is always asking people to delete or move data manually to preserve space and hopefully defer costs. This tactic never solves the problem and doesn’t reduce IT costs; it often results in burning valuable man hours and costing more. With Komprise, that conversation stops. From the end-user perspective, Komprise makes data management and tiering transparent. Being vendor agnostic is another huge benefit. Our customers can leave Komprise whenever they want. It’s about managing their data, not locking them into yet another proprietary storage solution. What are data storage people like? RG: Storage administrators have one of the most thankless and stressful jobs in the IT industry. The only feedback storage admins get is when things go south and people can’t find their data or access is slow. Storage admins tend to take their job very seriously and many are hyper-focused on making sure things run smoothly every day. Those who have been working in the industry for years hold onto their knowledge, feeling that they are irreplaceable. Vendors with the right solutions can make the job of storage managers and architects easier and less stressful. With the proper tools, like Komprise, storage admins can perform deep analytics that save a lot of time and hassle. Komprise can not only show how a storage manager can save $500,000 a year, but also give them the tools to do it. Then, they have an opportunity to become a hero across the organization. This results in greater trust among peers and stakeholders and frees up time to focus on what they love to do— which is evaluating new technologies and focusing on corporate or business goals as opposed to fighting fires day in and day out. How do you get through the hype of tech when engaging with a senior IT director? RG: The conversation has to be about them and their needs. Sales engineers sometimes struggle because they get too technical. Take the time to find out what the customer cares about and speak to that. This might be cutting data storage costs or facilitating easier migrations or moving to the cloud faster. Be honest about what you can and can’t do for them. At the end of the day, you just want to have a conversation with the IT leader. Resist the urge to “just do the pitch.” What are the big gaps right now in data storage technologies from the customer perspective? RG: Most of the time, storage managers do not fully understand the data that they manage. How much is personal data? How much is video files that are taking up too much space and nobody needs them anymore? Is all the data that they are storing on expensive technology warrant the same level of performance and reliability? Ironically, many enterprise IT infrastructure people have been burned by the move to the cloud because it was supposed to be cheaper, but it ended up being more expensive over time. Most often this is a result of not understanding their data or not knowing what to move to the cloud and what to keep on premises. What if we could show you a picture of your entire environment, indicating that you don’t need to buy a new storage array but you can take 70 or 80% of all your data and either delete it or store it in cheaper secondary storage in your data center or in the cloud? That allows executives to make the right decisions. Outside of work, what do you do? RG: I love to cook, and I especially like baking bread and dry aging steaks. I also enjoy cycling and scuba diving. I go to a quarry near where I live in St. Louis or travel to North Carolina – although of course the Caribbean is the best for scuba diving. Remote working and no travel because of COVID allowed me to explore lots of unusual hobbies. This past winter, I tapped my maple tree in the back yard and made syrup, just to see if you could do it in Missouri. However, my biggest passion is to fix bottlenecks—any type of bottleneck at home, work, or play. This is perhaps why I like Komprise so much. Komprise solves some of the largest IT bottlenecks in the industry: knowing, controlling and managing data. ### Komprise Deep Analytics Actions Unstructured data is tricky to manage because of its volume, velocity and variety. Komprise solves the problem of rapidly finding small data sets in petabyte-volume environments and moving it to new environments for analytics and compliance needs. Data continues to pile up at the edge, in data centers and in clouds. In a large enterprise, this can easily be billions of files. Data stewards often need to search through all this data and find what they need for regulatory compliance or to support analytics for customer intelligence or new product research. For instance, pharmaceutical companies routinely need to provide the raw data files during an inspection—even if some of that data might be in the cloud and some might be at different datacenters. A company going through a merger may need to split some data to a new entity or identify files for deletion. How can they easily find this subset of data and move it securely with a full audit trail? This can be a highly manual process which doesn’t deliver what stakeholders need or in a timely manner. To that end, the Komprise Intelligent Data Management Fall 2021 release introduces Deep Analytics Actions, a systematic way to find specific data across hybrid cloud storage silos and move just the right subset of data for new uses such as cloud analytics. This gives IT and storage departments the ability to drive closer connections with end users by liberating the nuggets of useful data from petabytes of files, so that new value and customer-facing benefits can be discovered.   Key Features of Deep Analytics Actions Unstructured data is tricky to manage because of its volume, velocity and variety. Traditional database-driven indexing does not work on unstructured data because of its sheer volume: it can routinely be several billions of files. Any analysis of unstructured data needs to run in the background and not interfere with active users and applications. Unstructured data piles up in multiple silos, so an index needs to be global across different datacenters, storage architectures and clouds. And analytics alone is not enough—data needs to be actionable. Finally, how do you make this easy to spin up, easy to operate, and easy to manage without burdening the customer? These are the challenges that Deep Analytics Actions solves: Global File Index across datacenters, clouds, file and object architectures: Once you connect Komprise to your file and object storage, Komprise indexes the data and creates a Global File Index of all your data. You do not have to move the data anywhere; but you now have a single way to query and search across all file and object stores. For instance, say you have some NetApp, some Isilon, some Windows servers, some Pure Storage at different sites and you have some cloud file storage on Amazon, Azure, and Google. You get a single index via Komprise of all the data across all these environments. You and your users can search and find exactly the data you need across all these environments with a single console and API. No central database or bottlenecks: Komprise is designed to handle unstructured data at scale and uses no central databases or central servers. The solution delivers a completely distributed Elastic Grid architecture both for indexing and data movement. Elastic scaling in the cloud: You can start by pointing Komprise at a few storage servers and add more on the fly. Komprise elastically scales the index in the cloud so you don’t have to manage the infrastructure. Policy-driven data movement on Deep Analytics queries: Once you find the data you want to operate on, you can systematically move it using Komprise. For example, if you want to tier files generated by certain instruments to the cloud, you can create a policy in Komprise so that as new files are generated, they are continuously and automatically moved by Komprise. This makes it easy to systematically leverage analytics to move and operate on data. Extensible with tagging, APIs: You can set tags to augment the data that Komprise indexes. Tags can be set outside of Komprise via API or within Komprise. For instance, an organization that is acquiring another entity tags some data from the acquiree as going to one department and other data as going to another department. Deep Analytics queries can then be used to find data based on the tags as well as standard metadata. Komprise also makes all Deep Analytics Actions capabilities available via API so you can incorporate it easily into your business workflows. No infrastructure to manage: The best part is that setting this up is easy. Deep Analytics Actions runs as a managed hybrid cloud service; the index is maintained in the cloud by default and you don’t have to manage any infrastructure to run it or scale it. Use Cases for Deep Analytics Actions Expanding the popular Komprise Deep Analytics capabilities, Deep Analytics Actions allows customers to: Find and ingest the right file data into cloud analytics, data warehouse and data lakes. For example, a manufacturer wants to analyze customer maintenance data for a new line of equipment from a two-day period and then compare it to similar data sets from other periods. The target application could be Snowflake, Databricks, or other popular cloud data management and cloud analytics technologies. Find and delete specific obsolete data. A common example is to purge ex-employee-generated emails that have not been accessed in over three years but handle exceptions such as for legal hold. Comply with regulations. Identify just the data that needs to be retained and move it to an object-locked cloud bucket. For instance, pharmaceutical companies are often required to produce the raw files during a drug inspection to avoid regulatory violations called “quality of concern.” Komprise can find and move the raw files using Deep Analytics Actions. Enable “user-driven data management”. Users can partner with IT on data management by identifying specific data sets they are interested in through Deep Analytics queries, which are then fed into the data movement and data management plans run by IT.   Benefits of Komprise Deep Analytics Actions: Expanding the popular Komprise Deep Analytics capabilities, Deep Analytics Actions allows customers to: Users only move the data they need with the ability to create queries on countless file attributes and tags such as: data related to a specific tag or project name, projects that are no longer active, file age, user/group ID’s, path, file type (aka JPEG) and specific extensions, data with unknown owners. Eliminates the manual effort of finding custom data sets and moving them separately from different storage silos since Komprise can create a virtual data set based on the query and systematically and continuously move data from multiple file and object silos to the target location. Improves IT and business collaboration around data, as data owners/users can participate in data tiering decision-making by defining Deep Analytics queries that create a specific curated data set they want moved.     _______________________ Other Komprise Fall 2021 Updates With each release Komprise adds features to support a broader set of customer requirement and conditions: Enhanced security for data in flight to EFS with Encryption in Transit; Ability to migrate hard links and Unix special files; Option to migrate over NFSv4 using ctime; Improved capacity management for migrations: Add new warning when destination is running out of space Improved support for migrations from arrays that have reached full capacity. More Resources on Komprise Fall Update: Press Release Short Video Learn more about our latest releases: www.komprise.com/whatsnew. ### Closer Look: Komprise Deep Analytics Powered by Elasticsearch This blog will cover how we use Elasticsearch to power Komprise Deep Analytics Service and how we are creating a massive and secure Global File Index for our customers to help them manage their data. Data is having a bit of a moment. We know data is used to track, model, and make decisions for practically every facet of life so it makes sense that data is critical to managing… wait for it: data. What do we mean? We’re talking metadata: data about data. This is where Komprise comes in. Our vision is for enterprises to move from managing storage to managing data. We help customers by providing an intuitive UI (and also APIs) to create custom queries based on file metadata: Where the data is located (e.g., what file server, share, or cloud) Who owns it When it was created, modified, and accessed File name, extension, type, size Custom tag – add your own data, like project ID Using this data you can find the needle in the haystack – or more likely millions of needles across many haystacks – and once you find those needles you can now access, protect, replicate or take other action on a very specific set of files. This is the breakthrough that lets IT make granular decisions about data according to business requirements, rather than just making sure there is enough physical disk space to house it. Here are a few examples of how our customers use metadata to manage their data: Collect specific data sets from multiple sites and clouds and copy to another location for analysis by AI/ML to get value out of data; Hunt down data from former employees to confine for deletion, free resources and comply with regulations; Archive clinical data while enabling availability for use in future studies and speeding the development of new therapies. Our goals: Enable customers to gain insights across massive data sets; Precisely locate granular file sets to support research needs and decision-making; Protect the security of customer’s metadata; Maintain a simple architecture for ease of operation and performance. The Metadata Mandate Komprise was founded in 2014 and since then has helped customers index and store a staggering amount of metadata – hundreds of billions of records or observations to date. How does Komprise manage this metadata? We chose the open-source indexing engine Elasticsearch. Elasticsearch indexes, stores, protects, and is the engine behind Komprise’s global metadata index that enables you to zero in on specific data sets over multiple data centers and hybrid cloud. This approach keeps the Komprise architecture simple. Using a dedicated metadata solution means you can store as much metadata as needed (creating additional tags for example) without the concern that Komprise’s performance will be impacted. Other solutions try to do everything in a single central database that restricts scale and suffers performance penalties as the metadata load grows. How Does Komprise Run and Secure Elasticsearch? Komprise is by default a SaaS offering, with both the Komprise Director and Elasticsearch infrastructure running in the cloud on behalf of our customers. Komprise manages the security, configuration, patching, and protection of Elasticsearch. The Observers deployed as a grid are deployed on premises adjacent to the data and stream the metadata to the Deep Analytics index service where it is indexed in the Elasticsearch cluster. Just like other tasks handled by the Observers, the indexing is distributed over the scale-out grid. Get a closer look at the Komprise elastic software architecture. Read about Elastic Grid here. The diagram below illustrates how the Komprise Grid analyzes data over multiple data centers or clouds and streams the metadata to Elasticsearch while the Director queries and caches the results. Read the Komprise Deep Analytics white paper Customers use the Komprise console to create and execute queries using Deep Analytics hosted by their dedicated Director running in the cloud. The Director then executes these queries against the Elasticsearch cluster. To secure communications between the Director and the Elasticsearch, a secure ID is used to map the Director to the dedicated indexes hosted by Elasticsearch. For customers that need to retain all data and metadata behind their firewall, Komprise can also run Elasticsearch in their data center. Even with on-prem deployment, we provide a fully-managed experience. The nodes running Elasticsearch are deployed from the on-prem Director as VM appliances and managed as an integrated component of the Komprise solution. Emerging Deep Analytics Use Cases Today we use the metadata to help customers make decisions about how to “right place” their data across storage resources. Tagging is the next step. Customers can tag data with a project ID for charge back, or tag X-ray images with demographic information to support clinical studies. Object storage made the concept of metadata tags mainstream, enabling advanced AI/ML workflows to query and act on specific data sets. This ability is new to the realm of NFS and SMB and we are excited to see how customers put it to use. Conclusion We see an evolution of data management beyond just the storage infrastructure team. Analytics will enable the owners or creators of the data to help decide how their data is stored and leveraged for future value. The ability to collect, store, index and enable search of this metadata in an intuitive manner is the critical component that will move us to data-centric management. Saving money on data management is the first step for most customers. Being able to do more with that data will help them drive real innovation. ### Boone County and Komprise: Preserving Bodycam Footage This article was adapted from SearchStorage.com. Government organizations are finding their storage needs increasing, exacerbated by employees working from home due to COVID-19 lockdowns. They're also seeing an exponential rise in unstructured data from state surveillance devices such as police body cameras. Some municipal organizations are heading to the cloud for their archives, particularly for storing surveillance footage, while others have doubled down on fast on-premises systems. Uses and needs can vary due to laws and regulations for public over private data, but protecting and storing data remains mission-critical to local governments. Failure to do so can cause innumerable issues. Earlier this month, the Dallas Police Department made headlines when it acknowledged the department permanently lost 8 TB of data earlier this year, after a failed migration from the cloud to an on-premises server. Boone County's Move to Azure Boone County, Ind., decided to invest in archival cloud data storage on Azure in 2018 to house a massive amount of data generated by the sheriff's department. The department covers five towns, numerous unincorporated areas and one populous city and had begun issuing body cameras for officers. Additionally, the county's court system required storage of files relating to ongoing and closed cases and had to factor in other police departments' body camera files. Boone County storage needs on Azure have increased from 25 TB to 35 TB in the past three years, according to the county's IT contractor, Government Utilities Technology Service (GUTS). As more departments across the county begin utilizing cameras, GUTS contractors said they expect those needs to keep increasing. "Every type of case has a different retention schedule," said Sean Horan, a manager of IT services for Boone County at GUTS, the county's IT contractor. "There are some which only have a four- or five-year retention cycle. There are others that are forever. The problem for us as an IT group is we cannot sit down and say we can delete X or Y, because we don't know." Using Komprise for Data-Driven Decisions Boone County Case Study Data management software by Komprise helps sort what data heads to archive storage or what remains on premises, Horan said. He estimated that annual bills with Azure total $25,000 to $30,000 but come out significantly cheaper compared with the on-prem-exclusive mentality of prior decades. "When you put that into perspective for what that would cost with SANs, it's a no-brainer," Horan said. Data stored on Azure by the county is also backed up on tape. Previously, Horan and his team had considered making their on-premises storage entirely flash storage but settled on a hybrid flash and hard drive array totaling 55 TB from Dell EMC. The all-flash configurations he investigated totaled almost $1 million, Horan said, while the hybrid approach rang up to a more reasonable $125,000. Still, constant expansion into the cloud isn't the all-encompassing storage product for Boone County, Horan added. Instead, smart use of devices can help cut down on the need for redundant recordings and data generations. "It just snowballs," he said. "Bodycams are a good thing, don't get me wrong, but it's not just the storage. We always try to look at ways to improve processes." ### Closer Look: Komprise Elastic Grid When we set our sights on the data management space, we wanted to take an approach that fit with our core principles. Most of the incumbents in the space were complex, they locked data in proprietary formats, and they had significant issues at scale in terms of capacity and performance. The Komprise solution needed to be simple, resilient and scalable to meet the challenges of enterprise data management and the explosion of unstructured data. To analyze, tier, move, and provide non-disruptive access to petabytes of data and billions of files you need an elastic scale-out approach. The Komprise Elastic Grid is a distributed, shared-nothing, scale-out software architecture that was designed from the ground up to manage data at massive scale. The Members of the Komprise Elastic Grid are: Komprise Observers, which are software virtual appliances installed close to storage devices and connected via a cooperative, distributed algorithm enabling scalability with load balancing and high availability. The Komprise Director, which manages the Observers and runs as a cloud software service or on-premises. The Komprise Grid provides the underlying scalable layer upon which the Komprise Intelligent Data Management solution is built. This ensures that all analytics and management functionality provided by the solution is scalable and highly available. The Komprise Intelligent Data Management Solution Provides the Following Core Functionality: Analytics to provide insight into your data and enable planning data management functions and capacity planning. Data management functions, which include migration, cloud tiering, and replication/cloud DR. Deep analytics that enables you to run custom queries to search, tag and manage your data at a file level in order to create a Global File Index. The Grid dynamically expands and shrinks to enable high availability, scalability, load balancing and failover. The Komprise Grid runs adaptively in the background so there is no noticeable performance impact of running Komprise in an environment. If your environment is overloaded during the day, Komprise will adaptively slow down the analysis rate and automatically speed it up at night when the load subsides. Key functions of the Komprise Elastic Grid: The Grid migrates, replicates, tiers and provides easy access to tiered data from the original location. It analyzes heterogenous storage environments, on-prem and in the cloud, and is not tied to any particular storage or cloud. The Grid load balances over software nodes for scale and resiliency. The Grid is highly available with respect to data access: software nodes can fail or be removed and remaining nodes take over to ensure that you have highly available access to any tiered data. Key architectural principles of the Komprise Elastic Grid: Easy to set up and requires no dedicated hardware or infrastructure. Built to scale dynamically to exabytes. Virtual machines can be added and removed as needed. Easy interoperability via standard protocols and requires no proprietary linkages. 100% non-proprietary and does not create any lock-in when moving data. Stays outside the hot data path. Has no single point of failure or single performance bottleneck. How the Komprise Elastic Grid Works The Grid scales both performance (needed to move data fast) and capacity (needed to manage billions of files and petabytes of data) by parallelizing work both within and across Observers with the ability to elastically add more Observers on the fly. Deployed as standard VMs, the Observers are added to the Grid through the Komprise Director user interface. The more nodes added the greater the scale; shares are load balanced across the Observers in the Grid. What Makes the Komprise Elastic Grid Unique 1. Scalable: The Komprise Elastic Grid balances operations on the many shares managed by the system across the Observers in the Grid. As Observers are added or removed, the Grid automatically rebalances the load of the shares and the operations to achieve a high degree of parallelism and scale in analysis and transfer. In cases where even more parallelism is needed, e.g., to migrate a large share, an Observer will engage other Observers to assist. 2. Stateless: The Komprise Elastic Grid is stateless and can be readily brought back to life even if all the Observers have failed. Each Observer maintains its high-level configuration (e.g., which shares it is handling) with the Director and periodically updates it. If an Observer goes down, a new Observer communicates with other Observers in the Grid to ensure it has the latest state before becoming a productive member of the Grid. This statelessness enables simpler recovery and enables us to create a large grid of Observers. 3. Singular: Even though different shares are mapped to different Observers, the end users see Komprise as one logical file system. That is, the grid of Observers is not visible to the end user. Komprise provides a “Komprise Access Address” (KAA) that is used to mount Komprise as a file system. The KAA is used to transparently access any data that has been tiered by Komprise from any share. Komprise ensures that any access request coming in to the KAA is properly routed to the Observer that is managing the share from which the request originated. If that Observer is down, Komprise will automatically forward that request to another Observer which has been selected to manage that share. The Komprise Grid uses a watchdog mechanism to continuously ensure the KAA is hosted on a healthy Observer. Conclusion: Faster, Smarter, Proven Data Management Each customer we work with has a different data management challenge depending on industry, growth rate, infrastructure, geography, and so on. The Komprise Intelligent Data Management solution is a flexible service giving enterprise IT organizations the ability to first get a holistic picture of their storage, backup and cloud data and then to take charge. By controlling their unstructured data, they get control of their IT budget and can make the optimal choices to accelerate their business rather than simply expanding legacy storage. Komprise allows customers to start out small and grow as their data grows by simply adding more Observers. The shared-nothing aspect of the solution allows our customers to expand the solution without restriction. Learn more about the Komprise Architecture and watch a Chalk Talk ### What is the State of Unstructured Data Management in the Enterprise? This blog post reviews the 2021 State of Unstructured Data Management Report. The 2023 report is now available: 2023 State of Unstructured Data Management.   Also, the 2022 Report is available here: 2022 State of Unstructured Data Management Report You’ve heard it before: unstructured data is growing in ways that are becoming troublesome for the enterprise. Unstructured data is stretching the limits of on-premises storage devices and comprising increasingly larger chunks of the IT budget. It’s easy for some large companies with big budgets to keep on buying storage – but that may not be the smartest way out. Money could be spent on training, investing in new cloud apps and services and developing new digital initiatives for the next chapter of growth. The CFO might suggest that saving money for future needs is nice, too. There’s also the energy cost of continuing to invest in physical data centers– sustainable business practices are on the rise. To ascertain how enterprise IT and storage managers are thinking about unstructured data management solutions, we surveyed 320 professionals across the United States and the UK, in June. The Komprise State of Unstructured Data Management Report found a prevailing interest in analytics, followed closely by data lakes, to foster better ROI from unstructured data management. We Uncovered a Few Key Trends: Storage is expensive. Well, you knew that. But 65% of organizations spend more than 30% of their IT budgets on data storage and unstructured data management solutions. And IT managers expect to spend more on data storage this year compared with 2020. Hybrid cloud storage is dominating. Half of participants are storing their data across a mix of on-premises and cloud-based data storage. We expect to see this number rise sharply in the near future as cloud data storage options increase and become viable not just for backups and archives but primary storage too. Automated data tiering is in demand. To cope with data growth, storage leaders are looking to understand the right approach to data tiering. IT leaders will need to look for unstructured data management solutions that offer flexibility for hybrid cloud environments while avoiding vendor lock-in so that data can move freely between storage products and services without breaking the bank and creating anxiety amongst IT staffers. Cloud tiering is a strategy many enterprises are considering today. Learn more about cloud tiering use cases at Komprise.   The first Komprise 2021 State of Unstructured Data Management Report is chock full of additional data points regarding storage, unstructured data management investments and new tactics that IT organizations are considering to tame data chaos and be more proactive for business gain. Download the 2021 State of Unstructured Data Management Report. The 2022 State of Unstructured Data Management Report is available: READ NOW The 2023 State of Unstructured Data Management Report is available: READ NOW   Sign up for a Komprise Intelligent Data Management Demonstration. ### Storage and Data Management Views from Steve Pruchniewski Steve Pruchniewski is the new director of product marketing at Komprise. Hailing most recently from NetApp, he brings deep knowledge in enterprise storage and data management. We spoke with Steve about the storage industry and related technology trends. You’ve been working in the storage industry for well over 10 years. What is interesting to you about the sector and what’s kept you in it? SP: Even when I wasn’t in storage, it was the foundation for everything I was doing. My customers needed disaster recovery, business continuity, compliance, and so on and they needed storage to make it happen. Customers that innovated with storage were able to accomplish amazing things. During the time you were at NetApp, can you describe the key phases of the company’s evolution regarding impact on the storage industry? SP: NetApp was of course known for being an early leader in NAS but their focus on application intelligence versus storage for storage’s sake is what set them apart. The ability to have a database protected by a consistent snapshot, then make a clone of that data instead of having have to dump all data to a copy and move the copy, was a game changer. The explosion in virtualization really brought them to the forefront. To drive full benefits out of virtualization you needed shared storage. NetApp brought that same feature set of snapshots, replication, clones, to virtual storage. NetApp also understood the importance of object storage and made an early investment by acquiring StorageGRID in 2010. When AWS established the S3 object storage API, NetApp was well positioned to help customers with on-prem and hybrid cloud object storage. What brought you to Komprise? SP: Over the last few years I had the opportunity to work with Komprise while at NetApp and was impressed with the not only the product but also the people. Komprise’s approach to data management resonates with my experiences and what I see as challenges and opportunities to be more efficient and strategic with storage. How will cloud storage growth impact strategies and plans of the major storage vendors over the next few years? SP: It’s going to be a challenge for traditional storage vendors that don’t have a real cloud strategy. More and more applications will be managing their own storage. Think back to when an application owner would need to create a ticket for the storage team to create a LUN with a certain capacity and with a specific block size. Now IT engineers and application owners expect to provision their own storage like they do in the cloud. Storage vendors must enable that same level of simplicity on premise. I don’t believe the world will move to 100% cloud; new offerings like Azure Stack and AWS Outpost validate that there will always be on-prem gear. Storage vendors that can provide an application driven, cloud-like experience on-prem and make it easy for customers to consume the right storage are going to excel. Earlier in your career you worked as an engineer. Do you have any regrets about moving over to the marketing side of the house? SP: I loved being hands on and being able to point at something I built, but I also love to learn from others and share my experiences. The move to marketing means I get to listen to a broader group of engineers and experts and then share those stories with others. I feel like I can have a bigger impact. I still get hands on in POCs and run my own home lab and build PCs. For me marketing is the best of all worlds. In closing, what do you think will be the biggest impact of the post-Covid world on CIOs and other IT leaders? SP: I think IT leaders will need to work on reducing the general resistance to change. Covid forced us to change the way we operate, and in many ways showed us how resilient we could be. Customers don’t want to get back to the way things were; they want better. “We always did it this way” is no longer an excuse. People are ready for change. Consider how many people are changing jobs now in 2021--myself included. The hesitancy to move away from status quo has been torn down by our experiences in the Covid shutdown. What do you mean by that? SP: When you talk to a customer who has been running IT infrastructure for more than a few years, much of their architecture evolved not because of conscious decisions but rather organic growth and happenstance. Organizations inherit data centers and gear through acquisitions; legacy systems persist because it’s a pain to get rid of them. This year, we see customers taking action. With remote workforces it’s not just about consolidating office space and having the right collaboration tools. Data center consolidation and move to the cloud is a great example of the change we see in industry. Welcome to the team, Steve! ### Komprise July Update: Product, People & News At the midway point of 2021, it’s a time of reckoning for human progress. The Covid-19 virus has abated dramatically in some countries and regions, while dangerous variants persist in Asia, Latin America, Africa and parts of Europe. There is still troubling uncertainty for business and personal life, yet human inventions march on. Elon Musk’s SpaceX venture announced plans earlier this year to send the first-ever all civilian crew into the ether later this year. Other technology advancements across personal gadgets and enterprise computing have progressed without delay in the past 18 months; we’d all have had a far worse time without connectivity and our personal devices during the pandemic. In a recent Future article, Marc Andreesen talked about the impact of technology on how organizations fared during 2020: “Much of the economy kept operating, and in fact many parts of the economy started operating even better under lockdown than before.” This leads to the first of the headlines for our industry update today: Selling the code that changed it all. Can you even remember the time before the invention of the Internet? It’s fuzzy or non-existent, depending upon your age. This is why Sotheby’s late June auction of the early version of the World Wide Web source code for $5.4 million with fees, seems paltry if you consider the unquantifiable impact of the internet on our world today. To compare, a 500-year-old painting of Christ believed to have been painted by Leonardo da Vinci was sold at Christie's in New York for a record $450 million in 2017. Now let’s take a look at the latest happenings in the world of data management and storage: It’s the data, stupid. This article in Datanami explores the emerging big data conundrum: it is not so much the technology, as machine learning and AI applications and the infrastructure to run all of it has advanced greatly. Nowadays, what CIOs and data science officers need to focus on is how exactly to sort, manage and leverage the massively large and complex volumes of data: “The average number of data sources used by organizations is 27, with a high of 90, according to a recent study by Precisely. About 75% of the chief data officers (CDOs) surveyed said that dealing with multiple data sources and complex data formats is very or quite challenging, the author reports. Another survey cited in Datanami found that 96% of data professionals are at or over capacity. Automated tools for security, privacy, backups, governance, cleansing and analytics will be part of the answer to help data management and data science teams get ahead. Back it up to the cloud. This TechTarget article discusses the increasingly common use case of NAS backups to the cloud, with some advice on different ways to go about it. The author also goes into a few of the drawbacks of file backups, including data residency. When a customer can’t be certain that a cloud service provider won't store [their data] within restricted areas, they will revert to control the storage themselves, which often means going back to tape or setting up private cloud, according to Fred Chagnon, principal research director at Info-Tech Research Group, who was interviewed for the article. Chagnon’s second warning is about data egress costs, saying: "It's like a toll highway for your data; it's free on the way in, but you pay on the way out.” Read how Komprise mitigates those cloud egress fees by enabling direct access to data stored in the cloud. Speaking of backups, here’s a new book. Blocks and Files penned a review of “Modern Data Protection — Ensuring Recoverability of all Modern Workloads,” by W. Curtis Preston, aka Mr. Backup. The book aims to explain what has become an increasingly twisted area of data management, as summarized by the reporter: “The complicating rot started with the public cloud, extended to SaaS application providers like Salesforce, and then went bananas with containerised applications and backup-as-a-service. All of a sudden the backup sources multiplied and the backup targets did likewise.” Never a dull moment in storageland! Need a quote for your cloud move? SearchCIO drew upon the advice of John Burke from Nemertes Research to help IT leaders understand the costs of cloud migrations, which include preparing for the migration, the costs of cloud migration itself and the post-migration operating costs of the migrated workloads. There is quite a bit to consider here, from staffing, training and new tools to disaster recovery and the need to maintain dual environments for an indefinite period of time. Chew on this: “On average, enterprises spend about 12% more to run a workload in IaaS than they do running it in their own data center. That average encompasses both workloads on which they manage to achieve great savings and those on which costs double or triple when compared to the cost of providing the service themselves. The more work that is lifted and shifted without modification, the more likely it is that the CIO will have to budget for cost increases.” Moving to the cloud is still the right tactic in many cases and for many reasons, despite the cost uncertainties. IT leaders will need to manage the balance of organizational gain and potential cost increase in some areas to achieve the long-term goals possible by moving away from capital-intensive, non-agile ways of managing IT in the on-prem world. It’s wise to deeply analyze which cloud migration options can incur more costs than others. Compare the tools used on the migration path to the cloud. The latest from Komprise In June, Komprise released the latest version of its platform, which focused on global management with multisite controls. Read all about Komprise Intelligent Management 4.0 here in the blog. As part of our coverage for the release, The Next Platform interviewed our COO and president Krishna Subramanian, who delivered a high-level overview of the data management challenges we are working to meet on behalf of our customers. Krishna was also honored by the Silicon Valley Business Journal in June, as a Top Woman of Influence in Silicon Valley. See how else the media is covering us on our News Page. Events On July 22, we’ll be meeting with data storage experts from AWS and Pfizer to learn how Komprise helped Pfizer stop 20 years of increasing storage costs and leverage the data tiered to AWS for research, all without changing how users and applications access their files. Register here. Also this month, get ready for another fast and fun demo with Komprise product experts, whom will deliver a short demo of selecting and running data migrations and show how you can achieve 7-25x performance with your migrations. Register for the Migrating NFS & SMB Data with Komprise webinar today. You can peruse other technical sessions on our Events page, including “Cloud Data Migration and Cloud Data Tiering: Know Your Choices,” and “Cloud-to-Cloud Data Replication.” ### Komprise Execs: President/COO Krishna Subramanian on Career and Komprise Roadmap Krishna Subramanian, co-founder, president and COO of Komprise, was recently named a “2021 Top 100 Women of Influence” by Silicon Valley Business Journal. Subramanian’s history of influence in Silicon Valley includes building three successful venture-backed IT businesses and holding senior leadership positions at major tech companies, including Sun Microsystems and Citrix. She has successfully generated over $500M in new revenues, applying her industry expertise in SaaS, cloud computing and data management. Below are excerpts from an interview published in Silicon Valley Business Journal. How has the pandemic changed how you lead others? KS: Remote work can be very isolating, so I use chats and texts to replace the hallway conversations at work, and we always have our webcams on so it feels more personal. How has the pandemic changed your outlook on life and work? KS: It has taught me the importance of enjoying the little things in life — baking cookies with my daughter or sharing a laugh with a colleague. What’s your strategy for getting your business back to normal after the pandemic? KS: As a software company, we transitioned pretty easily to remote work, but we are going to a hybrid in-person model. What would you like to accomplish in the next year? KS: Scaling our go-to-market expansion and revenue momentum globally. What’s the best piece of advice you have ever received? KS: Successful entrepreneurs are not afraid to take risks, but learn to not repeat the same mistake. What is the biggest challenge facing women who want to take on leadership roles? KS: The lack of sufficient role models means each woman has to push forth and break glass, which is hard. A female CEO or business icon you admire? KS: Indra Nooyi, CEO of PepsiCo — a successful female immigrant leader who created growth with positive societal impact. How do you unwind after work? KS: Hiking and hanging out with my family. What was your first job? KS: High-performance software engineering to make parallel machines run faster. What was your favorite pop-culture discovery during the pandemic? KS: Podcasts — I am currently listening to Obama's podcast on my walks with our dog. Komprise Intelligent Data Management and Path to the Cloud for File Data Subramanian was also recently interviewed by The Next Platform, discussing Komprise’s trajectory, customer challenges and our latest product release which focused on global data management with multisite control. Here are a few excerpts: On customer challenges with data management: “Customers don’t have a single vendor, they have a heterogeneous strategy, mainly because there’s so many options to them,” said Subramanian. “They may have different flash storage, different cloud data storage, different file storage, and so on and so on and different backup vendors. Visibility into data is a big problem. IT doesn’t know what users are doing with the data and yet IT is supposed to manage all those environments.” On Komprise Intelligent Data Management 4.0: “With the multisite architecture, which is available now, enterprises can retain local control while getting centralized management across multiple sites through what Subramanian refers to as a sort of ‘uber-Director’ to help drive both performance and cost improvements. It’s an important step as large enterprise customers like Qualcomm, Pfizer, Cadence Design Systems and NYU Langone Health expand their use of the technology. “A lot of our customers started using us at a departmental level and now they’re using us across their enterprise,” Subramanian says. “They’re making us their standard and that’s where multisite helps because it covers the entire enterprise and users across all their geographies. It’s showing the evolution we are making in the market where we are becoming the data management platform for these companies. We’re also getting demand from service providers who want this capability.” Going forward, the company will continue to focus on the deep analytics in the platform, with more capabilities being announced later this year, Subramanian says. Large enterprises can have billions of data files across their global environment and need to be able to draw on that data to the find the information they need. Komprise now can take queries and create a virtual data lake based on those queries.” To learn more about Komprise’s latest product release, check out the blog. ### Komprise COO, President and Co-Founder Krishna Subramanian Honored as a Top Woman of Influence in Silicon Valley San Jose, Calif., June 22, 2021 - Komprise, the leader in analytics-driven data management as a service, today announced that Krishna Subramanian, co-founder, president and COO, was named a "2021 Top 100 Women of Influence" by Silicon Valley Business Journal. The prestigious award honors 100 remarkable women leaders and innovators, their contributions to their respective industries, and overall impact on business in Silicon Valley. This award validates Subramanian's history of innovation in the technology industry over her 20-plus year career of founding, building, merging and acquiring numerous technology companies. Subramanian has a long-standing history of influence in Silicon Valley. She has built three successful venture-backed IT businesses and held senior leadership positions at major tech companies, including Sun Microsystems and Citrix. Furthermore, Subramanian has successfully generated over $500M in new revenues, applying her industry expertise in SaaS, cloud computing and data management. "I first met Krishna nearly a decade ago while she was in business development at Sun Microsystems," said Maha Ibrahim, General Partner, Canaan Partners. "I was incredibly impressed by her aptitude and drive, as well as her ability to be technical and sales-oriented. When I saw the Komprise business plan and their opportunity with the current big data trends, I backed the company. This award further validates her leadership." "Simplifying complex problems through technology motivates me," says Subramanian. "I co-founded Komprise to tackle the two biggest problems companies have with data – managing the explosive growth of unstructured data and unlocking the business value of data. The right data management strategy has become a competitive differentiator for organizations today across all sectors and it's exciting to build a company where our mission centers on changing the way the world manages data across clouds, data centers and today's increasingly hybrid IT infrastructure." For more information about Komprise, please visit: https://www.komprise.com About Komprise Komprise is the industry's only multi-cloud data management-as-a-service that frees you to easily analyze, mobilize, and access the right file and object data across clouds without shackling your data to any vendor. With Komprise Intelligent Data Management, you are able to know first, move smart, and take control of massive unstructured data growth while cutting 70% of enterprise storage, backup, and cloud costs. www.komprise.com Media Contact: Tara Lefave Stred komprisepr@watersagency.com ### Komprise June Update: Product, People & News In the Northern Hemisphere, summer is (nearly) here, and with it renewed hope for personal life and work life as the vaccines make their way through civilization. We at Komprise are grateful for our customers, partners, our employee family, and the opportunity to make a difference in the world of IT. Below we highlight some recent trends in the data management industry as well as what’s going on at our company. Latest Happenings in the World of Data Management and Storage: Google launches 3 new cloud services: The third-largest cloud provider is making big data investments with the recent launch of new services including Google Dataplex, which Google says can centrally manage, monitor, and govern data across data lakes, data warehouses and databases from a single view. [Komprise Intelligent Data Management also  brings unified visibility to data with analytics to help IT managers make smart decisions about where to store it]. Google Analytics Hub for data access and sharing and Google Data Stream for data replication across multi-cloud environments are the two other new services as reported in Silicon Angle. Simplifying data management and movement is today no easy quest, but these new services speak to the need to make it easier and less risky while still giving IT leaders flexibility to store data wherever they wish. Decentralized storage management: Storage is going multi-cloud, as reported in Tech Target. Decentralized storage management is an “emerging technology which breaks data into fragments and scatters them across a variety of cloud-based storage devices, rather than centrally storing in any one cloud.” This can give IT organizations better reliability, guarding against outages by storing all data in one provider, and may also give CSOs more confidence on the privacy front since no provider has a full copy of the data. Deals on flash: Spectra Logic’s 2021 Data Storage Outlook reported that the flash market is in over-supply, resulting in lower prices of 10% to 15% in the fourth quarter of 2020 with further reductions expected throughout 2021. The authors findings also covered tiering strategies: “Economic concerns will push infrequently accessed data onto lower cost media tiers.” Backup failures: The point of a backup is to protect against damage or loss of the original copy. Yet 37% of backups fail, according to a survey by Veeam. Issues with storage media, backup software, infrastructure and human error are the culprits, reports ComputerWeekly. Computational storage benefits: When you need real-time analytics, a computational storage system can deliver the power boost that you need. These systems integrate compute resources (CPUs) either directly with storage or between the host and the storage and can reduce performance bottlenecks. Explains Andrew Larssen at PA Consulting in this article: “When a computer needs to do calculations on a data set, the data needs to be read from storage into memory and then processed. As storage sizes normally vastly exceed memory, the data has to be read in chunks. This slows down analytics and makes real-time analytics impossible for most data sets. By having processing capabilities directly in the storage layer, computational storage lets you avoid this.” Improving cloud-native storage: Cloud native investment choices should take into account performance monitoring, data placement, technology upgrades and high availability, according to The New Stack.  The author ends the article with a nod to the importance of observability: “This is critical because, in today’s stateful applications, storage problems are among the key reasons behind application performance problems and availability.” The Latest From Komprise Here at Komprise, we’ve been thinking and writing a lot lately about some of the challenges that organizations face in migrating workloads to the cloud as well as tiering and archiving files. The Komprise Path to the Cloud site details the choices IT managers face in these critical strategies. Related to this topic is a new white paper: Cloud Tiering - Storage-Based vs. Gateways vs. File Based. The paper started as a blog post by Komprise CEO and co-founder Kumar Goswami: What you need to know before jumping into the cloud tiering pool. Events Our most recent webinar featured an interactive discussion and live demo of Cloud Migration and Transparent Cloud Tiering with Komprise. Check out our Cloud Data Migration and Cloud Tiering webinar. You can peruse other technical sessions on our webinars page, including: Async Replication for Pure FlashArray Files, Cloud-to-Cloud Data Replication and many more data management webinars. Media Komprise has authored and been featured in a few articles lately: DevOps.com: Tiering Cold Data to the Cloud without Tears Authority Magazine: 5 Things I Wish Someone Told Me Before I Began Leading My Company Channel EYE: Komprise Makes Two Channel-Related Hires HelpNetSecurity: How do I select a data management solution for my business? ### Komprise Technical Professional Training and Certification Working in the technology business entails continuous learning, as the products and use cases are always evolving. In some cases, independent e-learning can be effective but here at Komprise we also believe in the power of the community to learn and adopt new technologies. To that end, in 2020 we launched the Komprise Technical Professional (KTP) program, an interactive, hands-on training experience and certification on the Komprise solution. KTP includes lively discussions between students on best practices and learnings related to deployment, use cases and more. We’ve already certified over 400 individuals, roughly 80 per quarter, through the KTP program. These invite-only sessions cover three major areas: Architecture Configuration and deployment considerations Operations (administration and maintenance) The approach is multidisciplinary: we aim for a blended mix of students across channel partners, alliance partners, customers and prospects. Why is this important? Customers are the best educators and evangelizers of a product. In our sessions, customers will often answer the questions which prospects ask. In turn, our partners, solution engineers and customers can learn from the concerns and challenges which prospects convey. Often, customers and prospects will swap contact details and speak after the course. Our goal is to excite participants about the technology and the opportunities that Intelligent Data Management brings to an organization, which ultimately can engender confidence and motivate for learning. And, engineers and developers love to get certifications, which they can showcase in their online profiles. Here’s an example, from a partner: Our partners find that KTP helps them be more prepared to work with customers and show value quickly. “KTP was a great use of a few hours of our time,” said Manny Punzo, KTP Graduate at Technologent. “The training gave us in-depth technology and hands-on knowledge to understand how Komprise helps with data management.” It’s also wonderful when customers attend the sessions, as it helps them continue to maximize the value from Komprise. I invited one of my customers to the KTP session last year and his organization had not yet upgraded to the latest version of Komprise; after attending the KTP, he learned about new use cases to help justify the upgrade. If you’d like to learn more about the KTP certification program, visit komprise.com/ktp or contact your account, partner or customer success manager. You can also email Training@komprise.com. Also be sure to subscribe to our blog and YouTube channel to keep up with what’s new at Komprise. ### Komprise May Update: Product, People & News It is Cinco de Mayo: a day to celebrate many accomplishments, and that includes new developments in the data management sector. Here’s what’s new: GigaOm has published a new Radar for High-Performance Object Storage, and according to Blocks and Files: “[GigaOm] claims these products are being developed because combining object storage’s scalability and accessibility with flash storage media makes it a good fit for new interactive and high-performance applications.” Funding for data protection companies has reached $652 million in the past four months across these companies: Acronis, Druva, HYCU, and Ownbackup. Cloud data storage company Wasabi announced that it raised $112 million in series C financing, bringing its total equity financing raised to $219 million. Komprise was included in the updated Gartner Market Guide for Hybrid Cloud Storage. TechTarget reported on the latest advances in DNA storage, with evidence of real-world applicability: “Twist Bioscience recently worked with Netflix to demonstrate the feasibility of DNA for video preservation.” Read on for recent product, people and thought leadership updates from Komprise. Intelligent Data Management Updates If there’s a common theme in managing IT infrastructure these days, it’s choice. Customers may today store their data in many places and in many different ways: the world of storage is increasingly fragmenting based upon specialized use cases for data management. To that end, this month’s product update focuses on Komprise Intelligent Data Management’s support for two popular vendors: Nutanix and Cohesity. You can transparently archive data to or from Cohesity and Nutanix and run analytics on data usage in those environments, such as: when last accessed, types of files stored, their owners, and their size distribution. These technologies are automatically enabled in our latest product update; simply select the option for Cohesity SmartFiles or Nutanix Files from the drop-down menu in Add File Server. (see below). As our customers continue to diversify their storage ecosystem, we aim to be right there with them, them supporting those technologies as sources and targets for analysis, data migration, data tiering and archiving. Read the blog on the Komprise product update from last month. It discusses new support for cloud NAS storage, Azure Files, expanded support for NetApp datastores and more. Expanding EMEA Leadership Komprise has two new executives joining the company’s EMEA division based in the UK.   Martin Gibbons joins as Channel Director, EMEA, and is responsible for leading channel strategy across the region. Gibbons has over 25 years’ experience working in the channel, recently serving as EMEA Channel Director at Cohesity and prior to that, at CommVault.     Ben Conneely joined Komprise in January 2021 as Regional Sales Director for the UK, Ireland and Northern Europe and was promoted to VP of EMEA in April. Conneely brings more than 20 years in senior-level sale roles in the data management industry, including at HPE and EMC.   Read the full press release on Komprise expanding its executive team. On the Web Gestalt IT profiled Komprise recently. In an article on our approach to unstructured data management, the author Zach DeMeyer wrote: “When talking about one case where a pharmaceutical company needed a method to archive and access all of their lingering data, (CEO Kumar) Goswami gives a miraculous figure. Within 90 days, the cost benefits that the company reaped by leveraging Komprise outweighed the costs they spent by implementing it. With a return on investment like that, it’s no wonder that Komprise is radically transforming the enterprise data space.”   Komprise President and COO Krishna Subramanian wrote about the new aggregator style of unstructured data management across on-premise and cloud, for ITProPortal. Here’s an excerpt: “As enterprises shift to a multi-cloud architecture, they can no longer afford to manage data within each storage silo, search for data within each and pay a heavy cost to move data from one silo to another. Whether it’s file or object data – from user-generated data to home directories, file shares, or machine and application data such as genomics, PACS imaging, seismic data, electronic design data and IoT etc., traditional storage systems were not designed to cope with the modern explosion of unstructured data and multi-cloud architectures.”   Komprise was featured as a top 20 cool vendor in data management, by CRN. “Komprise develops analytics-driven data management that helps customers know, move and control data to save costs and extract business value without disrupting access.” Check out the slideshow covering other leaders in the data management space.    Komprise was included in Storage Newsletter’s Best Storage Products of 2021 list, appearing in the Data Management category. ### EMEA Channel and Sales Leaders in Data Management Sector Join Komprise Campbell, CA – May 5, 2021 – Komprise, the leader in analytics-driven data management-as-a-service, today announced two new executives joining the company’s EMEA division based in the UK. Martin Gibbons joins as Channel Director, EMEA, and is responsible for leading channel strategy across the region. Gibbons has over 25 years’ experience working in the channel, recently serving as EMEA Channel Director at Cohesity and prior to that, at CommVault. Ben Conneely joined Komprise in January 2021 as Regional Sales Director for the UK, Ireland and Northern Europe and was promoted to VP of EMEA in April. Conneely brings more than 20 years in senior-level sale roles in the data management industry, including at HPE and EMC. As enterprises in EMEA hasten their journeys to the cloud while also managing explosive unstructured data growth, IT leaders are seeking ways to reduce spend and be more strategic with data. Komprise is showing traction in the region as organisations can see 70-80% savings in data storage by using Komprise to put the right data in the right place at the right time with intelligent cloud data migrations, cloud data tiering and archiving. Komprise is growing its EMEA presence and in November 2020, Fortune 500 technology reseller/distributor Tech Data signed on to add Komprise to its portfolio of solutions for the pan-European market. “When you are a smaller, focused technology vendor so much relies upon a strong team pulling in the same direction and I felt that through every level of Komprise management, right to the top,” says Gibbons. “Komprise is a fantastic opportunity for our partners to change the game of data management. The old practice of throwing more storage at the problem is no longer a viable financial option. Customers are eager to take an analytics-first approach to data storage and leverage new technology and cloud models to be more productive, efficient, and cost effective.” Conneely has observed similar trends in the marketplace in recent years: “It’s clear that organisations are looking to get meaningful insight from their data, reduce their data centre footprint, stop the continual three-year purchase of yet more disk capacity and accelerate their adoption of public cloud. Komprise provides a cost effective, simple solution to an expensive and complicated problem: how do I know what data can move to the cloud and how do I get it there without disrupting operations?” About Komprise Komprise is the industry’s only multi-cloud data management-as-a-service that frees you to easily analyze, mobilize, and access the right file and object data across clouds without shackling your data to any vendor. With Komprise Intelligent Data Management, you are able to know first, move smart, and take control of massive unstructured data growth while cutting 70% of enterprise storage, backup, and cloud costs. www.komprise.com. ### Kumar Goswami on the Journey to Komprise Kumar Goswami is the CEO and co-founder of Komprise. A serial entrepreneur, Goswami shares his pearls of wisdom and how Komprise was founded to solve the growing problem of unstructured data management. This article is adapted from the original version on Medium. What was the impetus for Komprise? We saw that data was growing like crazy and there isn’t a lot of innovation, especially in the world of unstructured data. We (myself, and co-founders Mike Peercy and Krishna Subramanian) talked to customers whom we had previously sold to and other CIOs. They told us: we’re drowning in unstructured data, we don’t know how much data we have, and we wish there was something that would help us understand and do something about it. The key “Aha” for us was this feedback: “Don’t just tell me about the data, make sure you can do something about it.” That struck a chord. What’s the competitive advantage of Komprise Intelligent Data Management? What makes us stand out is that we’re solving a critical problem in a way that nobody else had thought to solve. But we didn’t just focus on the technical aspects of the solution, we also thought a lot about the business perspective. We wanted to be to be able to show a prospect one view of their data and their costs so they could immediately see the potential ROI. We have a donut chart that our customers love that used to be green, like the color of money. Another differentiator is that we built the product on open standards, so the customer is not locked into our solution. This was risky, because it meant that a customer could kick us out at any time. This is contradictory in the data storage industry where the popular mindset is: “own the data, own the customer.” Our approach forces us to deliver white glove treatment to ensure we’re really solving a customer’s problem. In the process, this has made Komprise stickier with our customers. The way I see it is, if you have data you need Komprise. Startups can be draining. What keeps you going? It does get easier as a serial entrepreneur. I can tell you this time was easier than last time. When we started our second company in 2008–2009, it was a tough time economically. This time we are more proven, having had a company acquired. But it’s never easy, especially when you’re doing something new. What keeps me going is customer feedback and knowing the impact we’re having. It’s inspiring when I hear a customer say: “Where the heck have you been all my life? Where were you three years ago?” These are the moments that keep you going—when you know you’re on to something and you just need to crack it open. We want to create something beautiful and elegant that really solves a key problem. Ultimately that’s what motivates us and I know this is the same passion that motivates our employees. What advice have you received that you now wish you never followed? Venture capitalists have a lot of advice, but you have to be careful to not blindly follow it as a founder and early-stage CEO. I remember at our first company in the dot-com era, the VCs were telling us to hire ahead of need. There was pressure, but we didn’t follow it completely. We’ve always tried to grow in step with our business. Is there such a thing as work-life balance as a tech startup founder? If you’re intense, everything matters. Yet, if everything matters, you’re just hyper and it doesn’t work. So how can you be intense and still be aloof? This is my struggle. This is my journey. I’m still learning this. If you look at our life, we’re all actors on a stage. This is the script I’ve been given. Do this with gusto but remember when the script ends you can get another script. You can move on to another play. This approach helps me to not put so much imperative on this one thing being successful. You give it everything you’ve got because that’s who you are, that’s your nature, but I try to use the weekends to do something with my family. In my first startup I had no balance. There was no such thing as a weekend or vacation. I burned out. Nowadays I do turn off. You can’t let it become your life. If you’re intense, everything matters. Yet, if everything matters, you’re just hyper and it doesn’t work. Read the full interview with Jerome Knyszewski on Medium. ### Komprise Konnects: Staying Agile and Adapting at PKA A couple of weeks ago I had a chance to catch up with Paul Cohen, Sales Vice President at PKA Technologies. PKA is a great Komprise partner based in the US North East. Since 1996, PKA has been supplying IT technology services and products to Fortune 500 companies, to organizations in the public sector, large commercial accounts and to smaller companies that think big. They specialize in providing the next generation of network servers, Big Data Solutions, Enterprise storage, Cloud Services, virtualization migration, wireless, hybrid cloud, and handheld technologies that are indispensable to business. Paul has a really interesting background. He started out as a customer – working in IT at Aetna and has gone on to run sales organizations at Lucent Technologies, Avaya, Carousel Industries and Hewlett Packard. How did you make the leap from working as an IT consultant to software sales? I was at a cutover in Cleveland, Ohio in January. It was a cold, rainy night – I can remember it like yesterday. It was a miserable night. Something started to go wrong with this datacenter cutover. I called my sales rep, who wasn’t there. I called his boss, who wasn’t there. They were in Hawaii. They had won the achievers trip on everything that I had bought. And I’m looking out the window in Cleveland, stuck with this baggage thinking, “There’s something wrong with this picture. I want what they got. I want to be in Hawaii.” That Monday, I called a friend in sales at Lucent and made the case that I had 8 years of customer experience. If there’s somebody who knows how to sell, it’s a customer. I got the territory sales rep role, and I worked my way up from there. Eventually I was recruited into the channels and alliances organization at HP, which led to coverage in servers, storage, and networking. I was there when Meg Whitman came in and turned the company around with her 5-year plan. I enjoyed my time at HP / HPE. Tell me about PKA? When I joined PKA in 2018 there was a VP of technology solutions, but they did not have a head of sales. The company was about 90% HPE when I joined. In the first year we really diversified, with Nutanix, Palo Alto Networks, Qumulo, Wasabi, RedHat, Komprise and other partners, including a number of security technology companies. I was very familiar with all of the channel partners in the North East and the New York metro for sure. I understand the business models of the channel partners and how they go to market, I understand their customers, their verticals, the segments they play in, the experience of the reps and the techs. It’s all very similar. One thing I’m very proud of with PKA is that our engineers participate in the sales process – from presales, they’ll get on the call with a prospective customer, they’ll design the solution, and then they implement the solution. Most, if not all other partners have a salesperson, a solutions architect and an engineer. The engineers don’t implement what they design. We do. The containment for how we run things is so tight, there’s very little room for error. PKA was founded in 1996. We started as a consulting firm – Peter Katz Associates, which morphed into the partnership with HP. We have about 30 employees today. How do you work with your technology partners? Some partnerships are stronger than others. HPE is still a very big partner of ours. Veeam, Cisco – we can do these implementations with our eyes closed. Some smaller vendors, we’re still learning. In some cases, we’ll work with our professional services partners if we don’t have enough expertise in house. How has the pandemic changed your business? Before Covid, a lot of what we were focused on was relationship based, solution selling, logistical – supply chain, for example. We were on a good roll. I joined 3 years ago in June. The foundation and trajectory were already there. There was some tightening. There are always relationships with OEMs you’re constantly improving upon, the support, the marketing. We were doing a bit of work in digital marketing in early 2020, which turned out to be very good investments – driving traffic to our website, social media, etc. When Covid hit we saw a lot of customers in New York and New Jersey pare down or even go out of business. We had health clubs as clients. They closed. We had higher education POs in hand that were taken back because of the unknown. Things improved in the summer, but still the unknown was there. We’re a nationally certified women-owned business and a New Jersey certified small business. This has helped us with access to a lot of state and local education organizations. Large commercial accounts that have quotas they need to fill. We went where there was opportunity and need. On a normal year we’re probably 55-45 commercial to public sector revenue – this flipped in 2020. We’ve done a lot in the public sector and public-school systems 2020. We also continue to be an HP partner. During the pandemic when everyone has been working remote, there’s been a greater need for VDI, laptops, Chromebooks. In 2020, we moved to Platinum-level partner with HP as a result of this demand and our expertise. I don’t love the expression, but you can say we’re small by design. It allows us to be flexible and adapt as needed. And we’re big enough that we can facilitate very large deals. What is happening now? As I mentioned, there was a pause in many sectors. Take Higher Education as an example. There’s been a lot of questions about when and how students will return. We have a lot of flagship accounts that are still feeling their way through, not sure when they’ll need WIFI on the networking side, for example. The environment is still a wait and see. There’s not a 100% confidence that things will be back to normal in the Fall. It’s the same in commercial. We’re seeing a big spike in the demand for HPE GreenLake and the as-a-service model – from CapEx to OpEx, pay for what you consume – this is very popular now. Companies still see the need to move forward with their IT projects. They don’t want to over-provision and overspend the CapEx dollars. With OpEx, they still have a revenue flow and can pay month to month. This model of only paying for what you consume is extremely attractive right now. You right-size their environment and that’s what they pay for on a month-to-month basis. It’s not a lease. Change orders in this model become cloud like. We have done some managed service work at PKA, but we still primarily do statements of work, we’ll partner and we resell. Tell me about the Komprise partnership. Komprise came to us through HPE. Our HPE storage partner business manager (PBM) brought Komprise to us. It was part of their Qumulo sales strategy and they thought Komprise was a perfect fit to come in, assess the environment, understand hot and cold data, and offload cold data from expensive storage platform, free up the more expensive storage platform. I know this is a simplistic summary, but we thought, “Wow, that’s fantastic. Let’s see what customers this can apply to.” This is what set us on the journey with Komprise. What is your guidance for enterprise IT leaders right now? Most of our business is on-prem today with HPE, except with Nimble Cloud Volumes or Wasabi cloud storage. The traditional storage vendors are still out there. I see the hyperconverged market as hot. Nutanix, SimpliVity with HPE. The server, storage bundle – hyperconverged all in one. A single pane of glass to manage, administer, eliminate silos. Everybody wants everything simpler now. GreenLake, or AaaS makes total sense. You’re not over-provisioning, which is key. I’m all about simplicity. If an IT manager can make their life easier, that’s attractive. The cloud has been popular for simplicity, but a lot of customers are finding out that with egress fees it’s actually quite expensive. We overpaid and we don’t have control. Many IT organizations want it back and they want it more manageable – they’re bringing infrastructure back on prem. Hybrid is the way to go. There will definitely be a component of data going to the cloud. It’s all about doing it right. Find ways to move your less-critical data workloads, so it’s not all expensive and keep your most precious data on-prem, that’s the way you need to go. A lot of our customers agree with that. There is definitely a need for a tool that will help an organization know what to move to the cloud before some of these basic mistakes are made. Then we have a compelling argument for more of a hybrid strategy. Offset or augment some things (management, admin, security) and establish a happy medium. The world is going to be hybrid. How have you had to change your game over the past year? In sales, you’re a creature of sociability. We’re in New York. We want to be out. You have to adapt. When Covid hit, nobody was prepared for the new “normal”. We’ve never seen anything like this. We’re all working from home. Businesses are shuttering. People are afraid to go outside. You have to be nimble and adapt. That’s what we did. We sent people home right away. I haven’t been back to the office since March 11th. It became Zoom and Teams. We had to find ways to keep customers interested, connected and engaged online – pizza parties, bourbon tastings, beer tasting, comedians. Even that got old. We had to keep it fresh. We tried to do everything possible to keep it fresh month to month. Keeping company morale up has been key. Keeping people productive. We had to keep things fun and lively, but also disciplined. We had to find the industries that were thriving and needed what we do most – media and entertainment, healthcare, state and local government. We went to utilities – everybody still needs gas, water and utilities. We found the verticals that had stronger demand on them. Now I’ve been vaccinated. I still mask and social distance. I’m optimistic, but I still wonder what the world will look like 6 or 7 months from now. Thanks Paul! Interested in becoming a Komprise Konnect partner? Visit the  Komprise Konnect page for more information. ### Getting to the New Hybrid Reality with Ivan Abis at Total Computer Networks For this Komprise Konnects Partner Spotlight, I was able to connect with Ivan Abis, Senior Account Manager at Total Computers, an IT service provider and Komprise reseller in the UK. Ivan and his colleague Connor Young have been instrumental in bringing Komprise into the Total Computer Networks portfolio and expanding our European business. Tell me about your company and your role? Total Computer Networks is a well-established provider of IT services and products. Recognized by CRN as Corporate Reseller of the Year in 2019, we’ve got strong relationships with the key vendors as well as technical expertise that’s backed up by top accreditations. We have a wide range of IT services and a proven methodology. What really sets us apart from others in our industry are our customer relationships. We try to keep things simple. What I hear most from my customers is they love the fact that we’re reliable and genuinely care about what we do for them. As for me, I studied business and economics at university in Bristol. I have always been really interested in how technology can improve our day to day lives and how tech can transform businesses at such a rapid pace. I’m currently a senior account manager at Total and work with a variety of organizations, so I’m familiar with the types of challenges IT professionals face. I work closely with customers to understand their business objectives and introduce only relevant technology and services that will help them achieve those goals. What are the most common challenges you’re helping clients with today? Of course, COVID has really changed how organizations view IT as well as their overall IT priorities. Everyone is looking to get more value from their current investments. Cost savings, particularly related to cloud and on-premise infrastructure, are critical. Finding better ways to run applications, workloads and services in faster timescales is always top of mind. I talk to a lot of IT leaders, and it’s clear that enormous data growth isn’t being matched by growing IT budgets. This has resulted in a search for ways to make better use of their data to help gain competitive advantage and continuously improve their company operations. Tell me about the Komprise partnership? The partnership really was driven by our belief that Komprise can bring significant value to enterprise IT organizations – now more than ever given all of the budget constraints. I have found Komprise to be a great value-add conversation at the IT leadership level as it helps address many of the challenges they’re facing in the increasingly hybrid, multi-cloud infrastructure world. We are strategic partners with Dell-EMC and HPE and can work with just about any storage vendor. We also have strong partnerships in place with Microsoft and AWS. How has the cloud changed the conversation for you? A few years ago, it seemed to be cloud first by default. But, while that can be right for some organizations, it isn’t right for all organizations and a more measured approach is required. Increasingly we’re talking to our clients about a hybrid strategy – how to use both on-prem and cloud and truly understand which applications and workloads to use where. We’re seeing more and more organizations looking at their environments in this manner. The ones that are doing it right are gaining a competitive advantage and saving huge amounts on cloud and on-prem costs. Has the past year only accelerated these trends? We’ve been going through a digital revolution for some time now, but in March 2020 everything sped up. Most organizations were forced to react quickly. They had to move fast and didn’t really have time to scope the risk of these projects because they had to speedily get people remote access to key applications, so they could continue with their jobs. As the year went on, they started to see their cloud and egress costs go up. In 2021 there’s a realization that’s it’s time to think about the longevity of our IT infrastructure and that looks like a hybrid strategy in most cases. When I talk to IT leaders today it’s important to be able to review: What do you have on-prem and in the cloud today? What are the skillsets of your current team? What is your organization trying to do strategically? Will establishing a plan to implement a hybrid strategy align with and accelerate the meeting of these goals? How will this have an impact on your current budget? This approach ensures you’re not just pushing everything to the cloud and expecting great results. How would you introduce Komprise to a CIO? I always want to address an industry-specific pain point and not just come in with a generic solution. It’s important to share examples of challenges we’ve seen other organizations face and to see if they are dealing with the same issues. For Komprise, there would be a discussion about data growth, shrinking budgets and the impact that is having on the business. I would want to learn about cloud adoption and costs. And, I would want to understand the impact of not knowing what data you have, who is using it, or where it resides, and how data can be analyzed and mobilized to free up budget to spend on other areas. From there we would discuss the possibility of a workshop or demo to learn more about where Komprise could help address their storage, backup, and cloud challenges. Great stuff. What’s your personal takeaway from 2020? I live in an old cottage and the garden was the last area to get my attention. I’ve been putting a lot of effort into clearing it back, bringing light back into the garden and getting things growing again. It’s been a lot of hard work, but a great a pleasure to see this garden evolve through the seasons. You can see where hard work now can pay off in the future. You really do reap what you sow! It’s similar to what our customers are going through and has a lot of parallels not just to work but to life in general. ### Komprise, force tranquille du data management Le jeune éditeur en data management Komprise a clôturé 2020 sur des indicateurs commerciaux et financiers au beau fixe. La société multiplie les partenariats d'intégration et aussi de distribution dernièrement avec TechData pour doper notamment son activité en Europe. ### Talking Unstructured Data Management with Technologent’s Manny Punzo, Jr. Manny Punzo, Jr. is Vice President of Data Management Strategy at Technologent, an IT solutions provider focused on enterprise-class infrastructure and data center solutions. In this Komprise Konnects partner spotlight, I sat down with Manny to learn more about the evolution of his role at Technologent, the partnership with Komprise, and get his perspective on the industry. Tell me about Technologent and your role? Technologent has been in business for 19 years. Instead of just being a value-added reseller, we’ve grown into a solution integrator, delivering edge-to-edge data center, multi-cloud, automation, risk avoidance, and cyber secure data protection solutions. Over the last year or two, the conversation has shifted to data management instead of just backup and recovery or data protection. Part of the data management conversation deals with managing and protecting unstructured data, hence our partnership with Komprise. We look at unstructured data through a data protection lens. Our goal is to help organizations have better insight into their data so they can manage it, protect it, and store it more effectively. What’s changing in the world of storage and unstructured data management? I’ve been focused on data protection for 25 years. We used to talk about unstructured data as – archive it first, back it up second. But, that culture went away with the inception of the data deduplication appliances, when people didn’t care so much about the amount and type of data – they thought just de-dupe it and it was fine. Fast forward to today and there are a lot of factors that are forcing customers to think differently about their data. Unstructured data growth has and continues to be a constant challenge by itself, but when you factor in cybersecurity issues, ransomware, new data privacy regulations – all of these and other factors are driving the unstructured data management conversation– what do we have, what are we protecting, why are we storing it, and can we do something with it? Data management is a new practice at Technologent – it used to be called Data Protection Solutions. When you look at metadata management and the intersection of structured data and analytics, that’s an area where we will also be focusing on at Technologent this year. We specialize in data protection solutions, but ultimately provide an entire ecosystem for our customers around data management. Finding value for your data and then protecting and storing data accordingly – we want to provide management solutions for the entire data lifecycle. What’s changing in your client conversations about storage? The storage conversation is changing. There’s a lot of players out in the cloud. There’s software defined storage. But, if you look at the big vendors today, they’re building a lot of these capabilities into their storage. Some are talking about data management capabilities, but it becomes specific to that storage array. Our partnership with Komprise is more agnostic. It doesn’t matter what storage we’re looking at and where it resides – cloud or on-prem. And many organizations are consolidating data centers. They’re moving to the cloud and data management is limited unless you bring in a 3rd party product like Komprise. What is the best time to bring Komprise into a client conversation? A common customer pain point is, “We’re running out of NAS space/storage.” Our approach is to work with customers to re-evaluate their data and what they’re doing with it. If I tell you that 60-70% of your data is stale, why would you want to increase the cost of storing it? Of course, we can sell a customer another shelf or two of disc, but we’re trying to change the culture and mindset, so they don’t just keep growing their array, when eventually they’re going to really need to know what they have. Now is the perfect time to start having these conversations. If a customer says, 'I've got a petabyte of data on my XYZ storage, how do I back it up?' I’d say, 'Don’t back it up. I can get rid of 70% of that data so your active data is backed up and you’re not putting a strain on your data protection solutions.' Ultimately, we want our customers to make intelligent decisions on how to store and protect their data. How do you get in front of the mindset of just buying more storage or thinking you should back up all your data first? The key is to have conversations with the right audience. It’s not just a conversation with the IT manager, who might run out of space and want to call a partner to buy another shelf of disc. This continues to happen, but c-level people in the organization are concerned about mitigating risk and how to prevent the organization from being exposed. This is when you can have a conversation about understanding what you’re really protecting, how much data you have and what’s in that data. Then you’re talking about how we can give you insight into the data and how they can start managing it appropriately. I’ve been doing this for a long time. There used to be a huge gap between IT and the business. That gap is closing. Now, what IT does impacts the bottom line more than ever. If there’s a breach, the business is exposed. Legal is also involved with data privacy regulations. It’s a much more strategic conversation, beyond just adding hardware to an array. We want to help bring this all together to ensure the business knows what’s happening in IT and vice versa. Otherwise, they don’t know what they don’t know. What about the cloud? Think about what organizations are facing today in their own data centers. They’re going to run into the same issues in the cloud. They’ll be limited as to what the cloud vendors will provide as options from a data management perspective. The question is what are you going to do with your unstructured data on-prem? Are you going to lift and shift it to the cloud? It’s probably not a good idea. It’s not cheap. There are costs associated with getting data in and out. There are different tiers of storage. More and more customers are dipping their toes in or migrating to the cloud. The key is to take a look at your data to make better decisions. We’re also helping customers ensure that the teams building and running cloud native apps understand what we’ve learned in the data center world when it comes to unstructured data management, protection, privacy, and security. Tell me about the Komprise partnership? The partnership started over 3 years ago. I had a slide back then that illustrated that unstructured data problem. We needed a solution that was really friendly with NAS and open protocols and could help us give our customers an unstructured data visibility assessment. Komprise allowed us to help our customers get the visibility they needed and then customers would continue to use Komprise for on-going data management. The data protection practice was born here in the TOLA region. Now Data Management is national practice with growing momentum. It is core to what we do at Technologent and external factors mean customers need to do something about their growing volumes of unstructured data. We like Komprise because we can be independent and ultimately get the customer what they need. Tell me a bit about yourself and what you’ve learned from this crazy year? I’m the oldest in my family and we’re the only ones in Texas. Everyone else is back in Chicago. I’m a big golf fan. I love to shoot. I have a daughter who is a respiratory therapist at Children’s Health in downtown Dallas who is in the thick of things with Covid. I have a son who is a sophomore in college on a golf scholarship who hasn’t been able to play golf. Covid and everything we’ve all been through has really put things into perspective for me. If there’s one thing that this past year has taught me, it is to be empathetic. You think things are going wrong with you but you need to think of the bigger picture. It’s just a small piece of what everyone else is experiencing. My approach to 2021 is: Don’t take anything or anyone for granted. You can’t. Spend time with your family and close friends. Take care of your health. Life is too short. It’s not guaranteed. So make the most of it. Thanks for sharing your experiences and perspective, Manny! To learn more about Technologent’s Data Management practice visit https://www.technologent.com/ ### Under the Hood: Failover and Failback in Komprise Asynchronous Data Replication Last Tuesday, Pure Storage announced key updates to its Purity software for FlashBlade® and FlashArray™, including that Komprise has partnered with Pure to provide Asynchronous Replication for reliable data replication for Pure FlashArray™ file customers. Last week, I popped open the hood to explain how Komprise Asynchronous Replication works. I explained how Komprise ensures that a point-in-time copy is always safe and available, and how Komprise handles errors, failures, and schedule overruns during replications. Now, let’s examine what happens if the worst case happens… and how Komprise can help you manage either a temporary or permanent loss of availability of a FlashArray Files system. Handling Failover Let’s say that you’re using Komprise Asynchronous Replication to protect your FlashArray Files systems. Now, assume something happens to one those FlashArrays, rendering it unavailable. The first order of business will be to ensure continued business operations, by providing your end users and applications access to the data that had resided on the affected source FlashArray. This means failing over the end users and applications to the recovery copy, which is the latest replication copy for the unavailable FlashArray, located on the replication destination FlashArray. (Note: Any changes that were made to the data on the replication source after the recovery copy snapshot was taken, up until the time that the source became unavailable, will not be included in the recovery copy on the replication destination. The potential number of changes lost in the failover process will depend on the replication’s schedule frequency, which essentially defines your RPO – recovery point objective.) Once your end users and applications have been transitioned to the recovery copy, they can continue working, using the recovery copy as their primary file system. Next, you’ll log into your Komprise Director console and start “Failover” for the replication of the unavailable FlashArray. This puts the replication into the Failover state: Komprise will stop all future copy activity, since the replication source FlashArray is no longer available. In fact, you’d see an error displayed in your Komprise Director console informing you that the FlashArray are inaccessible. During failover, your end users and applications will be creating, modifying, and deleting files and directories on the recovery copy in the course of their daily work. If the source FlashArray cannot be recovered, then the destination FlashArray Files will need to serve as the live file system for your end users and applications. Since that FlashArray was likely located geographically distant from the original source, there may be increased latency experienced by your end users. If this access latency becomes intolerable, you can use Komprise’s Elastic Data Migration capability to migrate the data from the recovery copy to a new FlashArray Files more conveniently located to your end users and applications. Whether the previous replication destination or a new FlashArray Files becomes the replacement file server, you’ll next want to protect the data on the replacement server by configuring a new replication in Komprise with that file server as the replication source. You’ll accomplish this in the Komprise Director console, just as you had set-up the replication of the original FlashArray Files source. Handling Failback In a happier case, if the unavailable replication source is restored to operational condition, you can perform a failback process, where the changes that have accumulated on the recovery copy are copied back to the restored FlashArray, to enable its use again. Failback: Copy is being made back to the source. To perform failback, select “Failback” in the Komprise Director for each desired replication. Komprise will automatically configure the failback runs, to copy data back from the recovery copy to the restored source FlashArray. You may need to perform multiple failback runs to ensure that all changes that users made during failover are copied back to the source FlashArray—especially if your end users and applications continue to use the recovery copy during failback. It is recommended that a “final failback run” be performed while the recovery copy is set as read-only for your end users and applications, to copy over all last changes. This will ensure your recovered FlashArray will have all the latest data from the recovery copy. (Note: Performing failback will write data on the source FlashArray Files from the recovery copy. So, make sure that no data on the recovered FlashArrays is required, as it may be overwritten.) Finally, select “Complete failback” in the Komprise Director. This will signal Komprise to automatically restart replications of the source FlashArray Files, just as was occurring prior to the source becoming unavailable, reinstating protection for it. You can then transition your end users and applications back to the restored FlashArray, where they can find all their data and resume business as usual.   Learn more about Komprise for Pure Storage. ### Summary of 38th IT Press Tour Following the IT Press Tour a few weeks ago, we had very recently a new tour – the 38th – with again a series of vendors developing hot technologies. Several of them took the opportunity to launch product and share corporate and strategy updates: Datameer, FujiFilm, HYCU, Komprise, Nasuni, Pavilion Data, Qumulo, StrongBox Data and Vast Data. ### Pure Storage and Komprise Extend Data Management Partnership to enable File Replication Komprise and Pure Storage announced that Pure will partner with Komprise to provide Komprise Asynchronous Replication to deliver reliable data replication for Pure FlashArray file customers. As an existing partner, the expanded agreement adds data replication capabilities to the company's existing reseller offerings. ### Pure Storage Enhances Purity for FlashArray & FlashBlade Today Pure Storage announced new Purity software versions for both its FlashBlade (3.2) and FlashArray (6.1) products. These new versions will be delivered through its Evergreen program. The company is promising that the enhancements will deliver new features for every customer, furthering the company’s vision for a Modern Data Experience. ### Under the Hood: Komprise Asynchronous Data Replication for Pure Storage In case you missed the news, on Tuesday, Pure Storage announced key updates to its Purity software for FlashBlade® and FlashArray™, including that Komprise has partnered with Pure to provide Asynchronous Replication for reliable data replication for Pure FlashArray™ file customers. Let’s get into the technical details of Komprise Asynchronous Replication. Asynchronous Replication Overview Komprise provides asynchronous replication to enable FlashArray file users to protect data by periodically copying it from a source to a destination. (For full protection, it’s recommended that the source and destination file servers should be physically disparate—and even better, geographically distant.) Replication uses snapshots on a source to create a point-in-time copy on a destination. Using the snapshot, Komprise copies all directories, files, and links—including Pure managed directories—from a source to a destination, on a user-configured schedule. Asynchronous replication is run by the Komprise Elastic Data Migration engine, which provides automated, high performance data transfers, with data integrity checked at each step, and all attributes, permissions, and access controls from the source applied at the destination. Starting a Replication Configuring a replication in Komprise is simple: you select the source to be protected and the destination for the replication copies, you specify the schedule, start time, and a name, and then press Start. Komprise will start replicating files at its first scheduled run time by taking a snapshot of the source share, then copying the snapshot to the destination. In the first replication run, all the directories and files will be copied from the source to the destination. All subsequent runs will only copy files that have changed since they were last copied to the destination. If files or directories are deleted on the source, they will be deleted on the destination in the next replication run. Ensuring Recovery Copy Availability To ensure that a safe, consistent replication copy always exists on the destination storage, two shares on the destination—called Copy A and Copy B—are used in the replication process. The first replication run will copy the snapshot to Copy A. If all the data are copied correctly to the destination, the replication run will be marked as Succeeded and Copy A will contain this copy, called the "recovery copy." In the second replication run, the source snapshot will be copied to the destination – this time to Copy B. If that run succeeds, then Copy B will hold the recovery copy, and the share for Copy A will be the working copy for the next replication run. Thereafter, Komprise will periodically perform replication runs based on the configured schedule. When the recovery copy is on Copy A, then Copy B will be the working copy, and vice versa. This ensures that the replication process never disturbs the availability of the safe, consistent recovery copy. Opening the Hood: view the details of a specific replication. Handling Errors and Failures During a replication run, failures could disrupt the copy process. For example, the source or destination storage could have issues limiting availability; the destination could run out of storage capacity; the network could suffer brownouts; or a Komprise component could become unavailable. All such failures generally lead to errors in copying the data, if a replication run is in progress. Komprise provides resiliency against transient failures and issues by automatically retrying individual file errors, then collecting each replication run's set of failures and retrying them again. These represent Komprise's best effort to successfully replicate the source snapshot on the destination. If after all the automatic retries there remains any data that could not be copied correctly to the destination, then the replication run will be marked as Failed. Any existing current recovery copy will remain undisturbed, and the next replication run will attempt to replicate a new snapshot into the same destination share as was used on the previous, failed run. Handling Schedule Overruns In some situations, a replication run may still be in progress when the next scheduled run is due to start. In such cases, Komprise will allow the in-progress replication to continue, thereby overrunning the next scheduled start time. The current run will proceed, ideally to successful completion. As soon as it is finished, the next replication run will start automatically, rather than waiting until the next scheduled start time. In this way, Komprise attempts to "catch up" to the configured schedule. The rationale for allowing an in-progress run to overrun the next scheduled start time is that if a replication run is running long, it might be due to issues that would also affect the next replication run. For example, there may be too much data on the source to copy it during the configured frequency, or files that failed to copy in the current run would continue to fail in a future run. So rather than stopping the current run and starting the next scheduled run, only to have that run also run long, Komprise believes the best course of action is to allow the current replication run to proceed to its conclusion. Coming Up: Failover and Failback Watch this space for the next blog post, in which we’ll cover how Komprise provides failover and failback in the event that a replication source experiences an outage and becomes unavailable to your end users and applications.   Learn more about Komprise for Pure Storage. ### Pure Storage and Komprise Extend Data Management Partnership Komprise, Inc. and Pure Storage, Inc. announced that Pure will partner with Komprise to provide Komprise Asynchronous Replication to deliver reliable data replication for Pure FlashArray file customers. ### Pure Storage and Komprise extend data management partnership Pure Storage (PSTG +1.8%) expands partnership with Komprise, a leader in analytics-driven data management-as-a-service, to provide Komprise Asynchronous Replication to deliver reliable data replication for Pure FlashArray file customers. ### Pure Storage Enhances Purity for FlashArray & FlashBlade Today Pure Storage announced new Purity software versions for both its FlashBlade (3.2) and FlashArray (6.1) products. These new versions will be delivered through its Evergreen program. The company is promising that the enhancements will deliver new features for every customer, furthering the company’s vision for a Modern Data Experience. ### Pure resells Komprise for FlashArray file replication and disaster recovery Pure Storage is to resell Komprise software technology to replicate files from one FlashArray to another, so providing a level of disaster recovery. Shawn Hansen, FlashArray GM at Pure Storage, said in a statement today: “Together with Komprise, innovating in robust file replication will extend our market leadership in unified enterprise storage.” ### Pure Storage and Komprise extend data management partnership Komprise and Pure Storage have today announced that Pure will partner with Komprise to provide Komprise Asynchronous Replication to deliver reliable data replication for Pure FlashArray file customers. As an existing partner, the expanded agreement adds data replication capabilities to the company’s existing reseller offerings. ### Pure Storage Partners with Komprise to Deliver Asynchronous Data Replication Today, Pure Storage announced key updates to its Purity software for FlashBlade® and FlashArray™. FlashArray is the leading unified block and file storage platform for mission-critical applications and secondary workloads and today we announced that Pure will partner with Komprise to provide Komprise Asynchronous Replication to deliver reliable data replication for Pure FlashArray™ file customers. Pure Storage already resells Komprise Elastic Data Migration to enable unstructured data migrations at scale into Pure FlashBlade and FlashArray environments. The expanded strategic partnership adds data replication capabilities to the company’s existing reseller offerings. Flexibility, simplicity and performance are the cornerstones of Pure’s technology. Together with Komprise, innovating in robust file replication will extend our market leadership in unified enterprise storage. Shawn Hansen, FlashArray General Manager, Pure Storage Komprise Asynchronous Replication for FlashArray Files provides a safe, consistent recovery point for disaster recovery scenarios. Key capabilities include: Granular Data Replication: Since file data sets can be very large, customers can choose whether to replicate entire shares or specific managed directories. Replications can also be scheduled to fit customer needs. Streamlined Management: Manage multiple FlashArray replications with a single intuitive console. Automate enterprise-scale replications using APIs. Increased Availability: Minimize data loss with a consistent replication copy always available. Replicate to another FlashArray with speed and agility. View a summary and monitor the replications to Pure FlashArray in progress in Komprise. Adding Replications in Komprise: select source, select destination, and specify the schedule of replications. Dive even deeper into the details and settings of each replication in Komprise. Learn More About Asynchronous Data Replication for Pure FlashArray™ On Wednesday, March 10, at 9am PST / 12pm EST/ 5pm GMT our alliances and product management teams will be hosting a joint webinar focusing on how you can: Increase availability and minimize data loss by having a consistent replication copy always available Replicate entire shares or specific managed directories and schedule them to fit your organization’s needs Manage for all your Pure FlashArray replications from a single intuitive console, and automate enterprise-scale replications using APIs Register Today In the meantime, be sure to download the Solution Sheet to learn more about Komprise for Pure Storage. Later this week Paul Chen, Director of Product Management at Komprise, go into more details on the new Asynchronous Data Replication for Pure FlashArray™ file customers. ### Top Fastest Growing Storage Companies in 2020 This article is regularly updated when we will get more information from other companies. Yearly since 2013, we are ranking the top fastest growing storage companies in sales, either start-ups, private or public firms, here from 2019 to 2020 (calendar year or fiscal year), based on published figures only. ### 15 Key Questions To Help Tech Leaders Prioritize Projects Every leader needs to master the ability to strategically prioritize projects. This essential skill helps managers develop solid action plans for completing the many tasks on their team’s plates. When adjusting to evolving factors is the only constant, as in the highly competitive world of tech, it’s especially critical for tech leaders to have a strong handle on prioritization. ### Tech Data to offer Komprise Intelligent Data Management solutions Tech Data has signed a pan-European agreement to offer solutions from Komprise, experts in analytics-driven data management as a service. Komprise Intelligent Data Management solutions enable users to handle large volumes of unstructured data and easily analyse, mobilise, and access the right file and object data across clouds and hybrid IT environments. ### Tech Data devient le distributeur européen des solutions de Komprise Tech Data annonce la signature d’un contrat paneuropéen pour offrir les solutions de Komprise. Les solutions Komprise de gestion intelligente des données permettent de gérer de gros volumes de données non structurées et d’analyser, mobiliser et utiliser facilement les données de fichiers et d’objets distribuées sur différents systèmes cloud et hybrides. ### A New Class of Interns at Komprise At Komprise India, we started our 6-month internship program in January of 2020. The internship program offers Computer Science under-grads opportunities to work in the different engineering teams within Komprise, with exposure to various engineering aspects including backend and frontend development, file systems, manual testing and automation. This gives them a comprehensive, end-to-end grasp of our Intelligent Data Management platform, dealing both with highly scalable cloud services and highly performant file system and low-level components. They also get to experience modern engineering processes, which helps us deliver our innovative solution to customers at a very quick pace. In addition, working on our product also helps them understand how analytics-driven data management works and how cloud infrastructure is key to the story. We recruit our interns from the top engineering colleges in the country during the campus recruitment cycles conducted every year. In 2019, we visited 3 colleges and hired 4 interns and 3 full time engineers. The interns started with us in January of 2020 and went on to join our ranks as full time Software Engineers upon graduation in July. We are now happy to welcome our next batch of promising interns. We look forward to working with them and having them join us full-time once they have successfully completed the rigorous action-packed internship and have graduated from college. Despite all of the difficulties in the world, 2020 was a great year for campus recruitment for Komprise. Komprise visited 9 campuses where close to 1000 students appeared in our online coding test. We shortlisted close to 100 students for interviews, which helped us select 6 interns and 5 full-time hires, hand-picked from these 9 colleges around the country. Introducing our New Class of Interns Kashish Oberoi I am ecstatic to start my journey in Komprise, which will significantly unfold my underlying potential to build a laudable career in software development. I hope to cherish every bit of this new learning journey and contribute my bit to the Komprise family.     Someshwar Balaraman I look forward to learning new things in this internship and gaining hands on experience in the subjects I have studied till now. I am eager to know how the industry functions and I believe Komprise is the right place to gain this experience. I am very excited to join Komprise and start my career here.   Nishikanth CS I am excited and thrilled to join Komprise and start my journey. I wish to learn and to contribute to the team. I am thankful to Komprise for this opportunity.     Sahil Gupta I'm looking to learn and work on new technologies to improve my skills and for some great personal growth.     Sourav Agrawal To improve my skills by continuous learning and constantly grow as an individual by making the most of this opportunity     Aparna Mrityunjay I look forward to gain knowledge and work skills from everyone and to be able to put my best efforts and help build a great product.     We would like to welcome the interns to Komprise and wish them great success. Of course, we are currently operating remotely, but our hope is that we’ll be able to connect face-to-face in our Bangalore office sometime soon. If you are interested in knowing more about Komprise or join our internship program for the next year, please visit our careers site or emails to us at jobs@komprise.com. ### Tech Data signs European deal with data management vendor Komprise Distributor Tech Data has signed a pan-European agreement for Komprise's analytics-driven data management as-a-service. Komprise Intelligent Data Management solutions enable users to handle large volumes of unstructured data and “easily analyse”, mobilise and access the right file and object data across clouds and hybrid IT environments. ### Tech Data to offer Komprise Intelligent Data Management Solutions Tech Data has announced that it has signed a pan-European agreement to offer solutions from Komprise as a service. Komprise Intelligent Data Management solutions enable users to handle large volumes of unstructured data and easily analyse, mobilise, and access the right file and object data across clouds and hybrid IT environments. ### Tech Data to Offer Komprise Intelligent Data Management Solutions in Europe Solutions enable enterprise customers to analyse, move, manage and harness data anywhere Barcelona, Spain – February 4, 2021 – Tech Data today announced that it has signed a pan-European agreement to offer solutions from Komprise, the leader in analytics-driven data management as a service. Komprise Intelligent Data Management solutions enable users to handle large volumes of unstructured data and easily analyse, mobilise, and access the right file and object data across clouds and hybrid IT environments. The agreement between Tech Data and Komprise comes at a time when enterprise IT teams are already straining to do more with less, and the complexity of the data under management is exploding as they now have to work across multiple clouds and storage vendors. Komprise Intelligent Data Management is elastically supporting enterprise IT organisations to migrate large workloads or move data across storage classes and tiers and optimise costs, all through policy-based automation. The Komprise platform offers integration opportunities with many of Tech Data's existing storage and cloud vendors and delivers extensive insight, mobility and transparency across customers’ data estates. For customers, understanding their data, and being agile around where they store it, is a key component in enabling digital transformation, hybrid cloud and data lifecycle initiatives. “Our partnership with Komprise will enable more partners and enterprise IT organisations to take control of the massive unstructured data growth challenge they are dealing with, while dramatically reducing their enterprise storage, backup, and cloud costs,” said Craig Smith, vice president, Analytics & IoT, Europe at Tech Data. “Komprise is one of the leading providers of analytics-driven data management. Their solutions will help our partners to create solid infrastructures to manage and unlock the true potential of their customers' data.” “We are delighted that Tech Data partners can now leverage our technology to analyse, move and manage file and object data at any scale,” said Clare Loveridge, vice president, EMEA Sales at Komprise. “As the pace of digital transformation and cloud adoption accelerates, it’s never been more important to be able to intelligently manage unstructured data anywhere without compromise.” To find out more about this offering, partners are invited to contact their local business representative or send a message to iot@techdata.eu. About Tech Data Tech Data connects the world with the power of technology. Our end-to-end portfolio of products, services and solutions, highly specialized skills, and expertise in next-generation technologies enable channel partners to bring to market the products and solutions the world needs to connect, grow and advance. Tech Data is ranked No. 90 on the Fortune 500® and has been named one of Fortune’s World’s Most Admired Companies for 11 straight years. To find out more, visit www.techdata.com or follow us on Twitter, LinkedIn, Facebook and Instagram. About Komprise Komprise is the industry’s only multi-cloud data management-as-a-service that frees you to easily analyze, mobilize, and access the right file and object data across clouds without shackling your data to any vendor. With Komprise Intelligent Data Management, you are able to know first, move smart, and take control of massive unstructured data growth while cutting 70% of enterprise storage, backup, and cloud costs. www.komprise.com Media Contact: Tara Lefave Stred komprisepr@watersagency.com ### The 10 Principles of Komprise Technology In our Komprise Intelligent Data Management Architecture Overview white paper we summarize the 10 principles of Komprise technology. I’m often asked to talk about our technology and roadmap with employees, customers, partners, and prospects, so I thought it would be useful to also share these principles on our blog. Not in any specific order, they are: Simple Komprise is simple to deploy and operate—it requires no proprietary interfaces or complex infrastructure setup. Open Komprise works using open standards—NFS, SMB/CIFS and REST/S3—without the use of proprietary stub files or agents. Vendor Agnostic Komprise is built on open standards and works with any storage supporting those common standards—allowing you to keep your preferred vendors and manage data seamlessly across multiple vendor storage devices. Komprise future-proofs you and allows you to switch vendors at any time. Analytics-Driven Komprise uses the analytics from your data usage and growth patterns to provide an ROI-driven approach to optimally manage your data based on your unique data needs. Transparent Komprise moves data transparently which means it is fully accessible from the source as files, exactly as before, and the data is accessible as files or objects from the target. This ensures users are not disrupted and can still find the cold data where it was originally located on the source. Komprise maintains native access to the data on the target, so you are always in control of your data no matter where it resides. Elastic Scaling Komprise scales elastically on demand—there are no central bottlenecks, databases, or servers to limit scalability. No Lock-In Data is always accessible from your source storage and your target storage, even if Komprise is taken offline. Data Management-as-a-Service Komprise does not require dedicated hardware or upfront infrastructure investments. Komprise runs as a hybrid cloud service or as a fully managed unstructured data management-as-a-service (DMaaS) solution in the cloud. Non-Intrusive Komprise analyzes and manages data in the background, with no impact to storage or network performance, and stays outside of the hot data and metadata paths. Adaptive Komprise throttles back as needed when your storage or network are in active use, so you never have to monitor or schedule when Komprise runs. Our Tech Field Day chalk-talks on the Komprise architecture and Komprise Transparent Move Technology are available here. We also recently published a Why Komprise solution brief on our website, which highlights many of these points. ### Unstructured Data Management Challenges In our Komprise Intelligent Data Management Architecture Overview we review the principles of Komprise technology, the 6 components of the Komprise architecture, and how it works. In this post we’ll cover some of the common unstructured data management challenges we talk to our customers and partners about every day and the benefits of analytics-driven data management-as-a-service from Komprise. Massive Growth Within Flat Budgets Data is growing fast—nearly 90% of the world’s data was created in the last two years and enterprise data is doubling every two years. The challenge is how to retain all this data (as much of it is valuable) while keeping within flat budgets. Enterprise IT organizations need to do more with less by using the right mix of cloud and on-premises file and object storage options. Right Data, Right Place, Right Time Over 60 to 90% of enterprise data is cold and infrequently accessed within months of creation, but is often stored, backed-up, replicated, and managed in the same way as active data. This is because there have been no easy approaches to systematically identify, move, and access data without tedious, manual processes and disrupting users. Cold data storage costs are even higher with the cloud given the ongoing OpEx costs of storing and retrieving data from the wrong cloud tier. A systematic way to continuously understand data usage and dynamically move the right data to the right place at the right time, without impeding user access, is required. Legacy Data Management Challenges Legacy data management solutions are too costly and complex, and cloud tiering solutions provided by storage vendors are too limited, proprietary, and create unexpected cost overruns. Costly – Require expensive enterprise licenses and upfront infrastructure investments. • Complex – Multiple moving parts such as storage agents, hardware, software, and databases to manage. Brittle – Static stubs can be corrupted or orphaned, agents need to be kept up to date as the storage evolves, and detailed rules need to be specified and managed. Disruptive – Performance slowdowns due to the management overhead they generate and user disruption by not maintaining transparent access to moved data. Cloud Tiering and Storage Pool/Tiering Challenges Cloud vendor tiering solutions don’t provide enough flexibility to meet your unique needs and only work on a subset of your enterprise storage. Managing multiple cloud data archive and tiering solutions creates unnecessary costs and complexity. Limited – Intelligent tiering and cloud tiering solutions are usually limited to a few simplistic policies (e.g. any data not used in 60 days) and limited to a few storage choices. Enterprises need a robust data management solution with flexible policies to address different groups and their unique needs. Proprietary – Tiering solutions move data at the block level, so the data cannot be directly used from a lower-cost tier such as the cloud. The cloud is used as a cheap under-the-hood storage tier, meaning that cold data cannot be directly accessed in the cloud nor can it be used with native tools in the cloud or 3rd party applications for AI, ML, or compliance use cases. Cost Overruns – Cloud tiering and storage tiering or “Storage Pool” solutions have unexpected costs since they are proprietary and access typically causes rehydration of the cold data and results in expensive egress costs from the cloud. Difficult to Manage – Although storage vendor-based tiering may sound like a simple choice, they are not suited for tiering to the cloud and will lead to performance degradations of the file system. These solutions are limited in the storage they support, create proprietary silos, do not provide visibility into the data that is being tiered, and they lock you in. Switching to another vendor in the future is not simple. Review your cloud tiering choices. Why Komprise? Komprise puts you in control of your unstructured data—not your storage, cloud, or backup vendor, so you can know, move, manage, and harness data anywhere. With analytics-driven data management-as-a-service from Komprise you get: Data Insight: Know before you buy more storage, backup, or cloud infrastructure. Data Mobility: Migrate, archive, tier, and move the right data to the right place at the right time. Open Standards: Work across any storage using standard protocols and no proprietary interfaces. Cloud Native Access: Use data as files or objects anywhere without lock-in, without expensive rehydration. In the next post, we’ll review the principles of Komprise technology. Be sure to download the Komprise Intelligent Data Management architecture overview white paper to learn more. ### Vendors Predictions for 2021 Just a few days after the publication of a vendors compilation about the last storage decade, here is the opinion of 64 vendors for the coming year. It is particularly interesting as we continue to live in a stressful period under the Covid-19 threat. ### Unstructured Data Management Glossary of Terms What started as an internal document to help new employees get up to speed on our market has become a useful tool on our website for customers, partners, and prospects - the Komprise Glossary of Terms for Unstructured Data Management. It's still a work in progress and constantly growing, but I wanted to highlight some of the most popular entries. We want this to be a helpful resource. You can suggest additional terms to be added on Twitter. Here are the most popular pages in 2020 based on page views: Metadata Secondary Storage Digital Business Data Management Policy Data Archiving Data Migration Cloud Storage Gateway Adaptive Data Management Cloud Data Management Network Attached Storage It will be interesting to see how this changes in 2021 and how this aligns with our hybrid, multi-cloud data management predictions. ### Looking to the Future of Tech: Predictions for 2021 It's no surprise that 2020 was a year of evolution for all sectors, due to the COVID-19 pandemic. Many tech leaders had to pivot their strategies by shifting to a largely remote-work model and look to new markets that were impacted by unforeseen economic impacts. ### Komprise Year in Review: Unstructured Data Management in the Spotlight According to this Forbes State of Data 2020 summary, IDC predicts that 59 zettabytes is the amount of data created, captured, copied, and consumed in the world in 2020, up from 33 zettabytes (33 trillion gigabytes) in 2018. In fact, IDC’s Global DataSphere forecast predicts: The amount of data created over the next three years will be more than the data created over the past 30 years, and the world will create more than three times the data over the next five years than it did in the previous five. The need for an analytics-driven approach to unstructured data management across disparate data silos has never been more clear. In our last post, we reviewed our multi-cloud data management predictions for 2021. Now, for a roundup of Komprise news in 2020. It was a difficult year for everyone and we are extremely grateful to our customers, partners, and employees for helping us achieve strong growth despite the challenges of the pandemic. January and February The first 2 months of 2020 seem like a totally different time. Remember trade shows? In January, the Komprise team was a sponsor at the Storage Tech Field Day. You can watch the videos of our founders and SVP of engineering reviewing our technology and interacting with industry gurus here. March 2020 Komprise Elastic Data Migration announced. As noted in the press release, customers can migrate data across heterogeneous storage and cloud environments more than six times faster than the status quo—at less than half the cost. Businesses will benefit most when migrating data between their Network Attached Storage (NAS) and the cloud, or from cloud to cloud, because it’s optimized to address latency across wireless area networks (WAN). With the announcement, a white paper was published to highlight the performance benefits, which Krishna Subramanian summarized in ChannelBuzz.ca: Over a LAN, we were 27 times faster than rsync, and over the WAN simulation, we were much faster than that. While Komprise completed the task in minutes over the WAN, rsync had not finished after 48 hours. Komprise was also able to cut the degradation due to higher latencies on WAN by 250%. June 2020 Komprise Intelligent Data Management extends cloud data management capabilities. Building on Komprise Cloud Data Growth Analytics, which had been announced at AWS re:Invent 2019, the press release noted that new updates allow enterprises to save at least 40% on their public cloud spend with intelligent cloud migration, transparent data archiving and data tiering based on analytics-driven user-defined policies across cloud storage classes. Dan Frith noted in his Komprise Announces Cloud Capability review: There’s support for all unstructured data (file and object), so the benefits of Komprise can be enjoyed regardless of how you’re storing your unstructured data. It’s also important to note that there’s no change to the existing licensing model, you’re just now able to use the product on public cloud storage. In the article Komprise adds another brick to hybrid cloud data management wall, Chris Mellor from Blocks & Files noted: Komprise is building a data management abstraction layer that covers on-premises and the three main public cloud environments. Its goal is to enable customers to tier files to/from on-premises file stores to on-premises object stores and public cloud object stores. The article included this diagram: Blocks & Files diagram: Komprise Intelligent Data Management June was a busy month for Komprise. Two other announcements of note: GigaOm announced that Cohesity, Komprise and Commvault lead the unstructured data management pack. Check out our summary of the 2020 GigaOm Radar for Unstructured Data Management and our position. 2020 GigaOm Radar for Unstructured Data Management New VP of EMEA Sales, Clare Loveridge, joins Komprise. With the announcement, Philippe Nicolas from Coldago Research noted: It’s no surprise that Komprise receives strong adoption by users and partners, and especially in EMEA, for its comprehensive, analytics-driven data management platform, illustrating businesses’ constant and rapid innovation with new features, protocols and platforms. It confirms that customer needs, such as data analytics, tiering/offloading, replication, migration and cloud-native access, represent key functions to optimize the enterprise data landscape and streamline associated costs. July and August Over the summer, our product team continued to deliver: In June, we delivered an up to 500% Elastic Data Migration performance increase on NAS migrations over NFSv3, including kcp and Elastic Shares. The June release also included reduced memory usage in our Observer, faster Observer start-up, and less VM memory requirements. In July, we delivered a 500% performance increase on Plan move and copy over NFSv3. We also launched archival within Amazon S3 to lower cost storage classes including archival to Glacier and Glacier Deep Archive. In August, we delivered additional source and target support, including Qumulo File Fabric and Pure Flash Array File Services as sources as well as support for SNMP enabling users to monitor their Komprise deployments using the standard SNMP protocol. There were major enhancements for UI scalability (handling a large numbers of shares) as well as support for enabling access logging on Amazon S3 buckets from the UI in bulk when analyzing object data in Amazon. August also saw the launch of the Komprise free trial to analyze usage, costs and growth of object data. We also hosted a series of webinars with our alliance partners: AWS: Efficiently Your Hybrid Cloud Data Strategy IBM: Your Data is Doubling, Your Budget Isn’t Wasabi: Cut 80% of NAS and Cloud Costs with Wasabi and Komprise Cloudian: Komprise and Cloudian Got the Memo, but Data Didn’t: Contain Rising Storage Costs. September Mike Munoz joined Komprise as our Chief Revenue Officer. Mike shared his thoughts on the company, the enterprise data storage market, and how we think about partnerships in this interview with eChannel News. Our September Intelligent Data Management update was a big one. A few highlights included continued performance gains for Komprise Elastic Data Migration, expansion of our support for Google Cloud, AWS and Microsoft Azure, better dashboards and reporting and many ease-of-deployment updates. You can get more details in this blog post. October We announced Komprise addresses demand for file data management in the cloud with new capabilities for Microsoft Azure Files and Azure NetApp Files. Additionally, Komprise was selected as a co-sell ready partner for Microsoft Azure with Komprise Intelligent Data Management available on the Microsoft Azure Marketplace. Once again, Chris Mellor from Blocks & Files included a useful visual in his article: Komprise identifies cold Azure data and sends it to Blobs: Blocks & Files: Komprise Cloud Data Growth Analytics concept with expected targets. Other October highlights: Our customer Carhartt was featured in this ComputerWeekly spotlight: Carhartt shifts old data to the cloud with Komprise. We kicked off our popular TechKrunch webinar series for unstructured data management practitioners. We hosted a webinar with IBM: How to cut spiraling NAS costs: IBM and Komprise data management for the new era. Our CEO Kumar Goswami participated in a GestaltIT podcast: You Can’t Afford to Use Cloud Storage. We were a Sapphire sponsor of the NetApp Insight 2020 virtual event. Get more details in our Q3 2020 Intelligent Data Management Innovation Update. November We announced Komprise Expands Intelligent Data Management Partner Program to Accelerate Cloud Data Management. In 2020 over 50 new partners were on-boarded and hundreds of partner employees went through our Komprise technical training and certification. We are committed to making our partners and customers successful by simplifying the way unstructured data is stored and managed data and we are focused on building strong partnerships. We qualified as Amazon Web Services (AWS) Outposts Ready, which means that customers can now simplify migrating and managing file and object workloads on AWS Outposts. Manage file and object data on AWS Outposts with Komprise And at the end of the month Ransomware Protection with Support Amazon S3 Object Lock was introduced, which allows customers to store objects using a Write-Once-Read-Many (WORM) model. Once Komprise archives data into such an Amazon s3 bucket configured with Object Lock, the data cannot be overwritten or deleted, providing file retention that meets compliance regulations and protects data from being encrypted by malware or ransomware. December It’s been another busy month for our product team: We introduced usage-based analytics and cloud migration for Microsoft Azure We announced Komprise now enables data management for public sector in AWS Marketplace for AWS GovCloud And we’ve been working on some great new capabilities that we’re excited to be announcing in early 2021. Stay tuned! So you see, it’s been a very busy and productive year. Unstructured data volumes continue to grow and the Komprise Intelligent Data Management platform capabilities continue to deepen and expand across hybrid and multi-cloud environments. Thanks again to our customers, partners, employees for your contributions to our mission of transforming enterprise data management with intelligence. We look forward to working with you in 2021. Happy New Year! ### TechKrunch Review: The “Confine Function” – What the Heck is it? Last week we squeezed in our last TechKrunch of the year and it featured what Randy Hopkins calls, “the best kept secret at Komprise.” Well, it’s a secret no more. In about 10 minutes, Randy and Glenn Speer reviewed what it is, how to set it up and how to use it – and of course it was mostly done through a Komprise demonstration, not slideware. So what is the Confine function? In Randy’s words, “it is a function that allows users to go out and find that hoarded data that’s been sitting out there for a long time that is just meaningless. Maybe it’s student data at universities that’s been sitting there loitering for 10 years on primary storage. It could be old log or trace files – things that you want to clean up and you want to do it in a declarative programing paradigm, meaning you set it once and from then on any old file that reaches certain characteristics / restrictions will be automatically be cleaned up.” Glenn then jumps right into the demo. As he notes, “sometimes there are candidates for deletion rather than archive.” He goes right to the Usage tab to zero in on data for which you don’t want to use our patented transparent move technology (TMT) to archive. You just want to get rid of it. The Komprise confine function helps you do that. Figure 1: Usage Tab View Within Komprise Intelligent Data Management The Usage tab view goes beyond just seeing the age of the data, allowing you to see location, ownership, size, etc. In this demonstration, we’re going to focus on file type. Right away you see you have a lot of cold (blue) data sitting on virtual machines (old OVAs for example). Glenn focuses on log data, which you generally don’t need to keep around for more than a month. Here you can see that 90% of the log data hasn’t been accessed in over a month, which means it’s a good candidate for deletion. The next step is to use Komprise Deep Analytics to understand where this log data resides. He runs a query by file type to get list of the top 1000 files. Figure 2: Using Komprise Deep Analytics to Run a Query by File Type Figure 3: Summary Analysis of Last Accessed Time by Filtered File Type He then digs deeper to get to space used by top shares to see which location has the most cold data. Figure 4: Analysis of Space Used by Top Shares and Last Accessed Time Now he returns to the Plan tab, where multiple groups have been configured with different policies. One is for Confine. In the Confine group, he’s already set up a Source Share for the cold log files. Here you can migrate, replicate, archive or confine data. He sets up the Confine policy to be not accessed in over 1 month. Based on this policy, he sets it up to move data to a new folder he calls .KompriseTrash and he notes that the dot is important as it hides the folder from normal users. Figure 5: Plan Tab, Choosing to Move the Group Identified as Confine Files Not Accessed in Over 1 Month to the Folder .KompriseTrash He then goes out to the file directory to verify that yes, there is a lot of old log file data there that should be removed. Running the Confine function will move the data from this folder to the trash folder. Now this will run daily, weekly or however you configure it. To demonstrate in real time, he goes to his test share logs and tells it to transfer. Figure 6: Komprise Test Share Logs, Choosing to Transfer the Identified Confine Data by Policy to .KompriseTrash The files have been moved to the new .KompriseTrash folder, which by default is hidden in Windows. Figure 7: The files have been moved to the new .KompriseTrash folder, which by default is hidden in Windows With the Confine function, we’re making it easy for you to remove the data, but we are not doing the actual deletion. From here you can automate how frequently you delete your data. We just want to make it easy to move it out and get ready for that final step. That’s it. That’s the Komprise Confine Function in 10 minutes. It will go out and capture any old text, log or whatever files that meet your specific criteria, driving efficiency and on-going cost savings. Questions? What happens if the cold file is a symbolic link? If we come across a symbolic link when we’re running a Confine function, we skip that file. Same thing if we do archive or any function. We don’t follow the link. We essentially ignore it. How does an admin clean up the Komprise trash folder? There are a lot of ways you can do this. It’s just a folder. You can set up a cron job to clean it up periodically. You can set up a backup job and clean it up after that. You can do it manually after you inspect it. Customers typically start manually to ensure they can trust that Komprise is finding the right data and then they’ll set up a process to do it programmatically. Thanks for another great TechKrunch, guys. Cheers! Figure 8: Cheers from Randy Hopkins and Glenn Speer!   View this session on-demand as well as all of our TechKrunch sessions here. https://www.youtube.com/watch?v=qwXOd-xIrbo ### Quantifying the Business Value of Komprise Intelligent Data Management The world will store 175 Zettabytes of data by 2025, according to oft-cited research from IDC. It’s estimated that 463 exabytes of data will be created each day globally: the equivalent of 212,765,957 DVDs per day. IDC also predicts that in 2025, 80% of data will be video or video-like and 79.4ZB of data will be created by almost 42 billion connected IoT devices. You may already know about and are trying to deal with the massive growth of unstructured data in the enterprise, but did you know that 60-90% of enterprise data is cold data? Yet, this cold data is often stored and managed in the exact same way as your hot or active data—which is highly inefficient and costing organizations millions. The good news is the problem of so-called “data hoarding” is gaining awareness. The bad news is that it may actually be contributing to global energy wastage. In this post we summarize how to quantify and track the ROI of analyzing and managing cold data efficiently with Komprise. Organizations across industries are running Komprise Intelligent Data Management to cut 50%+ of the overall storage costs, and 70%+ of cold data costs by: Identifying hot and cold data across NAS, file and object storage. Providing interactive ROI analysis of how much an organization saves, based on different data management policies. Transparently archiving (or tiering) cold data to cost-efficient secondary storage such as cloud or object storage. Delivering transparent access to archived data from the source, so there is no disruption to users or applications. Providing a cost-efficient ransomware defense strategy by eliminating cold data from the active footprint. Scaling on-demand to handle massive data growth via a seamless, scale-out architecture that simply adds virtual appliances as the amount of data grows without any dedicated infrastructure. Providing an adaptive data management architecture that throttles back as needed to run in the background, without impacting storage or network performance. Figure 1: Komprise Intelligent Data Management Analysis of Data Usage and Savings Over 3 Years   DOWNLOAD THE PDF The TCO shown here is an aggregation of data from 10 organizations using Komprise to manage data across NAS, object storage, and cloud data storage. The data footprint in the organizations varied from 100 terabytes of NAS data to 10 petabytes of NAS data. Typical NAS storage used were NetApp, Dell EMC Isilon, and Windows File Servers. The customer profiles are as shown in the table below: Figure 2: Demographics of Organizations Used in TCO Analysis Financial Benefits and Savings of Komprise Data Management Reducing the Cost of Cold Data Storage by 70%: By identifying and transparently archiving cold data on secondary storage, Komprise reduces annual data storage and backup costs by an average of 70%. Reducing the Cost of Backups: Backups are getting more expensive, backup windows are getting longer, and backups are hard to manage due to of the sheer volume of unstructured data. By moving cold data out of the actively managed footprint, Komprise shrinks the backup footprint, making backups run faster, reducing backup licensing and reduces storage costs. Figure 3: Cost Metrics of the TCO Analysis – All costs are over a 3 year period per TB Komprise Cost Savings Analysis Based on the average data storage costs from the cost metrics above, on a 4PB NAS environment with a 30% year-over-year growth rate, Komprise saves customers an average 57% of overall storage costs and over $2.6M+ annually. Figure 4: Detailed Cost Savings Breakdown Going Beyond Cost Savings In addition to the operational savings, customers report several benefits of using Komprise, including: Plan Future Storage Purchase with Insight and Visibility: With an analytics-first approach, Komprise provides much-needed visibility into how data is growing and being used across a customer’s storage environment. IT no longer has to make critical storage capacity planning decisions in the dark. Optimize Storage, Backup, and DR Footprint: Komprise reduces the amount of data stored on Tier 1 NAS so customers can shrink backups, reduce backup licensing costs, and reduce DR costs. 27 Times Faster Cloud Migrations: Auto parallelize at every level to maximize performance, minimize network usage to migrate efficiently over WANs, and migrate more than 27 times faster than generic tools across heterogeneous cloud and storage. Risk Mitigation: Since Komprise works across storage vendors and technologies to provide native access without lock-in, organizations reduce the risk of reliance on any one storage vendor. Affordable Ransomware Defense: File tiering with Komprise eliminates cold data from the ransomware attack surface while giving users and applications seamless access. This shrinks your attack surface and reduces your ransomware defense costs while supporting technologies like tamperproof snapshots work seamlessly. Read the blog. Figure 5: ROI on 4PB of NAS, 60% Archived by Komprise In an increasingly hybrid, multi-cloud IT infrastructure environment, the need for visibility across data storage and intelligent data management to efficiently handle the pace of data growth is no longer just a nice to have. Komprise provides a simple to adopt, scalable, storage-agnostic, ROI-driven solution. Importantly, Komprise is non-intrusive, working in the background to optimize your storage and data management spend—without any changes to user or application access. I hope you find this ROI overview to be useful. Be sure to schedule a demonstration with our team if you’d like to learn more. Don’t compromise. Komprise. Learn more about data hoarding. ### New Komprise CRO reworks Go-to-Market to strengthen channel The big changes involve making the whole Go-to-Market strategy more systematic, and include the addition of an internal sales team to work with partners, but some enhancements have been made to the partner program as well to make it more effective. ### TechKrunch: Using Komprise Analysis, Deep Analytics and Data Modeling In this series of posts I’ll provide a summary of our TechKrunch sessions, which are designed to be informal, interactive sessions with our technical gurus on the topics that matter most to our customers, partners, and prospects. In this session Randy Hopkins, VP of Systems Engineering at Komprise, and Glenn Speer, Senior Systems Engineer, discuss and demonstrate how we use take an analytics-first approach to quickly provide cold data visibility so you quickly understand what you have and make better decisions on how to offload expensive network attached storage (NAS) data. Glenn jumps right into the Komprise demonstration, which starts with an out-of-the-box Komprise Analysis visualization of your hot and cold data. At a glance you can see that 50% of your data is cold – in other words it hasn’t been accessed in over a year. You can easily change policies to move data, leave links behind, allow users to transparently access data that hasn’t been accessed in over a year or two years, etc. The focus of this view is on overall cost savings and setting global policies. But for most enterprise organizations, making global data movement changes requires special permissions and is no easy task. With Komprise, you can get much more granular quickly to get a fast return. For example, you have arrays that are getting full and you need to move data out quickly. With Komprise you can find that data and move it without setting global policies. In the Usage tab, you see many more attributes of the data beyond age – location, top shares, top directories, ownership, file types, etc. So, what do you do next? Deep Analytics. Quickly create a custom query to dig into the issue you’ve identified with log data. From there you can dig into where the data resides and take action. In the next demonstration, Glenn starts with a visualization of space consumed by your top shares. You can delete it. You can archive it. There are lots of options with Intelligent Data Management from Komprise. He then looks at space consumed by top owners and talks about the common challenge of Zombie Data – data owned by users no longer with your organization. Quickly archive and free up space right away. Another common example is virtual machines that are no longer being used. The screenshot below shows a hot VM data example, but that’s not usually the case. Someone has used an OVA to stand up a lot of systems. They left the OVA out on the NAS device and forgot about it. Sound familiar? And finally, Glenn was asked about just being able to see how much of your data is hot. I’m thinking of moving to an all flash storage platform. When you’re buying a brand new, flash array, you don’t want to put expensive cold data on it. Many of our customers use Komprise to migrate just the hot data with links to anything cold to their brand new all-flash array. In a future TechKrunch session the gurus promise to cover tiering. You can check out the video below on our YouTube channel (be sure to like and subscribe) or on BrightTalk. Next week’s session is How to Access Archived Data in the Cloud. ### Inspirational Women Leaders Of Tech: Krishna Subramanian of Komprise On The Five Things You Need To Know In Order To Create A Very Successful Tech Company To create a successful tech company, you should start by asking yourself the following questions: Who has the problem that you’re trying to solve, why is it important to them, why can you solve it better than anyone else, and why now. ### Is Zombie Unstructured Data Haunting You? It’s that Halloween time of year again, which brings to mind all types of scary matters such as witches, ghosts, goblins, and ZOMBIES! But, we all know that Halloween is not the only time of year we should be worrying about these things. (They are all real, right?) Whether you believe in these scary apparitions or not, one thing that is very real is… ZOMBIE DATA! (cue the scary ch ch ch, ah ah ah Friday the 13th sound effect filling in zombie zombie zombie, data data data for full affect) What is ZOMBIE DATA? Different people and companies will have different definitions, but all will agree that ZOMBIE DATA is data that is no longer needed, but is still hanging around. In fact, you probably have a ton of Zombie Data on your computer, laptop, tablet, or phone right now – old pics of nothing, bills that you already paid and don’t need anymore, crappy recipes that you will never make again, stuff like that. Just like its namesake, ZOMBIE DATA is notoriously hard to kill and can cause you pain ($$$). At Komprise, when we talk about ZOMBIE DATA we are referring specifically to network attached storage (NAS) file data that is owned by users that are no longer with the company. Most of the time, this data is sitting on a company’s expensive NAS array taking up not only expensive primary storage space, but likely being replicated and maybe even backed up, so storing this ZOMBIE DATA comes at great expense to customers. Dealing with Zombie Unstructured Data Komprise is like the double-tap killshot for ZOMBIE DATA. We can help companies: identify data owned by users that are no longer with the company (see Diagram 1), show you exactly where that data resides (Diagram 2), and allow you to either move that data out to a cheaper tier of storage (think AWS, Wasabi, really any NAS or S3 / object target on-prem or off) or move to a trash folder to be deleted for good. Diagram 1: See those users listed at SIDs instead of names and long scary dark blue lines? That’s OLD crusty ZOMBIE DATA – Users are no longer active and the data hasn’t been accessed in over 3 years (!) Now, that IS scary! Diagram 2: Boo! Easily find and locate Zombie Data with Komprise Deep Analytics. We've got your back. For a deeper look into ZOMBIE DATA in action and how you can take control, watch our TechKrunch session on data analytics and modeling. Also check out this Potential Duplicates demo. Want to learn more? Get in touch. ### Konnecting with the Komprise Team This Fall It has been a busy few weeks here at Komprise and we are just getting started. With all of the digital events on the radar, we want to make sure you know where you can Konnect with the Komprise team.   Virtual Events We’re excited to be a Sapphire sponsor of NetApp Insight 2020 on October 26-29. Komprise allows NetApp customers to plan how to leverage the right mix of NetApp technologies to save costs. Find and offload cold data from any NAS and cloud to NetApp StorageGRID object-based storage and cut more than 70% of your costs without affecting user experience. With Komprise, you can quickly and reliably migrate petabytes of data from other NAS and clouds to NetApp storage at a third of the cost and easily move hot NAS data to NetApp ONTAP flash and S3 object data to NetApp StorageGRID. Learn more about Komprise for NetApp. Visit our virtual booth at the conference and you’ll be automatically entered to win an Oculus Quest 2 All-in-One Advanced VR System. You can also enter to win here. Webinars For webinars, live and online demos, and interactive tech talks be sure to subscribe to our BrightTalk Channel. Our webinars typically introduce new topics, challenges, and market dynamics and we often have partners participate. This week we have a great webinar scheduled with our partner IBM: How to Cut Spiraling NAS Costs: IBM & Komprise – Data Management for the new era Bruce Rakowski, Client Technical Specialist, IBM will be discussing this important topic with Krishna Subramanian, our president and COO. You’ll hear how customers are saving over 70% with IBM and Komprise and in the live demonstration we’ll show you a new way to manage your data and save: Understand data across your storage silos before making decisions Transparently archive cold data seamlessly to IBM Cos Migrate data quickly and reliably Build data lakes across all storage Create low cost, air-gap copy and protect it on IBM Cos Access archived data directly—no middleman or rehydration On October 22 we’ll be hosting a webinar for our European customers, partners and prospects:  How to Accelerate Cloud Data Migrations and Cut Cloud Costs And on November 5, we’ll be focused on our Microsoft Azure migration capabilities: Accelerate Cloud Data Migrations to Azure with Komprise Be sure to register and join us for these online events. TechKrunch Demos Last month we kicked off our TechKruch series, which digs into popular topics that we get asked about regularly by customers, partners, and prospects. These 20-minute sessions consist of a brief topic overview and demo, followed by an interactive Q&A session. This is a great opportunity ask questions and connect with the experts: On-Demand: TechKrunch: Using Data Analytics & Modeling October 15, 11am PDT: TechKrunch: How to find hot data for migrations Nov, 4, 11am PDT: TechKrunch: How to access archived data in the cloud   Check out our Events page for any additional information. We look forward to Konnecting! ### #1 Leverancier van bestandsanalysesoftware Our ears are burning—and we couldn’t be happier. We have been named the #1 vendor in File Analysis Software by Gartner Peer Insights. Nothing means more to us than hearing from satisfied customers: Highest Overall Rating from Customers: Our customers rated us at 4.8 out of 5 stars. 89% said they would recommend Komprise Intelligent Data Management to their peers. Highest Ranking in Services & Support: 4.9 out of 5 stars, which isn’t that surprising given our unusual dedication to customer service, but it’s always great to hear. Peer Insights is Gartner's peer-driven ratings and reviews platform for enterprise IT solutions and services covering over 300 technology markets and 3,000 vendors. Every review is verified before publishing to ensure only completely authentic insights are gathered. From Systems Administrators to Infrastructure Managers spanning multiple industries, feedback spanned from compelling POCs and ease of implementation to measurable results. Customers valued the insight into their data with analytics, the storage costs they’re saving, and the data migrations that are running much better. We also got some insight on areas to enhance, which we value just as much, and our team is hard at work. Below are a couple excerpts: "Komprise is a no-brainer!" "If you have data, you need Komprise. Not only does Komprise take action on the files that haven't been accessed since they were created, but it takes action transparently. No more end-users calling up wondering where their data is. They can keep their folder and file structure in place while you slip it to cheaper storage in the background. To add to this, you can give your executives visibility into just what's out there and how much it's costing you. Beautiful!" —Systems Engineer in the Healthcare Industry "Komprise - Great Solution, Great Company, Great People" "Great product and great team. They have gone above and beyond to educate us on the platform. They have done several POC installations to help several regions around the globe to test out the platform." —System Administrator in the Manufacturing Industry Take it from your peers and hear it in their own words in the Gartner Peer Insights report. See why Komprise Intelligent Data Management is creating more and more satisfied customers. Want to see first-hand what all the excitement’s about? Let us show you with a free assessment in your own environment. ### #1 File Analysis Software Vendor by Gartner Our ears are burning—and we couldn’t be happier. We have been named the #1 vendor in File Analysis Software by Gartner Peer Insights. Nothing means more to us than hearing from satisfied customers: Highest Overall Rating from Customers: Our customers rated us at 4.8 out of 5 stars. 89% said they would recommend Komprise Intelligent Data Management to their peers. Highest Ranking in Services & Support: 4.9 out of 5 stars, which isn’t that surprising given our unusual dedication to customer service, but it’s always great to hear. Peer Insights is Gartner's peer-driven ratings and reviews platform for enterprise IT solutions and services covering over 300 technology markets and 3,000 vendors. Every review is verified before publishing to ensure only completely authentic insights are gathered. From Systems Administrators to Infrastructure Managers spanning multiple industries, feedback spanned from compelling POCs and ease of implementation to measurable results. Customers valued the insight into their data with analytics, the storage costs they’re saving, and the data migrations that are running much better. We also got some insight on areas to enhance, which we value just as much, and our team is hard at work. Below are a couple excerpts: "Komprise is a no-brainer!" "If you have data, you need Komprise. Not only does Komprise take action on the files that haven't been accessed since they were created, but it takes action transparently. No more end-users calling up wondering where their data is. They can keep their folder and file structure in place while you slip it to cheaper storage in the background. To add to this, you can give your executives visibility into just what's out there and how much it's costing you. Beautiful!" —Systems Engineer in the Healthcare Industry "Komprise - Great Solution, Great Company, Great People" "Great product and great team. They have gone above and beyond to educate us on the platform. They have done several POC installations to help several regions around the globe to test out the platform." —System Administrator in the Manufacturing Industry Take it from your peers and hear it in their own words in the Gartner Peer Insights report. See why Komprise Intelligent Data Management is creating more and more satisfied customers. Want to see first-hand what all the excitement’s about? Let us show you with a custom demonstration. ### Komprise toont aanhoudend momentum in EMEA met strategische aanwerving De nieuwe VP EMEA Sales, Clare Loveridge, neemt de leiding over het Komprise-verkoopteam om het aanhoudende succes in de regio te bevorderen   Campbell, CA - June 18, 2020—Komprise, the leader in analytics-driven data management, has today announced the appointment of Clare Loveridge, VP EMEA Sales, in addition to focused regional EMEA sales teams. As a senior business leader for Komprise, Loveridge will lead the rapid growth and be responsible for all go-to-market activities, and delivering the industry leading intelligent data platform and multi-cloud capabilities to customers. "In today's digitally-driven, cloud-first world, data is an essential aspect for all businesses and being able to manage and analyze that data is incredibly important - Komprise's Intelligent Data Management Platform directly addresses this challenge," said Loveridge. "Komprise is one of the leading providers of analytics-driven data management, a quality that immediately piqued my interest in working for the company. I'm looking forward to working with the sales team and helping them to ensure they continue to understand the customers' businesses and the challenges they are looking to overcome, in order to continue driving continued momentum for Komprise in EMEA." Based in the London area, Loveridge joins Komprise with more than 20 years of sales and channel management experience. She was most recently in a senior leadership role at ExaGrid and prior to that Cloudcheckr, Nimble Storage and Data Domain. Loveridge's experience in leading sales in high growth companies will be beneficial as Komprise in EMEA continues to expand into new markets. Komprise Momentum Highlights: 400% year-over-year growth in Q1 of 2020. Komprise continues to expand customer and revenue momentum, with strong growth every quarter as more enterprises adopt and increase their use of Komprise. Strong Enterprise adoption. Komprise customers are increasingly using its data management software globally across multiple data centers. Komprise is now used by six of the top 10 companies in nearly every major vertical including Pharmaceutical, Biotechnology, Insurance, High-Technology, Higher Education and Genomics. Product Innovation in Multi-Cloud Data Management – Elastic Data Migration, Cloud Data Growth Analytics, Public Cloud Data Management and Deep Analytics are some of the key new product introductions in the last year. EMEA partner momentum. Komprise continues to add key partners and resellers in EMEA, including DataCentrix and Vox Telecom, two leading resellers in South Africa. "It's no surprise that Komprise receives strong adoption by users and partners, and especially in EMEA, for its comprehensive, analytics-driven data management platform, illustrating businesses' constant and rapid innovation with new features, protocols and platforms. It confirms that customer needs, such data analytics, tiering/offloading, replication, migration and cloud-native access, represent key functions to optimize the enterprise data landscape and streamline associated costs," says Philippe Nicolas, Analyst at Coldago Research. About Komprise Komprise stelt bedrijven in staat om controle te krijgen over hun data, zonder dat applicaties, gebruikers of hot data er last van hebben. Komprise Intelligent Data Management vormt de basis voor analysegestuurd databeheer, essentieel om data op de juiste plaats en op het juiste moment in alle opslagmedia te plaatsen. Analyseer, verplaats en vind uw data eenvoudig terug met Komprise. www.komprise.com. Media Contact: Declan Waters pr@komprise.com ### Eindejaarsbericht van de CEO 2019 2019 was opnieuw een fantastisch jaar met dubbele groeicijfers en ik wil onze klanten, partners en medewerkers persoonlijk bedanken. Ieder van jullie heeft bijgedragen aan de missie van Komprise: Data Management transformeren met intelligentie: We are in a rapid sales expansion mode – in the first half of 2019, we brought in strong sales leadership and have expanded our sales footprint by nearly 400% this year This effort is paying off - we saw 300% revenue growth in the second half of 2019 over the first half We also expanded the use cases we address in 2019 – with expansion of NAS Migration that we released in 2018, the addition of Deep Analytics to find just the data you need, and now the newly announced Cloud Data Management for better managing your cloud data. Over 50% of our customers now use Komprise for multiple use cases – analytics, archiving, and migration Our Technology Partner relationships continue to deepen – we have strong momentum with IBM and have closed multiple significant deals with IBM Global Technology Services (GTS), IBM Cloud and IBM Storage.  We announced our HPE worldwide reseller relationship in July and they have jointly started to resell Komprise in 2019, and we continue to build momentum with AWS, Azure and Google We are continuing to invest heavily in our channel and are launching some exciting go-to-market programs with our strategic channel partners ….and to make things interesting, my predications are that 2020 will continue to build on the predictions we made in 2019, with a greater emphasis in the cloud: Data will continue to grow at an annual rate of 50% on average, and 75% of it will be cold and should not consume expensive storage, backup resources Managing data in the cloud will become an even harder problem than on-premises as cloud data growth and bucket sprawl becomes untenable AI techniques, such as adaptive automation, deep analytics, and machine learning, will continue to be used more pervasively for data management Prettige feestdagen allemaal en blijf op de hoogte, want in januari volgt er een spannende aankondiging! Kumar Goswami CEO, Komprise ### 2019 Year-End Note from CEO 2019 has been another fantastic year of double-digit growth, and I want to personally thank our customers, partners and employees. Each of you has contributed to the Komprise mission of Transforming Data Management with Intelligence: We are in a rapid sales expansion mode – in the first half of 2019, we brought in strong sales leadership and have expanded our sales footprint by nearly 400% this year This effort is paying off - we saw 300% revenue growth in the second half of 2019 over the first half We also expanded the use cases we address in 2019 – with expansion of NAS Migration that we released in 2018, the addition of Deep Analytics to find just the data you need, and now the newly announced Cloud Data Management for better managing your cloud data. Over 50% of our customers now use Komprise for multiple use cases – analytics, archiving, and migration Our Technology Partner relationships continue to deepen – we have strong momentum with IBM and have closed multiple significant deals with IBM Global Technology Services (GTS), IBM Cloud and IBM Storage. We announced our HPE worldwide reseller relationship in July and they have jointly started to resell Komprise in 2019, and we continue to build momentum with AWS, Microsoft Azure and Google Cloud We are continuing to invest heavily in our channel and are launching some exciting go-to-market programs with our strategic channel partners ….and to make things interesting, my predications are that 2020 will continue to build on the predictions we made in 2019, with a greater emphasis in the cloud: Data will continue to grow at an annual rate of 50% on average, and 75% of it will be cold and should not consume expensive storage, backup resources Managing data in the cloud will become an even harder problem than on-premises as cloud data growth and bucket sprawl becomes untenable AI techniques, such as adaptive automation, deep analytics, and machine learning, will continue to be used more pervasively for data management Happy holidays to all of you and please stay tuned for an exciting announcement coming in January! Kumar Goswami CEO, Komprise ### Heb je moeite met het jongleren met je dataopslag? Hoe balanceer je je opslagbehoeften zonder de bal te laten vallen? Data blijft razendsnel groeien en vertoont geen tekenen van vertraging. Al deze informatie moet worden opgeslagen, beheerd en geanalyseerd, wat grote uitdagingen voor bedrijven oplevert. Krishna Subramanian, COO bij Komprise, onderzoekt de benaderingen van databeheer. ### Juggling your data storage? How to balance your storage needs without dropping the ball Data continues to grow at a rapid rate, showing no signs of slowing down. All of this information must be stored, managed and analysed presenting key challenges for businesses. Krishna Subramanian, COO at Komprise, explores the approaches to data management ### Komprise data management now available through HPE channels HPE’s channel will now also sell Komprise’s data management solution, after a deal which follows a similar Komprise/IBM deal last year. In February, Komprise announced a $24m series C rounding of funding to accelerate global expansion and claims record growth levels, with an emphasis on Europe.. ### Komprise neemt wereldwijde verkoopmanagers aan en sluit zich aan bij Hewlett Packard Enterprise Complete Program om wereldwijde expansie te stimuleren Komprise Scales to Deliver Intelligent Data Management Solutions to Global Enterprises Campbell, CA - July 16, 2019 -- Komprise, the industry-leader in intelligent data management, today announced two significant milestones in its global expansion - a deal with HPE to resell Komprise through the HPE Complete program, and the appointment of two key executives to accelerate international growth. The appointments come at a time of record growth for Komprise. In February, the company announced a $24M series C rounding of funding to accelerate global expansion. Komprise has appointed Chris Moore as SVP WW Sales and Randy Hopkins as VP Global Systems Engineering and Enablement. Moore is an accomplished sales leader with more than 25 years of experience in driving high growth and successful exits of multiple startups including Codenvy (Red Hat), Kace Networks (Dell), AvantGo (IPO; Sybase) and Genesys (IPO, Alcatel). Hopkins has over 25 years in technical leadership roles creating enablement programs for Enterprise companies, including two startup companies Data Domain(IPO) and Nimble Storage(IPO) and leadership roles at Pure Storage, EMC, Dell, and HPE. “We are excited to have Chris and Randy on board especially with their proven background accelerating growth in successful startups,” said Kumar Goswami, Founder and CEO at Komprise. Komprise in HPE Complete: Komprise is joining the HPE Complete Program, enabling HPE to resell the Komprise data management software together with its own scale-out storage solutions to enterprise customers across the globe. After several months of progressing through the pilot and validation phase, the Komprise and HPE collaboration is now being offered to customers. The best-of-breed solution combines the Komprise data management software that works across NAS and clouds with robust and efficient HPE storage. As a result, enterprise customers around the world now can dynamically and cost-effectively manage unstructured data growth while reducing complexity. Please see the Komprise – HPE Reference Architecture. “Komprise’s data management platform provides exceptional analytics capabilities and highly flexible management of data assets, enabling more efficient on-premises and hybrid cloud architectures,” said Marty Lans, GM Storage Connectivity & HPE Complete, at HPE. “Our collaboration with Komprise extends our focus on helping our customers create solid infrastructures to manage and unlock the true potential of their data.” Under the HPE agreement, combined Komprise and HPE solutions are available now throughout the Americas and EMEA HPE channel and enterprise sales force. The HPE Complete Program is built on best-in-class technologies and products that complement HPE servers and storage backed by the reliability of HPE’s interoperability assurance. Additional Resources: Komprise Product Page on HPE.com Video overview of Komprise and HPE About Hewlett Packard Enterprise Hewlett Packard Enterprise is a global technology leader focused on developing intelligent solutions that allow customers to capture, analyze, and act upon data seamlessly from edge to cloud. HPE enables customers to accelerate business outcomes by driving new business models, creating new customer and employee experiences, and increasing operational efficiency today and into the future. About Komprise Komprise, the industry-leader in intelligent data management across clouds, empowers businesses to efficiently manage today’s massive scale of data growth while unlocking its value. The Komprise mission is to radically simplify data management through intelligent automation. Komprise partners include Western Digital, IBM, NetApp, EMC, Google Cloud Platform, Amazon Web Services, HPE, and Azure.  Komprise is used by enterprises to intelligently manage data at scale. For more information, visit Komprise. Media Contact: Monica Giannella McNay pr@komprise.com ### Komprise Hires Global Sales Leaders and Joins Hewlett Packard Enterprise Complete Program to Fuel Global Expansion Komprise Scales to Deliver Intelligent Data Management Solutions to Global Enterprises Campbell, CA - July 16, 2019 -- Komprise, the industry-leader in intelligent data management, today announced two significant milestones in its global expansion - a deal with HPE to resell Komprise through the HPE Complete program, and the appointment of two key executives to accelerate international growth. The appointments come at a time of record growth for Komprise. In February, the company announced a $24M series C rounding of funding to accelerate global expansion. Komprise has appointed Chris Moore as SVP WW Sales and Randy Hopkins as VP Global Systems Engineering and Enablement. Moore is an accomplished sales leader with more than 25 years of experience in driving high growth and successful exits of multiple startups including Codenvy (Red Hat), Kace Networks (Dell), AvantGo (IPO; Sybase) and Genesys (IPO, Alcatel). Hopkins has over 25 years in technical leadership roles creating enablement programs for Enterprise companies, including two startup companies Data Domain(IPO) and Nimble Storage(IPO) and leadership roles at Pure Storage, EMC, Dell, and HPE. “We are excited to have Chris and Randy on board especially with their proven background accelerating growth in successful startups,” said Kumar Goswami, Founder and CEO at Komprise. Komprise in HPE Complete: Komprise is joining the HPE Complete Program, enabling HPE to resell the Komprise data management software together with its own scale-out storage solutions to enterprise customers across the globe. After several months of progressing through the pilot and validation phase, the Komprise and HPE collaboration is now being offered to customers. The best-of-breed solution combines the Komprise data management software that works across NAS and clouds with robust and efficient HPE storage. As a result, enterprise customers around the world now can dynamically and cost-effectively manage unstructured data growth while reducing complexity. Please see the Komprise – HPE Reference Architecture. “Komprise’s data management platform provides exceptional analytics capabilities and highly flexible management of data assets, enabling more efficient on-premises and hybrid cloud architectures,” said Marty Lans, GM Storage Connectivity & HPE Complete, at HPE. “Our collaboration with Komprise extends our focus on helping our customers create solid infrastructures to manage and unlock the true potential of their data.” Under the HPE agreement, combined Komprise and HPE solutions are available now throughout the Americas and EMEA HPE channel and enterprise sales force. The HPE Complete Program is built on best-in-class technologies and products that complement HPE servers and storage backed by the reliability of HPE’s interoperability assurance. Additional Resources: Komprise Product Page on HPE.com Video overview of Komprise and HPE About Hewlett Packard Enterprise Hewlett Packard Enterprise is a global technology leader focused on developing intelligent solutions that allow customers to capture, analyze, and act upon data seamlessly from edge to cloud. HPE enables customers to accelerate business outcomes by driving new business models, creating new customer and employee experiences, and increasing operational efficiency today and into the future. About Komprise Komprise, the industry-leader in intelligent data management across clouds, empowers businesses to efficiently manage today’s massive scale of data growth while unlocking its value. The Komprise mission is to radically simplify data management through intelligent automation. Komprise partners include Western Digital, IBM, NetApp, EMC, Google Cloud Platform, Amazon Web Services, HPE, and Azure.  Komprise is used by enterprises to intelligently manage data at scale. For more information, visit Komprise. Media Contact: Monica Giannella McNay pr@komprise.com ### Komprise Raises $24 Million in Series C Funding Intelligent data management provider Komprise secured $24 million in a Series C investment round led by Top Tier Ventures, with strategic investment from new investor Western Digital Capital (the strategic investment fund of Western Digital Corp.), and participation from existing investors Canaan Partners and Walden International... ### Data Management is best done via Software, not Hardware We all know unstructured data management is a huge problem that is growing bigger every day, as data growth continues to explode. We continue to see businesses announcing huge funding rounds in the hundreds of millions of dollars to try and tackle this problem. But why? Why is it so expensive to build a technology solution that addresses data management? The conventional approach to address data management is through hardware. It is technically easier to build a hardware appliance that controls all the data landing on it and then moves that data across clouds, but this approach is proprietary. Most large customers have a heterogeneous storage environment, so they need a data management solution that works across different vendors and clouds, and gives them the flexibility to evolve. A proprietary solution locks them in and a hardware-based approach is very expensive. Shipping and managing hardware boxes is capital, intensive, and inefficient. Businesses taking this approach require hundreds of millions of dollars to build a scalable, profitable business. We have never believed that wrangling the cloud from a hardware box is the right approach. In all of our companies, we have taken a non-proprietary, 100% software-based approach. We are gratified to see our customers, partners, and investors continue to invest in us and help us grow. This week, Komprise closed a $24M Series C round after experiencing strong growth. Additionally, Komprise was just awarded a patent on its Transparent Move Technology™. By taking a lightweight, software-based approach to data management, Komprise is able to provide customers an efficient way to understand, analyze, and manage their data no matter where it lives, and evolve without lock-in. Read the white paper: Data Management Must Replace Storage Management ### Transforming Your Data Management Strategy Unstructured data growth is exploding. IDC recently predicted that the sum of the world’s data will grow to 163 zettabytes by 2025. Obviously, existing data management approaches were never built to handle this growth. Tune into this audio webcast featuring Komprise COO Krishna Subramanian and IDC Research Director Phil Goodwin as they discuss why the time to start planning for an effective data management solution is now. ### Disruptive Technology Doesn't Only Disrupt, It Transforms Krishna Subramanian, COO at Komprise, discusses the benefits disruptive technology can bring to a business as more and more companies adapt to Digital Transformation. She also talks about the transformative effects of AI and ML in data management and how this will allow businesses to become more agile, adaptable and efficient. ### Komprise Raises $24M Series C Funding to Support Global Expansion Latest funding round led by Top Tier Ventures with strategic investment from Western Digital Capital February 5, 2019-- Komprise, the industry-leader in intelligent data management, today announced it has secured $24M in Series C Investment. The funding round is led by Top Tier Ventures, with strategic investment from new investor Western Digital Capital, the strategic investment fund of Western Digital Corp. (NASDAQ: WDC), along with participation from existing investors, Canaan Partners and Walden International. This new funding round, which brings the total financing since inception to $42M, will be used globally to boost sales, marketing, product development and customer support. In 2018, Komprise achieved a number of milestones including: Solid revenue growth of more than 200% quarter-over-quarter A 600% growth of footprint in existing customers who are using Komprise to manage more of their data Enterprise customers are now using Komprise across their entire infrastructure to manage hundreds of petabytes of data Doubling of its employee headcount globally Growth in EMEA with significant customers and partners now in the UK, Ireland, Switzerland, Germany, Spain and the Netherlands The launch of three major product updates including Ongoing Lifecycle Management, Data Access Anywhere, and NAS Migration Komprise enables customers to swiftly adapt their data management to suit a range of business requirements. The Komprise Intelligent Data Management Platform allows organizations to manage the complete data lifecycle with data analytics, replication, archive and effortless migration. The solution is designed to address today’s massive scale of data and enables customers to dynamically opt for the right storage solution at the right time, saving money and enhancing data management. SUPPORTING QUOTES “We have been watching the company closely, and are impressed with how quickly enterprises are adopting Komprise,” said Garth Timoll, general partner at Top Tier Ventures. “Data is growing exponentially and the Komprise software-based business model is uniquely positioned to address this space. We know Komprise is poised to scale massively, led by its proven founders and executive team.” “The increasing volume, variety, velocity and value of unstructured data is driving the need for more efficient and flexible storage architectures,” said Mark Long, president, Western Digital Capital. “Komprise’s data management platform provides exceptional analytics capabilities and highly flexible management of data assets, enabling more efficient on-premises and hybrid cloud architectures. Our investment in Komprise complements our focus on helping our customers create infrastructures to manage, preserve, experience and unlock the true potential of their data.” “We are pleased that both new and existing investors have demonstrated their support and we will look forward to using this new cash infusion to significantly grow our product, customer and partner base,” said Kumar K. Goswami, CEO Komprise. “We are always looking for new and innovative ways to work, and as organizations continue to store more data, at an unprecedented scale, we are seeing more customers reevaluate their data management system.” "I am impressed by Komprise and the way it solves unstructured data management issues. The solution just works, seamlessly, and this is why end users love it,” said Enrico Signoretti, IT Analyst, GigaOm. ADDITIONAL RESOURCES Video overview of Komprise Quantifying the Value of Intelligent Data Management About Komprise Komprise, the industry-leader in intelligent data management across clouds, empowers businesses to efficiently manage today’s massive scale of data growth while unlocking its value. The Komprise mission is to radically simplify data management through intelligent automation. Komprise partners include Western Digital, IBM, NetApp, EMC, Google Cloud Platform, Amazon Web Services, and Azure.  Komprise is used by enterprises to intelligently manage data at scale. For more information, visit Komprise. Western Digital and the Western Digital logo are trademarks or registered trademarks of Western Digital Corporation or its affiliates in the US and/or other countries. Other trademarks, registered trademarks, and/or service marks, indicated or otherwise, are the property of their respective owners.   Media Contact: Monica Giannella McNay pr@komprise.com ### Year-End Note from CEO This year has been an amazing year of growth for Komprise and I want to express my heartfelt thanks to our customers, partners, and employees. Each of you has contributed to the Komprise mission of Transforming Data Management and I am excited about our mutual achievements in 2018: Increased revenues by more than 200% quarter-over-quarter for every quarter in 2018 A 600% footprint growth in existing customers who are using Komprise to manage more of their data Doubling Komprise employee headcount globally and adding new sales regions in EMEA Strengthening our Technology Partner relationships with an IBM Reseller relationship, Azure co-sell partnership, AWS Advanced Tier partnership and a strategic partnership with Western Digital The launch of three major product updates, including Ongoing Lifecycle Management, Data Access Anywhere, and NAS Migration Tripling of our channel partnerships ….and to make things interesting, my predications are that 2019 will bring new challenges and opportunities for managing unstructured data to us all: Data will continue to grow at an annual rate of 50% on average 75% of data on average will be cold and not accessed in over a year, and offloading it from expensive storage and backups will become even more of an imperative than it is today AI techniques, such as adaptive automation, deep analytics, and machine learning, will be used more pervasively for data management Happy holidays to all of you and please stay tuned for an exciting announcement coming in January! Kumar Goswami CEO, Komprise ### Managing HPC Data At Today's Scale The Komprise team is at the SuperComputing Conference in Dallas this week (come and see our team at booth 4413 if you happen to be attending). I thought I would take a moment to share my thoughts on the challenges I hear from our HPC customers. Our HPC customers are generating data at a rapid pace. I’d like to share a top medical research center customer based out of New York as an example. This particular research organization has, through its various research projects and studies, accumulated petabytes of data over the years. This data is immensely valuable, even lifesaving, and must be retained and kept accessible—either due to the value of the data for future research or for compliance reasons. The challenge they faced is that while their data footprint was growing by 45% YOY the budget they had to manage that growing footprint was only growing 2.5%. The pressure that IT is put under to stretch a relatively flat budget is a common theme that I have noticed in my conversations with our users. As a result of the scale of data being produced today in HPC environments, I believe it is no longer feasible for organizations to continue to keep all their data on Tier One NAS solutions and it comes as no surprise to me that Komprise is helping more and more HPC organizations adopt a tiered approach to keep costs under control while still keeping data accessible to their users. In the case of the medical research center, our analysis showed 76% of data had not been accessed or modified in over a year. For them, this meant that they could cut more than 70% of their storage costs by moving this data to IBM tape which the organization already owned but had not fully utilized. Because the data was moved via our Transparent Move Technology TM, it is still accessible to the organization as if it was still on the primary filer from the original namespace with no change to user access. And users can search and access all the metadata locally for data that is now on tape(albeit with the latency upon full retrieval that is inherent to tape).   After talking with our HPC customers over the last months one of the more requested features was support for the Lustre protocol. I am happy to share that we announced our Lustre Beta at SuperComputing this week. If you are interested in signing up for the Lustre Beta, send an email to lustrebeta@komprise.com ### Komprise Intelligent Data Management 2.9 Delivers Data Access Anywhere Customers, for the first time, can evolve their data management strategy swiftly as business needs change. Las Vegas, NV – October 23, 2018 -- Komprise, the industry-leader in intelligent data management, announced today at NetApp Insight 2018 the general availability of Komprise Intelligent Data Management 2.9. As your data strategy evolves across on-prem and cloud, Komprise evolves with you. Komprise 2.9 enables multi-tier data management with full data access from any storage without rehydration. Traditional tiering solutions focus just on cost and move blocks of data to lower storage tiers while metadata stays on the primary tier. This cripples the ability to evolve and natively run on tiers such as the cloud since data is only accessible from the primary tier and requires rehydration. Unlike traditional solutions, Komprise moves data progressively down classes of storage with full file integrity so customers can directly access data and fully take advantage of native capabilities of each tier. This enables customers to evolve, so the cloud could begin as a lower tier and over time become a primary tier. Komprise will be showcasing Version 2.9 at NetApp Insight 2018, Booth #801. “We needed a new storage strategy as our data kept growing,” says Jay Smestad, Sr Director IT Architecture at Pacific Biosciences. “With Komprise, we are able to move data as it ages across Flash, Disk, Tape, and Cloud from multiple vendors. The best part is there is no change to our user access.” Komprise enables businesses to handle unstructured data growth by identifying cold data, transparently moving data across multi-vendor storage and clouds, and delivering ongoing policy-based archiving, data replication and data migration. New Data Access Anywhere Capabilities Multi-Vendor Data Lifecycle Management: : Set policies on how data moves across classes of storage regardless of vendor. Komprise seamlessly moves the data as it ages. Powered by Transparent Move Technology™: As Komprise moves data progressively across storage classes, the moved files continue to be accessed from the original source without any disruption to users or applications. No stubs or agents. Data Access from Any Tier, On-Prem or Cloud: Unlike array tiering solutions that are block-based and do not allow direct access from secondary tiers, thus severely limiting their use, Komprise fully preserves file integrity so data can be accessed directly from any tier, on-premises or cloud. Komprise Intelligent Data Management 2.9 is available now for no additional charge.  For more information and a complimentary assessment of your data, visit Komprise.   About Komprise Komprise, the industry-leader in intelligent data management across clouds, empowers businesses to efficiently manage today’s massive scale of data growth while unlocking its value. The Komprise mission is to radically simplify data management through intelligent automation. Komprise partners include IBM, Western Digital, NetApp, EMC, Google Cloud Platform, Amazon Web Services, and Azure. Komprise is used by enterprises to intelligently manage data at scale. For more information, visit Komprise.   ##   Media Contact: Monica Giannella McNay pr@komprise.com ### Komprise keeps growing Komprise recently announced that Andy Hill, the former VP Sales for EMEA at Nexsan, has been appointed as EMEA VP sales, with the assignment to further advance Komprise in our region. Article in Dutch ### 4 Business Reasons for the Komprise Data Management Architecture A Look at the Key Business Reasons for the Komprise Data Management Architecture & Why They Matter We built Komprise based on customer feedback. As enterprises told us they were literally drowning in data, we realized there was a massive innovation gap. Most data management architectures were built more than a decade ago, back when data footprints were still relatively small and standard protocols for storage were still emerging. Products to address different aspects of data management, such as Information Lifecycle Management (ILM), Hierarchical Storage Management (HSM), Data Virtualization, Data Analytics, and Storage Resource Management (SRM), were all too complex, too costly, too niche, too proprietary, and hard to adopt. We designed the Komprise Data Management architecture to create an easy to manage, easy to scale, storage independent, no lock-in solution that delivers business results. In this blog, I will walk through the four key business problems the Komprise architecture addresses and why they matter. You can get an under the hood look at the pillars of the Komprise Intelligent Data Management architecture in a blog written by our CTO, Mike Peercy. Four Key Business Priorities The four business priorities that customers tell us they care about when it comes to data management are: Need to manage data at scale across current and future storage Need to manage data without impeding data access Need to migrate and move data without disrupting current users and apps Need to extract and harness data value, not just manage infrastructure Let’s look at each of these in a bit more detail… Business Need #1: Need to Manage Data at Scale across Current & Future Storage (i.e. Simplify managing data without lock-in) Data growth is exploding in nearly every industry. As technology becomes more sophisticated, the more data growth accelerates. IT budgets and resources are constrained and businesses want to improve how they store and manage data. The good news: there are a lot more options than ever before to store and manage data – you have flash, disk, object, cloud, even tape as storage options at different price/performance points. You also have multiple backup and data protection solutions: from software-only to appliance-based and cloud-based approaches. The bad news: this confusing array of options is often dizzying and overwhelming. How does IT decide which options to pick? How do they decide what data goes where? What if they change their minds tomorrow? How locked in are they to a given choice? How do they know how much each option is going to cost? IT is left to figure all of this out on their own: somehow glean visibility into how data is growing and being used across their current silos of storage, somehow put data into the right place at the right time, and to report and assess whether their current strategy is working. What is needed is a data management solution that works across storage without being locked-in. A solution that also enables you to scale up and down as needed without heavy infrastructure investments. Therefore, we created Komprise as a non-storage, software solution that works across all your storage with a scale-out data management architecture. Watch this video of a customer who was able to leverage Komprise to analyze their data, create a go-forward strategy, and manage data across silos. Business Need #2: Need to Manage Data without Impeding Access (i.e. Don't get in the way!) One way to manage data across storage is to add another layer in front of the storage that you want to manage - this was the popular concept behind Data Virtualization.  Companies have tried data virtualization via software (e.g. most recent example is PrimaryData), via the network (e.g. Acopia), via storage (e.g. cloud gateways, array tiering) or via metadata catalogs. These approaches have never taken off… why? This is because customers do not want a “middleman” in front of all their data, especially hot data and metadata. Imagine buying expensive flash storage and then having to stick a data management layer in front of it that is brokering access. This layer now becomes a bottleneck, a chokepoint, and a source for lock-in. How can you have a data management architecture that sits to the side of your storage, works with all your storage without creating lock-in, doesn't sit in front of your important data or metadata, and still enables access to all the data?  That is what Komprise does. We built the solution to be storage agnostic, work via open standards, and provide access without fronting hot data or metadata. Read our case study on How an Engineering firm was able to transform its storage and backup architectures without engineers noticing any change, using Komprise. Business Need #3: Need to Migrate & Move Data without Disrupting Users & Apps (i.e. Users shouldn't have to lose access or look elsewhere or rewrite apps) Archiving data has traditionally meant that the data either goes offline into a long-term archive such as tape or the data moves to another namespace such as object or cloud, which also involves a paradigm change from file to object. Neither of these approaches are ideal because creating behavior change for users and asking them to look for data in multiple places is hard and it’s equally hard to rewrite applications from file to object. This is why Komprise creates a redundant, transparent, hierarchical filesystem that preserves file access to moved data, even when the data is stored as objects in the cloud. These files show up in the original namespace, so if you moved data from a NetApp filer or from a Windows File Server, you will see all the moved files as if they were still on that filer. Users and applications maintain access to the files, exactly as they did before, without any disruption. You can also directly access these files from the cloud itself, or natively as objects, so there is no lock-in. Check out our white paper on Data Management in Boone County to learn how the county was able to handle the onslaught of bodycam and dashcam data by migrating cold data to Azure through Komprise — without any user disruption. Business Need #4: Need to Harness Data Value by Finding Relevant Data (i.e. Enable new uses such as big data analytics on data no matter where it lives) Ultimately, businesses are storing data not just for compliance purposes, but also to harness the value of all the data.  Yet, businesses often struggle to search and find relevant data easily — since data is often trapped in multiple storage silos and was created by multiple users, many of whom may not even be in the company any longer. A key need for businesses is to have a simple way to search and find relevant data sets across storage pools. For example, imagine that you are an insurance company and you wanted to find all files related to earthquakes across all your storage.  Imagine if you could easily search and create a virtual data volume of any dataset, even if it is strewn across multiple data stores, and then copy this data into Hadoop or some analytics engine for big data analytics.  Komprise provides deep analytics capabilities through a distributed search framework on all metadata across storage.  We are getting a lot of customer interest and usage of this feature in beta. As Komprise continues to evolve, we are committed to continue delivering on these four business benefits. Learn more about the Komprise architecture. ### Caringo and Komprise Partner to Slash Storage TCO AUSTIN, TEXAS (PRWEB) JUNE 13, 2018 Caringo, Inc. today announced a technology partnership with Komprise to solve issues caused by the astonishing rate of growth of unstructured data, such as documents, videos, photos and audio files, with increased quality and resolution by pairing Komprise intelligent data management technology with Caringo Swarm, hassle-free, limitless storage. As file creation and access has changed, the traditional Network Attached Storage (NAS) devices often used to store and provide access to an organization's files are no longer adequate for today’s rapidly scaling data stores. Many organizations are turning to the cloud to offload data; however, daily transfer rates and bandwidth constraints quickly become an issue as does data that contains sensitive information. “At Caringo, we’ve long known that traditional NAS devices are a limiting technology and that, in time, organizations would need to look for more sophisticated tools to expand their storage capabilities and securely hold massive amounts of data on premise, eliminating the worry of ransomware and hacks,” said Adrian Herrera, Caringo VP of Marketing. “With Komprise and Caringo, that transition is quite easy and you can reap the many benefits of object-based storage.” With the joint solution, you can easily identify data via the Komprise user interface to move from NAS, realize the cost reduction, then securely transfer data based on its value to Caringo Swarm scale-out object storage. Once in Swarm, data is continuously protected without backups and instantly and securely available internally or externally. With automated archiving from NAS, a single-pane management interface and the ability to scale to 100s of PBs on any hardware, organizations can now align storage and access with data value while protecting archives from hacks and ransomware and significantly reducing backup cycles, speeding recovery and lowering storage Total Cost of Ownership (TCO)—all without disrupting users or applications. “Customers are realizing that with today’s explosive data growth, a one-size-fits-all approach to store and manage data is no longer tenable,” said Krishna Subramanian, COO Komprise. “We are excited to partner with Caringo and enable enterprises to continue leveraging NAS and Flash investments for hot data while transparently offloading cold data management and storage without any disruption to users or applications.” On Thursday, June 21, Caringo will host a webinar titled “Slash Storage TCO for Rapidly Scaling Data Sets” on BrightTALK  at 10 AM PT/1PM ET. This educational webinar will feature Glen Olsen, Caringo Product Manager, and Krishna Subramanian, Komprise COO. Register now to watch live or on demand and learn more about how Komprise and Caringo have partnered to solve today’s most pressing storage  issues by pairing Komprise’s intelligent data management technology with Caringo Swarm hassle-free, limitless object storage. For more information, visit http://www.Caringo.com. Follow Caringo LinkedIn: https://www.linkedin.com/company/caringo-inc- Twitter: https://twitter.com/CaringoStorage About Caringo Caringo was founded in 2005 to change the economics of storage by designing software from the ground up to solve the issues associated with relentless data growth. Caringo’s flagship product, Swarm, decouples data from applications and hardware providing a foundation for continued data access and analysis that continuously evolves while guaranteeing data integrity. Today, Caringo software-defined object storage solutions are used to preserve and provide access to rapidly scaling data sets across many industries by organizations such as NEP, iQ Media, Argonne National Labs, Texas Tech University, Department of Defense, the Brazilian Federal Court System, City of Austin, British Telecom and hundreds more worldwide. About Komprise Komprise, the industry-leader in intelligent data management across clouds, empowers businesses to efficiently manage today’s massive scale of data growth while unlocking its value. The Komprise mission is to radically simplify data management through intelligent automation. Komprise is used by enterprises to intelligently manage data at scale. Komprise has won numerous awards including being named as a Gartner’s Cool Vendor in Storage Technologies 2017. For more information, visit Komprise. ### Caringo and Komprise Partner to Slash Storage TCO AUSTIN, TEXAS (PRWEB) JUNE 13, 2018 Caringo, Inc. today announced a technology partnership with Komprise to solve issues caused by the astonishing rate of growth of unstructured data, such as documents, videos, photos and audio files, with increased quality and resolution by pairing Komprise intelligent data management technology with Caringo Swarm, hassle-free, limitless storage. As file creation and access has changed, the traditional Network Attached Storage (NAS) devices often used to store and provide access to an organization's files are no longer adequate for today’s rapidly scaling data stores. Many organizations are turning to the cloud to offload data; however, daily transfer rates and bandwidth constraints quickly become an issue as does data that contains sensitive information. “At Caringo, we’ve long known that traditional NAS devices are a limiting technology and that, in time, organizations would need to look for more sophisticated tools to expand their storage capabilities and securely hold massive amounts of data on premise, eliminating the worry of ransomware and hacks,” said Adrian Herrera, Caringo VP of Marketing. “With Komprise and Caringo, that transition is quite easy and you can reap the many benefits of object-based storage.” With the joint solution, you can easily identify data via the Komprise user interface to move from NAS, realize the cost reduction, then securely transfer data based on its value to Caringo Swarm scale-out object storage. Once in Swarm, data is continuously protected without backups and instantly and securely available internally or externally. With automated archiving from NAS, a single-pane management interface and the ability to scale to 100s of PBs on any hardware, organizations can now align storage and access with data value while protecting archives from hacks and ransomware and significantly reducing backup cycles, speeding recovery and lowering storage Total Cost of Ownership (TCO)—all without disrupting users or applications. “Customers are realizing that with today’s explosive data growth, a one-size-fits-all approach to store and manage data is no longer tenable,” said Krishna Subramanian, COO Komprise. “We are excited to partner with Caringo and enable enterprises to continue leveraging NAS and Flash investments for hot data while transparently offloading cold data management and storage without any disruption to users or applications.” On Thursday, June 21, Caringo will host a webinar titled “Slash Storage TCO for Rapidly Scaling Data Sets” on BrightTALK  at 10 AM PT/1PM ET. This educational webinar will feature Glen Olsen, Caringo Product Manager, and Krishna Subramanian, Komprise COO. Register now to watch live or on demand and learn more about how Komprise and Caringo have partnered to solve today’s most pressing storage  issues by pairing Komprise’s intelligent data management technology with Caringo Swarm hassle-free, limitless object storage. For more information, visit http://www.Caringo.com. Follow Caringo LinkedIn: https://www.linkedin.com/company/caringo-inc- Twitter: https://twitter.com/CaringoStorage About Caringo Caringo was founded in 2005 to change the economics of storage by designing software from the ground up to solve the issues associated with relentless data growth. Caringo’s flagship product, Swarm, decouples data from applications and hardware providing a foundation for continued data access and analysis that continuously evolves while guaranteeing data integrity. Today, Caringo software-defined object storage solutions are used to preserve and provide access to rapidly scaling data sets across many industries by organizations such as NEP, iQ Media, Argonne National Labs, Texas Tech University, Department of Defense, the Brazilian Federal Court System, City of Austin, British Telecom and hundreds more worldwide. About Komprise Komprise, the industry-leader in intelligent data management across clouds, empowers businesses to efficiently manage today’s massive scale of data growth while unlocking its value. The Komprise mission is to radically simplify data management through intelligent automation. Komprise is used by enterprises to intelligently manage data at scale. Komprise has won numerous awards including being named as a Gartner’s Cool Vendor in Storage Technologies 2017. For more information, visit Komprise. ### Top Media Storage Trends from NAB 2018 Last week, the Komprise team packed up and headed to the National Association of Broadcasters Show (NAB) in Las Vegas, Nevada. At the show, we spoke with a few of our storage partners about the innovations we were seeing, such as the growth of Ultra High Definition Video, and how media organizations are leveraging Komprise and secondary storage to manage the explosion of data resulting from them. I wanted to take a second to share some of the highlights from those discussions. Beyond Ultra High Definition Video At NAB this year, solutions took UHD to a new level. High Dynamic Range 8K workflows were on display in demonstrations like the production/post-production breakdown of the upcoming film The Week Of — held by Panasonic, Light Iron, and Netflix. Also at the show, Spin Digital and Intel debuted 16K video playback and video processing. As these video formats increase in quality, the amount of data captured for each project is increasing exponentially. Watch our conversation with Jeff Braunstein of Spectra Logic about the UHD and its impact on storage. Media Assets Need to Stay Accessible One of the recurring themes in our discussions with our customers at the NAB was that, as media companies are looking to drive more value out of their existing assets, offline media is no longer an option. For most organizations, however, continuing to purchase tier one production storage to store this data on is cost prohibitive. Customers in this situation can benefit from an Active or "always on" media archive which provides the cost benefits of using secondary storage while preserving access to the files if and when they are needed for future projects. Watch our conversation with Jon Toor of Cloudian about the need to keep assets online. Most IT budgets are not growing to match the increased storage needs. Most of the customers we spoke with at NAB are faced with a similar mandate, to adapt to increasing storage demands while staying within a flat budget. They want to invest in resources that expand the capability and efficiency of their creatives, but instead, have to use that spend to purchase additional network attached storage capacity. By offloading inactive media assets to more cost-effective secondary storage tiers, companies are able to significantly free up the capacity of their production storage. This allows them to focus resources on additional creative tools. Watch Komprise discuss this mandate to cut costs with David Wohlford of IBM. To Learn More Read: Data Sheet: Komprise Overview Case Study: Large Media & Entertainment Company Cuts Storage Costs with “Always-On” Live Media Archives ### What Will be Big at NAB 2018 & What it Means for Data Storage Judging by industry predictions & marketing buzz... here a few hot topics to expect at NAB 2018 & what they mean for data storage strategies:   Increased M&A Activity by Studios, Broadcasters, Vendors — Requires Capacity Planning and Consolidation Across Entities: Vendors, broadcasters, and production companies are all consolidating and forming ever bigger groupings. Acquisitions such as Walt Disney’s purchase of 21st Century Fox, means consolidation in the media industry.  Such consolidation typically has major impact on the storage and data center footprint. What does this mean? It means that you need solutions that provide visibility into data storage, across multiple vendors and organizational silos, for cross-storage, cross-vendor capacity planning and seamless data migration. More Sophisticated Data Rendering with 4K, HDR, Virtual Reality and Augmented Reality — Driving Data Growth: New and better imaging, sound, and fidelity, are all coming our way with exciting technologies: such as Ultra High Definition 4k (and even 8k) capture, High Dynamic Range (HDR) content, and the explosion of Virtual Reality and Augmented Reality options — which created the stunning images that we saw at the PeyongChang Olympics... But all of these new techniques have one thing in common, they generate more data than before. Additionally, this major growth in data needs to be squeezed into (already tight) IT budgets. The bulk of this data becomes inactive/cold within weeks to months of creation... imagine if you could seamlessly offload the cold data to a lower cost secondary storage and eliminate its DR and backup overhead—without media artists and users ever having to remember asset tags in an offline archive or look elsewhere for their media assets. Transparent data archiving that identifies and moves cold data to lower cost cloud, object or tape storage of your choice, while preserving access from the original location, is important. Advancements in Internet of Things (IoT), Artificial Intelligence (AI), and the Cloud — Opening New Ways to Harness Data: As AI, IoT, and the cloud advance, we are entering a truly fascinating era, an era where automation can “learn” from data.  Of course, this learning requires mining extensive data sets. This means we are going to have to learn how to not only store vast amounts of data, but also quickly find relevant cuts of the data, to feed AI and big data analytics. The ability to build dynamic data lakes on the fly, quickly and easily, regardless of where the data lives, is going to become important. A transparent namespace across all your data silos, both on-premises and in the cloud, enables this.   What do you see as major trends and pain points? Come stop by the Komprise booth (SL14217) @ NAB 2018 to share your thoughts, see how intelligent data management addresses these key issues for media and entertainment companies, and win cool prizes! ### NetApp Podcast: Komprise & NetApp Storage Systems This week Kumar Goswami, Komprise CEO, joined the NetApp Tech ONTAP Podcast to chat about data management and how Komprise works with NetApp to help organizations get even more out of their storage systems! Tech ONTAP Podcast ft. Komprise This week Kumar Goswami, Komprise CEO, joined the NetApp Tech ONTAP Podcast to chat about data management and how Komprise works with NetApp to help organizations get even more out of their storage systems! Listen to the podcast and find out why Kumar had such a great time talking about Komprise with these guys... ### A Woman with Vision – Krishna Subramanian, COO Komprise Co-founder and COO, Krishna Subrmanian, was named to the list of 2017 Women of the Channel. A prestigious list to be named to, Krishna was recognized for her influence on women in technology. Exemplifying a woman in leadership, Krishna encourages women to grow, both personally and professionally, and see no limit. Komprise COO Named to 2017 Women of the Channel CRN's 2017 Women of the Channel: among a list of key engineering leaders and architects, sales and channel leaders, investors and board members, you'll find Komprise co-founder and COO, Krishna Subramanian. A prestigious list to be named to, Krishna was recognized for her influence on women in technology. Exemplifying a woman in leadership, Krishna encourages women to grow, both personally and professionally, and see no limit. “Women have an amazing opportunity in this market to leverage their strengths and creativity. I would encourage young women to not hold back and to give it their all – with passion, dedication, knowledge – they can achieve anything they want; from being an innovator, to starting and running your own technology business, anything is possible” says Krishna. Komprise has women across the organization – from female engineers, data scientists, sales representatives, channel leaders, marketing executives, a woman venture capitalist on our board, and a woman COO! If Krishna could be any movie character, who would she be? “I would want to be Princess Leia from Star Wars – she was fearless on the battlefield, and dedicated to ending the tyranny of the Empire. She showcased strength, power, and compassion. She is an icon and a hero for women and men alike,” answers Krishna. From Star Wars to Komprise We can see a direct correlation between the fearlessness and dedication Princess Leia and our own icon, Krishna Subramanian. It is an honor to work with such a compassionate leader. We thank you, Krishna, and look forward to seeing where your leadership takes us.   Read the CRN 2017 Women of the Channel interview with Krishna ### The problem with primary data Data Virtualization Data virtualization as a concept, makes perfect sense — imagine not having to care where data lives or how it is being stored, just knowing that it will be available whenever it is needed. Yet, in practice, data virtualization solutions have not worked — Why?  The problem with data virtualization is in how it has been implemented and what it does with primary data. Imagine this… you are trying to hire a housekeeper to keep your house tidy. A great candidate comes along and says, “I’ll keep your house spotless, but there is a catch, you must wake up when I tell you to, eat what I tell you to, and leave the house when I tell you to. Essentially, if you follow my rules, everything will work perfectly.” Would you hire this housekeeper? This has been the same challenge that data virtualization solutions have faced. They get in front of the metadata and require all requests — including requests for primary data, which is hot data and accessed a lot, to go through them. As a result, these solutions become chokepoints and create tremendous lock-in – which is why customers do not want to use them. The solution is to provide the benefits of data virtualization without fronting the primary data.  Imagine being able to provide a redundant name space that lets you easily access any data, when you need to, and does not interfere with your access to primary data. On top of that, it does not change any user behavior on secondary, cold data… Komprise provides intelligent data management across storage, using standard protocols, without getting in front of the hot primary data or metadata paths. Download our white paper to learn more about the Komprise architecture. So, the real problem with data virtualization is not the concept, but its implementation. Now you can experience the benefits of data virtualization, without impacting your primary data, with Komprise Intelligent Data Management — the invisible data housekeeper. ### 2017 End of the Year Message from the CEO From the desk of Komprise CEO, Kumar Goswami. As 2017 comes to a close, I wanted to take a moment to reflect back on all of the strides that we made this year and thank all of our customers, partners, and employees.It has been a year of monumental growth for Komprise. From the desk of CEO: Kumar Goswami As 2017 comes to a close, I wanted to take a moment to reflect back on all of the strides we made this year and thank all of our customers, partners and employees. It has been a year of monumental growth for Komprise. We scaled our team and hired exceptional talent, including our VP World Wide Sales, and established new field offices around the world. In 2017, we expanded our customer base over five-fold, welcoming new customers into the Komprise family — reaching nearly every major vertical, globally. A special thank you to our customers for trusting Komprise with petabytes of data and for sharing how they leverage Komprise — Customer Stories. We look forward to helping our customers solve more data management challenges and to sharing more of their stories with you. Additionally, our Komprise Intelligent Data Management solution has expanded significantly, thanks to customer feedback. We added key features such as deep analytics, data confinement, and cloud data cost controls. Our engineering team is still hard at work and excited to share more advances. Make sure to watch for a very exciting announcement, early in the new year! We are very appreciative of the awards we received in 2017, as well as the media recognition — View Recognition — highlighting our focus on intelligent data management and commitment to eliminating the cost, complexity and disruption of legacy data management approaches. Once again, many thanks to our customers, partners, employees and well-wishers — here is a toast to 2018. Happy Holidays! Kumar Goswami CEO of Komprise ### Komprise extends data management capabilities in 2.6 flagship release, shows record growth Full lifecycle management abilities, the ability to confine defined data to make it invisible without deleting it, and the ability to build custom queries. Komprise adds full lifecycle management abilities, as well as the ability to confine defined data to make it invisible without deleting it, and the ability to build custom queries: Read the Article ### Komprise Broadens Support for Dell EMC Systems Komprise has expanded the Dell EMC solutions they work with reacting to customer requests they support their mid-market line... Komprise has expanded the Dell EMC solutions they work with reacting to customer requests they support their mid-market line—which could wind up being rebranded: Read the Article ### How Pacific Biosciences Manages Data at Scale with Komprise & Netapp Pacific Biosciences (PacBio) is accelerating the pace of genomic discovery, utilizing a data fabric that is enabled by technology partners: NetApp and Komprise Intelligent Data Management. Genome Analysis Genome analysis is transforming the world — from finding cures for diseases, such as Parkinson’s and Cancer, to wildlife conservation, and even improving the world’s food supply. Pacific Biosciences (PacBio) is accelerating the pace of genomic discovery, utilizing a data fabric that is enabled by technology partners: NetApp and Komprise Intelligent Data Management. The PacBio story highlights the common challenges that customers face with today’s massive scale of data and how to address them. “You know the cloud is there, what the new storage offerings are, you have this need to consolidate on premise. And you have this massive data growth.  You get scared – how am I going to massively manage all of this data?” said Jay Smestad, Senior Director of IT at Pacific Biosciences. Plan Capacity Growth and Hybrid Cloud Strategies with Insight Data is growing fast and IT budgets are tight... Key decisions must be made: how to handle the exploding data with a flat budget, when and how to adopt new storage technologies, and what storage infrastructure update is right for the organization…This can feel overwhelming. Komprise provides visibility across an organization’s storage infrastructure, into their data—showing how the data is growing, by who, and how often it is being accessed. “What’s great is Komprise showed how much data hadn’t been touched in two years, one year, six months, three months, etc. It showed that 60% of the data could actually be moved off to capacity storage; be it object, scale-out, tape or off site to the cloud, and the ROI. It showed that we had 35% data growth within three months – and I could use it to explain why we needed more money for storage,” said Jay Smestad. Optimize Data Footprint for Today and the Future Armed with a plan of how to handle hot and cold data, the next key is to put the plan into action—without disrupting user and application access. Since data extends across millions of files, thousands of folders, and multiple storage servers… a scale-out, policy-based, storage automation solution is needed. One that focuses performance and capacity. “Komprise gives us that single pane of glass where I can see what the costs are, where the data is, and be able to manage that from a single place and it’s easy to use.  We standardized on NetApp storage because it offers the most flexibility on a proven platform, with primary storage on NetApp FAS systems and ultra-dense archive on E-Series. NetApp understands we want choice, and is delivering on the vision of the data fabric; both through their broad storage portfolio and integration with partners like Komprise,” said Jay Smestad Accelerate Business with Big Data Planning for the future and finding ways to glean relevant information easily, from data across on premise and cloud data stores, becomes important. Komprise integrates with NetApp StorageGRID Webscale object storage to make tagging and finding data faster and easier—providing new ways to search, find and gain insights from relevant data. “We’re talking about having users being able to find data. How do you find data when you have six, ten, hundred petabytes? That becomes really hard. How do you have the flexibility to move things to cloud and back?  You need something to do that – you need massive scale data management," said Jay Smestad. ### Intelligent Data Management at Cadence Cadence Design Systems, Inc. selects Komprise Intelligent Data Management Solution. Cadence Design Systems Selects Komprise Cadence Design Systems, Inc. is deploying the Komprise Intelligent Data Management solution. Cadence plays a key role in the electronics and semiconductor industry, providing design automation software and engineering services, vital for innovation. By trusting Komprise, Cadence gains visibility across its global storage infrastructure and transparent data archiving—cutting costs and increasing efficiency. The Komprise Architecture A key reason why Komprise is a great fit for engineering and semiconductor firms, is our scale-out, adaptive architecture. The Komprise software runs on-demand and non-intrusively in the background, while analyzing and managing data with performance. Major factors that led Cadence to choosing Komprise: Visibility into data growth & usage across storage – Komprise provides visibility into how data is growing and being used, across all NFS, SMB/CIFS, REST/S3. Since over 50% of data is rarely accessed after a year in these environments, the savings of moving this footprint to the cloud are substantial. No disruption to production engineering usage – Since engineering is actively using the primary storage, they cannot afford to have any data management solutions that disrupt performance on these shares. Komprise runs in the background and throttles itself back to be invisible to active usage on the primary storage.  Transparent file-based access to moved data in the cloud – Komprise moves data to targets of the customer’s choice (cheaper NAS, object storage, or cloud) and preserves transparent file-based access to the moved data. Users and applications continue to see and access their data exactly as before, without any disruption. This way, the business can save substantially by leveraging cloud/object storage without any changes to user and application access. Linear scaling to handle billions of files, petabytes of data – Komprise scales linearly by simply adding more virtual machines – there are no central databases, servers or bottlenecks that limit scalability. Komprise is designed to be highly efficient with minimal footprint and to handle billions of files, petabytes of data, and thousands of shares, with ease. The Modern Approach to Data Management Komprise eliminates the cost and complexity of legacy data archiving solutions. An analytics-driven data management software, working across on-premise and cloud storage, Komprise empowers businesses to manage data growth at massive scale, while unlocking data value. A patent-pending solution, No Agents, No Complexity No agents, no dedicated hardware or storage, no complex setup or proprietary integration. Komprise delivers native access to data in the cloud and is delivered as a hybrid cloud Software-as-a-Service (SaaS). So, Why Komprise? “Komprise stood apart from other solutions we evaluated because it provides visibility across our storage infrastructure and manages data at scale at a fraction of the cost,” said Carl Siva, senior IT group director, Architecture and R&D Solutions at Cadence. “We had Komprise up and running in minutes, and we quickly gained insight into how much of our data was not being actively used, so that we could archive it to the clouds of our choice, all without any disruption to users or applications. As our business grows, our data grows, and Komprise offers flexibility to meet the requirements of our business.” Learn more about Komprise for engineering and semiconductor enterprises. ### Major Funding Secured by Komprise After 3 years of successful growth, we have secured an additional $12M in investment funds, helping to fuel our continued expansion and growth. Funding was led by Walden International, with participation from existing investor Canaan Partners and notable luminaries - including Bill Moore (co-founder ZFS, EMC Fellow) and Sanjay Mehrotra (former CEO of SanDisk). $12M B series funding secured After 3 years of successful growth, we have secured an additional $12M in investment funds, helping to fuel our continued expansion and growth. Funding was led by Walden International, with participation from existing investor Canaan Partners and notable luminaries - including Bill Moore (co-founder ZFS, EMC Fellow) and Sanjay Mehrotra (former CEO of SanDisk). Innovation is the key to success, that is why we are continuously striving to improve the Komprise Intelligent Data Management software. At Komprise, we have been disrupting the multi-billion dollar, unstructured data management industry for the past couple years. We utilize a customer-led strategy and are committed to helping our customers manage their data with insight and cut storage costs. With the additional support, we can continue to grow our customer and partner base, as well as expand into new markets. ## Glossary of Key Terms > Authoritative definitions for terms used across Komprise documentation, product content, and industry discourse. ### AI and Machine Learning Concepts > Definitions for AI, agentic AI, shadow AI, LLMs, inferencing, and related terms in the context of enterprise data management. #### AI Data Preparation AI data preparation is the process of discovering, filtering, organizing, enriching, and delivering the right data, at the right time, to fuel AI and machine learning models. AI data prep includes: Data Discovery: Finding the relevant data across fragmented silos Data Curation: Selecting high-quality, representative, and useful datasets Metadata Enrichment: Adding context through metadata tagging, classification, and labeling Data Validation: Ensuring completeness, compliance, and quality Data Ingestion: Moving or virtualizing the data into AI pipelines or model training environments While data preparation has traditionally focused on data that resides in structured databases, data warehouses and data lakes, the real challenge today is unstructured data, which lacks schemas, varies widely in format, and often lives in storage systems that are not visible or accessible to to AI teams. AI is only as good as the data that feeds it While much attention has been paid to structured data sources and traditional ETL data pipelines, the next frontier of AI innovation lies in unstructured data: documents, images, videos, logs, sensor files, etc. that are often reside in NAS devices in the enterprise, which make up more than 80% of enterprise data. Yet unstructured data is often overlooked, poorly governed, and underprepared, leading to delays, inaccuracies, and unnecessary costs in AI initiatives. To deliver meaningful, enterprise-grade AI outcomes, organizations must rethink how they prepare, govern, and manage unstructured data for AI, starting at the source. Why Unstructured Data Is Often Overlooked—But Critically Important Many AI projects stall because teams spend too much time looking for, duplicating, or cleaning data rather than building or training models and driving business outcomes. Here’s why unstructured data preparation is often neglected: Hard to access: Locked in SMB/NFS/S3 file systems, often deep in cold storage or backups No schema: Lacks a clear structure, making it harder to classify or filter at scale Siloed ownership: Managed by IT or storage teams, not data scientists (see data silos) Metadata gaps: Missing context (who owns it, what's in it, is it sensitive?) And yet, unstructured data holds the richest signals for AI, including natural language, conversations, documents, imagery, and behavior logs that can feed foundation models, copilots, and predictive analytics. Key Steps for Successful AI Data Preparation (Especially for Unstructured Data) Global Data Discovery & Visibility: Identify where unstructured data lives across hybrid storage environments—on-prem, cloud, archives, etc. Metadata Enrichment & Classification: Use intelligent tagging, NLP, and PII detection to classify data by content, owner, usage, and risk. Data Curation & Filtering: Avoid copying petabytes. Use smart filters (age, last access, sensitivity, project tags) to extract only what’s needed. Automated Data Movement or Virtualization:  Migrate or tier selected datasets to AI pipelines or cloud environments, without disrupting users or production systems. Governance & Access Control: Ensure data access aligns with compliance, usage policies, and audit requirements. Iterative Refinement: Allow AI teams to request, refine, and improve data sets over time—ideally via governed self-service. The Role of Komprise: From Storage Optimization to AI Enablement Komprise was built to help enterprise IT organizations optimize storage at scale, but its true power lies in delivering value-added data services that enable AI and data-driven transformation. Here's how: Value Komprise Delivers Why It Matters for AI Global metadata index across all file/object data Enables rapid discovery of useful unstructured datasets Intelligent tagging, classification, and search Adds the context AI teams need to curate and understand data Smart data movement based on policies and usage Reduces data sprawl and accelerates pipeline readiness In-place analytics and non-disruptive scans Avoids costly re-ingestion or duplication of petabytes of data Role-based access and governance policies Ensures responsible, compliant data access for AI use cases Komprise Intelligent Data Management bridges the gap between storage teams and data consumers, helping organizations shift from simply storing unstructured data to strategically activating it for AI, analytics, and innovation. Unstructured data is no longer a cost to manage, it’s an asset to mine. But to do so effectively, enterprises need to modernize their data preparation approach, rethink collaboration between storage and data teams, and invest in the right unstructured data management solutions that can unlock the potential of unstructured data without breaking the budget or workflow. #### AI Data Extraction AI Data Extraction is the automated process of identifying, retrieving, and structuring relevant information from raw data sources, especially unstructured data or semi-structured data content like documents, emails, images, and logs, to make it usable for AI models. This is a critical first step in AI data pipelines because AI systems require well-organized, context-rich inputs to deliver accurate and meaningful results. The Challenges of Traditional ETL for AI Data Preparation & Ingestion Traditional ETL (Extract, Transform, Load) pipelines were designed primarily for structured data in relational databases, not for the complexity of modern, large-scale unstructured data, which now represents 80–90% of enterprise data. Here are the key challenges: ETL is a Poor Fit for Unstructured Data: ETL tools are optimized for tabular data. They struggle with files like PDFs, videos, CAD drawings, or medical images that have no fixed schema. Rigid Pipelines: ETL processes are often brittle, with predefined workflows that don’t adapt well to dynamic or diverse file types and metadata schemas typical in unstructured environments. Heavy Preprocessing Burden: AI models need rich metadata, context, and sometimes embedded content (like from a document or slide). ETL doesn’t natively extract this or handle things like file relationships, time-series from logs, or cross-file context. Scalability and Cost: Moving petabytes of file data into central repositories for transformation is costly and slow, especially across hybrid or multi-cloud architectures. Loss of Data Context: Important attributes like access patterns, user behavior, storage tier, or security policies are lost when data is flattened or transformed outside its native environment. How Komprise Takes a Different Approach to Data Preparation for AI Workflows Komprise offers a modern, Intelligent Data Management architecture purpose-built for unstructured data in distributed environments. The Komprise approach to AI data preparation is distinct in three key ways: 1) In-Place Analytics and Metadata Extraction Komprise scans and analyzes file and object data in place, without needing to move it. The Komprise Global Metadatabase collects rich deep metadata, including access times, user activity, file lineage, tags, and content snippets, which is crucial for AI data filtering and enrichment. 2) Smart Data Tiering & Virtualized Views Rather than force a lift-and-shift ETL model, Komprise allows AI tools to access just the data they need, wherever it lives—on-premises or in the cloud. The Komprise metadatabase is a Global File Index, which powers Deep Analytics and enables federated search and virtual curation of training data without disrupting storage or access controls. 3) Tagging & Curation at Scale Komprise supports custom tagging of files at scale across heterogeneous storage platforms. This helps enterprises prepare domain-specific datasets (e.g., for RAG or fine-tuning LLMs) with consistent context, without copying or reprocessing entire data sets. While traditional ETL is inflexible and not optimized for unstructured, distributed data, Komprise offers a metadata-driven, storage-aware, and cloud-native approach. This enables AI and analytics teams to: Discover and extract the right data quickly Maintain compliance and unstructured data governance Accelerate time-to-insight without bloated infrastructure #### AI Data Management AI data management is the set of processes, tools, and practices used to manage the data that feeds AI models, both during training and inferencing. It includes: Finding and curating the right data Moving and preparing data for AI pipelines Ensuring data is high-quality, compliant, and properly tagged (see data tagging) Optimizing where data is stored and how it’s accessed Tracking data lineage and governance for responsible AI AI data management is the infrastructure and process layer that makes sure AI has the right data to work with. The Important Role of Unstructured Data in AI Data Management Most enterprise data today is unstructured: files, documents, images, videos, audio, PDFs, emails, sensor logs, etc. AI (especially foundation models and generative AI) thrives on unstructured data. Most AI use cases in the enterprise involve unstructured data. For example: LLMs → text, emails, reports Multimodal AI → images + text + video AI search & retrieval → documents, PDFs, data lakes AI-powered compliance → identifying sensitive files and preventing AI data leakage So, managing unstructured data is critical to making enterprise AI effective. Komprise for AI Data Management Komprise helps enterprises manage and prepare unstructured data for AI. Here are some examples of Komrpsei AI data management: Data Discovery & Curation The Komprise Global File Index, or Metadatabase, catalogs unstructured data across storage silos. Find and classify relevant data for AI projects Tag and enriches data so AI models can understand it Data Mobility & Preparation Komprise can move or copy data to AI-friendly environments (e.g., cloud object stores, data lakes). With Komprise you can ensure only high-value, relevant data is fed to AI pipelines, which reduces costs and noise. Data Tiering & Cost Optimization Komprise customers are able to optimize data storage by keeping hot AI data on fast, high-performance storage, while moving cold data to lower cost tiers of stage. This approach saves on data storage costs and cloud egress costs, which can explode in AI pipelines. Storage-Agnostic Metadata Catalog The Komprise Global File Index is a searchable, vendor-neutral metadata layer AI tools and pipelines can query this catalog to discover useful data. Governance & Compliance Komprise helps track data lineage (where data came from, how it was transformed), which is essential for trustworthy and auditable AI. Komprise Smart Data Workflows also can identify and protect sensitive data in AI pipelines (e.g., PII, IP). To summarize, without Komprise unstructured data is siloed, hard to find or move. With Komprise, unstructured data is cataloged and easy to curate for AI. Without Komprise Intelligent Data Management, AI pipelines can waste compute on noisy data AI models, whereas with Komprise you get only relevant, high-value data.  AI data management can be an expensive and manual proposition. Komprise is focused on delivering automated, optimized data workflows with clear governance, tagging, and lineage tracking. #### AI Inferencing AI inferencing is the process of using a trained machine learning (ML) or deep learning model to make predictions or decisions based on new input data. It’s the phase after training, where the model applies what it has learned to real-world scenarios, such as classifying images, transcribing audio, or generating text. Inferencing happens in environments where speed and efficiency are critical: cloud platforms, edge devices, mobile apps, or enterprise applications. For example: A recommendation engine showing content on Netflix A chatbot responding to customer questions An autonomous car identifying road signs in real time The Importance of Unstructured Data for AI Inferencing AI models, especially large language models (LLMs) and multimodal models, rely heavily on unstructured data for both training and inferencing. Examples include: Text documents, emails, PDFs (natural language processing) Images, videos, medical scans (computer vision) Audio recordings, sensor logs (speech and signal processing) At inference time, these models often consume unstructured inputs, such as: A radiology image for diagnostic classification A customer service transcript for sentiment analysis An enterprise document for summarization or Q&A Without access to high-quality, context-rich unstructured data, AI inferencing can't produce relevant, accurate results,  especially in enterprise use cases where private data (vs. public web data) is the most valuable. How Does Komprise Intelligent Data Management Support AI Inferencing? Komprise specializes in unstructured data management, with capabilities highly relevant to AI inferencing workflows: Data Discovery & Indexing: Komprise indexes and classifies unstructured data across heterogeneous data storage systems (on-prem, NAS, cloud), helping organizations find relevant data for inference tasks. Metadata-Driven Insights: Komprise builds a metadata catalog of files, or metadatabase, which can be searched and filtered based on content, age, usage, owner, etc. This supports use cases like: Finding unstructured datasets for inferencing Identifying compliance risks (e.g., inference using PII-containing data). Learn more about Komprise sensitive data detection and PII data. Data Preparation for AI Pipelines: While not an ETL tool, Komprise can help curate and organize unstructured data so it’s ready for ingestion into AI pipelines, e.g., by exporting data to object stores (S3) used by inference engines or ML frameworks like Databricks, AWS SageMaker, or Azure ML. Intelligent Data Tiering: Komprise enables policy-based movement of cold or inactive data to lower-cost storage, while maintaining access, which is essential for keeping inference pipelines cost-effective. Learn more about Komprise Transparent Move Technology. #### AI Agents AI agents are software programs that autonomously perform tasks using artificial intelligence. Also referred to as agentic AI, AI agents should be able to perceive their environment, have built-in logic to make decisions, and take appropriate actions to achieve specific outcomes. Examples of AI agents include: Customer service bots Autonomous data analysis tools AI copilots for IT operations Agents orchestrating workflows in tools like Microsoft Copilot, OpenAI, or Databricks The 2025 Trends – Artificial Intelligence (AI) report by Mary Meeker, Jay Simons, Daegwon Chae, and Alexander Krey noted: AI is changing how we interact with the world around us. With affordable satellite connectivity expanding access to remote and underserved regions, the next wave of internet users will likely come online through AI-native experiences – skipping traditional app ecosystems and jumping straight into conversational, multimodal agents. The report goes on to note that, "platform incumbents and emerging challengers are racing to build and deploy the next layers of AI infrastructure: agentic interfaces, enterprise copilots, real-world autonomous systems, and sovereign models." AI Agents Need Unstructured Data Enterprise AI agents are increasingly being designed to interact with unstructured data, which includes files like documents, emails, images, videos, and logs. This is because: 90%+ of enterprise data is unstructured. (source IBM) Unstructured data contains rich context and institutional knowledge essential for training LLMs and powering generative AI. AI agents require unstructured data to: Answer user questions accurately. Summarize or translate internal documentation. Extract insights from contracts, emails, or logs. Make informed decisions based on historical data. But AI agents can't use what they can't access. That's where the challenge lies. Challenges in Feeding Unstructured Data to AI Data silos across storage platforms (on-prem NAS, cloud object storage, etc.) contribute to a variety of issues: Massive data volumes to search from as many enterprises have petabytes of file data. High cost of storing and processing irrelevant or cold data. Data governance and security requirements. Lack of visibility into what data is useful for AI. Komprise Helps Enterprise IT with AI Data Readiness Komprise gives enterprise IT a flexible platform to manage unstructured data at scale, independently of data storage platform. Here's how Komprise can be part of an agentic AI strategy: Data Discovery & Curation Komprise Deep Analytics and custom tagging are used to search, find and classify and unstructured file and object data across environments. Helps curate the “right data” for AI uses on based on age, type, owner, access frequency, and other characteristics Supports intelligent data selection for LLM training or inferencing Intelligent Data Tiering Komprise automatically moves cold data to cheaper storage based on a data management policy (e.g., cloud object storage) without disrupting user or application data access. Optimizes data storage costs while ensuring AI agents can still reference older data when needed. Metadata Catalog & Indexing Komprise builds a rich metadata catalog, or metadatabase, across all unstructured data, decoupled from storage. Integrate with AI data pipelines, enabling search, filtering, and ingestion of specific unstructured datasets. Data Mobility Komprise can be used to find, move, copy, etc. curated datasets to AI/ML platforms. Supports multi-cloud and hybrid cloud use cases. Storage-Agnostic AI Data Foundation Because Komprise works across data storage vendors (Pure, VAST, Dell, NetApp, AWS, Azure, Wasabi, etc.), it liberates unstructured data from vendor lock-in, which is critical for enterprises building vendor-neutral AI strategies. AI Agents Need Unstructured Data Here is a summary of the importance of unstructured data for AI agents and agentic AI success as well as the role Komprise Intelligent Data Management can play: Data sprawl: Find and feed the right data with a global file index (metadatabase), analytics and data tagging. Cost management: Efficient access to data locked in storage silos with intelligent data tiering and archival. Scalability: Handle petabyte-scale data workloads with parallelized indexing and mobility. (Learn more about the Komprise architecture.) Data curation: Prepare AI-ready data with a metadata catalog and smart data filtering and unstructured data classification. AI/ML integration: Input to LLMs and models with a data movement to feed AI platforms. #### Shadow AI Shadow AI is the unknown, unauthorized or unmanaged use of AI tools, models, or services within an organization, outside the visibility and governance of enterprise IT, security, and compliance teams. It’s an evolution of Shadow IT, but with significantly higher data risk, regulatory exposure, and ethical complexity, especially in the age of GenAI and LLMs. Read the white paper Unstructured Data Management In the Age of Generative AI. Shadow AI is a Growing Problem The 2025 Komprise IT Survey: AI, Data & Enterprise Risk showed IT organizations are concerned about shadow AI, with nearly half stating that they are “extremely worried” about the security and compliance impact of unauthorized and unsanctioned use of AI tools. Data-heavy enterprises are especially vulnerable to the risks created of shadow AI due to: Vast amounts of unstructured data in shared drives, clouds, and personal folders Employees feeding sensitive data into tools like ChatGPT, GitHub Copilot, Claude, or private AI apps Business units or developers deploying AI models on local or cloud environments without centralized oversight Proliferating shadow AI can lead to: Data leakage or IP exposure Regulatory non-compliance (e.g., GDPR, HIPAA, SEC) Model misuse or bias Security and privacy blindspots Inaccurate, false or suboptimal results due to lack of standards and guidelines for proper use of AI Strategies to Prevent or Manage Shadow AI Here are some strategies enterprise IT organizations are implementing to address shadow AI:   Data Management & Security Technology Most enterprise IT leaders (75%) are planning to use data management technologies to address risks from shadow AI, followed closely by AI discovery and monitoring tools (74%), according to the 2025 Komprise IT Survey: AI, Data & Enterprise Risk. Organizations are also focusing on classifying sensitive data and using workflow automation to prevent its improper use with AI (73%), according to the survey. Policy + Education Develop an AI Acceptable Use Policy (AUP) and disseminate it broadly with training Run awareness campaigns about the risks of unauthorized AI usage Provide sanctioned alternatives to consumer AI tools (e.g., Azure OpenAI with enterprise governance) Centralized AI Data Governance Establish a cross-functional AI governance committee (IT, Legal, Compliance, Security, Data Science) Define AI model approval, lifecycle, and audit requirements Use ML model registries and access control platforms (e.g., MLflow, ModelDB) Adopt data governance tools and standards for AI Establish Security Controls Use AI discovery tools and/or CASBs (Cloud Access Security Brokers) to detect unauthorized API access or AI tool usage Implement data loss prevention (DLP) controls to detect uploads of sensitive data to AI tools Monitor file movement into AI-related apps or platforms using data governance and auditing tools Leverage unstructured data management solutions to create sensitive data management processes Audit and Monitor Data Sources Know what data is being accessed and by whom—especially large, unstructured datasets Enforce least-privilege access and activity logging for data lakes, cloud buckets, NAS, etc. How Komprise & Unstructured Data Management Can Help Address Shadow AI in the Enterprise Unmanaged unstructured data is where Shadow AI risks often begin. Komprise can play a critical preventative and monitoring role: Data Visibility: See Across Storage Silos Komprise scans and catalogs all unstructured data (across NAS, cloud, object storage) so that IT knows what data exists, where, and how it's being accessed. Shadow AI often feeds on "dark data." Komprise helps shine light on it. Unstructured Data Classification & Data Tagging Komprise supports tagging files by sensitivity, business unit, or keywords. IT and end users with the right data access controls can label regulated or high-risk data (e.g., PII, R&D, contracts) and restrict its use in AI training or prompts. Access Pattern Analysis Komprise analyzes who is accessing which data, how often, and from where. Unusual access spikes or transfers of large files to unauthorized cloud platforms can be flagged as potential Shadow AI indicators. Policy-Driven Data Management Komprise Intelligent Data Management automatically tiers or archive data so that non-essential files are less accessible. With Komprise Smart Data Workflows you can enforce policies that restrict copying, moving, or exporting sensitive unstructured data. Audit Trails and Forensics The Komprise metadata catalog, or metadabase, plus historical logs offer evidence in case of AI-related data leaks. This visibility helps with post-incident investigations and compliance reporting The Real Problem of Shadow AI Shadow AI is a serious and growing risk, especially for enterprises with large volumes of unstructured data. The solution is not to block tools our outlaw AI, which can backfire and is largely ineffective. Instead, IT leaders can deliver broad visibility, governance and responsible use of AI. Unstructured data management platforms like Komprise provide essential foundations for: Discovering hidden data risks Tagging and restricting AI-sensitive data Monitoring usage patterns that could signal Shadow AI activity #### AI and Corporate Data Corporate data and AI are deeply intertwined, with businesses increasingly running artificial intelligence technologies to extract insights, automate processes, and enhance decision-making. As 90% of data that is being created everyday in the enterprise is unstructured data, it's increasingly important to establish a comprehensive unstructured data management and data governance strategy. Some areas where AI is transforming corporate data management include: AI Data Management & Governance AI-powered Data Classification: Automatically categorizes structured and unstructured data. Data Quality & Cleansing: AI detects and corrects inconsistencies, missing values, and errors. Metadata Management: AI-driven tagging and indexing improve searchability and governance. AI for Business Intelligence & Analytics Predictive Analytics: Forecasts trends, customer behavior, and operational needs. Natural Language Processing (NLP): Enables conversational AI-driven data querying. Automated Insights: AI finds patterns and correlations in vast datasets. AI in Security & Compliance Anomaly Detection: Identifies fraud, security breaches, and operational risks. PII & Sensitive Data Protection: AI-driven redaction and masking. Regulatory Compliance: Automates policy adherence (e.g., GDPR, HIPAA). AI-driven Automation & Optimization Intelligent Process Automation (IPA): Automates workflows, reducing manual tasks. AI-powered Data Pipelines: Automates the kind of workflows historically done by ETL (Extract, Transform, Load) tools, but now are being performed by a new set of modern technologies. Read interview with Komprise COO on AI data pipelines. AI for IT & Infrastructure Optimization: Enhances cloud cost management and resource allocation. AI and Corporate Decision-making AI-assisted Decision Support Systems: Provides data-driven recommendations. AI-powered Financial Forecasting: Enhances investment and budgeting strategies. Customer & Market Intelligence: AI extracts insights from social media, surveys, and market data. Risks of AI with Corporate Data It is still early days for most companies when it comes to running AI on corporate data sets, but increasingly, data storage teams are responsible for data governance and compliance. Unfortunately most lack ways to do this systematically across their data estate. Some of the risks associated with AI and corporate data include: AI Data Privacy & Security Risks Unauthorized Access & Data Breaches: AI systems often process vast amounts of sensitive data, making them attractive targets for cyberattacks. The global average cost of a data breach reached $4.88 million in 2024, according to IBM. PII & Confidential Data Exposure: AI models may inadvertently expose personally identifiable information (PII) or proprietary business data. According to Menlo Security, attempts to input sensitive data such as PII into GenAI platforms represent over half of data loss prevention (DLP) events, followed by confidential documents (40%). Shadow AI Risks: Employees may use unauthorized AI tools, leading to compliance and security risks. AI Bias & Ethical Concerns Algorithmic Bias: AI models trained on biased datasets can produce unfair or discriminatory outcomes. Ethical Decision-making Challenges: AI-driven decisions (e.g., hiring, lending, promotions) may unintentionally reinforce systemic biases. Lack of Transparency: Many AI models, especially deep learning, operate as "black boxes," making it difficult to audit or explain decisions. AI Compliance & Regulatory Risks Regulatory Violations: AI-driven data processing must comply with laws such as GDPR, CCPA, and HIPAA, and non-compliance can lead to heavy fines. AI-generated Content Risks: Incorrect or misleading AI-generated reports, summaries, or insights can result in legal liabilities. Data Retention & Ownership Confusion: AI models may retain or generate derivative data, complicating compliance with data deletion policies. AI Data Integrity & Quality Risks Hallucination & Inaccuracies: AI models, particularly generative AI, may produce incorrect, outdated, or misleading results. Data Poisoning: Attackers can manipulate training data to skew AI outcomes, leading to faulty insights or decisions. Over-reliance on AI: Automating decision-making without human oversight can lead to critical errors if the AI model is flawed. AI Operational & Financial Risks Model Drift & Performance Degradation: AI models degrade over time as real-world data changes, requiring ongoing monitoring and retraining. High Implementation & Maintenance Costs: AI adoption requires significant investment in infrastructure, talent, and governance frameworks. Job Displacement & Workforce Challenges: Automating tasks with AI can create resistance from employees and require reskilling initiatives. AI Intellectual Property & Competitive Risks Data Dependency on Third-Party AI Providers: Businesses using third-party AI solutions risk vendor lock-in and loss of control over their data. IP Ownership Issues: AI-generated content and insights may create legal disputes over intellectual property rights. Competitor AI Spying & Model Inference Attacks: AI models can be reverse-engineered, exposing proprietary data patterns or trade secrets. Mitigating AI Risks in Corporate Data To minimize these AI risks, organizations should implement robust AI governance frameworks, including: Data encryption & access controls to protect sensitive information. Bias audits & fairness checks for AI models. Human oversight & explainability in AI decision-making. Regulatory compliance tracking to adapt to evolving laws. Cybersecurity measures to prevent AI data leakage and cyber attacks. #### AI Data Pipelines AI data pipelines are a process and supporting technology to curate data from multiple sources, prepare the data for proper ingestion, and then mobilize the data to the destination. Data pipelines for unstructured data have special considerations since unstructured data is large, diverse, and difficult to search, organize and move. AI Data Pipeline Requirements for Unstructured Data IT organizations need streamlined, automated ways to find and tag data for classification and search and deliver the right datasets to the right tools. AI data pipelines also must include methods to ensure data security and governance. A global file index that can look across all storage facilitates search and curation of unstructured data for AI, including metadata enrichment. AI data pipelines can also detect sensitive data and move it into secure storage where it cannot be discovered nor ingested into an AI tool. Most organizations have PII (Personal Identifying Information), IP and other sensitive data inadvertently stored in places where it should not live. Data pipelines can also be configured to move data based on its profile, age, query, or tag into secondary storage—such as cloud object storage where it is significantly cheaper to host and where researchers and data scientists can access it natively for use in cloud-based AI services. Since unstructured data often lives across storage silos in the enterprise, it’s important to have a plan and a process to manage this data for storage efficiencies, AI, data protection and compliance. Data pipelines aided by a global file index and metadata tagging can help with all these needs. You’ll need various capabilities, many of which are part of an unstructured data management solution. For example, metadata tagging and enrichment – which can be augmented using AI tools – allows data owners to add context and structure to unstructured data so that it can be easily discovered and segmented. Read the interview on Blocks & Files: AI Data Pipelines Could Use a Hand from Our Features, Says Komprise Komprise Smart Data Workflows for AI Data Pipelines Komprise Smart Data Workflow Manager, included in the Komprise Intelligent Data Management Platform, is a simple UI that allows users without specialized experience to set up, schedule and monitor workflows, including connecting via API to third-party AI services. Duquesne University used Komprise Smart Data Workflow Manager to create an AI data pipeline for rapid image search across millions of files in its digital archives. The workflow sent images to AWS Rekognition which analyzed file contents to find specific images needed for marketing campaigns, which Komprise then tagged for future search. The process reduced a 300-plus manual hour effort to less than two hours and demonstrated a repeatable use case for other departments. Read more Learn more about Komprise Smart Data Workflow Manager Watch a Demo #### AI Data Leakage AI data leakage occurs when sensitive, private, or proprietary information becomes accessible or exposed through the training, deployment, or usage of AI systems. This exposure can happen at various stages of the AI lifecycle and can lead to privacy violations, intellectual property theft, or misuse of sensitive data. The paper Unstructured Data Management Strategies in the Age of Generative AI includes an AI data governance framework that defines the governance priorities enterprises will need to address using the acronym SPLOG: Security: Data must be secured against unauthorized access or tampering by malicious actors. Privacy: Data must be private, meaning only those individuals who are authorized to view it can access it. Lineage: Businesses must track data lineage, which requires understanding the source (and accuracy/authenticity) of data used in AI models. Ownership: It must be clear who has stewardship over data and is therefore responsible for managing, securing, sharing and addressing any concerns that arise on its usage. Governance: Businesses must have explicit governance standards in place that reflect the priorities defined above, with tools and processes to execute and enforce them As AI data leakage stories continue to reach the mainstream press, the only way to ensure that generative AI tools and services can generate insights based on unstructured data while simultaneously protecting organizations from data leakage, privacy and ethics violations and even lawsuits is to define and execute an AI data governance framework with strong compliance measures built in. What are some types of data leakage in AI? Training Data Leakage: Occurs when sensitive data from training datasets is memorized by the model and unintentionally exposed during inference. Example: A model trained on customer service logs that inadvertently reveals personal details during responses. Inference Leakage: Happens when attackers extract sensitive information from a model by carefully crafted queries. Example: A membership inference attack determines if specific data was part of the training set. Model Leakage: Involves exposing the model's internal structure or parameters, which may reveal sensitive training data or intellectual property. Example: Reverse-engineering a model to understand its training data or proprietary algorithms. Deployment-Phase Leakage: Sensitive data might be leaked during the deployment of AI systems if security measures are inadequate. Example: Improper encryption of data in transit or storage. Data Pipeline Leakage: Occurs when data is intercepted or mishandled during preprocessing, transfer, or storage. Example: Unsecured APIs leaking raw input data during data collection. What are some potential risks and implications of AI data leakage? Privacy Violations: Breach of user privacy, potentially violating regulations like GDPR or HIPAA. Security Risks: Exposed data can be exploited for phishing, identity theft, or other malicious activities. Reputational Damage: Companies can suffer severe brand damage and loss of customer trust. Regulatory Penalties: Non-compliance with data protection laws can result in fines and legal consequences. Intellectual Property Theft: Sensitive business information or proprietary algorithms may be compromised. What are some of the causes of AI data leakage? Insufficient Data Anonymization: Sensitive data is inadequately anonymized before training or sharing. Overfitting Models: Models that memorize training data rather than generalizing can inadvertently expose sensitive information. Poor Security Practices: Weak encryption, improper access controls, or insecure data storage. Unfiltered Data Sharing: Sharing datasets without ensuring sensitive information is removed. Adversarial Attacks: Exploitation of AI system vulnerabilities by malicious actors. Read the solution brief: How to protect unstructured data from ransomware at 80% lower cost. What are some mitigation strategies to prevent AI data leakage? Data Preprocessing: Use data anonymization or pseudonymization to remove identifiable information. Implement differential privacy to ensure individual contributions cannot be distinguished. Model Design: Train models to avoid overfitting, which reduces the risk of memorizing sensitive data. Consider using federated learning to keep data localized and minimize exposure. Security Measures: Encrypt data at rest and in transit. Implement robust access controls and monitoring for data and systems. Regular Audits and Testing: Conduct penetration testing and vulnerability assessments to identify and address risks. Use techniques like membership inference testing to check for data leakage. Policy and Compliance: Follow data governance policies and ensure compliance with legal frameworks like GDPR, CCPA, or HIPAA. Post-Deployment Monitoring: Continuously monitor AI systems for signs of anomalous behavior or breaches. What are some emerging solutions to manage AI data leakage? Privacy-Preserving Machine Learning: Techniques such as homomorphic encryption, secure multiparty computation, and differential privacy. Explainability and Transparency Tools: Tools to audit and understand AI model behavior to detect potential leaks. Synthetic Data: Use synthetic datasets that mimic real-world data without containing sensitive information. AI-Specific Security Frameworks: Adoption of AI-tailored cybersecurity protocols and processes to protect data pipelines and models. Learn more about the Komprise Smart Data Workflow Manager for AI. #### AI Data Ingestion AI Data Ingestion (or AI ingestion) is the process of discovering, preparing and moving data from various sources such as applications and storage systems into AI tools and services for processing, analysis and/or training machine learning (ML) models. AI data ingestion in corporate environments consists primarily of leveraging unstructured data, such as user documents, PDFs, chat and text files, multimedia files, or instrument data. Since unstructured data is highly distributed across storage silos in enterprises, storage IT professionals need automated systems to search across petabytes of corporate data stores, check for sensitive data, tag data so that it can be discovered more easily and move data to AI with audit reporting. AI data governance is an important discipline to ensure safe AI data ingestion processes, since corporate data used in AI can lead to sensitive data leakage, compliance violations and inaccurate or unethical outcomes without the proper guardrails. Data management systems can help by classifying and segmenting data for use or restrictions in AI and also deliver a means to audit and investigate derivate works as needed for data security, privacy and overall compliance requirements. IT organizations need to establish processes and policies for collecting, storing, processing, and using data within AI systems. AI data workflows are also intrinsic to AI data ingestion as they deliver the automation and controls to quickly discover, classify and move data to AI tools, including enriching metadata, so users can more easily find and use data in projects. Enterprise IT directors overseeing large, petabyte-scale data estates will increasingly need to adopt highly-efficient, safe and accurate methods for AI data ingestion, as department heads expand their requests for AI projects. Read the blog and watch the video with Komprise COO Krishna Subramanian and eWeek on the related topic of AI inferencing. #### AI Data Workflows AI Data Workflows are a capability of unstructured data management platforms. AI and GenAI have created urgency on understanding and leveraging vast amounts of unstructured data within organizations. Despite the availability of free and low-cost AI tools, most enterprise data remains underutilized. This is problematic given the urgent need for AI in various sectors and since AI requires large amounts of unstructured data to deliver optimal results and to train its models. Read the article: AI Projects Need Intelligent Data Workflows AI Data Workflow Processes AI data workflows can help automate two essential processes: Discover, segment, classify and automate the movement of data to AI tools. Enrich the metadata of unstructured data, which makes it easier to find and use in a variety of analytics and AI projects. AI Data Workflows Capabilities You'll want the ability to search across vast data estates, ranging from terabytes to petabytes, to find relevant data. They ensure data governance by maintaining an audit trail and enforcing guardrails to ensure sensitive data is handled appropriately. To avoid high costs associated with AI’s pay-per-use models, it is vital to have a global index that tracks labels and tags, allowing users to search without reprocessing data. Read more about the Komprise Global File Index. Automation is an important feature of AI data workflows technology because it ensures that AI models are continuously trained on the latest data without manual intervention. It also allows IT to incorporate policies into data workflows. Sample use cases for these workflows include, in the life sciences sector, querying data silos to find all data for a specific project, executing a function to identify a DNA mutation, tagging the data, and then moving it to a cloud AI service for analysis. The process ends with archiving the data once it is no longer needed. A marketing team could use an unstructured data management system to search across billions of images, tagging those that feature specific people or objects using tools like Amazon Rekognition, thereby saving significant manual effort. AI data workflows are critical for unlocking the full potential of AI in organizations. By automating and streamlining data processes, ensuring compliance, and managing costs, organizations can harness AI to improve operations and outcomes across various sectors. Learn more about the AI data workflow use case for Komprise Intelligent Data Management. Learn more about Komprise Smart Data Workflow Manager for AI. #### AI Infrastructure What is AI Infrastructure? AI storage and data infrastructure are evolving rapidly to support the complex demands of training and deploying machine learning models. This technology plays a pivotal role in ensuring optimal performance, scalability, and reliability in AI applications. AI Storage  AI storage solutions are specifically designed to handle the unique challenges posed by AI workloads—specifically, efficiently managing the massive datasets used in training models. AI storage solutions prioritize high-throughput, low-latency, and scalable performance. Technologies such as solid-state drives (SSDs), distributed storage architectures, and parallel file systems are commonly employed. One of the primary challenges in AI storage is maintaining high performance when processing large datasets multiple times, which requires high-speed access to the data. AI storage infrastructure addresses this challenge by providing solutions that can handle the parallel processing needs of deep learning frameworks at the speed which AI applications demand. AI storage solutions must be highly reliable and deliver features for data replication, snapshots, and backups to ensure the integrity and availability of the training data. Given the sensitivity of the data often used in AI applications, organizations must develop strong data governance guidelines and policies to protect PII and IP data from leakage into commercial tools that can expose this data to other users and organizations. This blog details five key areas for AI data governance to consider across security, privacy, lineage, ownership and governance of unstructured data for AI – or SPLOG. Data Infrastructure for AI Data infrastructure for AI consists of an ecosystem of technologies and processes for managing and manipulating data for AI applications. This includes not only storage but also tools and frameworks for data preprocessing, cleaning, and transformation, along with unstructured data management solutions. Distributed computing frameworks, such as Apache Hadoop and Apache Spark, are integral to AI data infrastructure, delivering parallel processing of data across multiple nodes or servers. Graphics Processing Units (GPUs) also play a crucial role by accelerating the training and inference processes of machine learning models. GPUs, designed for parallel processing, handle the complex mathematical operations involved in training deep neural networks. They work with AI storage to ensure high-speed access to data, reducing latency and improving overall performance. GPUs also play a crucial role in the inference phase, bringing real-time predictions. AI storage solutions must be capable of supporting the high-throughput requirements of GPUs, to prevent data bottlenecks. Specialized AI accelerators from NVIDIA and AMD further enhance parallel processing capabilities with speed, efficiency, and scalability. Unstructured data management solutions play a pivotal role in AI infrastructure by delivering a unified, independent console for holistic data visibility across on premises, edge and cloud storage. An unstructured data management system such as Komprise can deliver a Global File Index for users to conduct ad hoc searches of file and object data to find the precise data sets they need for AI. Komprise also delivers automated workflow capabilities with Smart Data Workflows, so that users can search, tag, and move data to data links and other platforms for use by AI applications—or similarly, to exclude sensitive data from ingestion into AI tools by automatically finding and moving it into immutable, object storage in the cloud. Learn more about AI and Big Data capabilities in Komprise. Read the white paper on tactics for managing and protecting data for GenAI. The Role of the Cloud in AI Infrastructure Cloud storage services from industry leaders like Amazon Web Services (AWS) and Microsoft Azure also play a significant role in AI infrastructure. These cloud platforms offer scalable and flexible storage solutions that can be tailored to the specific needs of AI applications, such as AWS S3 and Azure's Blob Storage. AWS and Azure are also developing an expanding array of AI and machine learning tools and services, creating easily deployable AI-as-a-service offerings for companies. Learn more here: AWS AI Services AWS ML Infrastructure Azure AI As AI evolves, enterprise organizations will look to integrate the right combination of storage solutions, data infrastructure, unstructured data management systems, GPUs, and cloud services to efficiently manage and protect data used for AI initiatives. #### AI Data Governance AI Data Governance was identified in the third annual state of unstructured data management survey as a top concern for generative AI adoption in the enterprise, which includes privacy, security and the lack of data source transparency in vendor solutions. The press release noted: As the generative AI marketplace expands and executives push for departments to leverage new solutions for competitive advantage, the need for an unstructured data governance agenda is strong; IT leaders cannot forsake data integrity, data protection and risk faulty or dangerous outcomes from generative AI projects. Read the Blocks & Files interview with Chris Mellor: Metadata is the Key to Smarter AI and Data Governance In the post 5 Unstructured Data Tips for AI, Komprise cofounder and COO Krishna Subramanian reviewed five areas to consider across security, privacy, lineage, ownership and governance of unstructured data for AI. Data Security for AI Data confidentiality and security are at risk with third-party generative AI applications because your data becomes part of the LLM and the public domain once you feed it into a tool. Get clear on the legal agreements in place by the vendor as pertains to your data. There are new ways to manage this now: ChatGPT now allows users to disable chat history so that chats won’t be used to train its models, although OpenAI retains the data for 30 days. One way to protect your organization is to segregate sensitive and proprietary data into a private, secure domain which restricts sharing with commercial applications. You can also maintain an audit trail of your corporate data that has fed AI applications. Data Privacy for AI When you create a prompt for an AI tool to produce an output based on your query, you don’t know if the result will include protected data, such as PII, from another organization. Your company may be liable if you use the tool’s output externally in content or a product and the PII is discoverable. As well, since non-AI vendors are now incorporating AI tools into their solutions, perhaps even without their customers’ knowledge, the risk compounds. Your commercial backup solution could incorporate a pretrained model to find anomalies in your data and that model may contain PII data; this could indirectly put you at a risk of violation. Data provenance and transparency around the training data used in an AI application are critical to ensure privacy. Data Lineage for AI Today there is not much transparency with data sources in generative AI applications. They may contain biased, libelous or unverified data sources. This makes using GenAI tools circumspect when you need results that are factually accurate and objective. Consider the problem you are trying to solve with AI to choose the right tool. Machine learning systems are better for tasks which require a deterministic outcome. Data Ownership for AI The data ownership piece of generative AI concerns what happens when you derive a work: who owns the IP? As it stands today, copyright law dictates that “works created solely by artificial intelligence — even if produced from a text prompt written by a human — are not protected by copyright,” according to reporting by BuiltIn. As well, the article continues, copyrighted materials used in training AI models, is permitted under the fair use law. There are currently a batch of lawsuits under consideration, however, challenging this law. It will be increasingly important for organizations to track who commissioned derivative works and how those works are used internally and externally. Data Governance for AI If you work in a regulated industry, you’ll need to show an audit trail of any data used in an AI tool and demonstrate that your organization is complying. A healthcare organization, for instance, would need to verify that no patient PII data has been leaked to an AI solution per HIPAA rules. This requires a data governance framework for AI that covers privacy, data protection, ethics and more. Unstructured data management solutions help by providing a means to monitor data usage in AI tools and create a foundation for unstructured data governance. Other Considerations for AI Data Governance At a high-level, AI data governance is the framework, policies, and procedures organizations put in place to ensure that data used in artificial intelligence (AI) systems is managed, processed, and utilized in a responsible, ethical, and compliant manner. It involves establishing guidelines for collecting, storing, processing, and using data within AI systems. Key components of AI data governance typically include: Data Quality and Integrity: Ensuring that the data used in AI models is accurate, reliable, and free from biases or errors. This involves data validation, cleaning, and maintaining data integrity throughout its lifecycle. Data Privacy and Security: Implementing measures to protect sensitive data, adhering to relevant data protection regulations (such as GDPR, CCPA), and securing data against unauthorized access or breaches. Compliance and Regulations: Ensuring that AI initiatives comply with legal and regulatory frameworks. This involves understanding and adhering to laws and guidelines governing data usage, such as industry-specific regulations and international standards. Ethical Use of Data: Establishing ethical guidelines for the collection, storage, and usage of data in AI applications. This includes considering fairness, accountability, and transparency in AI decision-making processes. Data Lifecycle Management: Managing data throughout its lifecycle, from collection to processing, analysis, and disposal. This involves tracking the lineage of data, maintaining proper documentation, and ensuring responsible data handling at every stage. Risk Management: Identifying and mitigating potential risks associated with data usage in AI systems, such as bias, security vulnerabilities, or unintended consequences of AI decision-making. Accountability and Transparency: Establishing mechanisms to ensure accountability for AI models and making the decision-making process transparent to relevant stakeholders. This involves explaining AI model behavior and outcomes in an understandable manner. Effective AI data governance is critical to building trust in AI systems, ensuring that they operate in a manner that respects data privacy, security, and ethical considerations. It also helps organizations make more informed decisions, reduce risks, and maintain compliance with regulatory requirements. In this Data on the Move, we discuss AI and Unstructured Data Management. Here are 2025 predictions, which focus on unstructured data management and AI Data Governance. What is AI data governance? AI data governance is the set of rules, policies, and technologies that ensure the data used for AI is accurate, compliant, secure, and trustworthy. It addresses challenges like bias, transparency, and regulatory requirements so AI models are trained and run on the right data. Why it matters: Without governance, AI models risk producing inaccurate, biased, or non-compliant results that undermine trust and business value. What is the connection between AI governance and unstructured data management? Most enterprise data is unstructured: files, documents, images, and logs, which often feed AI models. AI governance sets the standards for how data should be used, while unstructured data management enforces those standards by classifying, securing, and tracking data across diverse storage environments. Why it matters: Together, governance and unstructured data management ensure enterprises can safely use vast volumes of data without violating compliance rules or feeding AI models low-quality inputs. What are strategies to ensure only the right unstructured data feeds enterprise AI models? Enrich metadata to make unstructured data searchable and contextual. Classify and tag data to filter out irrelevant or sensitive files. Apply governance policies and access controls to protect regulated data. Manage the data lifecycle so outdated or low-value data doesn’t enter AI pipelines. Continuously monitor data quality and usage to maintain trustworthy AI outcomes. Why it matters: These strategies prevent AI models from being overloaded with irrelevant or risky data, ensuring higher accuracy, compliance, and business relevance. Learn more about Komprise Smart Data Workflows and the Komprise Data Experience (KDX). #### Large Language Model (LLM) A large language model (LLM) is a type of artificial intelligence model that is designed to understand and generate human-like language on a large scale. LLMs are typically trained on massive amounts of text data from a wide range of sources, such as books, websites, articles, and other textual resources. These models utilize deep learning techniques, particularly using architectures like transformers, to capture complex patterns and dependencies in language. Read the article: What are LLMs, and how are they used in generative AI? LLMs are trained to process and generate coherent and contextually relevant responses based on the input they receive. They can understand and generate text in multiple languages and can perform a variety of language-related tasks, including language translation, text summarization, question answering, text completion, and more. One of the most well-known and influential LLMs is OpenAI's ChatGPT or just GPT (Generative Pre-trained Transformer) series, such as GPT-4. These models have achieved capabilities in generating human-like text and have been used in various applications, including chatbots, virtual assistants, content generation, and creative writing. The training process for LLMs involves exposing the model to a large corpus of text data and using techniques like unsupervised learning to learn the statistical patterns and relationships within the language. The models are trained to predict the next word or sequence of words based on the context provided by the preceding words. This process enables the models to capture syntactic, semantic, and contextual nuances of language. LLMs limitations and challenges LLMs can sometimes generate incorrect or nonsensical responses, struggle with understanding nuances, and may be sensitive to biases present in the training data. Additionally, LLMs require significant computational resources for training and inference, making them computationally expensive. Despite these limitations, large language models have demonstrated tremendous potential in advancing natural language processing capabilities and enabling human-like interactions with AI systems. Ongoing research and development efforts continue to push the boundaries of LLMs, aiming to improve their accuracy, interpretability, and ethical use. Data Management and AI In May 2023, Krishna Subramanian, cofounder and COO of Komprise wrote an article for Datanami entitled: Data Management Implications for Generative AI. She summarized 3 areas that need more attention: Data governance and transparency with training data Data segregation and data domains The derivate works of AI Her conclusion: Enterprises should tread carefully and ensure they clearly understand the data exposure, data leakage and potential data security risks before using AI applications. #### AI Compute The computing ability required for machines to learn from big data to experience, adjust to new inputs, and perform human-like tasks. Komprise cuts the data preparation time for AI projects by creating virtual data lakes with its Deep Analytics feature. AI compute refers to the computational resources required for artificial intelligence systems to perform tasks, such as processing data, training machine learning models, and making predictions. These resources can be provided by various hardware and software platforms, including GPUs, TPUs, cloud computing, and edge computing devices. The amount of AI compute needed depends on the complexity of the AI system and the amount of data being processed. Hosting AI compute infrastructure internally can be prohibitively expensive, making the prospect of cloud-based AI more attractive for many organizations. Unstructured data management solutions and unstructured data workflows are increasingly being used to improve storage efficiencies and speed time to value for AI. What is unstructured data in AI? AI needs unstructured data - are you ready? AI needs unstructured data Read the Duquesne University case study to learn more about Komprise Smart Data Workflows and AWS Rekognition for rapid image search across petabytes of data. ### Metadata, Tagging, and Indexing > Definitions covering metadata types, enrichment, cataloging, tagging, and indexing as they apply to unstructured data management and AI readiness. #### Data Storage Tags Data storage tags are custom labels or metadata attributes applied to files or objects to describe their content, context, or intended usage. Tags can represent anything from a project name, department, data sensitivity level (e.g., PII, confidential), to business relevance or AI-readiness. In modern data environments, tags are essential for organizing, classifying, and managing vast amounts of unstructured data across hybrid storage systems. Why Tags Matter for Unstructured Data Management Unstructured data (e.g., documents, images, logs, videos) lacks inherent structure or schema, making it difficult to classify, organize, and govern using traditional tools. Tags provide a flexible, scalable way to: Classify data for compliance, security, and lifecycle management Group files across disparate storage (on-prem NAS, cloud object stores) without physically moving them Enable intelligent automation, like tiering cold data, identifying AI-relevant files, or enforcing retention policies Enhance searchability and discovery, especially when metadata is incomplete or missing Tags turn dark, disorganized file systems into actionable, policy-driven datasets. Komprise and Storage Tagging Komprise plays a key role in enabling tagging at scale for enterprises managing unstructured data across diverse environments: 1. Global Smart Tagging Komprise allows users to create and apply custom metadata tags across billions of files and objects, regardless of where they’re stored. This includes: Department, project, cost center Sensitivity level (e.g., PII, HIPAA) Business purpose (e.g., archive, analytics, AI training) Tags are applied based on criteria such as file location, age, usage, owner, or even content (via integration with data classification tools). 2. Cross-Platform Tag Consistency Komprise maintains a unified tagging strategy across all your storage—on-prem and in the cloud—eliminating data silos and enabling centralized policy enforcement. This is especially important when dealing with: Heterogeneous NAS systems (NetApp, Dell, etc.) Cloud object storage (Amazon S3, Azure Blob, Google Cloud) Multi-cloud data lakes and AI platforms 3. Tag-Driven Workflows and Automation Tags in Komprise aren’t just labels—they power automation: Automatically archive files tagged as inactive or non-critical Route files tagged as “AI-Ready” to analytics platforms Protect or lock files tagged as “Legal Hold” or “Confidential” This enables metadata-driven data orchestration, not just storage visibility. With Komprise, tags become a powerful way to take control of your unstructured data—turning chaos into clarity, and cost centers into value drivers. #### Metadata Enrichment Metadata enrichment is the process of adding contextual, descriptive, or business-relevant information to your existing data to make it more discoverable, understandable, and useful - especially for downstream analytics and AI. Why Metadata Enrichment Matters for AI For AI to work effectively, especially in enterprise environments, it needs relevant, high-quality input data. But unstructured data (like files, images, videos, logs) typically has minimal or inconsistent metadata - just basic system info like file name, owner, and last modified date. (See System Metadata) Without metadata enrichment: AI tools can’t easily filter or prioritize the right training data Sensitive or irrelevant content may be included by mistake Data pipelines are bloated, costly, and harder to govern With metadata enrichment: You can tag files with business context (e.g., project name, department, PII status, language, sensitivity) AI systems can automatically prioritize, exclude, or fine-tune based on smart metadata You're able to benefit from faster, safer, and more targeted AI data ingestion How Komprise Enables Metadata Enrichment Global Metadatabase (KMDB) KMDB is a global file index that continuously indexes metadata across all your storage (on-prem and cloud), giving you centralized visibility into billions of unstructured files, without moving the data. Smart Data Workflows With Smart Data Workflows, you can: Apply tags, labels, or policies based on criteria like file type, usage, owner, location, or content characteristics Identify and tag sensitive data (e.g., files with PII or financial info) using custom or external classifiers Add custom metadata to drive policy-based automation, such as archiving, moving to AI platforms, or restricting access Example: Tag and extract .pdf and .tiff files last accessed by R&D in the last 12 months and route them for AI model training, while excluding files with flagged PII. These are use cases Komprise is working customers to develop. Metadata Enrichment Outcome for Enterprises By enriching unstructured data with meaningful metadata, Komprise empowers enterprises to: Feed AI with curated, relevant, and governed datasets Avoid overloading pipelines with noisy or risky data Accelerate AI time-to-value while maintaining compliance Metadata is the new index and Komprise gives enterprises the tools to enrich file and object data at scale, across disprate storage environments and locations. Why is Komprise Intelligent Management a Better Approach than ETL or iPaaS Tools? Komprise is better approach to metadata enrichment for unstructured file and object data than traditional ETL or iPaaS tools because it is purpose-built for the scale, complexity, and structure-free nature of unstructured data, while ETL and iPaaS are primarily designed for structured data workflows. Why Komprise Intelligent Data Management? Built for Unstructured Data ETL/iPaaS tools excel at moving structured data between databases and SaaS apps, but struggle with large-scale file and object storage systems. Komprise directly connects to file (NAS) and object (S3, Azure Blob, etc.) storage, indexing billions of files without moving them, and operates at petabyte scale. Metadata Visibility Without Data Movement ETL tools often require data to be moved into a processing environment for metadata to be extracted or transformed. This is slow, expensive, and risky for unstructured data. Komprise uses a Global Metadatabase to collect and enrich metadata in place, enabling fast search, tagging, and decision-making without disrupting production systems. Smart, Policy-Based Enrichment ETL/iPaaS workflows require complex manual scripting to enrich data or apply rules, which is brittle and hard to scale. Komprise Smart Data Workflows let you automate enrichment based on file attributes, content, access patterns, and third-party classification tools—like tagging files with PII, project names, or compliance labels dynamically. (See Sensitive Data Management.) No Rehydration or Egress Penalties ETL tools may trigger expensive rehydration or data egress costs when accessing archived cloud storage. Komprise operates transparently, preserving native file access and avoiding cloud storage “penalties.” (See Transparent Move Technology (TMT)) Enterprise-Ready for AI and Governance Komprise is designed for enterprises managing hybrid cloud, multi-vendor storage environments, and prepping data for AI, analytics, governance, and compliance use cases. (See storage agnostic data management.) Komprise offers visibility + enrichment + movement + access control—in a single, storage-agnostic platform. #### System Metadata System metadata is the set of attributes that a file system or object storage platform automatically generates and maintains about a file or object. It describes the data without being part of the data itself (see metadata) and is one of the most under-leveraged assets in enterprise IT, especially when managing massive, distributed unstructured data environments. Common Examples of System Metadata File name and extension (.pdf, .mp4, etc.) Path/location in the file system File size Creation date, last modified date, last accessed date Owner and group permissions Access control lists (ACLs) File type or MIME type Why Is System Metadata Important for Storage Admins and IT Teams? Especially in environments with multiple NAS platforms (on-prem + cloud), system metadata is critical because it enables: 1. Visibility Across Silos System metadata allows IT teams to understand what data exists, how it’s structured, and how often it’s used—without opening or moving the files. 2. Storage Cost Optimization Using metadata (like last access date or file size), you can optimize data storage costs by: Identifying cold data Data tiering to lower-cost storage Reclaiming high-cost NAS space 3. Efficient Data Migrations Metadata tells you which files are active, who owns them, and whether they’re locked—enabling smarter migration strategies. (See Smart Data Migration.) 4. Governance & Risk Management Metadata helps track ownership, access rights, and usage history. Critical for audits, compliance, and reducing shadow data risks. Why Is System Metadata Crucial for Unstructured Data Management? Unlike structured data (which lives in well-defined schemas), unstructured data is messy and distributed. System metadata becomes the only consistent structure you can rely on. System Metadata Enables: Data classification and policy-driven decisions Search across billions of files Indexing without data scanning Automation for retention, migration, and lifecycle management System metadata is the foundation for understanding unstructured data at scale. Komprise Unstructured Data Management and System Metadata 1. Distributed Metadata Indexing The Komprise Global File Index is a metadatabase that scans and indexes system metadata in place across on-premises NAS (NetApp, Dell Isilon, etc.), cloud NAS (Amazon FSx, Azure Files), and object stores, without moving data. The Komprise Global File Index enables: Deep search and filtering Cold data detection AI/ML data pipeline preparation (see AI Data Workflows) 2. Metadata-Driven Policies With Komprise you can create intelligent data management policies using metadata. Examples include> “Move files not accessed in 3+ years over 1 GB to cloud archive” “Tag and confine files owned by ex-employees” 3. Transparent Data Tiering & Movement Komprise moves data based on system metadata policies (e.g., last access), while leaving dynamic links behind, so apps and users see no change. This approach avoids rehydration and retains full path and permissions, even after moving data. 4. Enabling AI-Ready Data Curation System metadata is used to pre-filter large volumes of unstructured data before more expensive AI preprocessing or enrichment. This can reduce costs and accelerate model training by sending only relevant, curated datasets to AI pipelines. Komprise Intelligent Data Management and System Metadata Komprise on-place global indexing across storage silos provides visibility into file usage and aging. Metadata-based tiering and lifecycle automation provide cost control via cold data detection. Metadata search, tagging, and ownership analysis provide unstructured data governance and access control auditing. The ability to curate and move data based on size, age, owner, and more are essential ingredients to smarter AI and data migration initiatives. The bottom line is system metadata is your first line of intelligence in managing unstructured data. Komprise turns that metadata into actionable insight and automation - cutting costs, boosting control, and enabling faster use of data for AI data services. #### Data Indexing Data indexing is the process of scanning, extracting, and organizing metadata from data assets so they can be easily searched, filtered, and analyzed, without moving or modifying the data itself. Think of it like the index of a book: it doesn’t hold the content, but it tells you where to find it, and what’s inside. In unstructured data environments, indexing captures: File name, path, size, type, and timestamps Ownership and access controls Content-specific metadata (e.g., PII, tags, custom attributes) Why is Data Indexing Important for Unstructured Data Management? Unstructured data (documents, images, videos, logs, etc.) lacks inherent structure. It’s scattered across silos—NAS, object stores, cloud storage —and traditional tools struggle to understand it. Without data indexing, you’re flying blind. Indexing enables: Visibility across multi-vendor, multi-cloud environments Searchability without scanning petabytes manually Policy-driven actions like data tiering, deletion, archiving, or data tagging Audit and compliance by identifying sensitive or orphaned data Data Indexing vs. Data Classification: What's the Difference? Data indexing is about knowing what you have. Data classification is about understanding what it means. Data Indexing: Organize and make data discoverable. It captures metadata (e.g., file size, type, date, access). The first step of indexing is during scanning. Data Classification: Group and label data based on type, sensitivity, etc. It typically captures content meaning (e.g., confidential, personal). Classification is often built on top of indexing. Both are foundational for unstructured data governance and AI. Data Indexing and AI Success AI models rely on high-quality, well-prepared data. Indexing ensures: The right data can be found and curated Redundant or irrelevant data is filtered out (see ROT data) Sensitive or regulated data is handled properly Labeled or tagged datasets can be used for training AI/ML models Without indexing, your AI initiatives waste compute on noise—or worse, expose your enterprise to risk. How Komprise Does Data Indexing—and Why It Matters Komprise uses a deep, distributed, storage-agnostic global file index (metadatabase) that: Crawls across NAS, cloud, and object stores Gathers both standard and custom metadata Does this in-place—without needing to move or copy data Supports tagging, search, and data workflows based on indexed attributes Komprise Global File Index benefits include: Cost savings by identifying cold data to tier or delete Data-driven decisions about what to move to cloud or AI pipelines Improved compliance by surfacing stale, sensitive, or ownerless data Faster AI project execution by delivering relevant, labeled, and accessible data Indexing and Unstructured Data Management Data indexing is the foundation for making unstructured data usable and is recognized to be an essential ingredient to AI data readiness. AI data pipelines depend on having indexed, curated, and context-rich data. Komprise provides intelligent, in-place indexing to help enterprises reduce cost, manage risk, and fuel AI success—without being tied to any one storage vendor. #### Metadata Catalog A metadata catalog is a structured repository that stores and organizes metadata, which is data about data. In the context of unstructured data, a metadata catalog could track key information such as: File name, size, and format Creation/modification/access dates File owner or creator Storage location/path Tags and classifications (e.g., sensitive, archived, project-based) Access frequency and last access time Content-derived information (via indexing or AI/ML) Think of a metadata catalog as a searchable, filterable index that lets organizations understand, organize, and act on their unstructured data. What is the role of a Metadata Catalog in an Unstructured Data Management Strategy? In environments with massive amounts of unstructured data (e.g., files, images, documents, videos), there are many potential benefits of a metadata catalog, also known as a global file index or metadabase, including: 1. Data Visibility & Inventory: Provides a centralized view of data across NAS, object stores, and cloud tiers. Helps identify data that is redundant, obsolete, or infrequently accessed. 2. Search & Discovery Allows users or system admins to search files based on metadata, not just names. Critical for compliance, legal holds, or data subject access requests. 3. Classification & Tagging Supports data classification for compliance, cost optimization, or access control. Enables policies for sensitive or regulated data. 4. Lifecycle Management Helps automate data tiering, data archiving, and data deletion policies. Improves storage efficiency (see data storage optimization) by aligning data location with usage patterns. 5. Data Governance & Compliance Assists with audits, access reviews, and meeting regulatory standards like GDPR, HIPAA. Metadata Cataloging and Intelligent Data Management Komprise provides many metadata management capabilities as part of the Intelligent Data Management platform, including: Deep Metadata Indexing Komprise scans data across storage systems without agents and builds a deep metadata catalog, including access patterns and ownership. This information is stored in a global file index, called the Komprise Metadatabase (KMBD). Read the KDX white paper for details. Global File Index Lets users query metadata across environments (on-prem and cloud) using custom search filters. Useful for legal, security, or business use cases, including self-service tagging as part of an AI data workflow. Read the solution brief. Smart Data Workflows Using the metadata catalog, Komprise enables automated workflows: Tier cold data to cheaper storage. Archive, copy, confine data based on age, access, or tags. Tag files with business context for searchability. Komprise Deep Analytics A metadata-driven analytics engine that lets users explore file attributes at scale. Helps visualize storage usage, growth, and optimization opportunities. Tagging & Custom Metadata Support Users can apply custom tags to files or file sets (e.g., “Finance FY23”, “Legal Hold”). These tags can be used in policy rules, search, or reporting. Why a Metadata Catalog for Unstructured Data? A metadata catalog is the backbone of any modern unstructured data strategy. it enables visibility, control, and automation. Komprise addresses this by offering a deep, scalable, storage-agnostic metadata index, tied to actionable policies and intelligent tiering. (Read the unstructured data tiering best practices guide.) This makes a metadata catalog especially valuable in environments where data sprawl is rampant and cost optimization or compliance is a priority. #### Header Metadata Header metadata is the term used for information that is included in the header section of a document, web page, or data file that provides metadata (data about data). The definition and content of header metadata depend on the context in which it is used. Header Metadata Examples 1. Web Page (HTML) In HTML documents, header metadata is placed inside the element and includes tags that provide information about the web page to browsers, search engines, and other services. HTML Example: Example Page 2. HTTP Headers In HTTP communications, headers include metadata about the request or response. HTTP Example: Content-Type: text/html; charset=UTF-8 Content-Length: 348 Date: Wed, 29 May 2025 12:00:00 GMT 3. File Formats (e.g., Images, PDFs, Audio) Many file formats include a metadata header section that contains information like author, creation date, software used, etc. File Format Example (EXIF metadata in images): * Camera model * Exposure time * Date taken * GPS coordinates 4. Programming (e.g., JSON, XML, or API requests) Headers may carry metadata like content type, authorization tokens, etc. Programming Example (API call): Authorization: Bearer Content-Type: application/json User-Agent: MyApp/1.0 Header Metadata: Why is it Important? Header metadata is any metadata placed in the header portion of a file, message, or document to describe, control, or facilitate processing of the main content. It varies by context (web, HTTP, file formats, etc.) but generally helps identify, describe, and manage the content. Header metadata plays a critical role in how data is interpreted, displayed, and processed across different systems and contexts. #### Unstructured Metadata Unstructured metadata is information about unstructured data (emails, documents, videos, images, PDFs, etc.) that does not follow a rigid, predefined format. It provides context or descriptive details that help identify, understand, or manage the data, yet, unlike structured metadata, it is often inconsistent, loosely formatted, and may be embedded within the data itself. Key Characteristics of Unstructured Metadata Flexible format: Lacks a consistent schema or structure. Derived from unstructured content: Often extracted using tools like AI, NLP, or pattern recognition. Can be implicit or inferred: Not always explicitly tagged or labeled. Examples of Unstructured Metadata: File creation/modification dates. Author or owner names. Document topics, keywords, or themes inferred by AI. Access logs or usage patterns. Sentiment analysis from text. Image content tags (e.g., “contains face” or “outdoor scene”). Why does Unstructured Metadata Matter? Unstructured metadata adds meaningful context to otherwise hard-to-organize data. It helps with: Data discovery – finding relevant files based on inferred attributes. Compliance – identifying sensitive or regulated information. Security – tracking usage or flagging unusual behavior. Storage optimization – understanding which data is active, stale, or high-value. Unstructured metadata is the contextual or descriptive information linked to unstructured data, often generated or inferred rather than explicitly stored in databases or network attaches storage (NAS) systems. It plays a vital role in organizing, analyzing, and securing large volumes of complex data—especially in environments where structured categorization is impractical. Komprise and Unstructured Metadata Komprise provides powerful, non-intrusive analytics to help organizations analyze unstructured metadata across file and object storage. The Komprise platform is built to give deep visibility into unstructured data, enabling smarter storage management, security, and ransomware protection strategies. Examples of How Komprise Analyzes Unstructured Metadata Deep File-Level Scanning: The Komprise Global File Index, or metadatabase, scans file shares and object storage to collect and analyze metadata like: file names and extensions, file sizes, file creation, modification, and last accessed times, file owners and permissions, directory and folder structures. This allows you to understand what data you have and how it's being used—without touching the content itself. Metadata-Driven Data Insights: Komprise uses this metadata to generate reports on: Data growth trends, aging and staleness (e.g., files not accessed in X years), usage patterns by department or user, data types and formats stored across environments, etc. This information and insight empowers IT and data teams to identify redundant, obsolete, or rarely accessed data that can be archived or deleted. Custom Metadata Tagging: With Komprise, you can create custom metadata-based queries and tags to segment and categorize data based on business needs (e.g., "legal hold," "sensitive," or "stale"). Komprise supports automated workflows based on metadata conditions. This approaches helps with data governance, compliance, and retention policies. Search and Filtering: Komprise offers advanced filtering tools using metadata fields. For example: Show all files > 1GB not accessed in 3 years. List files owned by a specific user or department. Locate old video or image files for archival. This helps prioritize what data to protect, tier, or monitor more closely. Integration with Smart Tiering & Archiving: Once metadata analysis identifies cold or risky data, Komprise can automatically tier it to lower-cost or immutable storage - helping reduce ransomware risk and optimize storage costs. In a ransomware protection context, analyzing unstructured metadata with Komprise helps: Discover where sensitive or critical data resides. Identify abnormal access or file activity (potential ransomware indicators). Reduce the attack surface by archiving stale, unused data. Build data-driven policies to protect against ransomware using real usage data. In conclusion, Komprise enables detailed, scalable analysis of unstructured metadata by scanning data environments without disrupting user or application access. Komprise transforms raw metadata into actionable insights for governance, cost savings (and cost avoidance), and ransomware protection strategies, making it easier to control and defend sprawling unstructured data growth and costs. #### Metadata Tagging Metadata tagging is the process of assigning descriptive tags or labels to data, files, documents or other resources to make them easier to organize, search, and retrieve. These tags provide additional context and meaning, helping users and systems better understand and interact with the tagged content. What is Metadata? Read: Google-Like Search and Tagging for All Your Cloud Buckets, Objects and Files Komprise Deep Analytics uses metadata tagging, where custom, user-defined tags enable easier future searches. Komprise metadata tagging can also be used to combine related data that are logically or temporally separated into one result set using a common tag or tags. Tags stored in applications can also be used and made available no matter where your data moves. Watch this on-demand webinar to learn more about Komprise tagging. The Elements of Metadata Tagging Types of Metadata Descriptive Metadata: Provides information to identify and describe the content (e.g., title, author, keywords). Structural Metadata: Describes the structure and relationships of data (e.g., chapters in a book, sections in a document). Administrative Metadata: Offers details about the creation, rights, and technical characteristics (e.g., file format, creation date, permissions). Tagging Methods Manual Tagging: Users assign tags based on their knowledge of the content. Automated Tagging: Uses algorithms, machine learning, or natural language processing (NLP) to extract and assign tags automatically. Tagging Standards Standards like Dublin Core, IPTC, or custom taxonomies ensure consistency and interoperability. Controlled vocabularies or ontologies can help maintain consistency in the tagging process. Implementation Flat Tagging: Assigns a simple list of tags to content. Hierarchical Tagging: Uses a tree-like structure where tags are organized in categories and subcategories. Facet Tagging: Allows content to be tagged across multiple dimensions (e.g., genre, location, date). What are the Benefits of Metadata Tagging? Enhanced Searchability: Tags make it easier for users to find relevant content through filters and keywords. Improved Organization: Tags group related items, helping to maintain structured collections. Contextual Understanding: Tags provide insight into the content’s purpose or characteristics. Interoperability: Standardized tags facilitate sharing and integration across systems. Automation: Tags enable automation in workflows, such as categorizing emails or recommending products. Common Metadata Tagging Use Cases Beyond the unstructured data management use cases Komprise supports, including searching, finding, delivering subsets of file and object data across storage silos and delivering data to cost effective or AI and analytical destinations, here are some other common use cases: Digital Asset Management: Tagging images, videos, and audio for better cataloging and retrieval. Content Management Systems: Assigning tags to articles, blogs, and other web content for navigation and SEO. E-commerce: Tagging products by category, brand, and features to enhance search filters. Data Analysis: Organizing datasets with relevant tags for easier analysis and visualization. Libraries and Archives: Tagging documents and books with subject categories, authors, and genres. What Are Some of the Different Tools for Metadata Tagging? Manual Tagging Tools: Built-in tagging features in platforms like Google Drive, Dropbox. Automated Tagging Tools: NLP and AI-powered tools like AWS Rekognition (for media), Tagtog (for text), and BrightEdge (for SEO). (Read the Komprise AWS Rekognition case study.) Enterprise Systems: Platforms like Adobe Experience Manager, Tableau, or Microsoft SharePoint often include metadata tagging functionality. Komprise Deep Analytics and Smart Data Workflows #### Metadatabase In unstructured data, a metadatabase is a virtual database of metadata (data about data) that provides additional structure and context to this data so that it is more usable and searchable for a variety of use cases. Unstructured data, due to its wide variety in formats, types, sizes and locations, is difficult to manage and understand. Metadata provides valuable keys to this data so that it can be leveraged across the organization for AI and analytics and also managed effectively for cost reduction and compliance. What's in a Metadatabase? The metadata in a metadatabase can include information such as file names, file types, creation dates, tags, authors, sizes, formats, and locations. Metadata is even more useful when enriched by analysis and tagging. For example, image files could be indexed based on facial or building recognition tags and text documents could be indexed based on keywords or sentiment. A critical use case for security and compliance is to index data based on its sensitivity – such as PII or IP data. That way, IT users can ensure sensitive data is segmented from AI data workflows and stored in compliant locations. For AI, tags could entail keywords describing file contents such as medical diagnosis or seismic data, so that precise data sets can be culled for model training or inferencing. A metadatabase can manage all these data tags at scale and provide a simple, rapid way for users to search data based on these tags and take actions accordingly. Benefits of a Metadatabase for Unstructured Data An unstructured data management solution with a metadatabase gives IT teams a way to collect, manage and enrich metadata across all storage systems, on-premises to the cloud. It delivers several benefits for IT, including data classification, search and querying across petabyte-scale data estates, access control, data provenance (history and lineage), full visibility and drill-down capabilities to manage data compliance, AI data governance and costs, and integration with automated data workflows. Learn more about the Komprise Global File Index, a metadatabase for file and object data across the hybrid cloud estate. Learn more about Komprise Smart Data Workflows, which integrates with the Global File Index to deliver automated processes for data search, data classification, data tagging, data movement and AI data ingestion. #### Metadata Indexing Metadata, which is data about data, is becoming more strategic to managing unstructured data and feeding data to AI, because it delivers more context about the data. This in turn is critical for managing data cost efficiently, protecting data and curating precise data sets for AI. Storage systems automatically create basic metadata for the unstructured data they store, such as author/owner timestamps, file size and type, and time of last access. Metadata indexing is a valuable capability that gives IT managers full visibility of unstructured data across hybrid storage—from on-premises to the cloud. This helps IT managers and storage administrators optimize storage by, for instance, identifying cold data that can be tiered or archived to cheaper storage and to see the rate of data growth, among other core metrics. Get Better Unstructured Data Insight with Metadata Indexing Metadata indexing is also valuable for ad hoc queries into data stores to understand common data types, costs per department or storage appliance/service, usage patterns, top owners by data volume and more. Tools that allow users to enrich metadata with additional tags, such as those identifying projects, PII or keywords, are especially useful so that IT and departmental users can quickly locate and find data sets for research and AI while ensuring that protected data is managed appropriately. Storage systems don’t allow for custom metadata tagging; you will need an unstructured data management system such as Komprise to enable that capability. A Metadata Index Across Data Storage Silos The Komprise Global File Index, included in Komprise Intelligent Data Management, is a metadata indexing service that runs in the cloud or that a customer can host on premises. Either way, Komprise manages the GFI, which indexes all files in place and analyzes all the metadata. Data and storage professionals can search, tag and create custom data sets across their storage silos and then copy and move those data sets in an automated fashion via plan. External scripts can also be used in the GFI. Read the blog series on metadata management for more detail on metadata, its pivotal role in unstructured data management, and how to optimize it for a variety of use cases. #### Metadata Management Metadata management is the process of collecting, organizing, storing, and maintaining metadata associated with an organization's data assets. Metadata means data about data – it provides context, structure, and information about various aspects of data, making it easier to understand, manage, and use. Effective metadata management is essential for ensuring data quality, data accuracy, and the right data accessibility across an organization's enterprise data landscape. Types of Metadata: Descriptive Metadata: Provides information about the content, structure, and context of data. This includes attributes such as data source, creation date, author, format, and keywords. Technical Metadata: Contains technical details about data, such as data type, data length, field names, and relationships between data elements. Operational Metadata: Tracks the usage and behavior of data within systems, including information about data transformations, processes, and workflows. Business Metadata: Relates data to the business context, such as data definitions, business rules, data ownership, and data lineage. Benefits of the Metadata Management Strategy: Data Discovery and Understanding: Metadata provides insights into the meaning and structure of data, making it easier for users to discover and understand available data assets. Data Governance: Metadata management supports data governance initiatives by enabling organizations to define and enforce data quality standards, security policies, and compliance requirements. Data Lineage: Understanding the lineage of data – its origin, transformations, and movement – helps ensure data accuracy and traceability, particularly in complex data environments. Data Integration: Metadata helps integration processes by clarifying how different data sources relate to each other, reducing the complexity of integrating disparate data systems. Data Analytics and Reporting: Accurate metadata supports effective data analysis and reporting by providing the necessary context for interpreting results. Search and Discovery: Well-managed metadata enables efficient search and discovery of data, saving time and effort when finding relevant information. Collaboration: Metadata fosters collaboration by providing a common understanding of data across teams and departments. Data Migration and Data Archiving: During data migration or data archiving projects, metadata helps in identifying what data to move, how to transform it, and what to retain for compliance purposes. Metadata Management Process: This can be done different across enterprises and industries, but the general components are: Capture: Metadata is collected from various sources, including databases, applications, files, and user input. Store: Metadata can be stored in a centralized metadata repository or catalog. This repository acts as a single source of truth for all metadata assets. Organize: Metadata is organized into categories, taxonomies, or hierarchies to facilitate easy navigation and understanding. Govern: Metadata is governed through established processes, ensuring data quality, accuracy, security, and compliance. Search and Access: Users can search and access metadata using intuitive tools and interfaces, allowing them to find relevant data assets quickly. Update and Maintain: Regularly update and maintain metadata as data assets evolve over time. This includes updating technical details, documenting changes, and managing data lineage. Metadata Standards and Tools: Metadata management often involves using standards such as Dublin Core, Metadata Object Description Schema (MODS), and industry-specific standards. Various metadata management tools and platforms are available to facilitate the capture, storage, organization, and retrieval of metadata. Metadata management is a crucial practice for any organization that values data quality, accessibility, and effective data governance. It has now broadened to include unstructured data in order to provide the context necessary to understand and utilize all data assets while supporting critical business initiatives, compliance efforts, analytical and AI activities. #### Data Tagging What is data tagging? Data tagging is the process of adding metadata to your file data in the form of key value pairs. These values give context to your data, so that others can easily find it in search and execute actions on it, such as move to confinement or a cloud-based data lake. Data tagging is valuable for research queries and analytics projects or to comply with regulations and policies. How does Komprise data tagging work? Users, such as data owners, can apply tags to groups of files and tags can also be applied programmatically by analytics applications via API. In the Komprise Deep Analytics interface, users can query the Global File Index and find the data for tagging. This is done by creating a Komprise Plan that will invoke the text search function to inspect and tag the selected files. The ability to use Komprise Intelligent Data Management to search, find, apply tags and then take action makes it possible for customers to get faster value from enriched data sets. Tagging and Smart Data Workflows Komprise Smart Data Workflows automate unstructured data discovery, data mobility and the delivery of data services. Define custom query to find specific data set. Analyze and tag data sets with additional metadata Move only the tagged data for analytics, AI/ML, etc. Move to a lower-cost data storage tier after analysis --------- #### Global File Index What is a Global File Index? Komprise Deep Analytics enables precise unstructured data management at enterprise scale, creating a Global File Index, which is a metadata catalog, delivering the benefits of Global Namespace or Global File System data access without sitting in front of the hot data path. Spanning petabytes of file and object data sources, the Global File Index allows enterprise customers to find specific data sets and then create a data management policy or Smart Data Workflow to systematically take action on your data set. Unstructured data ends up in multiple silos, so an index needs to be global across different data centers, storage, backup and cloud infrastructure and it must not sit in front of the hot data path to ensure there is no impact on data storage performance. Once you connect Komprise to your file and object storage, your data is indexed and a Global File Index, which is a global metadata catalog across disparate file and object data, is created. You do not have to move the data anywhere; but you now have a single way to query and search across your file and object stores. Say you have some NetApp, some Isilon, some Windows servers, some Pure Storage at different sites and you have some cloud file storage on AWS, Azure, and Google. You get a single index via Komprise of all the data across all these environments and now you can search and find exactly the data you need with a single console and API. Benefits of the Global File Index Users only move the data they need, with the ability to create queries on countless file attributes and tags such as: data related to a specific tag or project name, projects that are no longer active, file age, user/group ID’s, path, file type (aka JPEG) and specific extensions, data with unknown owners. A global metadata catalog eliminates the manual effort of finding custom data sets and moving them separately from different storage silos since Komprise can create a virtual data set based on the query and systematically and continuously move data from multiple file and object silos to the target location. Improves IT and business collaboration around data, as data owners/users can participate in data tiering.  Watch the TechKrunch session: Deep Analytics Actions with One Global File Index Search and Act on Unstructured Data Insights Deep Analytics Actions provides a systematic way to find specific file and object data across hybrid cloud storage silos and move just the right subset of unstructured data for new uses such as AI/ML and cloud analytics. This gives IT and storage departments the ability to drive closer connections with end users by liberating the nuggets of useful data from petabytes of files, so that new value and customer-facing benefits can be discovered. Smart Data Workflows take Deep Analytics Actions a step further by allowing IT users and/or storage admins to create automated workflows for all the steps required to find the right unstructured data across storage assets, tag and enrich the data and send it to external tools for analysis. This eliminates manual effort in unstructured data management and helps organizations speed time to value from cloud-native and other tools. #### Tagging data The often-lengthy process of annotating or labeling data (like text or objects in videos and images) to make it detectable and recognizable to computer vision to train the AI models through ML algorithm for predictions. Creating Virtual Data Lakes, the Global File Index with Komprise Deep Analytics makes this process much faster. Watch the customer success webinar and TechKrunch videos to learn more. #### Metadata Metadata means “data about data” or data that describes other data. The prefix “meta” typically means “an underlying definition or description” in technology circles. Standard metadata are storage system attributes such as: when the file was created, who created it, what type of file it is, its size, when it was last accessed, and when it was last modified. Uses of Metadata Metadata makes finding and using data easier so that the user can quickly find and categorize specific documents. Some examples of basic metadata are author, date created, date modified, and file size. Metadata is also used for unstructured data such as images, video, web pages, spreadsheets, etc. Web pages often include metadata in the form of meta tags. Description and keywords meta tags are commonly used to describe content within a web page. Search engines can use this data to help understand the content within a page. How do you create and manage metadata? Metadata can be created manually or through automation. System metadata creation is more elementary, usually only displaying basic information such as file size, file extension, when the file was created, for example. Users can tag their own data sets manually, with modifiers that identify the data based on its contents. AI tools can also enrich metadata by, for example, scanning file contents for keywords and creating curated data sets that can be tagged automatically using an unstructured data management system. Komprise delivers sensitive data tagging, to prevent PII, IP or other protected data from being stored in noncompliant locations. Learn more here. Metadata can be stored and managed in a database, however, without context, it may be impossible to identify metadata just by looking at it. Metadata is useful in managing unstructured data since it provides a common framework to identify and classify a variety of data including videos, audios, genomics data, seismic data, user data, documents, logs. Metadata and vector embeddings Vector embeddings provide a machine-readable representation of a file’s contents aka what the file is about. Metadata, on the other hand, offers contextual information that often goes beyond the file’s content, explaining why the file exists, how it's used, and by whom. While embeddings are powerful for content understanding, metadata is typically more concise and efficient for categorization and management. Embedding full file contents into metadata is not only inefficient, but it can also introduce data governance challenges, especially when applying AI models across all your data. Using both strategically ensures better context, performance, and compliance. Top Benefits of Metadata for Unstructured Data The right metadata strategy for unstructured data management brings many benefits, including: Metadata brings structure to unstructured data, valuable for search, data mobility, management, and analytics; Metadata delivers deeper insights on your data, such as: top data owners, top file types and sizes, and usage information such as last access date; It improves cost savings and decision-making for data storage; It supports compliance and AI data governance by tagging regulated or audited data sets; Users can find key data sets faster and move them to the right location for AI and research projects. How does Komprise manage metadata? Komprise indexes metadata across different storage and cloud environments and acts on it at scale. Komprise  extracts both system metadata and extended metadata such as PII or project codes into a global file index. This index retains the knowledge no matter where your data lives, and it does so without changing the original files. Komprise Deep Analytics helps you query and filter data based on this index and Komprise Smart Data Workflows allows you to search and feed the right data to the right AI process and retain its outputs as additional metadata. Metadata management is different than ETL when it comes to preparing data for AI. It delivers an ongoing workflow solution to find the right data, get it to the right compute, run the compute either locally or in the cloud, and then repeat this process again. A great example of this is our customer Duquesne University. Learn more about the Komprise Global File Index and Deep Analytics. Learn more about the Komprise Intelligent Data Management architecture. What is Metadata? Metadata is “data about data.” It is structured data that references and identifies data to give an essential extra layer of shorthand information. Metadata schema can be simple or complex but it provides an important underlying definition or description. Types of Metadata Today’s metadata ecosystem encompasses seven distinct types, each serving different purposes and requiring different approaches to capture and manage:  System metadata: Storage systems automatically generate attributes like creation date, file size, ownership and permissions. While essential, this represents just the starting point.  Header metadata: Technical format specifications embedded within files, such as camera settings in photos, document templates in Word files, or compression algorithms in media files. Applications and storage systems generate this automatically.  Application-based metadata: Workflow states, approval chains and process information from business applications. Lab notebooks automatically generate metadata about experiment phases, approval status and system integration.  Contextual metadata: Project identifiers, geographical tags, departmental associations and business context that gives meaning beyond technical properties. This requires sophisticated tools to capture and organizations add significant business value through this enrichment.  Sensitivity metadata: PII, intellectual property, regulated data type and security classifications. This requires specialized tools to uncover and classify, as it involves analyzing file contents rather than just properties.  User-based metadata: Manual tags, collaborative annotations and crowd-sourced insights that add human intelligence to data classification. While powerful, this approach faces scalability challenges as data volumes explode.  AI-generated metadata: The newest and most transformative category. AI analyzes file contents and automatically generates contextual tags and classification insights at scale.      Metadata Management Metadata management includes both standard metadata that most storage systems create and track as well as more custom metadata that gives more context about the contents of the file. Metadata management is the administration of data that describes other data and can include metadata enrichment, via tagging. AI tools can help enrich metadata by inspecting file contents and identifying new tags to indicate demographics, project keywords, sensitive data, individuals and objects discussed or included in the file. Metadata management is important for understanding, aggregating, grouping and sorting data for use. Over the last decade, the rapid growth of data has created the need for metadata management to provide a clear insight into what data to produce and what data to consume. This ensures data becomes a valuable enterprise asset. Advanced metadata is handled differently by file storage and object storage systems: File storage organizes data in directory hierarchies, making it hard to add custom metadata attributes. Object storage lacks the hierarchical directory structure of file storage, but you can customize it. For instance, a clinical image file would only contain metadata such as creation date, owner, location, and size. But if it is stored as an object, a user can enrich the metadata with demographics such as patient’s name, age, and diagnosis. Managing metadata requires strategy and automation: Choosing the best path forward can be difficult when business needs are constantly changing, data is growing explosively and data types are morphing from the collection of new data types such as IoT data, surveillance data, geospatial data and instrument data. Read more about metadata and its role in unstructured data management in this two-part blog series. Learn more about Komprise Smart Data Workflows Learn more about Komprise Deep Analytics and the metadata-driven Komprise Global File Index ### Unstructured Data Management > Core concepts in managing file and object data: preparation, classification, curation, and observability. #### File System Observability File system observability is the ability to continuously monitor, analyze, and understand activity, performance, and usage patterns across a file storage environment, going deeper than basic monitoring to reveal why things are happening, not just what is happening. File system observability combines metrics, logs, events, and metadata from the file system to give IT teams and data owners insight into: Capacity trends – how storage is filling up, by who, and with what types of data Performance metrics – latency, throughput, and I/O patterns File and directory activity – creates, reads, writes, deletes, modifications Data lifecycle – when files were last accessed or modified Anomalies or threats – unusual access spikes that might indicate ransomware or misuse Metadata changes – ownership, permissions, tagging, and classification shifts How is File System Observability Different from Traditional Monitoring? Traditional monitoring tells you that a system is under stress or that storage is 90% full. Observability tells you why, for example, a certain application dumped millions of small log files or a user group started scanning large datasets repeatedly. File system observability enables: Proactive capacity planning – avoiding costly emergency expansions Performance optimization – identifying and resolving I/O bottlenecks Data governance – ensuring sensitive data is stored securely and accessed appropriately Security – detecting abnormal access patterns early Cost control – moving cold or stale files to cheaper storage tiers What are examples of File System Observability in Practice? A tool like Komprise can ingest file system telemetry and metadata to create dashboards and alerts, so a storage admin can see: “70% of last month’s storage growth came from one project folder with CAD files last modified over 3 years ago.” That insight can drive action - archiving the data, reclaiming space, and reducing backup loads. How Does Komprise Deliver File System Observability? Komprise’s observer-based architecture connects non-disruptively to NAS, object storage, and cloud file systems to continuously collect metadata, usage patterns, and performance-related signals, without installing agents on the storage or affecting hot data paths. Key Komprise Intelligent Data Management capabilities include: Global Data Visibility Scans across on-premises, cloud, and edge storage silos. Consolidates file metadata (size, type, owner, location, last access/modified dates) into a single view. Deep Metadata Analytics Tracks trends in data growth, age, activity, and storage consumption. Surfaces hidden patterns such as dormant datasets, overused shares, or orphaned project folders. Activity & Access Insights Identifies who is accessing what data, how often, and where it resides. Can flag anomalies in usage or unusual data movement. Customizable Tagging & Classification Allows creation of business-specific tags (e.g., “AI-Ready,” “Compliance Hold”) for targeted management. Transparent Tiering & Migration Tools Lets you take action directly from insights—archiving, tiering, or migrating files without breaking access. Komprise Benefits Non-intrusive deployment – No performance hit on primary storage, unlike in-line monitoring tools. Vendor-agnostic – Works across heterogeneous storage environments, avoiding lock-in. Actionable intelligence – Not just “what’s on your file system,” but “what to do next.” Scalable visibility – Supports petabyte-scale datasets across multiple sites and clouds. Compliance-ready – Delivers chain-of-custody, audit trails, and governance metadata for regulated industries. Potential Komprise Business Outcomes Komprise delivers file system observability by providing a unified, non-intrusive view of file and object data across on-premises and cloud storage. With Komprise Analysis, customers can uncover usage patterns, growth trends, and dormant data, while enabling action with the full Intelligent Data Management platform, such as tiering, migration, and tagging - directly from storage insights. The result is lower storage costs, faster data-driven decisions, improved compliance, and accelerated AI and analytics readiness, all without vendor lock-in or disrupting performance. Common benefits include: Cost Reduction Identify and move cold/inactive data to cheaper storage tiers, cutting storage and backup costs by 50–70%. Delay expensive hardware refreshes by freeing up premium storage capacity. Operational Efficiency Reduce IT staff time spent manually tracking and cleaning up file systems. Speed migrations by up to 25× and at a fraction of the cost of manual processes. Improved Compliance & Security Locate and secure sensitive or regulated files before audits or breaches occur. Support chain-of-custody tracking for legal and industry requirements. AI & Analytics Enablement Rapidly find and prepare the right datasets for AI ingestion, improving time-to-value for machine learning and analytics projects. Better Cross-Department Collaboration Give business units a self-service view of their data usage to drive accountability and shared cost management. #### Unstructured Data Preparation Unstructured data preparation is the process of identifying, organizing, enriching, and curating unstructured data, such as files, images, videos, documents, and logs, so it can be effectively used for AI, analytics, or automation. Unstructured data preparation may includes Discovery: Finding the right data across data silos Classification: Tagging by type, owner, sensitivity, usage (see data classification) Filtering & Curation: Selecting only relevant or usable data (see data curation) Formatting/Conversion: Making data readable for downstream tools Metadata Enrichment: Adding context for AI/ML models Why Is Unstructured Data Preparation Critical for Enterprise AI? Unstructured data accounts for over 80% of enterprise data, yet it's often: Siloed across on-prem and cloud systems Poorly tagged or understood Costly to store and move in bulk Risky due to embedded sensitive or irrelevant content Enterprise AI projects, including GenAI, need clean, labeled, and relevant data to succeed. Without data preparation: Models get trained on noisy, redundant, or biased data Costs balloon due to unnecessary data movement Governance, compliance, and ethical AI become difficult Preparing unstructured data is not just a technical task - it's a business-critical step for trusted, efficient AI outcomes. Connecting Data Preparation for AI to Your Unstructured Data Management Strategy Unstructured data prep isn’t a standalone activity, it must be part of a broader data management framework that includes: Visibility: Know what data you have, where, and how it’s used Classification & Tagging: Group by content, sensitivity, owners, and usage Lifecycle Management: Archive or delete redundant/unneeded data (see cold data and ROT data) Access & Movement: Ensure secure, cost-effective delivery to AI platforms Policy Automation: Apply governance rules across systems (see data governance) Without an unstructured data management foundation, data preparation becomes manual, risky, and unsustainable at scale. How Komprise Enables Unstructured Data Preparation for AI Komprise helps enterprises prepare AI-ready data at petabyte scale by applying intelligent data management across all file and object storage - on-prem and cloud. For organizations with petabytes of unorganized data, Komprise provides global metadata indexing & search. For organizations building AI data pipelines that quickly become bloated with messy and potentially harmful data, Komprise provides Smart Data Workflows & filtering. For organizations looking to address data privacy and compliance concerns, Komprise provides PII detection and tagging. And finally, for organizations who are experiencing high data movement costs, Komprise provides intelligent data tiering and high-performance data mobility solutions. Here is a summary of the Komprise Data Experience for unstructured data preparation use cases: Global Data Visibility Komprise indexes file and object metadata across all storage silos—without moving data Komprise enables fast search/filtering across billions of files Smart Data Workflows Komprise can automate data classification, tagging, and enrichment (e.g., by file type, PII presence, owner, project) Policy-Based Data Curation Identifies and extracts only relevant, curated subsets of data for AI pipelines Filters by metadata (e.g., "Last accessed < 1 year", "Owner = R&D", "File type = .dcm") Sensitive Data Management Detects and flags PII or compliance risks before feeding data into AI models (read solution brief) Optimized Data Movement Moves selected datasets to AI platforms without breaking file paths or access permissions Avoids “rehydration” costs from archive tiers Example Komprise Unstructured Data Preparation Use Case A healthcare organization wants to train an AI model on radiology images stored across multiple NAS systems. With Komprise, they can: Identify all .dcm image files Filter for files accessed in the last 2 years from oncology teams Exclude files with flagged PII Move only the curated set to a cloud AI platform, saving cost and risk Komprise gives AI teams only the data they need, with the context they require, without the overhead of managing and moving unstructured data manually. #### Data Curation Data curation is the process of organizing, managing, and maintaining data so that it remains accurate, accessible, and useful over time. It involves not just storing data, but also enhancing the value of data through activities such as cleaning, validation, annotation, integration, and preservation. Data curation for unstructured data (text documents, images, videos, audio files, emails, social media posts, etc.) refers to the process of organizing, enriching, and managing data that doesn't have a predefined structure (such as tables or databases). Data Curation of Unstructured Data Increasingly enterprises are looking to unstructured data management (UDM) solutions like Komprise to handle data curation, especially across disparate file and object data storage (NAS) systems. Common data curation steps include: Data Ingestion: Collect data from various sources (e.g., sensors, emails, social media, scanned files). Data Classification: Identify and categorize data by type, source, or topic using AI/NLP tools or manual tagging. Metadata Enrichment:  Add metadata (e.g., author, timestamp, topic, language, sentiment) to help organize and retrieve the data. Data Cleaning: Remove noise or irrelevant parts (e.g., removing stop words from text, trimming silence from audio). Content Extraction: Use tools to extract meaningful information:  (OCR (Optical Character Recognition) for scanned documents, speech-to-text for audio, NLP for summarizing or tagging text. Data Annotation: Label parts of the content for AI training or classification (e.g., tagging entities, labeling emotions in text). Indexing and Storage: Organize the data in searchable repositories using data lakes, NoSQL databases, or content management systems. See Global File Index. Access Control and Governance: Apply rules to manage who can access the data and how it can be used. Preservation and Versioning: Archive the data, ensure format sustainability, and track versions over time. Growing Importance of Proper Data Curation of Unstructured Data As the category of unstructured data management emerges, enterprises are increasingly looking for data curation and data classification strategies to: Unlocks Insights: Makes dark data (unused unstructured data) useful for analysis and decision-making. Support AI & ML Initiatives: Clean, labeled unstructured data is critical for training machine learning models. Improve Searchability: Helps users and systems find relevant content faster. Ensure Compliance: Helps meet legal or regulatory obligations related to data management. Data curation for unstructured data transforms messy, raw information into a structured, searchable, and valuable resource. It combines technical tools (like UDM, NLP and OCR) with careful organization and governance to make unstructured data usable and meaningful. #### Unstructured Data AI AI Needs Unstructured Data Unstructured data is the fuel for Artificial intelligence (AI) and there is growing demand to use AI and machine learning techniques to analyze, process, and derive insights from unstructured data. Unstructured data is data that doesn't have a predefined schema or organized format, such as: Text: Emails, social media posts, chat logs, documents. Images: Photographs, scanned documents, and graphics. Audio: Voice recordings, podcasts, and call recordings. Video: Surveillance footage, movies, or user-generated content. Sensor Data: Logs from IoT devices without a clear structure. Most of this unstructured data is storage as files and objects in the enterprise. Read: Unstructured Data Growth and AI are Changing Executive Decision Making. AI Applications in Unstructured Data Here are some examples: Natural Language Processing (NLP): Sentiment analysis on social media or reviews. Chatbot development for automated customer support. Summarizing or translating text content. Computer Vision: Image recognition for tagging photos or medical imaging diagnostics. Video analysis for facial recognition or surveillance. Speech Recognition: Transcribing spoken words into text. Enhancing virtual assistants like Alexa or Siri. Predictive Analytics: Identifying patterns in unstructured logs or communication data. Forecasting trends based on textual or visual insights. Recommendation Systems: Using text reviews and user-generated content to suggest products or services. Knowledge Extraction: Extracting actionable information from documents, reports, or multimedia data. AI Technologies for Unstructured Data Deep Learning: Particularly neural networks like CNNs for images and RNNs/transformers for text. Transformers Models (e.g., BERT, GPT): Used for advanced text generation, classification, or summarization tasks. OCR (Optical Character Recognition): Converts images of text into machine-readable formats. Audio Processing Models (e.g., WaveNet): Analyze audio signals for transcription or sentiment analysis. Challenges Data Cleaning and Preprocessing: Handling noise, inconsistencies, and errors in raw data. Scalability: Managing large datasets, e.g., video archives or massive text corpora. Interpretability: Making AI outputs understandable and actionable. Integration: Combining structured and unstructured data for holistic insights. AI for unstructured data is becoming increasingly critical, as 80-90% of data generated today is unstructured, according to IDC. Tools like OpenAI's models, Google Cloud AI, and AWS AI services are instrumental in enabling businesses to leverage unstructured data effectively. Unstructured Data Management and AI At the end of 2024, Komprise CEO and cofounder Kumar Goswami made the following predictions for AI and data: IT leaders will get creative to deploy AI on a budget (see the survey) Unstructured data governance processes for AI will mature Systematic data ingestion for AI will be the first data storage mandate Hybrid cloud persists, mandating deep intelligence on data and costs Role of storage administrator evolves to embrace security and AI data governance He noted: AI mania is overwhelming, but so far, enterprise participation has been largely led by employees who are using GenAI tools to assist with daily tasks such as writing, research and basic analysis. AI model training has been primarily the responsibility of specialists, and storage IT has not been involved with AI. But this will change swiftly in the coming year. Business and public sector leaders know that if they get left behind in the AI Gold Rush, they may lose market share, customers and relevance. Corporate data will be used with AI for retrieval augmented generation (RAG) and inferencing, which will constitute 90% of AI investment over time. Everyone touching data and infrastructure will need to step up to the plate as a broader set of employees start sending company data to AI. Storage IT will need to create systematic ways for users to search across corporate data stores, curate the right data, check for sensitive data and move data to AI with audit reporting. Storage managers will need to get clear on the requirements to support their business, departmental and IT counterparts. #### Unstructured Data Storage What is Unstructured Data Storage? Unstructured data storage is the storage of data that does not adhere to a predefined data model or schema. Unlike structured data, which fits neatly into tables with rows and columns, unstructured data lacks a specific organization and may include various file types, such as text documents, images, videos, audio files, emails, social media posts, and more. Read the article: Here's How to Take Control of Unstructured Data Gartner on unstructured data storage Each year Gartner publishes the Magic Quadrant for Distributed File Systems and Object Storage. Gartner defines distributed file systems and object storage as software and hardware appliance products that offer object and distributed file system technologies for unstructured data. Their purpose is to store, secure, protect and scale unstructured data with access over the network using file and object protocols, such as Amazon Simple Storage Service (S3), Network File System (NFS) and Server Message Block (SMB). Gartner also has a Primary Data Storage Magic Quadrant, as summarized in this Blocks & Files article. Common requirements for unstructured data storage Flexibility: Unstructured data storage systems are flexible and can accommodate various types of data without requiring predefined schemas. This flexibility allows organizations to store and manage diverse data types efficiently. Scalability: Unstructured data storage solutions are often designed to scale easily, allowing organizations to handle massive volumes of data as their storage requirements grow over time. Indexing and Search: Effective management of unstructured data involves indexing and search capabilities to quickly locate and retrieve specific information within large datasets. This may involve metadata tagging, full-text search, and other techniques to facilitate data discovery. See unstructured data classification. Object Storage: Object storage is a common approach to storing unstructured data, where each piece of data is stored as an object with a unique identifier and metadata. Object storage systems provide scalability, durability, and accessibility for large-scale unstructured data environments. Cloud Storage: Many organizations leverage cloud storage services for unstructured data storage due to their scalability, reliability, and cost-effectiveness. Cloud providers offer a range of storage options, including object storage, file storage, and content delivery networks (CDNs), to accommodate different types of unstructured data. Data Governance and Security: Managing unstructured data requires robust data governance practices to ensure compliance, data security, and privacy protection. This may involve implementing access controls, encryption, data classification, and audit trails to safeguard sensitive information. Effective storage and unstructured data management are essential for organizations to derive insights, make data-driven decisions, and unlock the value of their data assets. Unstructured Data Storage Vendors Many vendors offer solutions for storing unstructured data, each with its own set of features, capabilities, and pricing models. Here are some notable vendors in the unstructured data storage space: Amazon Web Services (AWS): Amazon Simple Storage Service (S3) (AWS S3) is a highly scalable object storage service designed for storing and retrieving any amount of data. It is commonly used for unstructured data storage and offers features such as versioning, lifecycle management, and security features. Learn more about Komprise for AWS. Microsoft Azure: Azure Blob Storage provides scalable, cost-effective storage for unstructured data. It offers tiered storage options, access controls, and integration with other Azure services for data analytics and processing. Learn more about Komprise for Azure. Google Cloud Platform (GCP): Google Cloud Storage is a scalable object storage solution suitable for storing unstructured data. It provides features such as versioning, lifecycle management, and integration with other GCP services. Learn more about Komprise for Google.  IBM: IBM Cloud Object Storage: IBM offers Cloud Object Storage, a scalable, secure, and durable object storage service. It is designed to support large-scale unstructured data storage and offers features such as encryption, access controls, and global data distribution. Learn more about Komprise for IBM. Dell: Dell EMC Isilon, now Dell PowerScale, is a scale-out network-attached storage (NAS) platform designed for storing and managing large volumes of unstructured data. It offers high performance, scalability, and multi-protocol support for various data types. Learn about Komprise Elastic Data Migration for Isilon. NetApp: NetApp StorageGRID is an object storage solution from NetApp that enables organizations to store, manage, and protect unstructured data at scale. It offers features such as geo-distribution, data tiering, and policy-based management. Learn more about Komprise for NetApp. Pure Storage: Pure Storage FlashBlade is a scalable, all-flash storage platform designed for unstructured data workloads. It offers high performance, simplicity, and native support for file, object, and analytics workloads. HPE (Hewlett Packard Enterprise): For years it has been HPE Nimble Storage, which offers a range of storage solutions, including Nimble Storage dHCI and Nimble Storage All Flash Arrays, suitable for storing unstructured data. HPE now resells VAST Data solutions as HPE File Services. Qumulo: Qumulo’s Scale Anywhere™ platform is a 100% software solution for hybrid enterprises to efficiently store and manage file & object data at the edge, in the core, and in the cloud These are some examples of vendors providing solutions for unstructured data storage. Optimize unstructured data storage with Komprise Komprise Intelligent Data Management frees you to analyze, mobilize, and access the right file and object data across clouds without shackling your data to any unstructured data storage vendor. Komprise helps enterprise customers optimize data storage costs by right-sizing and right-placing data, while making it easy for users to unlock data value with smart data workflows. #### Unstructured Data Classification Unstructured data classification involves the process of categorizing and organizing unstructured data based on its content, context, or other characteristics. Unstructured data typically refers to information that does not have a predefined data model or is not organized in a structured manner, such as text documents, images, audio files and videos. Classifying unstructured data is increasingly recognized as essential for efficient unstructured data management, search, and analysis. Unstructured Data Classification: A Top Enterprise Data Storage Trend According to Gartner's Top Trends in Enterprise Data Storage 2023 (subscription required): By 2027, at least 40% of organizations will deploy data storage management solutions for classification, insights and optimization, up from 15% in early 2023. The report goes on to note that: Data classification or categorization helps improve IT and business outcomes such as storage optimization, data life cycle enforcement, security risk reduction and faster data workflows. Data classification and insights solutions are typically vendor storage agnostic, and work on any data that can be accessed over a file or object access protocols like NFS, SMB or S3. Why unstructured data classification matters Classification adds structure to unstructured data – which makes it easier to find and leverage across the organization. Classification starts with the metadata that’s automatically generated by data storage technology. System-generated metadata includes information about when the data was created, who created it, its type, its size, when it was last accessed and when it was last modified. This helps IT managers classify data by the department it belongs to and identify rarely accessed data as ready for archiving and tiering to lower-cost storage destinations. IT professionals can also search based on data types, such as video or medical imaging files, which may be consuming too much storage (and budget) and require action such as migration. Enriching metadata adds additional classification, such as to identify project data, demographic data, sensitive data or other content based on keywords. Use Cases for Data Classification Security and Privacy: Data classification is critical to discover personally identifiable information, IP and other sensitive data that may be hidden or has been copied and stored in noncompliant locations. An organization can apply levels of security classification too, such as low, medium or high risk. Audits and E-discovery Some organizations have regular audits, such as for proper management of financial or personal health information data, which requires IT to work with auditors and demonstrate compliance. Without classification and segmentation of audited data, an organization may face heavy manual work to locate audited data. For e-discovery, which happens out of the blue, a company may need to quickly locate and copy security video footage to facilitate an investigation, for instance. Data Retention Industry or corporate rules may dictate the retention of files for a period. Searching metadata for file type, such as medical images, and time of creation, IT can find files that are prime for deletion. This also saves money by avoiding the endless storage of data that is no longer needed or required. Komprise Smart Data Workflows can allow IT to create workflows that discover and confine or delete files by policy. Cost Savings Data classification by age and time of last access is a smart way to find data that is rarely accessed, or “cold,” and move it to archival storage where it can be retained for as long as necessary — at a fraction of the cost. Metadata indicating file type, such as instrument or research data, further informs long-term storage strategies. Learn more about Komprise Analysis here. Search and AI Deep classification of unstructured data sets, such as by keyword or project name, helps employees can find what they need without bugging IT. They can then feed it to analytics tools or other applications as needed. For instance, healthcare analysts may want to run a study of breast cancer images from a certain demographic and with a particular diagnosis code. Enriching metadata with these tags in a policy-driven, automated way means that the required data sets are always updated and easy to locate by researchers. Data Governance for AI IT and security teams can tag and segment proprietary data sets which are banned from ingestion by AI tools, as well. This is an important consideration when using GenAI tools in the public domain, since sensitive and protected data can be easily and unwittingly leaked into training models. Read more about Komprise Sensitive Data Management. What are some approaches and techniques for unstructured data classification? Text-Based Classification Natural Language Processing (NLP): NLP techniques, including text tokenization, sentiment analysis, and named entity recognition, can be used to analyze the content of textual data. Keyword Matching: Classifying documents based on the presence of specific keywords or key phrases related to predefined categories. Image-Based Classification Computer Vision: Utilizing computer vision techniques, such as image recognition and object detection, to classify and categorize images based on their visual content. Feature Extraction: Extracting features from images, such as color histograms or texture patterns, and using machine learning models for classification. Audio and Speech-Based Classification Speech Recognition: Converting spoken language into text for further analysis and classification. Audio Analysis: Extracting features from audio files, such as pitch or frequency, and using machine learning algorithms for classification. Metadata-Based Classification File Metadata: Utilizing metadata associated with files, such as creation date, author, or file type, for classification purposes. Exif Data: For images, extracting metadata embedded in the file, such as camera settings and location information. Exchangeable image file format (EXIF). Pattern Recognition Machine Learning Algorithms: Training machine learning models, including supervised or unsupervised learning algorithms, to recognize patterns and classify unstructured data based on historical examples. Clustering: Grouping similar data points together using clustering algorithms to discover natural groupings within unstructured data. Rule-Based Classification Predefined Rules: Establishing rules and criteria for classifying data based on certain characteristics or conditions. Expert Systems: Using expert systems that encode human expertise and rules for classification. Content Analysis Topic Modeling: Identifying topics or themes within unstructured text data using techniques like Latent Dirichlet Allocation (LDA). Sentiment Analysis: Determining the sentiment expressed in textual content, such as positive, negative, or neutral sentiments. Combination of Techniques Hybrid Approaches: Combining multiple techniques, such as text analysis, image recognition, and metadata examination, for a more comprehensive and accurate classification. Deep Learning Neural Networks: Leveraging deep learning models, such as convolutional neural networks (CNNs) for images or recurrent neural networks (RNNs) for sequential data, to automatically learn features and patterns for classification. Feedback Loop and Continuous Improvement Establishing a feedback loop where the classification system continuously learns and improves based on user feedback, corrections, and updates to the training data. Unstructured data classification is a challenging task, but advancements in machine learning, deep learning, and natural language processing have significantly improved the accuracy and efficiency of these classification methods and modern unstructured data management software solutions have emerged to address elements of data classification and ongoing data lifecycle management. Depending on the specific requirements and characteristics of the unstructured data, different techniques or a combination of approaches may be suitable for effective unstructured data classification. Unstructured Data Classification with Komprise Komprise Deep Analytics allows you to find the right data that fits specific criteria across all your data storage silos to answer questions, such what file types the top data owners are storing. Once you connect Komprise to your file and object storage, Komprise indexes the data and creates a Global File Index of all your data. Users can then create custom tags by enriching the metadata, such as for identifying sensitive data such as PII. Read more about data tagging with Komprise in the blog.   Read the article: How to Control Unstructured Data Komprise Use Case: Data Classification #### Unstructured Data Governance Unstructured data governance is a growing practice in enterprise IT as data volumes have exploded and organizations need to manage data assets to reduce risks and costs and ensure data is discoverable for new uses. Unstructured data includes text documents, emails, images, videos, social media posts, audio files, sensor data and other data types that do not fit neatly into traditional structured databases. Unlike structured data that can be organized into tables and fields, unstructured data lacks a predefined format, making it challenging to manage, search, and mine for new insights. Read the Blocks & Files interview with Chris Mellor: Metadata is the Key to Smarter AI and Data Governance An unstructured data governance strategy can involve many components: Data Discovery and Inventory: Organizations need to identify and catalog unstructured data to manage it properly. This involves locating data stored across various repositories, including file shares, cloud storage, email systems, and more. A thorough inventory delivers holistic visibility into data assets to inform decision-making. Data Classification and Tagging:  IT managers need the ability to tag and segment unstructured data based on its sensitivity, importance, and relevance to the organization.  This includes tagging data with metadata that indicates details such as owners, purpose or project, security (such as containing PII), compliance requirements and other identifying characteristics of the file contents. Access Control and Security: Implementing access controls ensures that only authorized individuals can access and modify sensitive unstructured data. This involves defining user roles, permissions, and authentication mechanisms to safeguard the data from unauthorized access or breaches. Data Retention Policies: Organizations need to establish policies that dictate retention policies for unstructured data.  Doing so helps ensure compliance with legal and regulatory requirements, lowers the risk of retaining unnecessary data and lowers costs of data storage and backups. Data Privacy and Compliance:  Data privacy regulations such as GDPR, HIPAA, or CCPA require proper handling and protection of personal and sensitive data. Unstructured data governance includes procedures to ensure compliance with these regulations—such as how and where regulated data is stored. Data Lifecycle Management: This involves managing data from creation to deletion. It includes processes for capturing, storing, migrating, archiving, and deleting unstructured data as its needs and value to the organization change. Search and Discovery: Deep search capabilities aided by metadata and content indexing help users find relevant unstructured data quickly. Data Analytics and Insights: Extracting valuable insights from unstructured data requires tools and techniques for data analysis, such as natural language processing (NLP), text mining and sentiment analysis. Data Stewardship: Assigning data stewards responsible for managing and overseeing specific sets of unstructured data can help ensure that data is properly maintained, accurate, and up-to-date. Monitoring and Auditing: Regularly monitoring and auditing unstructured data governance processes is important for compliance, security, and to reduce risks and improve outcomes from analytics and AI initiatives. Read more in the blog on data governance tips for generative AI. Unstructured data governance is critical for maintaining data quality, security, compliance, and deriving meaningful insights from the vast amounts of unstructured data that organizations generate and store. Proper governance practices contribute to better decision-making, reduced risks, and improved overall unstructured data management. Unstructured Data Governance in the News What to Expect in 2025: AI Data Governance Predictions How to make safe, ethical AI decisions in the age of unstructured data Learn how Komprise is bringing new data governance features to its unstructured data management solution in this blog. #### Unstructured Data Workflows Unstructured data workflows can include a variety of processes and technologies, such as data management tools, document management systems, content management systems, and collaboration platforms. Data is no longer static and needs to move between systems and clouds to satisfy changing requirements and to support big data and AI/ML initiatives. Technologies and processes that automate and streamline these workflows can shave significant time and costs from finding, preparing and moving data into data lakes and analytics platforms or to meet compliance requirements. Overall, unstructured data workflows play an important role in modern data management and are critical for organizations that generate and use large volumes of unstructured data. By implementing effective unstructured data workflows, organizations can ensure that data lives at the right place and at the right time to satisfy a variety of enterprise and departmental needs. Komprise Smart Data Workflows for File and Object Data Komprise Smart Data Workflows allow you to define and execute automated processes, which could be industry or domain specific, to search and fine, migrate and tier, and ultimately get greater value from unstructured data. With Smart Data Workflows you can create custom queries across hybrid, multi-cloud, on-premises and edge data silos to find the file and object data you need, which is often locked away in data storage silos, execute Komprise or external functions on a subset of data and tag the data with additional metadata. ​Move only the data you need and manage the lifecycle of unstructured data intelligently. Watch the unstructured data management workflow with Komprise CTO and co-founder at Cloud Field Day. #### File Data Management File data management is the process of organizing, storing, and retrieving digital files in an efficient and secure manner. This can include tasks such as: Naming files in a consistent and descriptive manner Creating folders and sub-folders to categorize and store files Regularly backing up important files to prevent data loss Purging old or unnecessary files to free up storage space Using appropriate software tools to manage, search and retrieve files Effective file data management helps improve productivity and organization, and reduces the risk of data loss or corruption. It is a critical aspect of overall data management, especially in businesses and organizations where large amounts of data are generated and stored on a regular basis. File Data Management Challenges Because we're talking about unstructured data, file data management can present a number of challenges, including: Data Growth: As more and more data is generated and stored, it can become difficult to manage and organize effectively. The majority is unstructured data. Data Duplication: Duplicate files can lead to confusion, waste storage space and make it harder to find the most up-to-date version of a file. Data Security: Protecting sensitive information from unauthorized access or cyberattacks is a major concern in file data management. (Read about cyber resiliency and saving on ransomware production.) Data Loss: Accidentally deleting or losing files can result in significant data loss and potential productivity loss. Compliance: Certain industries and organizations may have regulatory requirements for file data management, such as retention policies and data privacy laws. Integration with Other Systems: Integrating file data management systems with other applications, such as email, CRM, and collaboration platforms, can be complex and time-consuming. Scalability: As the amount of data grows, the file data management system must be able to scale to meet the demands of the organization. Compatibility: Ensuring that files can be opened and used by multiple users and systems can be a challenge, especially with different file formats and software versions. These challenges can be addressed through the use of appropriate software tools, best practices for file data management, and regular reviews and updates to the file data management policies. Komprise File Data Management Komprise Intelligent Data Management has been designed from the ground-up to simplify file data management and put customers in control of unstructured data, no matter where data lives. Analytics-first approach, Komprise works across file and object storage, across cloud and on-premises, and across data storage and data backup architectures to deliver a consistent way to manage data. With Komprise you get instant insight into all of your unstructured data—wherever it resides. See patterns, make decisions, make moves, and save money—all without compromising user access to any data. Komprise puts you in control of your data while simplifying file data management by creating a lightweight management plane across all your data storage silos without getting in the path of data access. Block vs File Level Data Storage Tiering A primary file data management technique is data tiering. Here is a summary of block-level versus file-level tiering and the impact. Also download the whitepaper and learn more about Komprise Transparent Move Technology (TMT). Read the Komprise Unstructured Data Tiering Guide #### Unstructured Data Analytics Unstructured data analytics or unstructured data analysis refers to the process of extracting insights and knowledge from large amounts of unstructured data, which is data that does not conform to a traditional structured model, such as relational databases (RDBMS). It includes text documents, images, audio and video files, emails, sensor data and other forms of data that do not have a pre-defined format. Unstructured data analytics involves several techniques and technologies to process and analyze the data, such as natural language processing (NLP), machine learning, text mining, image and video analysis, and data visualization. The goal of unstructured data analytics is to discover insights that can inform decisions, improve business processes, and drive innovation. The importance of unstructured data analytics is growing in many data-heavy industries, including healthcare, finance, retail and government and across many functions, including marketing, engineering, research and development. The right approach to unstructured data analytics can deliver a competitive advantage, help you understand customer behavior, suggest operational improvements and influence R&D initiatives. The challenge of unstructured data analytics is to manage and process large volumes of data in a scalable and efficient manner, and to extract meaningful insights from the data. Data Lakes, Data Lakehouses, and cloud data storage are typically part of an unstructured data analytics IT infrastructure. According to the Komprise 2022 State of Unstructured Data Management survey, 65% of IT organizations are delivering unstructured data to big data analytics programs. Komprise Smart Data Workflows is an automated process for all the steps required to find the right data across your storage assets, tag and enrich the data, and send it to external tools such as a data lakehouse for analysis. Komprise makes it easier and more streamlined to find and prepare the right data for analytics projects. #### Unstructured Data Management What is Unstructured Data Management? Unstructured data management is a category of software that has emerged to address the explosive growth of unstructured data in the enterprise and the modern reality of hybrid cloud storage. In the Komprise 2023 Komprise State of Unstructured Data Management, 32% of organizations report that they are managing 10PB of data or more. That equates to 110,000 ultra-high-definition (UHD) movies, or half of the data stored by the U.S. Library of Congress. Most (73%) of organizations are spending more than 30% of their IT budget on data storage. Data storage and data backup technology vendors are now recognizing the importance of unstructured data management as data outlives infrastructure and as data mobility is needed to leverage cloud data storage. Additionally, unstructured data is recognized as the fuel for enterprise AI. Managing it effectively is what makes that fuel usable, trusted, and cost-efficient. Unstructured data management must be independent and agnostic from data storage, backup, and cloud infrastructure technology platforms. There are 5 requirements for unstructured data management solutions: Must go beyond storage efficiency and help create greater data value Must be multi-directional Most not disrupt users and workflows (learn more about Transparent Move Technology) Should create new uses for your data (increasingly powering AI initiatives, for example) Put your data first and avoids vendor lock-In (see native data access) An analytics-based unstructured data management solution brings value by analyzing all data in storage across on-premises, cloud and edge environments to deliver deep insights. This knowledge helps IT managers make better decisions with users in mind, optimize costs and reduce security and regulatory compliance risks. These insights go beyond traditional storage metrics such as latency, IOPS and network throughput. Additionally, the right unstructured data management solution should deliver the right data sets in native format to analytics and AI services. Here are some of the new metrics made possible with unstructured data management software: Top data owners/users: See trends in usage and and possible compliance issues, such as individual users storing excessive video files or PII files being stored in an insecure location. Common file types: The ability to see data by file extension eases the process of finding all files related to a project and can inform future research initiatives. This could be as simple as finding all the log files, trace files or extracts from a given application or instrument and moving them to a data lake for analysis. Storage costs for chargeback or showback: Whether for chargeback requirements or not, stakeholders should understand costs in their department and be able to view metrics. This will help identify areas where low-cost storage or data tiering to archival storage is a viable cost-reduction opportunity. Data growth rates: High level metrics on data growth keeps IT and business heads on the same page so they can collaborate on data management decisions. Understand which groups and projects are growing data the fastest and ensure that data creation/storage is appropriate according to its overall business priority. Age of data and access patterns. In most enterprises, 60-80% of data is  “cold" and hasn’t been accessed in a year or more. Metrics showing percentage of cold versus warm versus hot data are critical to ensure that data is living in the right place at the right time according to its business value and to optimize costs. Read: File Data Metrics to Live By Beyond cost optimization, unstructured data management tools and practices can help deliver new value from data. Unstructured data is the fuel needed for AI, yet its difficult to leverage because unstructured data is hard to find, search across, and move due to its size and distribution across hybrid cloud environments. Tagging and automation can help prepare unstructured data for AI and big data analytics programs. Tactics include: Preprocess data at the edge so it can be analyzed and tagged with new metadata before moving it into a cloud data lake. This can drastically reduce the wasted cost and effort of moving and storing useless data and can minimize the occurrence of data swamps. Applying automation to facilitate data segmentation, cleansing, search and enrichment. You can do this with data tagging, deletion or tiering of cold data by policy and moving data into the optimal storage where it can be ingested by big data and ML tools. A leading new approach to is the ability to initiate and execute data workflows. Use a solution that persists metadata tags as data moves from one location to another. For instance, files tagged as containing key project keywords by a third-party AI service should retain those tags indefinitely so that a new research team doesn’t have to run the same analysis over again — at high cost. Komprise Intelligent Data Management has these capabilities. Plan appropriately for large-scale data migration efforts with thorough diligence and testing. This can prevent common networking and security issues that delay data migrations and introduce errors or data loss. Read the interview with Komprise cofounder and COO: AI Data Pipelines Could Use a Hand from our Features, says Komprise. Komprise allows you to analyze unstructured data costs and set data management policies. The State of Unstructured Data Management In August 2021, Komprise published the first State of Unstructured Data Management Report.  Read the 2024 report.   Highlights of the 2021 Unstructured Data Management Report Unstructured Data is Growing, as are its Costs 65.5% of organizations spend more than 30% of their IT budgets on data storage and data management. Most (62.5%) will spend more on storage in 2021 versus 2020. Getting More Unstructured Data to the Cloud is a Key Priority 50% of enterprises have data stored in a mix of on-premises and cloud-based storage. Top priorities for cloud data management include: migrating data to the cloud (56%) cutting storage and data costs (46%) and governance and security of data in the cloud (41%). IT Leaders Want Visibility First Before Investing in More Data Storage Investing in analytics tools was the highest priority (45%) over buying more cloud or on-premises storage or modernizing backups. One-third of enterprises acknowledge that over 50% of data is cold while 20% don’t know, suggesting a need to right-place data through its lifecycle. Unstructured Data Management Goals & Challenges: Visibility, Cost Management and Data Lakes 44.9% wish to avoid rising costs. 44.5% want better visibility for planning. 42% are interested in tagging data for future use and enabling data lakes. 2022 State Unstructured Data Management Report In August 2022, Komprise published the 2nd annual State of Unstructured Data Management Report: Komprise Survey Finds 65% of Enterprise IT Leaders are Investing in Unstructured Data Analytics. The Top 5 trends from the report are summarized here. They are: User Self-Service: In data management, self-service typically refers to the ability for authorized users outside of storage disciplines to search, tag and enrich and act on data through automation—such as a research scientist wanting to continuously export project files to a cloud analytics service. Moving Data to Analytics Platforms: A majority (65%) of organizations plan to or are already delivering unstructured data to their big data analytics platforms. Cloud File Storage Gains Favor: Cloud NAS topped the list for storage investments in the next year (47%). User Expectations Beg Attention: Organizations want to move data without disrupting users and applications (42%). IT and Storage Directors want Flexibility: A top goal for unstructured data management (42%) is to adopt new storage and cloud technologies without incurring extra licensing penalties and costs, such as cloud egress fees. Unstructured Data Management State of Unstructured Data Management 2023 In September 2023, Komprise published the 3rd annual State of Unstructured Data Management report. Read the press release Download the report The coverage focused on the fact that 66% of respondents said preparing data storage and data management for AI and GenerativeAI in general is a top priority and challenge. Data governance is top enterprise priority when introducing AI AI: Intelligent data needs intelligent solutions Nearly a third of enterprises are already prepping for AI Getting Data Governance Right Top AI Priority in 2023 State of Unstructured Data Management 2024 Highlights of the Survey: Nearly 50% of enterprises are storing more than 5PB of unstructured data and nearly 30% have more than 10PB. The top priorities for data storage in the next year include cost optimization (54%), preparing for AI (51%) and investing in data management and data mobility (41%). As in 2023, moving data without disruption to users/apps is the top technical unstructured data management challenge (54%), followed by using AI to classify and segment data (48%). Prepping for AI is the top business challenge for unstructured data management (57%). Only 13% restrict what corporate data can be used in AI, while 31% have no restrictions for users, apps or data in AI. Nearly half (44%) are creating AI-ready infrastructure and 32% are building their own learning models. Only 30% are increasing the IT budget to support AI projects. The leading challenge in prepping data for AI is managing governance/security​ concerns (45%), followed by data classification and tagging​ (41%). The leading tactic to address AI data concerns is to upgrade data storage/data management technologies (53%). AI data governance/security is the top future capability (47%) for unstructured data management, up from 28% in 2023. Nearly 60% need more staff with skills related to security, compliance and sensitive data. Why you need to manage your unstructured data? In a 2022 interview, Komprise co-founder and COO Krishna Subramanian defined unstructured data this way: Unstructured data is any data that doesn’t fit neatly into a database, and isn’t really structured in rows and columns. So every photo on your phone, every X-ray, every MRI scan, every genome sequence, all the data generated by self-driving cars – all of that is unstructured data. And perhaps more relevant to more businesses, artificial intelligence (AI) and machine learning (ML) – they depend on, and usually output, unstructured data too. Unstructured data is growing every day at a truly astonishing rate. Today, 85% of the world’s data is unstructured data. And it’s more than doubling, every two years. The importance of an unstructured data strategy for enterprise In part two of the interview, Krishna Subramanian noted: Unstructured data doesn’t have a common structure. But it does have something called metadata. So every time you take a picture on your phone, there’s certain information that the phone captures, like the time of day, the location where the picture was taken, and if you tag it as a favorite, it’ll have that metadata tag on it too. It might know who’s in the photo, there are certain metadata that are kept. All filing systems store some metadata about the data. A product like Komprise Intelligent Data Management has a distributed way to search across all the different environments where you’ve stored data, and create a global index of all that metadata around the data. And that in itself is a difficult problem, because again, unstructured data is so huge. A petabyte of data might be a few billion files, and a lot of these customers are dealing with tens to hundreds of petabytes. So you need a system that can create an efficient index of hundreds of billions of files that could be distributed in different places. You can’t use a database, you have to have a distributed index, and that’s the technology we use under the hood, but we optimize it for this use case. So you create a global index. Learn more about unstructured data tagging. The Future of Unstructured Data Management In an end of the year blog post, Komprise executives review unstructured data management and data storage predictions for 2023 and the implications of adopting data services, processing data at the edge, multi-cloud challenges, the importance of getting smart data migration strategies, and more. Here are the predictions for unstructured data management in 2024. Clearly AI has emerged as a top requirement, as summarized in this industry analyst interview: AI infrastructure and independent data management are on trend. Here are 2025 predictions, which again focus on AI and AI Data Governance. Why is unstructured data management important? Unstructured data management is important because it directly impacts cost, compliance, and business value — especially in the AI era. Key reasons: It’s the majority of enterprise data: Roughly 80–90% of enterprise data is unstructured (files, emails, images, video, sensor data). Without management, it grows unchecked, driving up storage costs. Cost control & storage optimization: Keeping all data on expensive, high-performance storage is wasteful. Classifying and moving cold or unused data to cheaper tiers can save millions annually. Data governance & compliance: Regulations (GDPR, HIPAA, CCPA) require knowing what data you have, where it lives, and who can access it. Poor oversight risks fines and reputational damage. AI readiness: Feeding AI all your unstructured data is inefficient and risky — irrelevant or poor-quality data can lead to inaccurate results. Management ensures only the right, trusted datasets are used. Faster data access & productivity: Well-managed data is easier to find, share, and analyze, accelerating business workflows and innovation. Unstructured data management shifts data from being a hidden liability to a strategic asset — enabling organizations to reduce cost, mitigate risk, and unlock value for analytics and AI. What is the role of unstructured data management in what Gartner calls DSMS? In Gartner’s view of Data Storage Management Services (DSMS), unstructured data management plays a central, enabling role because most of the storage management challenge today comes from the explosive growth of file and object data. Here’s how it fits: 1. Unstructured Data Is the Majority of What DSMS Manages Gartner notes that 70–90% of enterprise data is unstructured—spanning files, images, videos, logs, scientific data, and more. DSMS strategies that focus only on block storage or structured datasets won’t solve the dominant cost, performance, and compliance challenges. Unstructured Data Management (UDM) extends DSMS beyond capacity and performance to data awareness—knowing what you have, where it is, and how it’s being used. 2. UDM Enables Intelligent Tiering and Cost Optimization In Gartner’s DSMS framework, a key service is storage optimization. UDM solutions like Komprise Intelligent Data Management bring file-level analytics and policy-based movement across storage tiers (on-prem, NAS, object, cloud) to cut costs without disrupting access. Without this insight, DSMS risks overprovisioning expensive storage for cold or inactive data. 3. UDM Adds Context for Governance, Compliance, and Security DSMS includes data protection and governance as core functions. UDM enriches metadata—beyond file size and location—to include sensitivity, owner, access history, and content classification. This allows DSMS to enforce retention, privacy (e.g., GDPR/CCPA), and security policies more accurately. 4. UDM Powers DSMS for the AI Era Gartner now emphasizes that DSMS is not just about storage cost control—it’s also about delivering the right data to AI, analytics, and digital transformation projects. UDM enables DSMS to curate, classify, and prepare unstructured datasets so AI models aren’t trained on irrelevant or risky data. This bridges storage management with data engineering—something Gartner sees as essential for AI success. 5. Business Outcomes of Integrating UDM into DSMS Lower TCO – Reduce cloud and on-prem storage spend by moving inactive data to lower-cost tiers. Reduced Risk – Identify and govern sensitive or orphaned data. Faster AI/Analytics Projects – Deliver clean, relevant datasets directly from storage infrastructure. Improved SLA Compliance – Ensure the right data is in the right storage for performance and access needs. What are the benefits of a storage agnostic approach to unstructured data management? Freedom from Vendor Lock-in: You can classify, move, and access data without being forced into a single storage ecosystem. This enables negotiating power with storage vendors since your data workflows aren’t locked into proprietary formats or APIs. Unified Visibility Across Hybrid/Multi-Cloud: Provides a single pane of glass across NAS, object stores, and cloud tiers. IT and data teams can analyze usage, growth, and cost regardless of where the data resides. Consistent Policies and Governance: Apply the same classification, tagging, and retention rules across heterogeneous storage environments. This ensures compliance frameworks (e.g., GDPR, HIPAA) are met no matter where the data is stored. Optimized Data Placement for Cost and Performance: Move data across tiers or vendors based on actual usage patterns, not storage vendor constraints. Save costs by tiering cold data to cheaper platforms without breaking access. Faster AI and Analytics Enablement: Curate and deliver datasets from anywhere, without first consolidating into one vendor’s system.. Researchers and data scientists can access data in native format across different environments. Business Continuity and Flexibility: Easier cloud migrations, consolidations, or divestitures since you’re not tied to one platform.. Supports changing business priorities. (e.g., moving workloads from on-prem to Azure or AWS, without rearchitecting) Future-Proofing: As new storage technologies and cloud services emerge, you can integrate them without disrupting existing workflows. This reduces technical debt because your data management layer is portable. The biggest takeaway: storage-agnostic UDM separates “data value” from “data storage.” That means organizations manage data for its business impact (cost, compliance, AI readiness) instead of being limited by where it happens to be stored. Learn more about Komprise storage-agnostic unstructured data management. #### Data Classification Data classification is the process of organizing data into tiers of information for data organizational purposes. Data classification is essential to make data easy to find and retrieve so that your organization can optimize risk management, compliance, and legal requirements. Written guidelines are essential in order to define the categories and criteria to classify your organization’s data. It is also important to define the roles and responsibilities of employees in the data organization structure. When data classification procedures are established, security standards should also be established to address data lifecycle requirements. Classification should be simple so employees can easily comply with the standard. Examples of types of data classifications: 1st Classification: Data that is free to share with the public 2nd Classification: Internal data not intended for the public 3rd Classification: Sensitive internal data that would negatively impact the organization if disclosed 4th Classification: Highly sensitive data that could put an organization at risk Data classification is a complex process, but automated systems can help streamline this process. The enterprise must create the criteria for classification, outline the roles and responsibilities of employees to maintain the protocols, and implement proper security standards. Properly executed, data classification will provide a framework for the data storage, transmission and retrieval of data. Automation simplifies data classification by enabling you to dynamically set different filters and classification criteria when viewing data across your storage. For instance, if you wanted to classify all data belonging to users who are no longer at the company as "zombie data," the Komprise Intelligent Data Management solution will aggregate files that fit into the zombie data criterion to help you quickly classify your data. Data Classification and Komprise Deep Analytics Komprise Deep Analytics gives data storage administrators and line of business users granular, flexible search capabilities and indexes data creating a Global File Index across file, object and cloud data storage spanning petabytes of unstructured data. Komprise Deep Analytics Actions uses these virtual datasets (see virtual data lake) for systematic, policy-driven data management actions that can feed your data pipelines. #### Unstructured Data What's the difference between structured data and unstructured data? Data can be of two broad types: structured data and unstructured data. Structured Data: Structured data is data that can be organized by structured categories, such as rows and columns in an Excel spreadsheet or a database. For example, accounting records are structured data because you can organize them by customer, by geography, by product, etc. Structured data is typically stored in a database and can be queried using query languages such as Structured Query Language (SQL). Most data was predominantly structured until 2000 but since then we have seen an explosion of unstructured data. Today, structured data accounts for less than twenty percent of the world’s data. Unstructured Data: Unstructured data is data that doesn’t fit neatly in a traditional database and has no identifiable internal structure. This is the opposite of structured data, which is data stored in a database. Up to 80% of business data is considered unstructured, with this number increasing year over year. Examples of unstructured data are text documents, e-mail messages, photos, audio and video files, CAD / CAM files, genomics sequencing data, medical images,  presentations, IoT and machine-generated data, log files, user documents stored across teams and departments, and much more. Unstructured data usually does not include a predefined data model, and it does not match well with relational tables. Text heavy, unstructured data may include numbers and dates, as well as facts. This leads to difficulty in identifying this data using conventional software programs. What are Unstructured Data Types by Industry? Unstructured data is the predominant data type that is generated by most applications today – from self-driving cars, to Internet of Things (IOT) devices, to genome sequencers, to video and audio files, most of the data we generate and use today is unstructured. Here are some examples of unstructured data types industry: Life Sciences: Imaging, genome sequencing, research Healthcare: Imaging, PACS, digital pathology Media & Entertainment: Post-production, animation, VFX, content delivery Government: CAD/CAM, GIS, bodycam surveillance Oil and Gas: Seismic data, compliance Transportation: Autonomous vehicles Financial Services: Claims data, call center recordings Read: Why Harnessing Unstructured Data is a Top Enterprise Mandate Read: Getting an Upper Hand on the Unstructured Data Problem Read: Why Unstructured Data Matters - An Industry View Why is Unstructured Data Growing so Fast? The analyst firm IDC predicts that we will generate over 175 zettabytes of data by 2025 (one zettabyte is 4.4 Billion 1 terabyte drives!). They also predict that in the next three years we will generate more data than what we created over the past 30 years, and this growth trend will continue. Most of the data we generate today is unstructured because unstructured data has several advantages over structured data: Wider Use Cases for Unstructured Data: Structured data has a rigid pre-defined structure and it can only be used for its intended purpose. This narrows the number of use cases for structured data – while it is useful for transactional applications like revenue tracking or catalogs, it is not a good use for applications that generate data that is not so easy to categorize such as video or genomics. Various Formats: Unstructured data can be stored in a variety of formats – from a mp4 video to a genomics BAM file to a .log diagnostics file to an X-RAY image that may be stored as a digital PACS format, all of these are types of unstructured data. So, an accurate way to describe unstructured data is that it has a variety of formats and not just one format. This means more applications can generate unstructured data and tailor the format to their use. Various Sizes: Unlike a cell in a database, unstructured data does not have to be a specific size or character limit. For example, you can have small video files for short snippets and large video files for full length movies. This also increases flexibility in how unstructured data is generated and used. Since unstructured data is easier to create and use, more applications and users are working with unstructured data. Unstructured Data Management Managing growing volumes of unstructured data generated within an organization are leading to higher expenses. What are the 3 Vs of unstructured data? Volume: The sheer quantity of data will continue to grow in a incomprehensible rate Velocity: The quantity of data is coming in at a continually faster rate Variety: The types of data continue to be more varied These 3 Vs of unstructured data, originally defined by former Meta Group / Gartner industry analyst Doug Laney, means that managing unstructured data growth is critical for organizations as they find their budgets and resources are getting stretched to their limits. Unstructured data management requires an understanding of what data is hot and actively used, and what data is cold and rarely accessed. In most enterprises, over 80% of unstructured data becomes cold within a year of creation – yet it continues to be managed on the most expensive storage and it continues to consume expensive backup resources. Analytics-driven data management of unstructured data can change this by identifying hot data and cold data across storage and managing hot data on expensive environments while offloading cold data to lower cost passive management. Unstructured data management should be done without restricting access to the cold data – so users and applications continue to see and access the cold data exactly as before, while the organization saves on cold data storage and backups. To understand how Komprise enables enterprise IT organizations to analyze, move, and manage unstructured data and save costs on storage, backup and cloud infrastructure read the white paper: Komprise Intelligent Data Management Architecture Overview. What are Common Unstructured Cloud Data Migration Challenges? Migrating unstructured data to the cloud has grown in popularity to save data storage costs, consolidate data centers, modernize IT infrastructure and take advantage of cloud-based services such as AI, ML and analytics. But there are many challenges when it comes to unstructured data migrations to the cloud, including: A global enterprise typically has billions of predominantly small files, which have significant overhead, causing data transfers to be slow. Server message block (SMB) and NFS protocol workloads, which can be user data, electronic design automation (EDA) and other multimedia files or corporate shares, are problematic since the protocol requires many back-and-forth handshakes which increase traffic over the network. The SMB protocol in particular, is known to to have WAN transfer performance challenges, meaning cloud migrations can take much more time than IT organizations anticipate if not done correctly. File protocols are sensitive to high-latency network connections, which are unavoidable in WAN migrations. Bandwidth is often limited or not always available, causing cloud NAS migration data transfers to become slow, unreliable and difficult to manage. 25 times faster unstructured data migrations with Hypertransfer Why Does AI Need Unstructured Data? In a 2022 blog post, Komprise co-founder and CEO wrote about unstructured data management as the foundation for artificial intelligence (AI) and machine learning (ML) initiatives. Enterprises need to be ready for this wave of change and it starts by getting unstructured data prepped, as this data is the critical ingredient for AI/ML. This entails new data management strategies which create automated ways to index, segment, curate, tag and move unstructured data continuously to feed AI and ML tools. Unforeseen changes to society, fueled by AI, are coming soon and you don’t want to be caught flat-footed. AI needs unstructured data What is Unstructured Data? Unstructured data is information that doesn’t have a predefined data model or is not organized in a pre-defined manner. Unlike structured data, which is typically organized into tables and follows a specific schema, unstructured data lacks a clear and consistent structure. This type of data is often text-heavy but can also include images, videos, audio, social media posts, emails, and other forms of content. 90% of all data generated in today’s digital age is unstructured. The sheer volume of unstructured data makes it challenging to manage and analyze using traditional methods. There is an estimated 120 ZB of data in the world today, according to Statista. IDC expects data to grow to 175 zettabytes by 2025. What are Examples of Unstructured Data? Examples of unstructured data include: Text Documents: Word documents, PDFs, emails, and other textual content. Multimedia Files: Images, videos, and audio files. Social Media Feeds: Posts, comments, and multimedia content from social media platforms. Web Pages: Content from websites, which may include text, images, and multimedia elements. Sensor Data: Data from sensors, such as those in IoT (Internet of Things) devices. What is Unstructured Data Management? Unstructured data management is the processes and strategies involved in handling, storing, organizing, and extracting value from unstructured data. Effective unstructured data management is crucial for organizations looking to harness the potential insights and value contained within diverse and voluminous datasets. As technologies and best practices continue to evolve, managing unstructured data becomes an integral part of overall data management strategies. See the definition for Unstructured Data Management and download the State of Unstructured Data Management report. Why does AI need Unstructured Data? According to a IDC report sponsored by Box: In 2022, 90% of the data generated by organizations was unstructured, and only 10% was structured. That year, organizations globally generated 57,280 exabytes of unstructured data — a volume that is expected to grow by 28% to over 73,000 exabytes in 2023. To put this in perspective, an exabyte is 1 million terabytes, or 1 billion gigabytes. Seventy-three thousand exabytes of unstructured data is equivalent to the amount of data in over 97 trillion sequenced human genomes; it’s also equivalent to the amount of video streamed to 2.7 billion screens 24 hours per day for an entire year. Success with AI depends upon harnessing this data and feeding the right data at the right time to AI platforms. This is difficult and costly not only because of its tremendous volume, but also because of how unstructured data is dispersed across data storage siloes in the enterprise. Komprise delivers a Global File Index for granular search and tagging of data across silos. In addition, with Komprise Smart Data Workflows, you can create custom workflows to easily search, find, and tag the exact files you want across all your hybrid cloud storage and create a plan to move the right unstructured data to a data lake or AI tool. Komprise delivers a storage-agnostic, analytics-based unstructured data management platform that automates data workflows for AI. #### Observer (Komprise Observer) The Komprise Observer is a virtual appliance running at the customer site that analyzes data across NAS silos, moves and replicates data by data management policy, and provides transparent file access to data that’s stored in the cloud. Komprise handles any scale of data using an elastic scale-out architecture that has no central bottlenecks. Start with a Komprise Observer virtual machine and simply scale by adding more. Komprise automatically load balances and creates fault-tolerance across the elastic grid. There are no central databases or bottlenecks to limit scalability. Komprise scales to handle billions of files and tens of petabytes of data with a lightweight distributed architecture. There is no single point of failure. Komprise uses fault-tolerant architecture principles to create a resilient grid with Observer virtual instances that provide failover to one another in an active-active configuration without requiring any dedicated infrastructure. ### Storage Infrastructure and Data Mobility > Concepts covering data tiering, archiving, migration, NAS, object storage, cloud storage, and data mobilization. #### Data Mobilization Data mobilization is the ability to move, copy, or access data across different data storage environments, on-premises, cloud, hybrid, or multi-cloud, without disrupting user or application access or functionality. In practice, this means moving data where it makes the most sense: Based on cost (e.g., cold data to lower cost cloud storage / object storage) See data tiering. Based on performance needs (e.g., active data to fast local SSD) Based on regulatory, backup, or analytics requirements Data mobilization, sometimes called data orchestration, is not just about migrating data, but intelligently orchestrating its movement while maintaining visibility, control, and usability. Why is Data Mobilization Increasingly Important for Unstructured Data Management? Unstructured data (files, images, videos, logs, sensor data, etc.) now accounts for over 80% of all enterprise data, and it’s growing rapidly. Here are a few reasons why data mobilization is critical to an unstructured data management strategy: 1. Cost Optimization Vast volumes of cold, or infrequently accessed, data are sitting on expensive high-performance NAS or primary storage. Mobilizing this data to lower-cost storage tiers (e.g., Amazon S3 Glacier, Azure Cool Blob, or on-prem object stores) can reduce costs by 70% or more. 2. Storage Lifecycle Management As enterprise IT infrastructure is increasingly hybrid and multi-cloud, a modern data mobilization strategy require data tiering, data archiving, data replication, or cloud bursting. Data mobilization ensures data can move freely through its lifecycle without vendor lock-in. Read the path to the cloud without lock-in. 3. Cloud Adoption & Hybrid Strategy Enterprises need to move data into, across, and out of clouds for backup, DR, AI/ML, or collaboration. Data mobilization supports this fluidity without manual overhead or re-architecting applications. 4. Compliance & Governance Regulatory rules (e.g., GDPR, CCPA) may require data to be moved or deleted from certain locations. Data mobilization enables faster response to such needs. 5. AI/Analytics Readiness Data needed for training models or running analytics often resides in silos. Data mobilization enables preparing and staging data in locations suitable for processing. Komprise Solutions for Data Mobilization Komprise offers a policy-driven, transparent data mobilization framework tailored for unstructured data management. Key data features include: Transparent Move Technology (TMT): Moves data (e.g., cold files) to cheaper storage without breaking file paths or user access. Users and apps still see and access moved data via symbolic links or stubs—no retraining or reconfiguration needed. See Dynamics Links. Policy-Based Tiering: Komprise admins can set policies to automatically move data based on: Last accessed date File size File type or path Custom tags or metadata Read the white paper: File Level Tiering vs. Block Level Tiering. Read the Guide to Unstructured Data Tiering. Deep Analytics to Drive Action: Komprise uses a metadata catalog to surface what data should be mobilized and where. Users can simulate cost savings before executing tiering or migration. Cloud-Native and Cross-Vendor Support: Komprise mobilizes data across: On-prem NAS (NetApp, Dell EMC, etc.) Cloud (AWS, Azure, GCP, Wasabi, etc.) Object storage and archive tiers (S3, Glacier, Blob Archive, etc.) Data Migration Projects: For full data migrations (e.g., vendor replacement), Komprise offers a high-performance migration solution with integrity checks, cutover planning, and progress dashboards. Read our Guide to Unstructured Data Migration. Learn more about Elastic Data Migration. Smart Data Workflows: Mobilized data can be used to feed cloud-native tools for analytics, backup, or compliance scanning. Komprise allows automation of downstream actions once data is moved. Data mobilization is essential for managing the spiraling cost, risk, and complexity of unstructured data growth in the enterprise. Komprise is an analytics-based, non-disruptive platform to move the right data to the right place at the right time, turning data sprawl into strategic opportunity. #### Isilon Migration Isilon migration is the moving of unstructured file data workloads from Dell PowerScale, formerly known as Ision, to another NAS (network-attached storage) environment. Isilon Systems, now known as Dell PowerScale, was originally acquired by EMC on December 21, 2010, for $2.25 billion. This acquisition was aimed at enhancing EMC’s scale-out NAS capabilities, particularly for big data and high-performance computing (HPC) environments. Read: EMC Buys Isilon To Fortify High-End NAS Line. Isilon storage has gone through several name changes and branding updates, especially after EMC was acquired by Dell in 2016. Here are the different names it has been known by: Isilon: The original name when it was an independent company before being acquired by EMC in 2010. EMC Isilon: After the acquisition by EMC, the brand remained "Isilon" but was under the EMC umbrella. Dell EMC Isilon: Following Dell's acquisition of EMC in 2016, Isilon was rebranded under the Dell EMC name. Dell EMC PowerScale: In 2020, Dell officially rebranded the Isilon product line as PowerScale, marking a shift to a more unified storage branding strategy. Despite the name change, PowerScale still uses OneFS, the same scale-out file system that powered Isilon. The hardware models also transitioned from Isilon-branded nodes (e.g., X410, A200, F800) to PowerScale-branded nodes (e.g., F200, F600, H700, A300, A3000). Why migrate from Isilon? Migrating from Isilon (Dell EMC PowerScale) can be driven by several factors, depending on an organization's needs. Some common reasons include: End-of-Life (EOL) or End-of-Support (EOS): Older Isilon models (like X410, NL400, etc.) may no longer receive firmware updates, security patches, or hardware support.. Companies often migrate to newer PowerScale models or alternative solutions before reaching EOS. Performance and Scalability Needs: Older Isilon hardware may not support modern workloads like AI/ML, high-performance computing, or hybrid cloud environments. Performance limitations in older Isilon nodes (HDD-based) might drive a shift to all-flash or more scalable solutions from vendors like Pure Storage. Transition to Cloud & Hybrid Architectures: Isilon is mostly on-premises, and many organizations are moving toward cloud or hybrid cloud solutions (AWS, Azure, Google Cloud). While PowerScale can integrate with the cloud, some may prefer fully cloud-native storage solutions. Cost Optimization: Isilon can be expensive, especially for organizations looking for more cost-efficient storage solutions (e.g., object storage like Amazon S3, Azure Blob). Some companies move to alternatives with lower total cost of ownership (TCO). Vendor Consolidation & Strategic IT Decisions: Some organizations move away from Dell EMC products to consolidate with a single storage vendor (NetApp, Pure Storage, etc.). Others prefer open-source or software-defined storage (e.g., Qumulo, VAST Data). Feature Gaps or Modernization: Some organizations want better analytics, data protection, or multi-protocol support. Alternative solutions may offer better deduplication, compression, or security features than older Isilon versions. Smart Migration from Isilon with Komprise Elastic Data Migration Unlike Isilon CloudPools, Komprise puts you in control of your data, keeping your data in native file format in and ensuring you're not locked into proprietary vendor systems. By tiering cold data first, Komprise customers realize immediate cost reductions and reduce the data footprint for migration. This is in stark contrast to point migration tools that must treat all data the same. Komprise maintains app and user access without stubs and stays out the hot data path for zero impact to primary data workloads. Learn more about Smart Data Migrations from Isilon. Learn more about Komprise Elastic Data Migration. #### Hybrid Tiering Hybrid Tiering, or Hybrid File Tiering, is a storage-agnostic approach to data tiering that all organizations to establish fine-grained data management policies beyond the built-in cold data tiering limits often provided by data storage vendors. In addition to greater flexibility, hybrid tiering approaches offer the benefits of data portability without rehydration and no limits to innovation and cost savings by easily moving specific cold data sets to lower cost, deeper data storage solutions from other vendors (e.g. AWS Glacier, Wasabi, etc.). #### Unstructured Data Tiering See Data Tiering. Read the Komprise Guide to Unstructured Data Tiering. Unstructured data tiering is the process of organizing and managing unstructured data (such as text, images, videos, and other non-relational data types) into different levels or tiers based on various criteria. This approach helps optimize storage costs, performance, and access efficiency. Read: 5 Tips to Optimize Your Unstructured Data. When it comes to unstructured data tiering, it's important to consider: Data Classification Active Data: Frequently accessed and modified data. (aka hot data) Inactive Data: Rarely accessed but still valuable data. (aka cold data) Archive Data: Data that is infrequently accessed and primarily kept for compliance or historical reasons. What Storage Tiers Make the Most Sense? High-Performance Storage: Fast, expensive storage for active data (e.g., SSDs, NVMe). Standard Performance Storage: Cost-effective storage for moderately accessed data (e.g., HDDs). Archival Storage: Low-cost, high-capacity storage for infrequently accessed data (e.g., tape storage, cloud archival services). Automated Tiering Using data management software or services that automatically move data between tiers based on access patterns, age, or other policies. See Policy-Based Data Management. Block vs File-Level Tiering The the way data tiering is done can significantly change your cost savings and affect your options to access your cold data. It's important to understand the differences between block-level tiering (NetApp FabricPool, Dell PowerScale CloudPools), which moves blocks that can no longer be directly accessed from their new location without the vendor software, and file-level tiering, which is what Komprise uses to fully preserve file access at each tier by keeping the metadata and file attributes with the file—no matter where it lives. Read the white paper: Block vs File Tiering Learn more about Komprise Transparent Move Technology (TMT) Cost Optimization Storing data in the most cost-effective manner without compromising performance for frequently accessed data. Leveraging cloud storage options to dynamically scale and manage data storage costs. Access Efficiency Ensuring that data can be quickly retrieved when needed, especially for active and frequently accessed data. Implementing caching mechanisms to improve access times for data stored in lower tiers. Read: Why Cloud Native Data Access Matters Data Lifecycle Management Implementing policies for data retention, archival, and deletion to ensure that data is managed throughout its lifecycle. Read: Treating all enterprise data the same is ‘no longer viable’ Compliance and Security Ensuring that data is stored in accordance with regulatory requirements and is protected against unauthorized access and breaches. See: Unstructured Data Governance Data Analytics Using analytics to monitor data usage patterns and optimize tiering strategies based on real-world usage. At Komprise, we say: Know First. Move Smart. Take Control. By effectively implementing an unstructured data tiering strategy, organizations can achieve significant cost savings, improve data access performance, and ensure that their data storage infrastructure scales efficiently with their data growth. Read: Why Unstructured Data Classification Matters #### Cloud Archiving Cloud archiving, a term that is often used interchangeably with cloud tiering, is a data storage strategy that involves moving inactive or infrequently accessed data from primary storage systems to long-term archival storage in the cloud. The primary goal of cloud archiving is to free up space on primary storage systems while retaining access to archived data for compliance, legal, or historical purposes. Key characteristics and considerations of cloud archiving include: Long-Term Retention: Cloud archiving solutions are designed for storing data for extended periods, often spanning years or even decades. They typically offer features such as data durability and integrity checks to ensure that archived data remains intact and accessible over time. Cost-Effectiveness: Cloud archiving services often provide cost-effective storage options optimized for long-term retention. These storage options typically offer lower data storage costs per unit compared to primary storage systems, making them suitable for storing large volumes of inactive data. Scalability: Cloud archiving solutions offer scalability to accommodate growing volumes of archived data without the need for significant upfront investments in infrastructure. Organizations can scale their archival storage resources on-demand based on their evolving storage requirements. Data Security and Compliance: Cloud archiving solutions prioritize data security and compliance by implementing encryption, access controls, and other security measures to protect archived data from unauthorized access or tampering. Additionally, they may offer features such as audit logs and compliance certifications to help organizations meet regulatory requirements. Data Accessibility: While archived data is typically accessed infrequently, cloud archiving solutions ensure that archived data remains accessible when needed. They provide mechanisms for retrieving and accessing archived data, such as retrieval APIs or web-based interfaces, allowing organizations to retrieve specific data sets or perform data restores as necessary. Data Lifecycle Management: Cloud archiving solutions often include features for managing the entire data lifecycle, from initial archiving to eventual deletion or retention expiration. They may offer automated policies for migrating data to archival storage, as well as retention policies for specifying how long data should be retained in the archive. Overall, cloud archiving enables organizations to effectively manage their data storage needs by offloading inactive data to cost-effective, scalable, and secure cloud-based archival storage solutions. By doing so, organizations can optimize their primary storage resources, reduce data storage costs, and ensure compliance with data retention requirements. #### NAS Mirroring Network Attached Storage (NAS) mirroring, also known as NAS replication or mirroring, is a process where data from one NAS device is duplicated in real-time or on a scheduled basis to another NAS device. This mirroring process creates a redundant copy of the data, providing data protection, disaster recovery, and high availability. What are some of the primary aspects of NAS mirroring? NAS mirroring can be expensive, especially if you're replicating all of your growing volumes of unstructured data. With Komprise Elastic Replication, enterprise IT organizations have a lower-cost solution for replicating non-mission-critical unstructured data. This approach is also a key component of a more-affordable ransomware strategy. Learn more here. Here are some of the primary aspects of traditional NAS mirroring: Redundancy and Data Protection NAS mirroring creates a duplicate copy of data on a separate NAS device. This redundancy helps protect against data loss due to hardware failures, data corruption, or other unforeseen events. Real-Time or Scheduled Replication Depending on the NAS solution and requirements, mirroring can occur in real-time (synchronous) or on a scheduled basis (asynchronous). Real-time mirroring ensures that changes are immediately replicated, while scheduled replication may introduce a delay. High Availability NAS mirroring enhances high availability by providing a standby copy of the data. In the event of a failure on the primary NAS device, operations can switch to the mirrored NAS device to minimize downtime. Disaster Recovery Mirroring supports disaster recovery (DR) by maintaining a geographically separated copy of the data. If the primary location faces a catastrophic event, data can be restored from the mirrored NAS at a different location. Bandwidth Considerations When performing NAS mirroring, consider the bandwidth requirements, especially in cases of real-time replication over a network. Bandwidth constraints can impact the speed and efficiency of data mirroring. Consistency and Integrity Ensure that the mirroring process maintains data consistency and integrity. This is crucial to prevent issues such as data corruption in the mirrored copy. Failover and Failback Implement mechanisms for failover (switching to the mirrored NAS in case of primary NAS failure) and failback (returning to the primary NAS when it is restored). This ensures a smooth transition between the primary and mirrored environments. Monitoring and Alerts Regularly monitor the status of the mirroring process. Implement alerting mechanisms to notify administrators of any issues or failures in the replication process. Authentication and Security Ensure that the NAS mirroring process includes proper authentication and security measures to protect the data during replication. Testing and Validation Periodically test the failover and recovery processes to validate the effectiveness of the NAS mirroring solution. Regular testing helps ensure that the mirrored data is readily available when needed. Compatibility and Vendor-Specific Features Verify compatibility between NAS devices and understand any vendor-specific features or requirements associated with the mirroring solution. NAS mirroring is a valuable strategy for organizations that prioritize data availability, data protection, and disaster recovery. It is especially useful in environments where continuous access to data is critical for business operations. Given the costs of traditional NAS mirroring strategy, it is important to look for ways to reduce the cost of replicating non-mission critical unstructured data as part of a broader data management strategy. Komprise Intelligent Data Management should be part of your overall DR solution. #### Recovery Time Objective (RTO) The Recovery Time Objective (RTO) is an essential metric in disaster recovery (DR) and business continuity planning. RTO represents the maximum acceptable downtime for a system or service following a disruptive event. RTO defines the time within which an organization must recover its IT systems, applications and services to a state where normal operations can resume. See also Recovery Point Objective (RPO). More Information About Recovery Time Objective (RTO): Time Frame: RTO is expressed in time and signifies the maximum allowable duration for the recovery process to be completed after a disaster or disruption occurs. Business Impact: RTO is determined by the business impact analysis, taking into consideration the criticality of systems and services to the organization's operations. It reflects the amount of time the business can afford to be without certain functionalities before significant negative consequences occur. Dependency on Technology and Processes: Achieving a specific RTO requires careful consideration of the technologies, processes, and procedures in place for disaster recovery. It may involve backup and restoration processes, failover systems, or other strategies. Balancing Act with RPO: RTO is closely related to another key metric, the RPO. While RTO focuses on the time needed for recovery, RPO deals with the acceptable amount of data loss. Together, RTO and RPO help organizations establish comprehensive recovery strategies. Continuous Monitoring and Testing: Organizations should regularly review and test their disaster recovery plans to ensure that the stated RTOs are realistic and achievable. Regular testing helps identify and address any issues that could impact the recovery process. Technology and Infrastructure: The choice of technology and infrastructure plays a crucial role in meeting RTO objectives. High-availability configurations, redundant systems, and efficient backup and recovery mechanisms contribute to achieving shorter RTOs. Communication and Stakeholder Expectations: Clear communication with stakeholders about the RTO is important. Understanding the expected recovery time helps manage expectations and allows stakeholders to plan accordingly. Regulatory Compliance: Depending on the industry and regulatory requirements, organizations may need to adhere to specific RTO standards. Compliance with these standards is essential for avoiding legal and regulatory consequences. Recovery Strategies: Different systems and services may have different RTO requirements. Organizations may implement tiered recovery strategies based on the criticality of each system or service. By defining and understanding the RTO, organizations can design and implement effective disaster recovery plans that minimize downtime and ensure the continuity of business operations in the face of disruptions. Komprise Elastic Replication Cuts Disaster Recovery Costs for Unstructured Data by 70% #### Recovery Point Objective (RPO) The Recovery Point Objective (RPO) is a concept in business continuity and disaster recovery (DR) planning. It represents the maximum acceptable amount of data loss in the event of a disruption or failure. In simpler terms, RPO defines the age of the data to which an organization must be able to recover in case of a data loss incident. Learn More About Recovery Point Objective (RPO): Time Frame: RPO is expressed in time and represents the maximum allowable time period between the last data backup and the occurrence of a disruptive event. Data Loss Tolerance: The RPO is determined by business stakeholders and reflects the organization's tolerance for data loss. Different types of data may have different RPO requirements. Data Backup and Data Replication: Achieving a specific RPO involves implementing data backup and replication strategies. This can include regular backups, continuous data replication, or a combination of both. Impact on Technology and Costs: Achieving a low RPO often requires more frequent backups or real-time data replication, which can have implications on technology choices and associated costs. Recovery Time Objective (RTO) Relationship: RPO is closely related to another important concept known as the Recovery Time Objective (RTO). RTO defines the maximum acceptable downtime for systems and services. RPO and RTO together help organizations establish comprehensive recovery strategies. Criticality of Data: Different types of data within an organization may have varying criticality levels. The RPO for critical data may be much shorter than that for less critical data. Regulatory Compliance: Certain industries and regulatory bodies may mandate specific RPO requirements. Organizations must align their RPO with any legal or regulatory obligations. Continuous Monitoring and Adjustment: Enterprise needs and technologies evolve, so it's important for IT organizations to regularly review and, if necessary, adjust their RPO based on changes in their operations, data volumes, and recovery capabilities. Understanding and defining your RPO is a fundamental step in creating an effective disaster recovery plan. It guides the selection of appropriate backup and replication technologies and helps organizations make informed decisions about their data protection strategies. Komprise Elastic Replication Cuts Disaster Recovery Costs for Unstructured Data by 70% #### Hybrid Cloud File Data Services In February 2023, Gartner industry analyst Julia Palmer published published a research article: Modernize Your File Storage and Data Services for the Hybrid Cloud Future. (Blocks & Files summary). According to Gartner, hybrid cloud file data services provide data access, data movement, life cycle management and data orchestration. Komprise is listed as top vendor in this category. The other two categories included in the note are: Next-generation file platforms: on-premises filers adding hybrid cloud capability and new software-only file services suppliers (VAST Data, NetApp, Qumulo) Hybrid cloud file platforms: providing public cloud-based distributed file services (Ctera, Nasuni, Panzura) Hybrid cloud file data services refer to the use of file-based storage solutions in a hybrid cloud environment, which combines on-premises infrastructure with cloud resources, allowing organizations to leverage the benefits of both private and public clouds. File data services, in this context, involve the access, management, movement and even storage and retrieval of files and data within a hybrid cloud setup. In the article: Unstructured Data Growth and AI Give Rise to Data Services, Komprise cofounder and COO Krishna Subramanian summarized the benefits of a data services approach as: Holistic visibility and granular search across multiple storage systems and clouds; Analytics and insights on data types and usage for more accurate storage decisions; Automated, policy-driven actions based on that analysis; Reduced security and compliance risks; Full use of data wherever it is stored, especially in the cloud; User self-service access to support departmental and research needs for data storage, management, and AI workflows; Greater flexibility to adopt new storage, backup, and DR technologies because data is managed independently of any vendor technology. The article concludes: Above all, data management and storage infrastructure experts will need to shift their thinking and practices from managing storage technologies to understanding and managing data for a variety of purposes. A data storage and data management infrastructure that supports flexibility and agility to shift with organizational data needs will allow IT to make the shift faster and with better outcomes for all. What are some of the components and features of hybrid cloud file data services? Still an emerging category, in the Gartner Top Trends in Enterprise Data Storage 2023 report from Gartner (subscription required), it is note that, "by 2027, 60% of Infrastructure and Operations leaders will implement hybrid cloud file deployments, up from 20% in early 2023." Hybrid cloud file data services "provide data access and data management across edge, cloud and core data center locations through a single global namespace." The report goes on to note: "Increasingly, enterprises are creating, ingesting and accessing data in edge locations, factories, field offices and retail locations. The data services to analyze or enhance data are typically present in the public cloud, but the workers who collaborate on the data are spread across many geographic locations, raising the demand for a single global namespace." Read the white paper: Global Namespace vs Global File System: What is the Difference and Why Does it Matter? Some of the components and features of hybrid cloud file data services may include: Data Storage and Management On-Premises Storage: Traditional file servers or network-attached storage (NAS) devices located within an organization's physical premises. Cloud Storage: File storage services provided by public cloud providers (e.g., Amazon S3, Azure Blob Storage) for scalable and elastic storage options. Data Synchronization and Sharing Bidirectional Sync: Ensures that data remains consistent across on-premises and cloud environments, allowing users to seamlessly access and update files from either location. Collaboration Tools: Integration with collaboration platforms to enable efficient sharing and collaboration on files among users in different locations. In Gartner's definition of Hybrid Cloud File Platforms, these features would require a Global File System. Scalability and Flexibility Elastic Scaling: The ability to scale file storage both on-premises and in the cloud based on changing storage requirements. Multi-Cloud Support: Compatibility with multiple cloud providers, giving organizations flexibility in choosing the most suitable cloud services for their needs. Data Security and Compliance Encryption: Secure transmission and storage of files through encryption mechanisms, ensuring data confidentiality. Compliance Features: Tools and features to help organizations comply with data protection regulations and industry-specific standards. Data Access and Mobility Global Access: Enable users to access files from any location, promoting a mobile and distributed workforce. Data Mobility: Facilitate seamless movement of data between on-premises and cloud environments. Learn more about Komprise Intelligent Data Management: Unified data control plane for file and object data analytics, mobility and management without creating a bottleneck and never being in the hot data path. Backup and Disaster Recovery Snapshot and Backup: Regularly capture snapshots and backups of file data to protect against data loss or corruption. Disaster Recovery Planning: Implement strategies to quickly recover file data in the event of a disaster or data loss incident. Integrated Management and Monitoring Unified Dashboard: A centralized management interface for overseeing file data services across on-premises and cloud environments. Monitoring Tools: Tools for tracking performance, usage, and potential issues in the hybrid cloud file storage infrastructure. Implementing hybrid cloud file data services requires careful planning, integration, and management to ensure a seamless and efficient experience for users while maximizing the benefits of both on-premises and cloud-based data storage solutions. #### Enterprise NAS Enterprise Network Attached Storage (NAS) systems are specialized storage solutions designed to meet the high-capacity, performance, and reliability requirements of large organizations. (See NAS Software and Network Attached Storage.) These systems provide centralized storage that can be accessed over a network by multiple users and applications. Here are some key features and considerations for enterprise NAS: NAS Scalability Enterprise NAS solutions should be scalable to accommodate the growing data needs of large organizations. This includes the ability to add additional storage capacity and expand the system seamlessly. NAS Performance High-performance is crucial for enterprise environments with demanding workloads. Look for NAS systems with fast processors, ample memory, and support for technologies like SSD caching or tiered storage. NAS Redundancy and High Availability Enterprise NAS systems often feature redundant components (redundant power supplies, fans, and disks) and support for high-availability configurations to minimize the risk of downtime. NAS Data Protection and Security Robust data protection features, such as RAID configurations, snapshots, and backup integration, are essential. Security features like encryption and access controls help safeguard sensitive data. NAS Multi-Protocol Support Enterprise NAS solutions should support various network protocols such as NFS and SMB/CIFS to ensure compatibility with different operating systems and applications. Storage Management Intuitive and feature-rich storage management interfaces simplify the configuration, monitoring, and maintenance of the NAS system. This includes features like storage provisioning, volume management, and reporting tools. Integration with Enterprise Ecosystems Seamless integration with other enterprise systems and applications, such as backup solutions, directory services (LDAP/Active Directory), and cloud services, is crucial for a holistic IT environment. Data Deduplication and Compression These features can help optimize storage efficiency by reducing the amount of redundant data stored on the NAS, resulting in potential cost savings. NAS Snapshots and Replication Enterprise NAS systems often support snapshot technology for creating point-in-time copies of data and replication for mirroring data between multiple NAS systems for disaster recovery purposes. Compliance and Certification Ensure that the NAS solution meets industry compliance standards and certifications relevant to your organization, such as HIPAA, GDPR, or other regulatory requirements. Support and Services Availability of comprehensive support, warranty options, and professional services can be critical in ensuring the reliability and longevity of the NAS solution. Popular vendors that offer enterprise-grade NAS solutions include: NetApp Dell EMC HPE (Hewlett Packard Enterprise) IBM Pure Storage When selecting an enterprise NAS solution, it is important to carefully evaluate your organization's specific requirements, future data growth plans, and budget constraints to choose a system that aligns with your business needs. Additionally, consider factors such as the vendor's reputation, customer support, and the ecosystem of third-party applications and integrations. Komprise Enterprise NAS Partners Komprise partners with leading enterprise NAS providers to deliver unified data and storage analytics, unstructured data management, data lifecycle management and Smart Data Workflows. Partners include Pure Storage, NetApp, VAST Data, Qumulo, and Nutanix. #### Data Migration Software There are many data migration software tools available to facilitate the process of moving data from one system or platform to another. The choice of a specific data migration software tool depends on factors such as the type of data, the scale of data migration, source and target systems, and other specific requirements. For semi-structured and structured data sources, extraction, transformation and load (ETL or ELT) tools are often used for data migrations. For unstructured data migrations, where the data lacks a predefined data model or structure, the challenges are different from migrating structured data. Unstructured data can include text documents, images, videos, audio files, and other content that doesn't fit neatly into a relational database. Cloud Migration Software Options Cloud migrations of file and object data can be complex, labor-intensive, costly, and time-consuming. Enterprises typically consider the following options:  Free Tools: These tools require a lot of custom development are less reliable and resilient and generally aren't built to migrate massive volumes of data. It's important to look at broader unstructured data management requirements, not just one-off data migration requirements. Point Data Migration Solutions: These data migration tools typically have complex legacy architectures that were not built for the modern scale of data, which can create ongoing data migration and data management challenges. Komprise Elastic Data Migration: Designed to make cloud data migrations simple, fast, reliable and eliminates sunk costs since you continue to use Komprise after the migration, Komprise gives you the option to cut 70%+ cloud storage costs by placing cold data in Object classes while maintaining file metadata so it can be promoted in the cloud as files when needed. Learn more about Smart Data Migration. #### Data Migration Plan Creating a data migration plan (or data migration process) is crucial when you're moving data from one system to another, whether it's due to system upgrades, data center relocation, or other reasons. Komprise Elastic Data Migration is focused on unstructured data migrations. Here's a general outline for a data migration plan: Define Objectives and Scope: Clearly state the reasons for migration. Define the scope of the migration (what data will be migrated, and what won't). Assessment of Current Data: Analyze the existing data to identify dependencies, relationships, and potential issues. Document the data types, formats, and volumes. Risk Assessment: Identify potential risks and challenges. Plan for contingencies in case of data corruption, loss, or other issues. Resource Identification: Identify and allocate necessary resources (human, technical, and financial). Select Migration Method: Choose the appropriate migration method (parallel, serial, big bang). Decide whether to use a manual or automated approach. Data Mapping: Create a mapping between the source and target systems to ensure data consistency. Data Cleansing: Cleanse and sanitize data before migration to improve data quality. Remove duplicates, obsolete, or irrelevant data. Data Backups: Perform a complete backup of the existing data before starting the migration. Data Migration Testing: Conduct thorough testing in a controlled environment. Test for data integrity, accuracy, and completeness. Communication Plan: Communicate the migration plan to all stakeholders. Establish communication channels for updates and issue resolution. Training: Train the personnel involved in the data migration process. Provide documentation for reference. Data Migration Execution: Implement the migration plan. Monitor the data migration process closely for any issues. Validation: Verify the data integrity and completeness post-migration. Compare the migrated data with the source data. Post-Migration Support: Provide support for users to adapt to the new system. Address any issues that arise after migration. Documentation: Document the entire migration process for future reference. Include any lessons learned and recommendations for future migrations. Performance Monitoring: Monitor the performance of the new system. Address any performance issues that arise. Closure: Conduct a post-implementation review. Close out the migration project. The specifics of the data migration plan will depend on the unique aspects of your organization, the systems involved, the scale of the migration and the type of data being migrated. Regularly update and refine the plan based on feedback and outcomes during the migration process. #### Data Migration Chain of Custody Data migration chain of custody refers to the process of tracking and documenting the movement, handling, and changes made to data during a data migration project. See Chain of Custody. This chain of custody concept is borrowed from the field of forensics and evidence management, where the chain of custody ensures the integrity and admissibility of evidence in legal proceedings. In data migration, the chain of custody serves a similar purpose, helping organizations maintain the integrity, security, and traceability of their data as it moves from one location or system to another. Primary components and principles of data migration chain of custody Documentation: The process begins with the creation of detailed records documenting the data being migrated. This includes information such as data source, data destination, metadata, data ownership, and the purpose of the migration. Data Identification: Each piece of data is identified and assigned a unique identifier or tag. This identifier is used to track the data throughout the migration process. Secure Handling: Data must be handled and transported securely to prevent unauthorized access, tampering, or data breaches during the migration. Encryption and secure data transfer methods are often used. Transfer Records: Detailed records are maintained at every step of the data migration process. This includes information about when data was transferred, who performed the transfer, and any transformations or modifications made to the data. Data Validation: Before and after each data transfer, validation checks are performed to ensure that data remains accurate and intact. Any discrepancies or errors are documented and addressed. Access Controls: Access to data during the migration process is restricted to authorized personnel only. Role-based or (share-based in the case of data storage migration) access controls and permissions are often implemented. Data Integrity: Measures are taken to ensure data integrity is maintained throughout the migration, including checksums, data verification, and error correction. Versioning: If changes are made to data during the migration, versioning is used to track these changes and ensure that previous versions of data can be restored if needed. Auditing and Logging: Comprehensive auditing and logging mechanisms are employed to record all activities related to data migration. These logs are critical for tracking any unauthorized access or changes. Reporting: Regular reports are generated to provide stakeholders with updates on the progress of the data migration project. These reports include information on the status of data, any issues encountered, and the actions taken to address them. Legal and Regulatory Compliance: Organizations must adhere to relevant legal and regulatory requirements when handling and migrating data, such as data privacy laws (e.g., GDPR) and industry-specific regulations. Data Retention: Data migration chain of custody may include provisions for the retention of migration-related records for a specified period to address potential future audits or inquiries. By implementing a robust data migration chain of custody process, organizations can ensure that data remains secure, accurate, and compliant with regulations throughout the migration. This not only minimizes the risk of data breaches or data loss but also provides a strong foundation for successful data migration and ongoing data management projects. Read the Komprise Unstructured Data Migration Guide. #### Data Migration Warm Cutover A warm cutover in the context of data migration refers to a data migration strategy in which the process involves transitioning from the old data system to the new one with a limited downtime or service interruption. It is a phased approach that allows for the coexistence of both the old and new data systems during a specific period. Warm cutover strategies are often employed when it's essential to maintain data availability and minimize disruption to ongoing operations. Key steps and considerations in a warm cutover for data migration Preparation Phase Planning: Define the scope, objectives, and timeline for the data migration. Identify the specific data sets, systems, or databases that need to be migrated. Data Assessment: Assess the quality, completeness, and structure of the data in the source system. Clean and prepare the data as needed. Infrastructure Readiness: Ensure that the infrastructure for the new data system is set up and configured, including the hardware, software, and network components. Parallel Operation: Data Replication: Set up mechanisms for data replication or synchronization between the old and new data systems. This ensures that data changes made in one system are mirrored in the other in near real-time. Testing: Perform thorough testing of the new data system while it operates in parallel with the old system. Verify data integrity, performance, and functionality. User Training: Train end-users, administrators, and support teams on how to use the new data system effectively. Data Transition: Gradual Migration: Begin migrating data from the old system to the new one in stages. This can be done by migrating specific data sets, databases, or tables incrementally. Validation: Validate the migrated data to ensure that it matches the source data in terms of accuracy and completeness. Data reconciliation and verification are crucial at this stage. Monitoring and Verification: Monitoring: Continuously monitor the health and performance of both the old and new data systems during the transition period. User Acceptance Testing (UAT): Involve end-users in user acceptance testing to ensure that the new data system meets their requirements and expectations. Final Transition: Data Synchronization: Once the new data system is confirmed to be stable and accurate, perform a final data synchronization to ensure that both systems have the same data. Switch Over: Redirect users and applications to the new data system while minimizing downtime. Ensure that all data transactions are processed in the new system. Post-Cutover Activities: Validation: Conduct post-cutover validation to confirm that data remains consistent and accessible in the new system. Monitoring and Support: Continue monitoring the new data system and provide support as needed to address any post-migration issues. Documentation: Update documentation and procedures to reflect the new data system and its operational requirements. With the release of Komprise Intelligent Data Management 5.0, Komprise Elastic Data Migration supports warm cutover. Warm cutover strategies are particularly suitable for data migration scenarios where organizations cannot afford extended downtime or where data continuity is critical, such as in healthcare, financial services, and online commerce. Careful planning, rigorous testing, and meticulous data validation are essential to ensure a smooth transition from the old data system to the new one while maintaining data integrity and availability. #### Komprise Elastic Data Migration Komprise Elastic Data Migration is a SaaS solution available with the Komprise Intelligent Data Management platform or standalone. Designed to be fast, easy and reliable with elastic scale-out parallelism and an analytics-driven approach, it is the market leader in file and object data migrations, routinely migrating petabytes of data (SMB, NFS, Dual) for customers in many complex scenarios. Komprise Elastic Data Migration ensures data integrity is fully preserved by propagating access control and maintaining file-level data integrity checks such as SHA-1 and MD5 checks with audit logging. In 2022, Komprise introduced Hypertransfer, which creates dedicated virtual channels across the WAN to accelerate cloud data migrations. By establishing dedicated channels to send data, Komprise Hypertransfer minimizes the WAN roundtrips, which mitigates SMB protocol chattiness and dramatically improves data transfer rates. Tests done using a dataset dominated by small files shows Komprise accelerates cloud file migration 25x faster. As outlined in the white paper How To Accelerate NAS and Cloud Data Migrations, Komprise Elastic Data Migration is a highly parallelized, multi-processing, multi-threaded approach that improves performance at many levels. Read the Komprise Unstructured Data Migration Guide. #### Amazon Tiering What is Amazon Tiering? Amazon Web Services (AWS) offers several storage services that support data tiering based on different storage classes. These data storage classes allow customers to optimize their storage costs and performance by choosing the most suitable option for their data based on its access patterns and durability requirements. Learn more about Komprise file and object data migration, data tiering and ongoing data management. AWS Storage Tiering Options Amazon S3 Storage Classes: Amazon Simple Storage Service (S3) provides multiple storage classes to accommodate different data access patterns and cost requirements: Standard: This is the default storage class for S3 and offers high durability, availability, and performance for frequently accessed data. Intelligent-Tiering: This storage class automatically moves objects between two access tiers (frequent access and infrequent access) based on their usage patterns. It optimizes costs by automatically transitioning objects to the most cost-effective tier. Standard-IA (Infrequent Access): This storage class is suitable for data that is accessed less frequently but still requires rapid access when needed. It offers lower storage costs compared to the Standard class. One Zone-IA: Similar to Standard-IA, but the data is stored in a single Availability Zone, which provides a lower-cost option for customers who don't require data redundancy across multiple zones. Glacier, Glacier IT and Glacier Deep Archive: These storage classes are designed for long-term archival and data retention. Data stored in Amazon S3 Glacier is accessible within minutes to hours, while Glacier Deep Archive is for data with retrieval times of 12 hours or more. Amazon EBS Volume Types: Amazon Elastic Block Store (EBS) provides different volume types for block storage in AWS. While not strictly tiering, these volume types offer varying performance characteristics and costs: General Purpose SSD (gp2): This is the default EBS volume type and provides a balance of price and performance for a wide range of workloads. Provisioned IOPS SSD (io1/io2): These volume types are designed for applications that require high I/O performance and consistent low-latency access to data. Throughput Optimized HDD (st1): This volume type offers low-cost storage optimized for large, sequential workloads that require high throughput. Cold HDD (sc1): This volume type provides the lowest-cost storage for infrequently accessed workloads with large amounts of data. Amazon S3 Glacier and Glacier Deep Archive: These are the storage classes within Amazon S3 designed specifically for long-term data archival and retention. The retrieval times are longer compared to other storage classes, but they offer significantly lower storage costs for data that is rarely accessed. Amazon tiering options are designed to help AWS customers effectively manage their data storage costs and performance based on the specific requirements of their workloads and data access patterns. Komprise Intelligent Data Management for AWS Komprise is an AWS Migration and Modernization competency partner, working closely with AWS teams to follow best practices and support cloud data storage services including Amazon EFS, Amazon FSx and Amazon S3 (including Amazon S3 Glacier Flexible Retrieval and Glacier Instant Retrieval storage classes). The Komprise analytics-driven SaaS platform allows customers to analyze, mobilize and manage their file and object data using AWS allowing enterprise customers to: Understand AWS NAS & Object Data Usage and Growth Estimate ROI of AWS Data Storage Migrate Smarter to Amazon FSx for NetApp ONTAP Easily Integrate AWS Data Lifecycle Management Access Moved Data as Files Without Stubs or Agents Gain Native Data Access in the AWS Cloud Without Storage Vendor Lock-In Rapidly Migrate Object Data Into AWS Storage Reduce AWS Unstructured Data Complexity Scale On-Demand with Modern, SaaS Architecture Learn more about Komprise Intelligent Data Management for AWS Storage. #### Cloud Costs Cloud costs, or cloud computing costs, will vary based on cloud service provider, the specific cloud services and cloud resources used, usage patterns, and pricing models. See Cloud Cost Optimization. Gartner forecast that cloud spend will be nearly $600B in 2023 and in an increasingly hybrid enterprise IT infrastructure, cloud repatriation is making headlines: cloud repatriation and the death of cloud only. Why are my cloud costs so high? A number of factors can influence your cloud costs. Examples include? Compute Resources: Cloud providers offer various compute options, such as virtual machines (VMs), containers, or serverless functions. The cost of compute resources depends on factors like the instance type, CPU and memory specifications, duration of usage, and the pricing model (e.g., on-demand, reserved instances, or spot instances). Cloud Storage: Cloud storage costs can vary based on the type of storage used, such as object storage, block storage, or file storage. The factors affecting storage costs include the amount of data stored, data transfer in and out of the storage, storage duration, and any additional features like data replication or redundancy. See the white paper: Block-level versus file-level tiering. Networking: Cloud providers charge for network egress and data transfer between different regions, availability zones, or across cloud services. The cloud cost can depend on the volume of data transferred, the distance between data centers, and the bandwidth used. Database Services: Cloud databases, such as relational databases (RDS), NoSQL databases (DynamoDB, Firestore), or managed database services, have their own pricing models. The cost can be based on factors like database size, read/write operations, storage capacity, and backup and replication requirements. Data Transfer and CDN: Cloud providers typically charge for data transfer between their services and the internet, as well as for content delivery network (CDN) services that accelerate content delivery. Costs can vary based on data volume, data center locations, and regional traffic patterns. Cloud Services: Cloud providers offer a range of additional cloud services, such as analytics, AI/ML, monitoring, logging, security, and management tools. The cost of these services is usually based on usage, the number of requests, data processed, or specific feature tiers. Pricing Models: Cloud providers offer different pricing models, including on-demand (pay-as-you-go), reserved instances (pre-purchased capacity for longer-term usage), spot instances (bid-based pricing for unused capacity), or savings plans (commitments for discounted rates). Choosing the appropriate pricing model can impact overall cloud costs. To estimate and manage cloud costs effectively, enterprise IT, engineering and all consumers of cloud services need to monitor resource usage, optimize resource allocation, leverage cost management tools provided by the cloud provider and independent solution providers, and regularly review and adjust resource utilization based on actual requirements. Each cloud provider has detailed pricing documentation and cost calculators on their websites that can help estimate costs based on specific usage patterns and service selections. In an increasingly hybrid, multi-cloud environment, looking to technologies that can analyze and manage cloud costs independent from cloud service providers is gaining popularity. #### Hybrid Cloud Data Management Hybrid cloud data management is a broad term that can mean different things to different people and areas of the organization depending on the focus is unstructured data, data storage and data protection or data warehousing, data lakes, analytics and AI. Generally hybrid cloud data management refers to the technologies, processes and strategies used to effectively and efficiently manage data in a hybrid cloud environment. A hybrid cloud combines both on-premises IT infrastructure and cloud resources, allowing organizations to leverage the benefits of both environments. General areas of hybrid cloud data management Data Integration: In a hybrid cloud setup, data may reside in various locations, including on-premises systems and multiple cloud providers. Data integration involves ensuring seamless connectivity and integration between these different data sources and applications. It may involve using technologies such as data integration platforms, APIs, or data virtualization to unify data access and enable data movement between on-premises and cloud environments. An example of a hybrid cloud data integration vendor is SnapLogic. Also see Cloud Data Management. Data Governance: Data governance in a hybrid cloud environment focuses on defining policies, standards, and procedures for data management, ensuring compliance, data security, and privacy. It involves establishing data ownership, access controls, data classification, and data lifecycle management across both on-premises and cloud resources. Implementing consistent data governance practices helps organizations maintain data quality, security, and regulatory compliance across their hybrid cloud infrastructure. Data Backup and Disaster Recovery: Hybrid cloud data management includes implementing backup and disaster recovery strategies to protect data in case of data loss, system failures, or natural disasters. It involves replicating and backing up critical data from on-premises infrastructure to cloud storage or using cloud-based backup services. By leveraging the scalability and reliability of cloud resources, organizations can ensure data availability and minimize downtime during unforeseen events. See the post: Begun the Cloud File Services Wars Have Data Security and Privacy: Hybrid cloud environments require robust security measures to protect sensitive data. Data encryption, access controls, identity and access management (IAM), network security, and threat detection mechanisms should be implemented to safeguard data both in transit and at rest. Compliance with data protection regulations, such as GDPR (General Data Protection Regulation) or HIPAA (Health Insurance Portability and Accountability Act), should also be considered when managing data in hybrid cloud environments. Data Analytics and Insights: Hybrid cloud data management enables organizations to leverage cloud-based analytics tools and platforms to gain valuable insights from their data. Data can be processed, analyzed, and visualized using cloud-native services, such as data lakes, data warehouses, or machine learning platforms. By utilizing cloud resources for data analytics, organizations can take advantage of scalability, agility, and cost-efficiency to derive meaningful insights from their hybrid data sources. In 2023, every enterprise IT organizations is working on establishing clear data management policies and evaluating their requirements in each of these areas, working closely with cloud service providers and leveraging their managed services where appropriate. Gartner published summarized the following 4 trends shaping the future of cloud, data center, edge IT infrastructure: Trend 1: Cloud Teams Will Optimize and Refactor Cloud Infrastructure Trend 2: New Application Architectures Will Demand New Kinds of Infrastructure Trend 3: Data Center Teams Will Adopt Cloud Principles On-Premises "According to Gartner, 35% of data center infrastructure will be managed from a cloud-based control plane by 2027, from less than 10% in 2022. I&O professionals should focus this year on building cloud-native infrastructure within the data center; migrating workloads from owned facilities to co-location facilities or the edge; or embracing as-a-service models for physical infrastructure." Trend 4: Successful Organizations Will Make Skills Growth Their Highest Priority #### Archiving Archiving, in the context of technology and unstructured data management (also see Data Archiving), is the process of storing and preserving data in a systematic and organized manner for long-term retention. It involves moving data from active or primary storage locations to secondary storage systems or media, with the goal of freeing up primary storage space while ensuring data is securely preserved for future reference. Additionally, rising data storage costs, unstructured data growth, data sprawl, data center consolidation, cloud migration and new approaches to data tiering are all drivers of modern data archiving strategies. When data is archived, it is typically less frequently accessed or modified compared to active data. Archiving and process of archival data allows organizations to manage data growth, improve system performance, and maintain compliance with data retention policies and legal requirements. Key points to understand about archiving and archival data The primary purpose of archiving is to retain data that is no longer actively used but may still hold value for reference, regulatory compliance, legal reasons, or historical purposes. Archiving helps organizations maintain data integrity and accessibility while optimizing primary storage performance and resources. Data Selection: The process of archiving involves identifying and selecting data to be moved from primary storage to secondary storage. Organizations define criteria for data selection, such as age, usage patterns, relevance, or specific retention policies, to determine which data should be archived. Storage Systems or Media: Archived data is typically stored on secondary storage systems or media that provide cost-effective and scalable storage options. These may include network-attached storage (NAS), tape libraries, cloud storage, or dedicated archival storage solutions. The choice of storage medium depends on factors like data volume, access requirements, retention policies, and budget considerations. Indexing and Metadata: Effective archiving involves organizing and indexing the archived data to enable efficient retrieval. Indexing involves creating a catalog or database that records relevant metadata about the archived items, such as file names, dates, file types, and other attributes. This helps in locating and retrieving specific data when needed. See Global File Index. Data Security and Integrity: Data security and integrity are crucial aspects of archiving. Archived data should be protected from unauthorized access, loss, or corruption. Encryption, access controls, regular backups, and data integrity checks are implemented to ensure the security and reliability of archived data. Learn about ways to protect data in storage from ransomware in this blog. Retrieval and Access: Although archived data is stored in secondary storage, it should still be easily accessible when required. Organizations establish data retrieval mechanisms, search capabilities, and access controls to locate and retrieve specific archived data efficiently. This may involve using search indexes, metadata filters, or specialized archival software. Archiving practices may vary depending on the specific requirements and industry regulations. Organizations often develop archiving policies and procedures to govern the storage, retention, retrieval, and disposal of archival data, ensuring compliance, data governance, and efficient data management, and more specifically unstructured data management, practices. A variation of archiving is data tiering, which moves cold data to cheaper levels of storage or tiers where it can be easily recalled later if needed. Read more in the Guide. #### NAS Software NAS stands for Network Attached Storage, which is a type of data storage architecture that allows multiple devices to access shared storage over a network. NAS software is the software that powers these NAS systems. There are several NAS software options available, from FreeNAS, an open-source NAS software that supports various protocols and features, including CIFS/SMB, NFS, iSCSI, FTP, etc., to enterprise NAS vendors who deliver a combination of NAS software and NAS hardware (Pure Storage, NetApp, HPE, Dell are examples). The choice of NAS software depends on factors such as the size of your storage needs, budget, features required and personal preferences. NAS Hardware NAS hardware is the physical components that make up a Network-Attached Storage (NAS) system. Some of the key components of NAS hardware include: Storage drives: The most important component of any NAS system is the storage drives. These are the hard drives or solid-state drives (SSDs) that store the data. NAS systems typically use multiple drives in a RAID configuration to provide redundancy and improved performance. NAS enclosure: The enclosure is the physical housing that holds the storage drives and other components of the NAS system. Enclosures can vary in size, from small desktop models to large rack-mounted models for enterprise environments. Network interface: The network interface is the component that allows the NAS system to connect to a network. Most NAS systems have a built-in network interface card (NIC) that supports Ethernet connections. Processor and memory: The processor and memory are important components that affect the performance of the NAS system. A powerful processor and sufficient memory can improve the speed and responsiveness of the NAS system. Power supply: The power supply is responsible for providing power to the NAS system. It is important to choose a reliable power supply to ensure that the NAS system operates smoothly. Cooling system: NAS systems generate a lot of heat due to the high-speed operation of the storage drives and other components. A good cooling system is important to prevent overheating and damage to the components. Expansion slots: Some NAS systems have expansion slots that allow you to add additional components, such as network interface cards, to improve the functionality of the system. Read: Sustainable data management and the future of green business Enterprise NAS solutions Pure Storage, NetApp, Dell and Qumulo are all companies that offer enterprise NAS solutions. Pure Storage: Pure Storage offers FlashBlade, a high-performance, scalable NAS solution designed for modern workloads such as analytics, AI, and machine learning. FlashBlade is built on a software-defined architecture and provides features such as data reduction, encryption, and file replication. NetApp: NetApp offers several NAS solutions, including the FAS series and the AFF series. The FAS series provides midrange NAS capabilities and is suitable for small and medium-sized businesses. The AFF series provides high-performance NAS capabilities and is suitable for large enterprises. Dell: Dell offers several NAS solutions, including the PowerVault NX series and the PowerScale series. The PowerVault NX series provides midrange NAS capabilities and is suitable for small and medium-sized businesses. The PowerScale series provides high-performance NAS capabilities and is suitable for large enterprises. Qumulo: Qumulo offers a software-defined NAS solution that can be deployed on-premises, in the cloud, or in a hybrid environment. The solution is designed to provide high-performance file storage for a range of workloads, including video and audio content, medical imaging, and scientific research data. These are just a few examples of enterprise NAS solutions. Cloud NAS Cloud NAS is a type of network-attached storage architecture that allows users to access their data remotely over the internet. There are several cloud NAS vendors in the market that offer cloud-based storage solutions. Some of the well-known cloud NAS vendors include: Amazon Web Services (AWS): Amazon's cloud computing platform provides several storage services, including Amazon Elastic File System (EFS), which is a cloud-based NAS solution that provides scalable and secure file storage for EC2 instances. Microsoft Azure: Microsoft's cloud computing platform provides Azure File Storage, which is a fully managed cloud-based NAS solution that supports SMB and NFS protocols. Google Cloud Platform: Google's cloud computing platform provides Cloud Filestore, which is a cloud-based NAS solution that provides high-performance file storage for compute instances running on Google Cloud Platform. NAS Migration NAS migration is the process of transferring data from one NAS system to another. This may be necessary if you are upgrading your existing NAS system, or if you are moving your data to a new location. Also refer to Cloud NAS Migration. Here are the steps involved in a typical NAS migration: Plan the migration: The first step is to plan the migration. This involves identifying the data that needs to be migrated, estimating the size of the data, and choosing the new NAS system. Set up the new NAS system: Once you have chosen the new NAS system, you need to set it up. This involves configuring the network settings, creating shares and volumes, and setting up user accounts and permissions. Copy the data: The next step is to copy the data from the old NAS system to the new one. This can be done using various methods such as using a backup and restore process, using a file transfer protocol such as FTP, or using a third-party tool. Verify the data: After the data has been copied, it is important to verify that all the data has been transferred successfully. This involves checking that all the files and folders have been copied correctly and that there are no missing or corrupted files. Update the clients: Finally, you need to update the clients to point to the new NAS system. This involves updating the client configurations and testing to ensure that the clients can access the data on the new NAS system. It is important to ensure that you have a backup of all your data before you start the migration process. This will help you to recover your data in case anything goes wrong during the migration process. NAS Migration Challenges NAS migration can be a complex process and may present a number of challenges. Read the Guide to Migration. Here are some of the common challenges that organizations may face during NAS migration: Data transfer speed: Moving large amounts of data can be time-consuming, especially if you are using a slow network or if the data is being transferred over a long distance. This can result in prolonged downtime and potential data loss if the migration is not completed within the scheduled downtime window. Compatibility issues: Different NAS systems may have different file systems, protocols, and configurations, which can create compatibility issues during the migration process. This can lead to data corruption or loss, or it may require additional configuration changes to ensure that the data is compatible with the new NAS system. Data loss: Data loss is a common risk during any data migration process, and it is important to have a backup of all your data before you start the migration process. This will help you to recover your data in case anything goes wrong during the migration process. User access: During the migration process, users may lose access to their data, which can result in productivity loss and potential data loss. It is important to plan for user access and ensure that users are informed about any scheduled downtime or access restrictions. Data security: During the migration process, data may be exposed to security risks, such as unauthorized access or data breaches. It is important to ensure that your data is protected throughout the migration process. To overcome these challenges, it is important to plan the NAS migration process carefully, use appropriate migration tools and services, and involve all stakeholders in the process. It is also important to test the migration process thoroughly before the actual migration to identify and resolve any issues beforehand. Komprise for NAS Migration and Data Management Komprise specializes in analyzing and tiering, archiving and moving unstructured data from primary NAS to more cost-effective long-term storage without any disruption. Typically, 60% to 80% of enterprise file and object data has not been accessed in over a year. By tiering cold data and older log files and snapshots, the capacity of the storage array, mirrored storage array (if mirroring and/or replication being used) and backup storage is reduced dramatically. The right approach to transparently tiering cold data can reduce overall storage costs by as much as 70%. With Komprise you can migrate NAS and object data on-premises and in the cloud quickly, reliably, and at scale. Optimize cloud data storage costs with analytics-driven cloud tiering and archival. Build a Global File Index to easily find, tag and take action on the right data at the right time and feed the right data to analytics and AL / ML engines. Komprise uses open standards such as NFS, SMB / CIFS and REST/S3, making it “data storage agnostic.” #### Tiering What is Tiering? In the context of data storage, tiering refers to the practice of organizing data into different tiers based on its value or frequency of access. Each tier is assigned a different level of performance, cost, and capacity, with the goal of optimizing the use of storage resources and reducing costs. The most commonly used tiers are: Tier 1: This is the highest-performing and most expensive tier, typically using solid-state drives (SSDs) for fast access to critical data that is frequently accessed or requires low latency. Tier 2: This tier is less expensive than Tier 1 and is typically made up of hard disk drives (HDDs) or slower SSDs. It is used for data that is still frequently accessed but not as critical as Tier 1 data. Tier 3: This is a low-cost and high-capacity tier, typically using slower HDDs or object storage. It is used for infrequently accessed data or data that is older and less valuable. Unstructured data is typically moved automatically between tiers based on predefined data management policies that consider factors such as data age, access frequency, and cost. This ensures that frequently accessed data is stored in the higher-performing and more expensive tiers, while infrequently accessed data is stored in the lower-cost tiers. The goal of tiering is to optimize storage utilization and reduce costs while ensuring that data is accessible when needed. Storage Tiering, Data Archiving, and Transparent Archiving – What’s the Difference? File Migration Isn't File Archiving Cloud Tiering: Storage-based vs. Gateway vs. File Based #### Google Cloud Platform (GCP) What is Google Cloud Platform? Google Cloud Platform (GCP) is a suite of cloud computing services provided by Google. It offers a wide range of infrastructure and platform services, including computing, storage, networking, big data, machine learning, and security. Some of the key services offered by GCP include: Compute Engine - Virtual Machines (VMs) that can be used to run applications and services. App Engine - A platform for building and deploying web and mobile applications. Kubernetes Engine - A managed service for deploying, scaling, and managing containerized applications. Cloud Storage - A scalable and durable object storage service. Cloud SQL - A managed relational database service. BigQuery - A serverless, fully managed data warehouse for analytics. Cloud Pub/Sub - A messaging and streaming service for real-time data processing. Cloud AI Platform - A suite of machine learning services for building and deploying ML models. GCP is designed to be highly scalable, reliable, and secure, and it is used by many organizations for a wide range of use cases, from small startups to large enterprises. Komprise and Google Cloud #### Cloud Object Storage What is Cloud Object Storage? Cloud object storage is a type of cloud data storage that is designed to store and manage large amounts of unstructured data in the cloud. Unlike file-based storage systems, cloud object storage services are based on a simple key-value model that allows data to be stored and retrieved based on unique identifiers (or keys) that are associated with each piece of data. Also see Object Storage. Cloud object storage is ideal for storing documents, images, videos, and other unstructured data types that doesn't fit neatly into a structured (relational) database. Cloud object storage systems are designed to be highly scalable and can store large data sets, making them well-suited for big data applications and use cases such as backup and archiving, content distribution, and data analytics. Examples of Cloud Object Storage Some examples of cloud object storage include Amazon S3, Microsoft Azure Blob Storage, Google Cloud Storage, and IBM Cloud Object Storage Services. These cloud object storage services offer a range of features such as data durability and availability, built-in encryption, and flexible data access controls, as well as APIs and integrations for developers to easily incorporate object storage into their applications. Komprise TMT: Cloud File and Object Duality One of the core components of the Komprise Intelligent Data Management Platform is the patented Transparent Move Technology. When Komprise tiers files to a new target, typically object storage like AWS S3 or Azure Blob, moved files remain in native form, which means when a file becomes an object, a user sees it as a file. In addition to no end user disruption, preserving duality of file and object data across silos enables native cloud services on the data and ensures your data is not locked into a proprietary storage vendor format. This approach also ensures that hot data at the original source is handled by that storage vendor for optimal performance. In an interview, CEO and co-founder Kumar Goswami put it this way: Without using any agents, you can tier the data to the cloud and still access it from the original source as if it had never moved AND access it as a native object in the cloud to leverage cloud services like AI/ML cloud applications. This file to object duality, without agents, without getting in front of hot, mission-critical data is something no one else can tout. Komprise partners with cloud object storage vendors to deliver data-storage agnostic unstructured data management as a service. #### Object Data Migration Object data migration is a type of data migration that supports the movement of object-based data; object data storage uses a flat address space and assigns a unique identifier to each piece of data. There are several factors to consider when planning and executing object data migrations, including: Data compatibility: Organizations need to ensure that the new data storage system is compatible with the existing data and can support the same data formats, protocols, and applications. Data protection: Object data migrations can be complex and lengthy, and organizations need to ensure that their data is protected during the migration process. This may involve using backup and recovery tools, implementing data encryption and other security measures. Performance and scalability: Ensure that the new storage system can meet IT’s performance and scalability requirements. Planning a Successful Object Storage Data Migration Komprise has created a number of webinars and blog posts focused on unstructured data migration best practices. While object storage migrations maybe on-premises, this post reviews Tips for a Clean File Data Migration and many of the points are just as relevant for an object data migration: Define Data Storage Sources and Targets Establish Clear Data Migration Rules & Regulations Know Your Unstructured Data: Data Discovery Smart Data Migration. Know Your Topology Before You Migrate Data: Test, Test, Test Understand the Differences Between Free Tools for Cloud Migration vs. Enterprise Have a Good Data Migration Communication Plan Celebrate Wins General Steps and Considerations for a Successful Object Storage Migration Assessment and Planning: Understand the existing object storage environment. Identify the data to be migrated, including its size, type, and access patterns. Assess the compatibility between the source and target object storage systems. Choose the Right Migration Tools: Depending on the size and complexity of the migration, you may use different tools. Some object storage systems provide built-in migration tools, while third-party tools like Komprise Elastic Data Migration are also available. Data Preprocessing: Clean up unnecessary or obsolete data before migration. Consider data compression or deduplication to reduce the amount of data to be migrated. (This is where analyzing and tiering cold data fits into a Smart Data Migration strategy.) Metadata Mapping: Ensure that metadata associated with objects is correctly mapped between the source and target systems. Metadata might include information such as access permissions, creation date, and custom tags. Network Considerations: Assess the available network bandwidth for the migration process. Optimize network configurations to achieve the best possible data transfer rates. (Learn more about the Komprise ACE tool.) Testing: Conduct a pilot migration with a subset of data to identify and address any issues. Test the performance of the target object storage system with the migrated data. Incremental Migration: For large datasets, consider performing the migration incrementally to minimize downtime and impact on operations. Monitoring and Validation: Monitor the migration process to ensure it progresses smoothly. Validate the integrity and completeness of the migrated data. Update References: Update any references or links to the objects in your applications or systems to point to the new object storage location. Post-Migration Verification: Verify that all data has been successfully migrated. Confirm that applications and services dependent on the object storage are working as expected. Documentation: Update documentation to reflect the changes in the object storage environment. Rollback Plan: Have a rollback plan in case any issues arise during or after migration. Smarter, Faster, Proven Object Data Migration Learn more about NAS and object data migration with Komprise Elastic Migration. Whether migrating to the cloud, cloud NAS or to a NAS in your data center, with Komprise Elastic Data Migration you get the fast, predictable and cost-efficient data migration for file and object data. #### FinOps (or Cloud FinOps) FinOps (or Cloud FinOps) means financial operations that include practices such as cost optimization, cost allocation, chargeback and showback, and cloud financial governance. Some of the key challenges that organizations face with regards to cloud costs include: Cost visibility: Many organizations struggle to gain complete visibility into their cloud costs, which can make it difficult to ensure that they are not overspending on resources. Cost optimization: Organizations need to optimize their cloud costs by reducing waste, optimizing resource utilization, and ensuring that they are only paying for what they need. Cost allocation: Organizations need to allocate their cloud costs so that they are charged in a way that accurately reflects the resources that they are consuming. Cloud financial governance: Governance processes and controls can ensure that cloud spending is aligned with their overall business goals and objectives. Overall, FinOps is a critical aspect of modern cloud management, and is essential for organizations that want to effectively manage their cloud costs and ensure that they are maximizing value and ROI from their cloud investments. There are several vendors that specialize in FinOps solutions for cloud cost management and cloud cost optimization, but increasingly FinOps is built into other applications and technology platforms: Apptio CloudHealth by VMware RightScale (acquired by Flexera) CloudCheckr Azure Cost Management + Billing by Microsoft AWS Cost Explorer by Amazon Web Services Cloudability ParkMyCloud With the right Cloud FinOps strategy, organizations should focus on gaining the tools and expertise they need to manage their cloud costs and ensure that they are getting the most value from their cloud investments. FinOps and Unstructured Data Management How much does it cost to own your data? Cost modeling in Komprise helps IT teams enter their actual data storage costs to determine upfront new projected costs and benefits before spending money on storage. (Know First) Look at your current (and future) data storage platform(s). Does the company pay per GB (OPEX) or is it an owned technology (CAPEX)? For the latter, divide the current total amount of actual usable data by the cost to acquire the full system to attain cost/TB. For example, 1PB of physical storage may end up being just 500TB of actual usable capacity but only has 300TB of actual useable data on it. Use the 300TB because that is representative of today’s data ownership cost. Data ownership should also include the cost of data protection (data backup, disaster recovery, etc.). The FinOps capabilities in Komprise Intelligent Data Management allow you to compare on-premises versus cloud models or factor in cloud tiering or migrating to a new NAS platform. Komprise Cost Models According to GigaOm's 2022 Data Migration Radar Report: Komprise has, "the best set of Financial Operations (FinOps) features to date." Stop overspending on cloud storage: Know First. Move Smart. Take Control with the right FinOps for cloud data storage and data management strategy. #### Cloud Cost Optimization Cloud cost optimization is a process to reduce operating costs in the cloud while maintaining or improving the quality of cloud services. It involves identifying and addressing areas to reduce the use of cloud resources, select more cost-effective cloud services, or deploy better management practices, including data management. The cloud is highly flexible and scalable, but it also involves ongoing and sometimes hidden costs, including usage fees, egress fees, storage costs, and network fees. If not managed properly, these costs can quickly become a significant burden for organizations. In one of our 2023 data management predictions posts, we noted: Managing the cost and complexity of cloud infrastructure will be Job No. 1 for enterprise IT in 2023. Cloud spending will continue, although at perhaps a more measured pace during uncertain economic times. What will be paramount is to have the best data possible on cloud assets to make sound decisions on where to move data and how to manage it for cost efficiency, performance, and analytics projects. Data insights will also be important for migration planning, spend management (FinOps), and to meet governance requirements for unstructured data management. These are the trends we're tracking for cloud data management, which will give IT directors precise guidance to maximize data value and minimize cloud waste. Source: ITPro-Today Steps to Optimize Cloud Costs To optimize cloud costs, organizations can take several steps, including: Right-sizing: Choose the correct size and configuration of cloud resources to meet the needs of the application, avoiding overprovisioning or underprovisioning. Resource utilization: Monitor the use of cloud resources to reduce waste and improve cost efficiency. Cost allocation: Implement cost allocation and tracking practices to better understand cloud costs and improve accountability. Reserved instances: Use reserved instances to reduce costs by committing to a certain level of usage for a longer term. Cost optimization tools: These tools identify areas for savings and help manage cloud expenses. The Challenge of Managing Cloud Data Managing cloud data costs takes significant manual effort, multiple tools, and constant monitoring. As a result, companies are using less than 20% of the cloud cost-saving options available to them. “Bucket sprawl” makes matter worse, as users easily create accounts and buckets and fill them with data—some of which is never accessed again. When trying to optimize cloud data, cloud administrators contend with poor visibility and complexity of data management: How can you know your cloud data? How fast is cloud data growing and who’s using it? How much is active vs. how much is cold data Cold Data Storage? How can you dig deeper to optimize across object sizes and storage classes? How can you make managing data and costs manageable? It’s hard to decipher complicated cost structures. Need more information to manage data better, e.g., when was an object last accessed? Factoring in multiple billable dimensions and costs is extremely complex: storage, access, retrievals, API, transitions, initial transfer, and minimal storage-time costs. There are unexpected costs of moving data across different storage classes (e.g., Amazon S3 Standard to S3 Glacier). If access isn’t continually monitored, and data is not moved back up when it gets hot, you will face expensive retrieval fees These issues are further compounded as enterprises move toward a multicloud approach and require a single set of tools, policies, and workflow to optimize and manage data residing within and across clouds. Komprise Cloud Data Management Reduce cloud storage costs by more than 50% with Komprise. Cloud providers offer a range of storage services. Generally, there are storage classes with higher performance and costs for hot and warm data, such as Amazon S3 Standard and S3 Standard-IA, and there are storage classes with much lower performance and costs that are appropriate for cold data, such as S3 Glacier and S3 Glacier Deep Archive. Data access fees and retrieval fees for the lower cost storage classes are much higher than that of the higher performance and higher cost storage classes. To maximize savings, you need an automated unstructured data management solution that takes into account data access patterns to dynamically and cost optimally move data across storage classes (e.g., Amazon S3 Standard to S3 Standard-IA or S3 Standard-IA to S3 Glacier) and across multi-vendor storage services (e.g., NetApp Cloud Volumes ONTAP to Amazon S3 Standard to S3 Standard-IA to S3 Glacier to S3 Glacier Deep Archive). While some limited manual data movement through Object Lifecycle Management policies based on modified times or intelligent tiering is available from the cloud providers, these approaches offer limited savings and involve hidden costs. Komprise automates full lifecycle management across multi-vendor cloud storage classes using intelligence from data usage patterns to maximize your savings without heavy lifting. Read the white paper to see how you can save +50% on cloud storage cost savings. Watch the video: How to save costs and manage your multi-cloud strategy #### Cloud Data Analytics Cloud data analytics refers to the use of cloud computing resources to process, analyze, and extract insights from large amounts of data. These solutions can include data warehousing, big data processing, machine learning, and business intelligence and can ingest a wide range of data, including structured, semi-structured, and unstructured data. Cloud data analytics can deliver an agile and lower-cost method to analyze large amounts of data quickly for a variety of business outcomes including operational improvements, customer behavior analysis, competitive analysis, R&D and more. Some of the leading cloud data analytics providers include Amazon Web Services, Google Cloud, Microsoft Azure, IBM and many early-stage venture-backed startups. One of the first cloud analytics vendors was LucidEra. These companies offer a range of cloud data analytics services and tools, including data warehousing, big data processing, machine learning, and business intelligence. Komprise Smart Data Workflows can be created to search and find the right unstructured data and automate the delivery of data to cloud analytics infrastructure. #### File Archiving File archiving is the process of preserving digital files for long-term data storage and retrieval. The goal of file archiving is to retain important files and documents in a secure, easily accessible, and cost-effective manner, while freeing up space on primary storage systems. Manual file data management, backup and restore solutions, and dedicated file archiving systems are three ways to archive files. Manual file management moves files to a secondary storage location, such as a network share or external hard drive. Backup and restore solutions preserve files by creating snapshots of the data at regular intervals; snapshots can restore data in the event of data loss or corruption. Dedicated file archiving systems are specialized software solutions that are designed specifically for file archiving and provide features such as indexing, searching, and data retention policies. File Archiving Challenges File archiving reduces the risk of data loss, improves regulatory compliance, and reduces the costs associated with primary storage. Yet file archiving can present several challenges, including: Data Storage Costs: Storing large volumes of data for a long time can be expensive, especially if the data is stored on traditional storage solutions, such as tapes or hard disk drives. Scalability: As data volumes continue to grow, archiving solutions must be able to meet the increasing demand for storage capacity. Data Retrieval: Archived files are difficult to locate and retrieve if they are not properly indexed or if the index becomes corrupted. Data Retention: Organizations must ensure that their archiving solutions meet regulatory requirements for data retention, including data privacy and security laws. Data Integrity: Archived files must be preserved in their original format and remain readable over time, which requires proper data preservation and data migration strategies. Data migration: As archiving systems age or become obsolete, IT must migrate data to new systems, in particular cloud data migration, which can be time-consuming and complex. Integration with other systems: Archiving solutions must integrate with other systems, such as backup and restore solutions, to ensure streamlined access. Standards-based Transparent Data Archiving A true transparent data archiving solution creates literally no disruption, and that’s only achievable with a standards-based approach. Komprise Intelligent Data Management is the only standards-based transparent data archiving solution that uses Transparent Move Technology™ (TMT), which uses symbolic inks instead of proprietary stubs. True transparency that users won’t notice When a file is archived using TMT, it’s replaced by a symbolic link, which is a standard file system construct available in NFS, SMB, object store file systems. The symbolic link, which retains the same attributes as the original file, points to the Komprise Cloud File System (KCFS), and when a user clicks on it, the file system on the primary storage forwards the request to KCFS, which maps the file from the secondary storage where the file actually resides. (An eye blink takes longer.) This approach seamlessly bridges file and object storage systems so files can be archived to highly cost-efficient object-based solutions without losing file access. Learn more about Komprise TMT for File Archiving #### File Data Tiering File data tiering is a data storage management technique that automatically moves files from one storage tier to another based on usage patterns and access frequency. The goal of file data tiering is to optimize storage utilization and reduce storage costs by placing frequently used files on high-performance storage and less frequently used files (cold data storage) on lower-performance storage. Hardware-based tiering, software-based tiering, and cloud-based tiering are three methods of file data tiering. Hardware-based tiering moves files between different types of physical storage devices, such as solid-state drives (SSDs) and hard disk drives (HDDs), within a storage array. Software-based tiering moves files between different types of virtual storage volumes, such as high-performance and low-performance storage pools. Cloud-based tiering moves files between different storage classes within a cloud-based object storage service, such as Amazon S3. As part of a broader file data management strategy, file data tiering can help organizations improve storage utilization, reduce storage costs, and increase storage performance by automatically placing the right data in the right place at the right time. However, it's important for organizations to carefully consider their storage requirements and choose a file tiering solution that fits their needs, as not all tiering solutions are appropriate for all environments. File-Level Tiering vs Block-Level Tiering Learn the difference between storage-centric block tiering, which moves blocks that can no longer be directly accessed from their new location without vendor software (aka lock-in) and file data tiering, which is what Komprise uses to fully preserve file access at each tier by keeping the metadata and file attributes with the file—no matter where it lives. Know the difference to make the right cloud tiering choice for your data storage moves. #### S3 Data Migration S3 (Amazon Simple Storage Service) data migration entails transferring data stored in Amazon S3, a cloud-based object storage service offered by Amazon Web Services (AWS), to another system or S3 bucket within AWS. S3 data migration involves several steps, such as data extraction, data transformation, data loading, data verification, and data archiving. S3 data migration can be complex and time-consuming, especially for organizations with large volumes of data and strict security and compliance requirements. Smart Amazon S3 Data Migration and Data Management for File and Object Data Komprise Elastic Data Migration is designed to make cloud data migrations simple, fast and reliable. It eliminates sunk costs with continual data visibility and optimization even after the migration. Komprise has received the AWS Migration and Modernization Competency Certification, verifying the solution’s technical strengths in file data migration. A Smart Data Migration strategy for file workloads to Amazon S3 uses an analytics-driven approach to speed up data migrations and ensures the right data is delivered to the right tier in AWS, saving 70% or more on data storage and ultimately ensuring you can leverage advanced technologies in the cloud. S3 Migration Done Right When you're migrating data or resources to Amazon Simple Storage Service (S3), a scalable object storage service offered by Amazon Web Services (AWS), your migrating to S3 can involve moving data from on-premises storage, another cloud provider, or even within different S3 buckets. Additionally, there are many possible S3 migration scenarios for unstructured data - for example, S3 to S3 migration, File to Object migration, Object to Object migration, etc. Whatever your object storage data migration strategy is, there are a number of basic steps considerations to keep in mind, including: Assessment: Identify the data you want to migrate. Assess the size and type of data. Consider access patterns and performance requirements. Create an S3 Bucket: Log in to the AWS Management Console. Navigate to the S3 service. Create a new bucket to store your data. Set Up Permissions: Configure access control lists (ACLs) and bucket policies to manage permissions. Ensure that your IAM (Identity and Access Management) roles have the necessary permissions. Data Transfer: Many AWS customers start with AWS DataSync, AWS Snowball, AWS CLI, or SDKs to transfer data and then discover the analysis-first Komprise Elastic Data Migration solution. For large-scale migrations, consider using AWS Snowball for physical transfer of data. Read the blog post here. Update Applications: If your data is being accessed by applications, update their configurations to point to the new S3 location. Testing: Perform tests to ensure data integrity and that applications can access data from the new S3 location. Switch Over: Once testing is successful, switch over to using the new S3 location. Update DNS entries or configurations as needed. Monitoring: Set up monitoring and logging to track S3 usage and performance. Implement alerts for any unexpected issues. Clean-Up: Once you are confident in the migration, clean up the old data storage and associated resources. Documentation: Update documentation to reflect the changes made during the migration. Of course, the specifics of your S3 migration will vary depending on your use case, data volume, and existing infrastructure. It's also important to consider security best practices and compliance requirements during the unstructured data migration process. Learn more about Komprise for AWS. #### NFS Data Migration NFS protocol data migration refers to the process of transferring data stored in NFS protocol-based systems, such as Unix and Linux file servers, to another system, such as a new file server or a cloud-based storage service. The NFS (Network File System) protocol is a file sharing protocol used by Unix and Linux-based systems to access files and other resources on a server over a network. Like and data migration, NFS data migrations involves several steps, such as data extraction, data transformation, data loading, data verification, and data archiving. The goal of NFS protocol data migration is to ensure the accurate and secure transfer of data to the new system, while minimizing any disruptions to business operations and preserving the integrity of the data. Although a less chatty protocol than SMB, NFS data migrations can be challenging due to the complex nature of the NFS protocol and the large volumes of unstructured data that are often involved. To ensure a successful file data migration, organizations typically use specialized tools and services, such as data migration software, cloud data migration services, and managed data migration services. Komprise delivers 27x faster NFS migrations To address the critical NFS migration issues (and SMB and S3/object protocols) IT faces today, Komprise has developed Elastic Data Migration. This super-fast data migration solution is a highly parallelized, multi-processing, multi-threaded approach that works at two levels: Multi-level Parallelism: Maximizes the use of available resources by exploiting parallelism at multiple levels: shares and volumes, directories, files, and threads to maximize performance. Komprise Elastic Data Migration breaks up each migration task into smaller ones that execute across the Komprise Observers. Komprise Observers are a grid of one or more virtual appliances that run the Komprise Intelligent Data Management solution. All of this parallelism occurs automatically across the grid of Observers. The user simply creates a migration task and can configure the level of parallelism. Komprise does the rest. Protocol-level Optimizations: Reduces the number of round-trips over the protocol during a migration to eliminate unnecessary chatter. Rather than relying on generic NFS clients provided by the underlying operating system, Komprise has fine-tuned the NFS client to minimize overhead and unnecessary back-and-forth messaging. This is especially beneficial when moving data over high-latency networks such as WANs. Read the Komprise Elastic Data Migration white paper. #### SMB Data Migration SMB protocol data migration refers to the process of transferring data stored in SMB protocol-based systems, such as Windows file servers, to another system, such as a new file server or a cloud-based storage service. The SMB (Server Message Block) protocol is a network file sharing protocol used by Windows-based systems to access files and other resources on a server over a network. Like any file data migration, an SMB data migration involves several steps, such as data extraction, data transformation, data loading, data verification, and data archiving. The goal of an SMB data migration is to ensure that all data accurately and securely transfers to the new system, while minimizing any disruptions to business operations and preserving the integrity of the data. SMB protocol data migration can be challenging due to the complex nature of the SMB protocol and the large volumes of data that are often involved. To ensure a successful migration, organizations typically use specialized tools and services, such as data migration software, cloud data migration services, and managed data migration services. The Barriers to Fast SMB Migrations From the Hypertransfer white paper: Unstructured data is everywhere. From genomics and medical imaging to streaming video, electric cars, and IoT products, all sectors generate unstructured file data. Data-heavy enterprises typically have petabytes of file data, which can consist of billions of files scattered across different storage vendors, architectures and locations. And while file data growth is exploding, IT budgets are not. That’s why enterprises’ IT organizations are looking to migrate file workloads to the cloud. However, they face many barriers, which can cause migrations to take weeks to months and require significant manual effort. These include: Billions of files, mostly small: Unstructured data migrations often require moving billions of files, the vast majority of which are small files that have tremendous overhead, causing data transfers to be slow. Chatty protocols: Server message block (SMB) protocol workloads—which can be user data, electronic design automation (EDA) and other multimedia files or corporate shares—are often a challenge since the protocol requires many back-and-forth handshakes which increase traffic over the network. Large WAN latency: Network file protocols are extremely sensitive to high-latency network connections, which are essentially unavoidable in wide area network (WAN) migrations. Limited network bandwidth: Bandwidth is often limited or not always available, causing data transfers to become slow, unreliable and difficult to manage. Speed up SMB Migration with Hypertransfer Blocks and Files coverage: Komprise speeds SMB data migration to cloud  Hypertransfer for Komprise Elastic Data Migration delivers 25x performance gains compared to other tools. Komprise Elastic Data Migration is a SaaS solution available with the Komprise Intelligent Data Management platform or standalone. Designed to be fast, easy and reliable with elastic scale-out parallelism and an analytics-driven approach, it is the market leader in file and object data migrations, routinely migrating petabytes of data (SMB, NFS, Dual) for customers in many complex scenarios. Komprise Elastic Data Migration ensures data integrity is fully preserved by propagating access control and maintaining file-level data integrity checks such as SHA-1 and MD5 checks with audit logging.   ---------- #### File Data Migration File data migration or file migration is the process of transferring data stored in files, such as text documents, images, audio and video files, spreadsheets, and other types of data, from one system to another. IT organizations move data for many reasons including for system upgrades, data center relocations, during mergers and acquisitions, and when acquiring new data storage platforms. File data migration involves several steps, such as data extraction, data transformation, data loading, data verification, and data archiving. It’s important to ensure that all the data is accurately and securely transferred to the new system, while minimizing any disruptions to business operations and preserving the integrity of the data. File data migration can be complex and time-consuming, especially for organizations with large volumes of data, multiple file formats, and strict security and compliance requirements. To ensure a successful migration, organizations typically use specialized tools and services, such as data migration software, cloud data migration services, and managed data migration services. Komprise File Data Migration Komprise Elastic Data Migration is a fast, predictable and cost-efficient file data migration software solution. Elastic Data Migration is included in the Komprise Intelligent Data Management platform or is available standalone. Komprise Hypertransfer for Elastic Data Migration accelerates file data transfer to the cloud while strengthening cloud security. Komprise Hypertransfer optimizes cloud data migration performance by minimizing the WAN roundtrips using dedicated channels to send data, mitigating SMB protocol issues. File Data Migration to the Cloud Considerations Increasingly enterprise IT organizations are looking to migrate file data workloads to the cloud. (Read the State of Unstructured Data Management report to review data storage and cloud data migration trends.) This ITPro-Today article reviews some key considerations to know first before a file data migration initiative: What data do I have and where is it stored? What data sets are accessed most frequently (a.k.a. hot data)? What data sets are rarely accessed (a.k.a. cold data)? Who uses the data currently and is there value in enabling collaboration outside of your organization? What data/files haven't been accessed for more than 3-5 years and should be considered for deep archival storage or confinement and deletion? What types of files do we have and which comprise the most storage: a.k.a. image files, video or audio files, sensor data, text data. What is the cost of storing these different file types? Which types of files should be stored in a higher security level — a.k.a. those containing PII or IP data or belonging to mission-critical projects? Are we complying with regulations and internal policies with our data management practices? Read the whitepaper: Komprise Elastic Data Migration Overview This video discussion reviews cloud file data migration considerations: In this Data on the Move discussion we interview Benjamin Henry, Customer Success Architect at Komprise.  _______________________ ---------- #### Data Migration Data migration means many different things and there are many types of data migrations in the enterprise world. At it's core, it is the process of selecting and moving data from one location to another. For this Glossary, we're focused on Unstructured Data Migration, specifically file and object data. IT organizations use data migration tools to move data across different data storage systems and across different formats and protocols (SMB, NFS, S3, etc.). Data migrations often occur in the context of retiring a system and moving to a new system, or in the context of a cloud migration, or in the context of a modernization or upgrade strategy. When it comes to unstructured data migrations and migrating enterprise file data workloads to the cloud, data migrations can be laborious, error prone, manual, and time consuming. Migrating data may involve finding and moving billions of files (large and small), which can succumb to storage and network slowdowns or outages. Also, different file systems do not often preserve metadata in exactly the same way, so migrating data to a cloud environment without loss of fidelity and integrity can be a challenge. Two Data Migration Approaches Lift-and-Shift Many organizations start here, thinking they'll just migrate entire file shares and directories to the cloud. If this is your data migration plan, it's important to use analytics to plan and migrate to reduce errors, ensure alignment and multi-storage visibility while minimizing cutover. With Komprise Elastic Data Migration, you can readily migrate from one primary vendor to another without rehydrating all the archived data, so migrations are cheaper and faster. Cloud Data Tiering as a First Step: Smart Data Migration Since a large percentage of file data is cold and has not been used in a year or more, tiering and archiving cold data is a smart first step – especially if you use Transparent Move Technology so users can access the files exactly as before. You can follow this up by migrating the remaining hot data to a performance cloud tier. Data Migration Questions Here are some questions that will help you determine the best file and object data migration strategy: What data storage do we have and where?​ (primary storage, secondary storage) What data sets are accessed most frequently (hot) and less frequently (cold)?​ What types of data and files do we have and which are taking up the most storage (image files, video, audio files, sensor data, etc.)?​ What is the cost of storing these different file types today? How does this align with the budget and projected growth?​ Which types of files should be stored at a higher security level? (PII or IP data? Mission-critical projects?)​ Are we complying with regulations and internal policies with our unstructured data management practices? What constraints do my network and environment pose and how do I avoid surprises during migrations? Do we have the best possible strategy in place for WAN acceleration, such as Komprise Hypertransfer for Elastic Data Migration. #### Storage Tiering What is Storage Tiering? Storage Tiering refers to a technique of moving less frequently used data, also known as cold data, from higher performance storage such as SSD to cheaper levels of storage or tiers such as cloud or spinning disk. The term “storage tiering” arose from moving data around different tiers or classes of storage within a storage system, but has expanded now to mean tiering or archiving data from a storage system to other clouds and storage systems. Storage tiering is now considered a core feature of modern storage systems and recently has become part of default configuration for next generation storage like AWS FSx ONTAP. Block-level data storage solutions include: NetApp FabricPool and Dell PowerScale CloudPools. Storage-agnostic data management and data tiering have emerged as more and more enterprise organizations adopt hybrid, multi-cloud, and edge IT infrastructure strategies. See also cloud tiering and choices for cloud data tiering.   Storage Tiering Cuts Costs Because 70%+ of Data is Cold As data grows, data storage costs grow. It is easy to think the solution is more efficient storage. Or simply buy more storage. But data management is the real solation. Typically over 70% of data is cold and has not been accessed in months, yet it sits on expensive storage hardware or cloud infrastructure and consumes the same backup resources as hot data. As a result, data storage costs are rising, backup times are slowing, disaster recovery (DR) is unreliable, and the sheer bulk of this data makes it difficult to leverage newer options like Flash and Cloud. Data Tiering Was Initially Used within a Storage Array Data Tiering was initially a technique used by storage systems to reduce the cost of data storage by tiering cold data within the storage array to cheaper but less performant options – for example, moving data that has not been touched in a year or more from an expensive Flash tier to a low-cost SATA disk tier. Typical storage tiers within a storage array or on-premises storage device include: Flash or SSD: A high-performance storage class but also very expensive. Flash is usually used on smaller data sets that are being actively used and require the highest performance. SATA Disks: High-capacity disks with lower performance that offer better price per GB vs SSD. Secondary Storage, often Object Storage: Usually a good choice for capacity storage – to store large volumes of cool data that is not as frequently accessed, at a much lower cost. Increasingly, enterprise IT organization are looking at another option – tiering or archiving data to a public cloud. Public Cloud Storage: Public clouds currently have a mix of object and file storage options. The object storage classes such as Amazon S3 and Azure Blob (Azure Storage) provide tremendous cost efficiency and all the benefits of object storage without the headaches of setup and management. Cloud NAS has also become increasingly popular, but if unstructured data is not well managed, data storage costs will be prohibitive. Cloud Storage Tiering is now Popular Tiering and archiving less frequently used data or cold data to public cloud storage classes is now more popular. This is because customers can leverage the lower cost storage classes within the cloud to keep the cold data and promote them to the higher cost storage classes when needed. For example, data can be archived or tiered from on-premises NAS to Amazon S3 Infrequent Access or Amazon Glacier for low ongoing costs, and then promoted to Amazon EFS or FSX when you want to operate on it and need performance. Cloud isn’t just low-cost data storage  The cloud offers more than low-cost data storage. Advanced security features such immutable storage that can defeat ransomware. Cloud native services from analytics to machine learning can drive value from your unstructured data. But in order to take advantage of these capabilities, and to ensure you’re not treating the cloud as just a cheap storage locker, data that is tiered to the cloud needs to be accessible natively in the cloud without requiring third-party software. This requires the right approach to storage tiering, which is file-tiering, not block-tiering. Block Tiering Creates Unnecessary Costs and Lock-In Block-level storage tiering was first introduced as a technique within a storage array to make the storage box more efficient by leveraging a mix of technologies such as more expensive SSD disks as well as cheaper SATA disks. Block storage tiering breaks a file into various blocks – metadata blocks that contain information about the file, and data blocks that are chunks of the original file. Block-tiering or Block-level tiering moves less used cold blocks to lower, less expensive tiers, while hot blocks and metadata are typically retained in the higher, faster, and more expensive storage tiers. Block tiering is a technique used within the storage operating system or filesystem and is proprietary. Storage vendors offer block tiering as a way to reduce the cost of their storage environment. Many storage vendors are now expanding block tiering to move data to the public cloud or on-premises object storage. But, since block storage tiering (often called CloudPools – examples are NetApp FabricPool and Dell EMC Isilon CloudPools) is done inside the storage operating system as a proprietary solution, it has several limitations when it comes to efficiency of reuse and efficiency of storage savings. Firstly, with block tiering, the proprietary storage filesystem must be involved in all data access since it retains the metadata and has the “map” to putting the file together from the various blocks. This also means that the cold blocks that are moved to a lower tier or the cloud cannot be directly accessed from the new location without involving the proprietary filesystem because the cloud does not have the metadata map and the other data blocks and the file context and attributes to put the file together. So, block tiering is a proprietary approach that often results in unnecessary rehydration of the data and treats the cloud as a cheap storage locker rather than as a powerful way to use data when needed. With block storage tiering, the only way to access data in the cloud is to run the proprietary storage file system in the cloud which adds to costs. Also, many third-party applications such as backup software that operate at a file level require the cold blocks to be brought back or rehydrated, which defeats the purpose of tiering to a lower cost storage and erodes the potential savings. For more details, read the white paper: Block vs. File-Level Tiering and Archiving. #### Cloud File Storage What is Cloud File Storage? Cloud File Storage, also known as Cloud NAS is a method for storing data in the cloud that provides servers and applications access to data through file system protocols such as NFS and SMB. Cloud file storage allows customers to move file-based workloads to the cloud without code changes. Popular choices for cloud file storage are AWS FSx for Windows, AWS FSx ONTAP, AWS FSx ZFS, Microsoft Azure Files, Google Filestore, and Qumulo. In late 2021, Komprise COO Krishna Subramanian predicted that cloud file storage will accelerate. She wrote: First, it was cloud-native applications, then block workloads, but now it’s time for file workloads to move to the cloud. Explosive growth in unstructured file data has led to data centers bursting at the seams. Covid-19 has accelerated the shift to cloud for file workloads. Data management solutions are also enabling smart file migrations so that hot data is placed in cloud file storage and cold data is transparently and efficiently tiered at the file level to object storage. This means that customers can use data from both the file and object tiers. Another approach many vendors are taking is to provide cloud-like economics and pricing while the infrastructure remains on-premises — HPE Greenlake and Pure as a Service are examples of this trend. #### Hybrid Cloud Storage What is Hybrid Cloud Storage? As data moves from on-premises data centers to the public cloud and to edge computing devices, enterprise data storage has increasingly moved to a hybrid cloud storage model, where data is stored on the infrastructure that will leverage the processing power of the public cloud. In Gartner’s Hybrid Cloud Storage Market Guide (subscription required), they recommend that infrastructure and operations leaders identify the right workloads, types of data and use cases for cloud data storage and prioritize hybrid cloud storage solutions that support cloud-native access. In the 2021 Komprise Unstructured Data Management Survey, 50% of enterprises responded that they have data stored in a mix of on-premises and cloud-based storage and 56% stated that their top priority is cloud data migration. Download the State of Unstructured Data Management report.  In August 2022, Komprise published the 2nd annual State of Unstructured Data Management Report. Unstructured Data Management #### Object Lock What is Object Lock? Object Lock is the Amazon S3 object storage API implementation of immutable storage. Object Lock prevents objects from alteration or deletion for a set retention period. Object Lock is available in two modes: Governance mode, which allows privileged administrators to override the Object Lock protection . Compliance, the more strict mode, which cannot be overridden even by administrators for the length of data retention.   Many of our customers use Komprise to archive cold data to Amazon S3 and want these files to be immutable for compliance and regulatory purposes. They may want protection against ransomware or malware incidents that can infect NAS shares. For both of these use cases, Komprise supports Amazon S3 buckets configured with S3 Object Lock, which allows customers to store objects using a Write-Once-Read-Many (WORM) model. Once Komprise archives data into such a bucket, the data cannot be overwritten or deleted, providing file retention that meets compliance regulations and protects data from being encrypted by malware or ransomware. Learn more about Komprise for cyber resiliency, including optimizing your defenses against cyber incidents, system failure and file data. Read the blog post: How to Protect File Data from Ransomware at 80% Lower Cost #### File-level Tiering File-level tiering is a standards-based data tiering approach Komprise uses that moves each file with all its metadata to the new tier, maintaining full file fidelity and attributes at each tier for direct data access from the target storage and no rehydration. Read the white paper: Block-Level Tiering versus File-Level Tiering. #### Elastic Data Migration What is Elastic Data Migration? Data migration is the process of moving data (eg files, objects) from one storage environment to another, but Elastic Data Migration is a high-performance migration solution from Komprise using a parallelized, multi-processing, multi-threaded approach that speeds NAS-to-NAS and NAS-to-cloud migrations in a fraction of the traditional time and cost. 27x Faster NFS Migration vs. Rsync 25x Faster SMB Migration vs. Robocopy Standard Data Migration NAS Data Migration – move files from a Network Attached Storage (NAS) to another NAS. The NAS environments may be on-premises or in the cloud (Cloud NAS) S3 Data Migration – move objects from an object storage or cloud to another object storage or cloud Data migrations can occur over a local network (LAN) or when going to the cloud over the internet (WAN). As a result, migrations can be impacted by network latencies and network outages. Data migration software needs to address these issues to make data migrations efficient, reliable, and simple, especially when dealing with NAS and S3 data since these data sizes can be in petabytes and involve billions of files. Elastic Data Migration Elastic Data Migration makes its orders of magnitude faster than normal data migrations. It leverages parallelism at multiple levels to deliver 27 times faster performance than NFS alternatives and 25 times faster for SMB protocol performance. Parallelism of the Komprise scale-out architecture – Komprise distributes the data migration work across multiple Komprise Observer VMs so they run in parallel. Parallelism of sources – When migrating multiple shares, Komprise breaks them up across multiple Observers to leverage the inherent parallelism of the sources Parallelism of data set – Komprise optimizes for all the inherent parallelism available in the data set across multiple directories, folders, etc to speed up data migrations Big files vs small files – Komprise analyzes the data set before migrating it so it learns from the nature of the data – if the data set has a lot of small files, Komprise adjusts its migration approach to reduce the overhead of moving small files. This AI driven approach delivers greater speeds without human intervention. Protocol level optimizations – Komprise optimizes data at the protocol level (eg NFS, SMB) so the chattiness of the protocol can be minimized All of these improvements deliver substantially higher performance than standard data migration. When an enterprise is looking to migrate large production data sets quickly, without errors, and without disruption to user productivity, Komprise Elastic Data Migration delivers a fast, reliable, and cost-efficient migration solution. Komprise Elastic Data Migration Architecture What Elastic Data Migration for NAS and Cloud provides Komprise Elastic Data Migration provides high-performance data migration at scale, solving critical issues that IT professionals face with these migrations. Komprise makes it possible to easily run, monitor, and manage hundreds of migrations simultaneously. Unlike most other migration utilities, Komprise also provides analytics along with migration to provide insight into the data being migrated, which allows for better migration planning. Fast, painless file and object migrations with parallelized, optimized data migration: Parallelism at every level: Leverages parallelism of storage, data hierarchy and files High performance multi-threading and automatic division of a migration task across machines Network efficient: Adjusts for high-latency networks by reducing round trips Protocol efficient: optimized NFS handling to eliminate unnecessary protocol chatter High Fidelity: Does MD5 checksums of each file to ensure full integrity of data transfer Intuitive Dashboards and API: Manage hundreds of migrations seamlessly with intuitive UI and API Greater speed and reliability Analytics with migration for data insights Ongoing value #### Block-level Tiering Moving blocks between the various tiers to increase performance where hot blocks and metadata are kept in the higher, faster, and more expensive data storage tiers, and cold data blocks are migrated to lower, less expensive ones. Lacking full context, these moved blocks cannot be directly accessed from their new location. Komprise uses the more advanced file-level tiering. Read the white paper "Block-Level Tiering vs. File-Level Tiering" #### S3 Intelligent Tiering S3 Intelligent Tiering is an Amazon cloud storage class. Amazon S3 offers a range of storage classes for different uses. S3 Intelligent Tiering is a storage class aimed at data with unknown or unpredictable data access patterns. It was introduced in 2018 by AWS as a solution for customers who want to optimize storage costs automatically when their data access patterns change. Instead of utilizing the other Amazon S3 storage classes and moving data across them based on the needs of the data, Amazon S3 Intelligent Tiering is a distinct storage class that has embedded tiers within it and data can automatically move across the four access tiers when access patterns change. To fully understand what S3 Intelligent Tiering offers it is important to have an overview of all the classes available through S3: Classes of AWS S3 Storage Standard (S3) – Used for frequently accessed data (hot data) Standard-Infrequent Access (S3-IA) – Used for infrequently accessed, long-lived data that needs to be retained but is not being actively used One Zone Infrequent Access – Used for infrequently accessed data that’s long-lived but not critical enough to be covered by storage redundancies across multiple locations Intelligent Tiering – Used for data with changing access patterns or uncertain need of access Glacier – Used to archive infrequently accessed, long-lived data (cold data) Glacier has a latency of a few hours to retrieve Glacier Deep Archive – Used for data that is hardly ever or never accessed and for digital preservation purposes for regulatory compliance Also be sure to read the blog post about Komprise data migration with AWS Snowball Accelerating Petabyte-Scale Cloud Migrations with Komprise and AWS Snowball What is S3 Intelligent Tiering? S3 Intelligent Tiering is a storage class that has multiple tiers embedded within it, each with its own access latencies and costs – it is an automated service that monitors your data access behavior and then moves your data on a per-object basis to the appropriate level of tier within the S3 Intelligent Tiering storage class. If your object has not been accessed for 30 consecutive days it will automatically move to the infrequent access tier within S3 Intelligent Tiering, and if the object is not accessed for 90 consecutive days it will automatically move the object to the Archive Access tier and then after 190 consecutive days to the Deep Archive access tier. If an object is moved to the archive tier, the retrieval can take 3 to 5 hours and if it is in the deep archive tier it can take 12 hours. and if it is then subsequently accessed it will move it into the frequently accessed storage class. What are the costs of AWS S3 Intelligent Tiering? You pay for monthly storage, request and data transfer. When using Intelligent-Tiering you also pay for a monthly per-object fee for monitoring and automation. While there is no retrieval fee in S3 Intelligent-Tiering and no fee for moving data between tiers, you do not manipulate each tier directly. S3 Intelligent Tier is a bucket, and it has tiers within it that objects move through. Objects in the Frequent Access tier are billed at the same rate as S3 Standard, objects stored in the Infrequent Access tier are billed at the same rate as S3 Standard Infrequent Access, objects stored in the Archive Access tier are billed at the same rate as S3 Glacier and objects stored in the Deep Archive access tier are billed at the same rate as S3 Deep Glacier. What are the advantages of S3 Intelligent tiering? The advantages of S3 Intelligent tiering are that savings can be made. There is no operational overhead, and there are no retrieval costs. Objects can be assigned a tier upon upload and then move between tiers based on access patterns. There is no impact on performance and it is designed for 99.999999999% durability and 99.9% availability over annual average. What are the disadvantages of S3 Intelligent tiering? The main disadvantage of S3 Intelligent Tiering is that it acts as a black-box – you move objects into it and cannot transparently access different tiers or set different versioning policies for the different tiers. You have to manipulate the whole of S3 Intelligent Tier as a single bucket. For example, if you want to transition an object that has versioning enabled, then you have to transition all the versions. Also, when objects move to the archive tiers, the latency of access is much higher than the access tiers. Not all applications may be able to deal with the high latency. S3 Intelligent tiering is not suitable for companies with predictable data access behavior or companies that want to control data access, versioning, etc with transparency. Other disadvantages are that it is limited to objects, and cannot tier from files to objects, the minimum object storage requirement is 30 days, objects smaller than 128kb are never moved from the frequent access tier and lastly, because it is an automated system, you cannot configure different policies for different groups. S3 Data Management with Komprise Komprise is an AWS Advance Tier partner and can offer intelligent data management with visibility, transparency and cost savings on AWS file and object data. How is this done? Komprise enables analytics-driven intelligent cloud tiering across EFS, FSX, S3 and Glacier storage classes in AWS so you can maximize price performance across all your data on Amazon. The Komprise mission is to radically simplify data management through intelligent automation. Komprise helps organizations get more value from their AWS storage investments while protecting data assets for future use through analysis and intelligent data migration and cloud data tiering. Learn more at Komprise for AWS. What is S3 Intelligent Tiering? S3 Intelligent Tiering is an Amazon cloud storage class that moves data to more cost-effective access tiers based on access frequency. How AWS S3 intelligent tiering works S3 Intelligent Tiering  is a storage class that has multiple tiers embedded within it. For a monitoring fee data is moved to optimize costs. Each tier with its own access latencies and costs: Frequent – data accessed within 30 days Infrequent – data accessed within 30-90 days Archive Instant Access – data accessed greater than 90 days Deep Archive Access – data not accessed for 180 days or greater (Optional*) * Deep Archive Access: Also known as Glacier provides low cost with the tradeoff that data is not available for instant access. Retrieval time is within 12 hours and may cause time out condition for many applications. As such Deep Archive Access must be configured with the default configuration of S3 Intelligent Tiering What are the advantages of S3 Intelligent tiering? The advantages of S3 Intelligent tiering are that savings can be made for data where access pattern is unpredictable or unknown. There is no operational overhead, and there are no additional retrieval costs. Objects can be assigned a tier upon upload and then move between tiers based on access patterns. What are the disadvantages of S3 Intelligent tiering? The main disadvantage of S3 Intelligent Tiering is that it acts as a black-box – you move objects into it and cannot transparently access different tiers or set different versioning policies for the different tiers. For well-known workloads selecting the appropriate tier of storage can be more cost-effective vs S3 Intelligent Tiering. #### Data Archiving What is Data Archiving? Data Archiving, often referred to as Data Tiering, protects older data that is not needed for everyday operations of an organization. A data archiving strategy reduces primary storage and allows an organization to maintain data that may be required for regulatory or other needs. Benefits of a Data Archiving Solution Data archiving protects older information that is not needed for everyday operations but which users may  occasionally access. Data archiving tools deliver the most value by reducing primary storage costs, rather than acting as a data recovery tool. Unstructured data archive tools are in high demand because they can drastically reduce overall storage costs;  most data is unstructured and resides on expensive, high-performance storage devices. Archive data storage, meanwhile, is typically on a low-performance, lost-cost, high-capacity data storage medium. Types of Data Archiving Some data archiving products only allow read-only access to protect data from modification, while other data tiering and archiving products allow users to make changes. Data archiving take a few different forms: Options include online data storage, which places archive data onto disk systems where it is readily accessible. Archives are frequently file-based, but object storage is also growing in popularity. A key challenge when using object storage to archive file-based data is the impact it can have on users and applications. To avoid changing paradigms from file to object and breaking user and application access, use data management solutions that provide a file interface to data that is archived as objects. Another archival system uses offline data storage where data archiving software writes the data to tape or other removable media. using. Tape consumes less power than disk systems, translating to lower costs. A third option is using cloud data storage, offered by Amazon, Azure and other cloud providers. Cloud object storage is a smart choice for cloud tiering and data archiving because of its low-cost, immutable nature. This is inexpensive but requires ongoing investment.   New requirements for secure data archiving have resulted from more sophisticated cybersecurity and ransomware threats. Encryption of sensitive archives and multi-factor authentication for access and object lock storage (such as AWS S3) are a few ways to protect archival data from modification, corruption and theft. The data archiving process typically uses automated software, which will automatically move cold data via policies set by an administrator. A popular approach is to make the archive “transparent”  so that users and applications can access archived data from the same location as if it had never moved. (See Native Access) Learn more about Komprise Transparent Move Technology (TMT). #### Unstructured Data Migration What is Unstructured Data Migration? Unstructured Data Migration is the process of selecting and moving data from one location to another – this may involve moving data across different storage vendors, and across different formats. Data migrations are often done in the context of retiring a system and moving to a new system, or in the context of a cloud migration, or in the context of a modernization or upgrade strategy. When it comes to unstructured data migrations and migrating enterprise file data workloads to the cloud, data migrations can be laborious, error prone, manual, and time consuming. Migrating data may involve finding and moving billions of files (large and small), which can succumb to storage and network slowdowns or outages. Also, different file systems do not often preserve metadata in exactly the same way, so migrating data without loss of fidelity and integrity can be a challenge. NAS Data Migration Network Attached Storage (NAS) migration is the process of migrating from one NAS storage environment to another. This may involve migrations within a vendor’s ecosystem such as NetApp data migration to NetApp or across vendors such as NetApp data migration to Isilon or EMC to NetApp or EMC to Pure FlashBlade. A high-fidelity NAS migration solution should preserve not only the file itself but all of its associated metadata and access controls. Network Attached Storage (NAS) to Cloud data migration is the process of moving data from an on-premises data center to a cloud. It requires data to be moved from a file format (NFS or SMB) to an Object/Cloud format such as S3. A high-fidelity NAS-to-Cloud migration solution preserves all the file metadata including access control and privileges in the cloud. This enables data to be used either as objects or as files in the cloud. Storage migration is a general-purpose term that applies to moving data across storage arrays. Unstructured Data Migration Phases Data migrations typically involve four phases: Planning – Deciding what data should be migrated. Planning may often involve analyzing various sources to find the right data sets. For example, several customers today are interested in upgrading some data to Flash – finding hot, active data to migrate to Flash can be a useful planning exercise. Initial Migration – Do a first migration of all the data. This should involve migrating the files, the directories and the shares. Iterative Migrations – Look for any changes that may have occurred during the initial migration and copy those over. Final Cutoff – A final cutoff involves deleting data at the original storage and managing the mounts, etc., so data can be accessed from the new location going forward. Resilient data migration refers to an approach that automatically adjusts for failures and slowdowns and retries as needed. It also checks the integrity of the data at the destination to ensure full fidelity. Types of Unstructured Data Migrations When it comes to file data, there are NAS Migrations and Cloud Migrations. There are also NAS migrations to the cloud. Data migrations are often seen as a dreaded and laborious part of the storage management lifecycle. Free tools are often considered first but they can introduce risk, time and cost overruns and they are typically labor intensive and error-prone. On the other hand, traditional migration tools have complex legacy architectures and are expensive point products that do not provide ongoing value – resulting in sunk costs. Look for easy-to-use, fast, reliable data migration tools are not one-and-done point tools. The right data migration solution should be able to handle other unstructured data management use cases, including cloud data tiering and data replication. How to Plan a Smart NAS or Cloud Unstructured Data Migration? The typical steps for any unstructured data migration project are: Analytics: Before you start an unstructured data migration project, it's important to have visibility into:  How fast is your data growing?  How much data is hot vs. cold data?  Who is using your data? Savings: Estimate how much you’ll save by moving to the new NAS or cloud infrastructure. This information will guide which NAS or cloud storage mix is best for your data. Offload heavy lifting: Your data migration solution should be able to manage multiple iterations of the migration and handle problems by automatically retrying in a slowdown or a network or storage failure. Preserve data integrity: Your data migration solution should provide MD5 checksum on every file and assure all metadata and access controls migrate to the new environment. Avoid sunk costs: File data migrations are a lot of heaving lifting. Your data migration solution should include automatic parallelization at every level for elastic scaling and the ability to migrate petabytes of data seamlessly and reliably. Reduce downtime: It is recommended that your data migration solution runs multiple iterations for more efficient cutovers. Planning Your Cloud File Migration Komprise and Unstructured Data Migration Komprise Elastic Data Migration is included in the Komprise Intelligent Data Management platform or is available standalone. Designed for cloud migrations and NAS migrations, with Komprise Elastic Data Migration you can run, monitor, and manage hundreds of data migrations faster than ever at a fraction of the cost. Learn more about Komprise Smart Data Migrations. Unstructured Data Migration and the Cloud As unstructured data continues to grow exponentially, organizations struggle to control costs for file data storage. Many are turning to the cloud to scale and manage spend. However, choosing the right files to move can be challenging as there can easily be billions of files. Many enterprises have over 1 PB of data, which represents roughly 3 billion files. This unstructured data is growing exponentially and resides in multi-vendor storage silos for access by various applications and departments. For these reasons, organizations often lack visibility into file data and are making decisions in the dark. To be agile and competitive, IT teams must evolve storage management to become a holistic data management strategy. The right approach to data migration and the cloud for file and object data is to use analytics in cloud data management: Understand your data patterns Plan using a cost model Use data to drive stakeholder buy-in Eliminate user disruption Create a systematic plan for ongoing data management Read the eBook: 5 Ways to Use Analytics for Cloud Data Migrations Top Unstructured Data Migration Challenges Businesses today are looking at modernizing storage and moving to a multi-cloud strategy. As they evolve to faster, flash-based Network Attached Storage (NAS) and the cloud, migrating data into these environments can be challenging. The goal is to migrate large production data sets quickly, without errors, and without disruption to user productivity. The top cloud data migration challenges are: How do you manage cloud data migrations without downtime? How can you automate cloud data migrations to eliminate manual effort? How can you ensure all the permissions, ACLs, metadata are copied correctly during a cloud data migration so you can access the data in the cloud as files? You can overcome these challenges with some planning and automation that preserves file-based access both from on-premises and the cloud. Unstructured Data Migration Tools Free Tools: Require a lot of babysitting and are not reliable for migrating large volumes of data. Point Data Migration Solutions: Have complex legacy architectures and create sunk costs. Komprise Elastic Data Migration: Makes cloud data migrations simple, fast, reliable and eliminates sunk costs since you continue to use Komprise after the migration. Komprise is the only solution that gives you the option to cut 70%+ cloud storage costs by placing cold data in Object classes while maintaining file metadata so it can be promoted in the cloud as files when needed. Learn more > Learn more about Smart Data Migrations for unstructured file and object data: Read the eBook: 5 Ways to Use Analytics for Cloud Migrations Watch the Demo: Fast, Reliable Data Migration to NetApp Know your Cloud Data Migration Choices: The Fastest Path to the Cloud with Komprise 7 Reasons why Cloud Data Migrations Fail - watch the videos: Part 1 Part 2 What is Data Migration? Data Migration is the process of selecting and moving data from one location to another and can involve moving data across different storage vendors, and across different formats. How is data migration done? Data migrations are often done in the context of retiring a system and moving to a new system, or in the context of a cloud migration, or in the context of a modernization or upgrade strategy. What tools to use for data migration? There are a variety of free tools but these require the most babysitting. Point Data Migration solutions have complex legacy architectures and can create sunk costs. Komprise Elastic Data Migration makes cloud data migrations simple, fast and reliable and eliminates sunk costs. #### Data Tiering Data Tiering refers to a technique of moving less frequently used data, also known as cold data, to cheaper levels of storage or tiers. The term “data tiering” arose from moving data around different tiers or classes of storage within a storage system, but has expanded now to mean tiering or archiving data from a storage system to other clouds and storage systems. See also cloud tiering and choices for cloud data tiering. Data Tiering Cuts Costs Because 70%+ of Data is Cold As data grows, storage costs are escalating. It is easy to think the solution is more efficient storage. But the real cause of storage costs is poor data management. Over 70% of data is cold and has not been accessed in months, yet it sits on expensive storage and consumes the same backup resources as hot data. As a result, data storage costs are rising, backups are slow, recovery is unreliable, and the sheer bulk of this data makes it difficult to leverage new options like Flash and Cloud. Data Tiering Was Initially Used within a Storage Array Data Tiering was initially a technique used by storage systems to reduce the cost of data storage by tiering cold data within the storage array to cheaper but less performant options – for example, moving data that has not been touched in a year or more from an expensive Flash tier to a low-cost SATA disk tier. Typical storage tiers within a storage array include: Flash or SSD: A high-performance storage class but also very expensive. Flash is usually used on smaller data sets that are being actively used and require the highest performance. SAS Disks: Usually the workhorse of a storage system, they are moderately good at performance but more expensive than SATA disks. SATA Disks: Usually the lowest price-point for disks but not as performant as SAS disks. Secondary Storage, often Object Storage: Usually a good choice for capacity storage – to store large volumes of cool data that is not as frequently accessed, at a much lower cost. Cloud Data Tiering is now Popular Increasingly, customers are looking at another option – tiering or archiving data to a public cloud. Public Cloud Storage: Public clouds currently have a mix of object and file storage options. The object storage classes such as Amazon S3 and Azure Blob (Azure Storage) provide tremendous cost efficiency and all the benefits of object storage without the headaches of setup and management. Tiering and archiving less frequently used data or cold data to public cloud storage classes is now more popular. This is because customers can leverage the lower cost storage classes within the cloud to keep the cold data and promote them to the higher cost storage classes when needed. For example, data can be archived or tiered from on-premises NAS to Amazon S3 Infrequent Access or Amazon Glacier for low ongoing costs, and then promoted to Amazon EFS or FSX when you want to operate on it and need performance. But in order to get this level of flexibility, and to ensure you’re not treating the cloud as just a cheap storage locker, data that is tiered to the cloud needs to be accessible natively in the cloud without requiring third-party software. This requires file-tiering, not block-tiering. Block Tiering Creates Unnecessary Costs and Lock-In Block-level tiering was first introduced as a technique within a storage array to make the storage box more efficient by leveraging a mix of technologies such as more expensive SAS disks as well as cheaper SATA disks. Block tiering breaks a file into various blocks – metadata blocks that contain information about the file, and data blocks that are chunks of the original file. Block-tiering or Block-level tiering moves less used cold blocks to lower, less expensive tiers, while hot blocks and metadata are typically retained in the higher, faster, and more expensive storage tiers. Block tiering is a technique used within the storage operating system or filesystem and is proprietary. Storage vendors offer block tiering as a way to reduce the cost of their storage environment. Many storage vendors are now expanding block tiering to move data to the public cloud or on-premises object storage. But, since block tiering (often called CloudPools - examples are NetApp FabricPool and Dell EMC Isilon CloudPools) is done inside the storage operating system as a proprietary solution, it has several limitations when it comes to efficiency of reuse and efficiency of storage savings. Firstly, with block tiering, the proprietary storage filesystem must be involved in all data access since it retains the metadata and has the “map” to putting the file together from the various blocks. This also means that the cold blocks that are moved to a lower tier or the cloud cannot be directly accessed from the new location without involving the proprietary filesystem because the cloud does not have the metadata map and the other data blocks and the file context and attributes to put the file together. So, block tiering is a proprietary approach that often results in unnecessary rehydration of the data and treats the cloud as a cheap storage locker rather than as a powerful way to use data when needed. The only way to access data in the cloud is to run the proprietary storage filesystem in the cloud which adds to costs. Also, many third-party applications such as backup software that operate at a file level require the cold blocks to be brought back or rehydrated, which defeats the purpose of tiering to a lower cost storage and erodes the potential savings. For more details, read the white paper: Block vs. File-Level Tiering and Archiving. Know Your Cloud Tiering Choices File Tiering Maximizes Savings and Eliminates Lock-In Cost Effective File Tiering Services: File-tiering is an advanced modern technology that uses standard protocols to move the entire file along with its metadata in a non-proprietary fashion to the secondary tier or cloud. File tiering is harder to build but better for customers because it eliminates vendor lock-in and maximizes savings. Whether files have POSIX-based Access Control Lists (ACLs) or NTFS extended attributes, all this metadata along with the file itself is fully tiered or archived to the secondary tier and stored in a non-proprietary format. This ensures that the entire data can be brought back as a file when needed. File tiering does not just move the file, but it also moves the attributes and security permissions and ACLS along with the file and maintains full file fidelity even when you are moving a file to a different storage architecture such as object storage or cloud. This ensures that applications and users can use the moved file from the original location, and they can directly open the file natively in the secondary location or cloud without requiring any third-party software or storage operating system. Since file tiering maintains full file fidelity and native access based on standards at every tier, it also means that third party applications can access the moved data without requiring any agents or proprietary software. This ensures that savings are maximized since backup software and other third -arty applications can access moved data without rehydrating or bringing the file back to the original location. It also ensures that the cloud can be used to run valuable applications such as compliance search or big data analytics on the trove of tiered and archived data without requiring any third-party software or additional costs. File-tiering is an advanced technique for archiving and cloud tiering that maximizes savings and breaks vendor lock-in. It's time to adopt more cost-effective file tiering services and strategies that optimize data storage costs and unlock the potential of unstructured data in the enterprise. Data Tiering Can Cut 70%+ Storage and Backup Costs When Done Right In summary, data tiering is an efficient solution to cut storage and backup costs because it tiers or archives cold, unused files to a lower-cost storage class, either on-premises or in the cloud. However, to maximize the savings, data tiering needs to be done at the file level, not block level. Block-level tiering creates lock-in and erodes much of the cost savings because it requires unnecessary rehydration of the data. File tiering maximizes savings and preserves flexibility by enabling data to be used directly in the cloud without lock-in. Why Komprise is the easy, fast, no lock-in path to the cloud for file and object data. #### Cloud Data Growth Analytics 70% of data is most enterprise organizations is cold data and has not been accessed in months, yet it sits on expensive storage and consumes the same backup resources as hot data. 50% of the 175 zettabytes of data worldwide in 2025 will be stored in public cloud environments. (IDC) 80% of businesses will overspend their cloud infrastructure budgets, according to due to a lack of cloud cost optimization. (Gartner) Komprise provides the visibility and analytics into cloud data that lets organizations understand data growth across their clouds and helps move cold data to optimize costs. #### NetApp Cloud Tiering The NetApp Cloud Tiering solution is called FabricPool. FabricPool is a NetApp tiering technology that enables automated tiering of data from an all-flash appliance to low-cost object storage tiers either on or off premises. This technology is a form of storage pools which are collections of storage volumes exported to a shared storage environment. Cloud tiering and data tiering (or data archiving) can deliver significant data storage cost savings as part of a cloud storage strategy by offloading unused cold data to more cost-efficient cloud storage solutions. The approach you take to NetApp tiering can either create an easy path to the cloud with native access and full use of data in the cloud or it can create costly cloud egress and lock-in. Here is some background on storage tiering. Storage array vendors have historically done delivered minimal insight into the data stored on them. As unstructured data growth has created more cost and complexities for enterprises, storage vendors have introduced new solutions to externally tier data to the cloud. NetApp FabricPool for NetApp cloud tiering and Dell EMC Isilon CloudPools for Isilon cloud tiering, may reduce the  cost of super-fast, expensive flash-based storage by adding lower-cost choice in cloud storage, but there are trade-offs. Blocks vs. Files Storage-based cloud tiering provides limited data analytics and limited policies for data tiering. File storage arrays use an efficient block-based storage system to store files. Each file is represented by a set of equally sized blocks, which grow as the file grows. To reduce the cost of the storage arrays, vendors provide multiple tiers of storage, from fast flash storage to slower, lower cost SAS drives and SATA drives. Commonly, the storage tiering system places file metadata and the frequently accessed blocks (from any file)  in the highest tier and less accessed blocks stored on lower, less expensive tiers.  By applying their tiering system, designed to work efficiently with internal storage tiers, to tier data to the cloud, it tiers cold blocks rather than files to the cloud. Metadata resides on the storage vendor filesystem and all data access needs to occur through the storage filesystem.  Storage tiering solutions are good for tiering snapshots to the cloud, they result in unnecessary costs and lock-in for tiering  files. The Komprise hybrid tiering approach addresses these challenges by moving the entire file, so you don't experience rehydration penalties when you need to move the data again. You can adopt new storage anytime with no penalties, due to the standards-based approach. Users and applications can access the tiered files from the original location with no disruption, thanks to Komprise Transparent Move Technology. File tiering moves unused cold data from the active footprint so that cold data is stored on low-cost, resilient solutions such as object storage which can be 30 times cheaper than file storage.   Komprise hybrid tiering is both a complement and an alternative to built-in tiering capabilities from data storage vendors, delivering more benefits and ROI such as: 1.Reduce your ransomware attack surface. Unstructured file data is highly vulnerable to ransomware attacks due to its vast surface area and widespread access across enterprise users and applications. Komprise’s hybrid tiering strategy helps mitigate this risk by moving cold data to an object-locked destination, reducing the attack surface by up to 70% while also lowering storage costs. Additionally, features like versioning and tamperproof snapshots provide further protection by ensuring an intact copy of data is available for restoration even in the event of an attack. 2. Avoid rehydration when refreshing your storage. Enterprise data storage systems require frequent refreshes, leading to high costs when switching vendors due to data rehydration. Traditional tiering solutions, like NetApp FabricPool, force enterprises to buy more storage just to migrate off their existing platform, increasing expenses and complexity. Komprise’s hybrid tiering eliminates this issue by offering storage-agnostic, file-based tiering that preserves file fidelity and avoids costly rehydration during migrations. 3. Provide flexible data management polices while maintaining transparent access. Storage-based data tiering is limited by vendor-specific constraints, such as a fixed policy that prevents gradual data movement to lower-cost storage over time. In contrast, Komprise hybrid tiering offers a flexible, policy-driven approach that works across heterogeneous storage environments without disrupting users or applications, thanks to Transparent Move Technology (TMT™). For example, a higher education IT team using NetApp and StorageGrid can seamlessly tier older data to Wasabi with Komprise, maximizing cost savings and efficiency. 4. Ensure innovation and choice with cloud native data access. Cloud hyperscalers offer rapid innovation, making it essential to fully use cloud storage tiers for cost efficiency, such as Amazon FSx versus S3 Glacier Instant Retrieval. Komprise hybrid tiering allows for seamless data movement to lower-cost cloud storage while maintaining native access, allowing users to access data directly in the cloud and apply cloud services such as AI to the data. In contrast, traditional storage-based tiering moves data in blocks within proprietary systems, limiting its usability in the cloud beyond simple archiving. Read: Why Cloud Native Data Access Matters  5. Ensure your unstructured data is ready for AI. Most storage-tiering solutions are proprietary and restrict seamless cloud access, whereas Komprise hybrid tiering ensures data remains open and accessible to any AI service. This open approach ensure that data is not locked away in proprietary format. limiting its accessibility and use for AI. As well, AI readiness is a top challenge in unstructured data management, making it crucial to choose a tiering solution that supports automated AI data workflows with proper AI data governance, across all data regardless of where it is stored.    What you need to know before jumping into the cloud pool. Learn more about your cloud tiering choices. Learn more about Komprise for NetApp. #### Isilon CloudPools (Dell EMC) What are Isilon CloudPools? Dell EMC PowerScale (formerly Isilon) CloudPools software provides policy-based automated tiering that allows for an additional storage tier for the Isilon cluster at your data center. This technology is a form of storage pools which are collections of storage volumes that often blend different tiers of storage into a logical pool or shared storage environment. CloudPools supports tiering data from Dell PowerScale Isilon to public, private or hybrid cloud options. This technology moves archived files to the destination storage in a proprietary format and then references the moved files via stubs. File data access from the object storage is not possible, eliminating the use of cloud-based functions such as AI/ML. Functions such as backup by external application or migration to new storage array require full rehydration of data leading to egress fees from cloud storage and the need to retain on-prem storage capacity. Learn more about CloudPools. Read the blog post: What you need to know before jumping into the cloud tiering pool Read the white paper: Cloud Tiering: Storage-Based vs Gateways vs File-Based: Which is Better and Why? Learn how to save on storage with Dell EMC and Komprise. #### Isilon Tiering The Isilon Tiering solution from Dell EMC is called PowerScale CloudPools. Dell EMC PowerScale Isilon CloudPools software provides policy-based automated tiering that allows for an additional storage tier for the Isilon cluster at your data center. CloudPools supports tiering data from Dell PowerScale Isilon to public, private or hybrid cloud options. This technology is a form of storage pools, which are collections of storage volumes exported to a shared storage environment. Cloud tiering and data tiering (or archiving) can deliver significant cost savings as part of a cloud data strategy by offloading unused cold data to more cost-efficient cloud storage solutions. The approach you take to Isilon tiering can either create an easy path to the cloud with native access and full use of data in the cloud or it can create costly cloud egress and lock-in. Array block-level tiering is a mismatch for the cloud. Isilon cloud tiers blocks rather than entire files, which the following ramifications: Limited policies result in more data access from the cloud. Defragmentation of blocks leads to higher cloud costs. Sequential reads lead to higher cloud costs and lower performance. Tiering blocks impacts performance of the storage array. Read the blog post: What you need to know before jumping into the cloud tiering pool PowerScale Isilon Tiering Choices When it comes to considering PowerScale Isilon data tiering and PowerScale Isilon cloud tiering, it’s important to understand your cloud tiering choices. Cloud tiering and archiving can save you millions by offloading infrequently accessed cold data to cost-efficient cloud data storage. But, the approach you take can either create an easy path to the cloud for file data with full use of data in the cloud or it can create costly cloud egress and lock-in. Smart Migration from PowerScale Isilon with Komprise: Analyze your data first, tier off cold data, deliver 25x faster cloud data migrations and deliver transparency / no disruption to your users and native data access / no storage-vendor lock-in for your file and object data. Learn more about cloud tiering and your cloud tiering choices. Learn more about Komprise for Dell EMC. #### Network File System (NFS) What is NFS? A network file system (NFS) is a mechanism that enables storage and retrieval of data from multiple hard drives and directories across a shared network, enabling local users to access remote data as if it was on the user's own computer. What is the NFS protocol? The NFS protocol is one of several distributed file system standards for network-attached storage (NAS). It was originally developed in the 1980s by Sun Microsystems, and is now managed by the Internet Engineering Task Force (IETF). NFS is generally implemented in computing environments where centralized management of data and resources is critical. Network file system works on all IP-based networks. Depending on the version in use, TCP and UDP are used for data access and delivery. The NFS protocol is independent of the computer, operating system, network architecture, and transport protocol, which means systems using the NFS service may be manufactured by different vendors, use different operating systems, and be connected to networks with different architectures. These differences are transparent to the NFS application, and the user. #### Cloud Data Management What is Cloud Data Management? Cloud data management is a way to manage data across cloud platforms, either with or instead of on-premises storage. A popular form of data storage management, the goal is to curb rising cloud data storage costs, but it can be quite a complicated pursuit, which is why most businesses employ an external company offering cloud data management services with the primary goal being cloud cost optimization. Cloud data management is emerging as an alternative to data management using traditional on-premises software. The benefit of employing a top cloud data management company means that instead of buying on-premises data storage resources and managing them, resources are bought on-demand in the cloud. This cloud data management services model for cloud data storage allows organizations to receive dedicated data management resources on an as-needed basis. Cloud data management also involves finding the right data from on-premises storage and moving this data through data archiving, data tiering, data replication and data protection, or data migration to the cloud. Advantages of Cloud Data Management How to manage cloud storage? According to two 2023 surveys (here and here), 94% of respondents say they're wasting money in the cloud, 69% say that data storage accounts for over one quarter of their company's cloud costs and 94% said that cloud storage costs are rising. Optimal unstructured data management in the cloud provides four key capabilities that help with managing cloud storage and reduce your cloud data storage costs: Gain Accurate Visibility Across Cloud Accounts into Actual Usage Forecast Savings and Plan Data Management Strategies for Cloud Cost Optimization Cloud Tiering and Archiving Based on Actual Data Usage to Avoid Surprises For example, using last-accessed time vs. last modified provides a more predictable decision on the objects that will be accessed in the future, which avoids costly archiving errors. Radically Simplify Cloud Migrations Easily pick your source and destination Run dozens or hundreds of migrations in parallel Reduce the babysitting The many benefits of cloud data management services include speeding up technology deployment and reducing system maintenance costs; it can also provide increased flexibility to help meet changing business requirements. Challenges Faced with Enterprise Cloud Data Management But, like other cloud computing technologies, enterprise cloud data management services can introduce challenges – for example, data security concerns related to sending sensitive business data outside the corporate firewall for storage. Another challenge is the disruption to existing users and applications who may be using file-based applications on premise since the cloud is predominantly object based. Cloud data management service solutions should provide you with options to eliminate this disruption by transparently moving and managing data across common formats such as file and object. Komprise Intelligent Data Management Features of a Cloud Data Management Services Platform Some common features and capabilities cloud data management solutions should deliver: Data Analytics: Can you get a view of all your cloud data, how it's being used, and how much it's costing you? Can you get visibility into on-premises data that you wish to migrate to the cloud? Can you understand where your costs are so you know what to do about them? Planning and Forecasting: Can you set policies for how data should get moved either from one cloud storage class to another or from an on-premises storage to the cloud. Can you project your savings? Does this account for hidden fees like retrieval and egress costs? Policy based data archiving, data replication, and data management: How much babysitting do you have to do to move and manage data? Do you have to tell the system every time something needs to be moved or does it have policy based intelligent automation? Fast Reliable Cloud Data Migration: Does the system support migrating on-premises data to the cloud? Does it handle going over a Wide Area Network? Does it handle your permissions and access controls and preserve security of data both while it's moving the data and in the cloud? Intelligent Cloud Archiving, Intelligent Tiering and Data Lifecycle Management: Does the solution enable you to manage ongoing data lifecycle in the cloud? Does it support the different cloud storage classes (eg High-performance options like File and Cloud NAS and cost-efficient options like Amazon S3 and Glacier)? In practice, the design and architecture of a cloud varies among cloud providers. Service Level Agreements (SLA) represent the contract which captures the agreed upon guarantees between a service provider and its customers. It is important to consider that cloud administrators are responsible for factoring: Multiple billable dimensions and costs: storage, access, retrievals, API, transitions, initial transfer, and minimal storage-time costs Unexpected costs of moving data across different storage classes. Unless access is continually monitored and data is moved back up when it gets hot, you’ll face expensive retrieval fees. This complexity is the reason why only a mere 20% of organizations are leveraging the cost-saving options available to them in the cloud. How do Cloud Data Management Services Tools work? As more enterprise data runs on public cloud infrastructure, many different types of tools and approaches to cloud data management have emerged. The initial focus has been on migrating and managing structured data in the cloud. Cloud data integration, ETL (extraction, transformation and loading), and iPaaS (integration platform as a service) tools are designed to move and manage enterprise applications and databases in the cloud. These tools typically move and manage bulk or batch data or real time data. Cloud-based analytics and cloud data warehousing have emerged for analyzing and managing hybrid and multi-cloud structured and semi-structured data, such as Snowflake and Databricks. In the world of unstructured data storage and backup technologies, cloud data management has been driven by the need for cost visibility, cost reduction, cloud cost optimization and optimizing cloud data. As file-level tiering has emerged as a critical component of an intelligent data management strategy and more file data is migrating to the cloud, cloud data management is evolving from cost management to automation and orchestration, governance and compliance, performance monitoring, and security. Even so, spend management continues to be a top priority for any enterprise IT organizing migrating application and data workloads to the cloud. What are the challenges faced with Cloud Data Management security? Most of the cloud data management security concerns are related to general cloud computing security questions organizations face. It’s important to evaluate the strengths and security certifications of your cloud data management vendor as part of your overall cloud strategy Is adoption of Cloud Data Management services growing? As enterprise IT organizations are increasingly running hybrid, multi-cloud, and edge computing infrastructure, cloud data management services have emerged as a critical requirement. Look for solutions that are open, cross-platform, and ensure you always have native access to your data. Visibility across silos has become a critical need in the enterprise, but it’s equally important to ensure data does not get locked into a proprietary solution that will disrupt users, applications, and customers. The need for cloud native data access and data mobility should not be underestimated. In addition to visibility and access, cloud data management services must enable organizations to take the right action in order to move data to the right place and the right time. The right cloud data management solution will reduce storage, backup and cloud costs as well as ensure a maximum return on the potential value from all enterprise data. How is Enterprise Cloud Data Management Different from Consumer Systems? While consumers need to manage cloud storage, it is usually a matter of capacity across personal storage and devices. Enterprise cloud data management involves IT organizations working closely with departments to build strategies and plans that will ensure unstructured data growth is managed and data is accessible and available to the right people at the right time. Enterprise IT organizations are increasingly adopting cloud data management solutions to understand how cloud (typically multi-cloud) data is growing and manage its lifecycle efficiently across all of their cloud file and object storage options. Analyzing and Managing Cloud Storage with Komprise Get accurate analytics across clouds with a single view across all your users’ cloud accounts and buckets and save on storage costs with an analytics-driven approach. Forecast cloud cost optimization by setting different data lifecycle policies based on your own cloud costs. Establish policy-based multi-cloud lifecycle management by continuously moving objects by policy across storage classes transparently (e.g., Amazon Standard, Standard-IA, Glacier, Glacier Deep Archive). Accelerate cloud data migrations with fast, efficient data migrations across clouds (e.g., AWS, Azure, Google and Wasabi) and even on-premises (ECS, IBM COS, Pure FlashBlade). Deliver powerful cloud-to-cloud data replication by running, monitoring, and managing hundreds of migrations faster than ever at a fraction of the cost with Elastic Data Migration. Keep your users happy with no retrieval fee surprises and no disruption to users and applications from making poor data movement decisions based on when the data was created. A cloud data management platform like Komprise, named a Gartner Peer Insights Awards leader, that is analytics-driven, can help you save 50% or more on your cloud storage costs. Learn more about your options for migrating file workloads to the cloud: The Easy, Fast, No Lock-In Path to the Cloud. What is Cloud Data Management? Cloud Data Management is a way to analyze, manage, secure, monitor and move data across public clouds. It works either with, or instead of on-premises applications, databases, and data storage and typically offers a run-anywhere platform. Cloud Data Management Services Cloud data management is typically overseen by a vendor that specializes in data integration, database, data warehouse or data storage technologies. Ideally the cloud data management solution is data agnostic, meaning it is independent from the data sources and targets it is monitoring, managing and moving. Benefits of an enterprise cloud data management solution include ensuring security, large savings, backup and disaster recovery, data quality, automated updates and a strategic approach to analyzing, managing and migrating data. Cloud Data Management platform Cloud data management platforms are cloud based hubs that analyze and offer visibility and insights into an enterprises data, whether the data is structured, semi-structured or unstructured. #### Cloud Data Migration What is Cloud Data Migration? Cloud data migration is the process of relocating either all or a part of an enterprise’s data to a cloud infrastructure. Cloud data migration is often the most difficult and time-consuming part of an overall cloud migration project. Other elements of cloud migration involve application migration and workflow migration. A “smart data migration” to the cloud strategy for enterprise file data means an analytics-first approach ensuring you know which data can migrate, to which class and tier, and which data should stay on-premises in your hybrid cloud storage infrastructure.   Cost, Complexity and Time: Why Cloud Data Migrations are Difficult Cloud data migrations are usually the most laborious and time-consuming part of a cloud migration initiative. Why? Data is heavy and data footprints are often in hundreds of terabytes to petabytes and can involve billions of files and objects. Some key reasons why cloud data migrations fail include: Lack of Proper Planning: Often cloud data migrations are done in an ad-hoc fashion without proper analytics on the data set. Improper Choice of Cloud Storage Destination: Most public clouds offer many different classes and tiers of storage – each with their own costs and performance metrics. Also, many of the cloud storage classes have retrieval and egress costs, so picking the right cloud storage class for a data migration involves not just finding the right performance and price to store the data but also the right access costs. Intelligent tiering and Intelligent archiving techniques that span both cloud file and object storage classes are important to ensure the right data is in the right place at the right time. Ensuring Data Integrity: Data migrations involve migrating the data along with migrating metadata. For a cloud data migration to succeed, not only should all the data be moved over with full fidelity, but all the access controls, permissions, and metadata should also move over. Often, this is not just about moving data but mapping these from one storage environment to another. Downtime Impact: Cloud data migrations can often take weeks to months to complete. Clearly, you don’t want users to not be able to access the data the need for this entire time. Minimizing downtime, even during a cutover, is very important to reduce productivity impact. Slow Networks, Failures: Often cloud data migrations are done over a Wide Area Network (WAN), which can have other data moving on it and hence deliver intermittent performance. Plus, there may be times when the network is down or the storage at either end is unavailable. Handling all these edge conditions is extremely important. You don’t want to be halfway through a month-long cloud data migration only to encounter a network failure and have to start all over again. Time Consuming – Since cloud data migrations involve moving large amounts of data, they can often involve a lot of manual effort in managing the migrations. This is laborious, tedious and time consuming. Sunk Costs: Cloud data migrations are often time-bound projects; once the data is migrated, the project is complete. So, if you invest in tools to address cloud data migrations, you may have sunk costs once the cloud data migration is complete. Cloud Data Migrations can be of Network Attached Storage (NAS) or File Data, or of Object data or of Block data. Of these, Cloud Data Migration of File Data and Cloud Data Migration of Object data are particularly difficult and time-consuming because file and object data are much larger in volume. Cloud Data Migration Strategies Different cloud data migration strategies are used depending on whether file data or object data need to be migrated. Common methods for moving these two types of data through cloud migration solutions are described in further detail below. Cloud Data Migration for File Data aka NAS Cloud Data Migrations File data is often stored on Network Attached Storage. File data is typically accessed over NFS and SMB protocols. File data can be particularly difficult to migrate because of its size, volume, and richness. File data often involves a mix of large and small files. Data migration techniques often do better when migrating large files but fail when migrating small files. Data migration solutions need to address a mix of large and small files and handle both efficiently. File data is also voluminous – often involving billions of files. Reliable cloud data migration solutions for file data need to be able to handle such large volumes of data efficiently. File data is also very rich and has metadata, access control permissions and hierarchies. A good file data migration solution should preserve all the metadata, access controls and directory structures. Often, migrating file data involves mapping this information from one file storage format to another. Sometimes, file data may need to be migrated to an object store. In these situations, the file metadata needs to be preserved in the object store so the data can be restored as files at a later date. Techniques such as MD5 checksums are important to ensure the data integrity of file data migrations to the cloud. Cloud Data Migration for Object Data (S3 Data Migrations or Object-to-Cloud Data Migrations or Cloud-to-Cloud Data Migrations) Cloud data migrations of object data is relatively new but quickly gaining momentum as the majority of enterprises are moving to a multi-cloud architecture. The Amazon Simple Storage Service (S3) protocol has become a de-facto standard for object stores and public cloud providers. So most cloud data migrations of object data involve S3 based data migrations. 3 common use cases for cloud object data migrations: Data migrations from an on-premises object store to the public cloud: Many enterprises have adopted an on-premises object storage Most of these object storage solutions follow the S3 protocol. Customers are now looking to analyze data on their on-premises object storage and migrate some or all of that data to a public cloud storage option such as Amazon S3 or Microsoft Azure Blob. Cloud-to-cloud data migrations and cloud-to-cloud data replications: Enterprises looking to switch public cloud providers need to migrate data from one cloud to another. Sometimes, it may also be cost-effective to replicate across clouds as opposed to replicating within a cloud. This also improves data resiliency and provides enterprises with a multi-cloud strategy. Cloud-to-cloud data replication differs from cloud data migration because it is ongoing – as data changes on one cloud, it is copied or replicated to the second cloud. S3 data migrations: This is a generic term that refers to any object or cloud data migration done using the S3 protocol. The Amazon Simple Storage Service (s3) protocol has become a de-facto standard. Any Object-to-Cloud, Cloud-to-Cloud or Cloud-to-Object migration can typically be classified as a S3 Data Migration. Secure Cloud Data Migration Tools Cloud data migrations can be performed by using free tools that require extensive manual involvement or commercial data migration solutions. Sometimes Cloud Storage Gateways are used to move data to the cloud, but these require heavy hardware and infrastructure setup. Cloud data management solutions offer a streamlined, cost-effective, software-based approach to manage cloud data migrations without requiring expensive hardware infrastructure and without creating data lock-in. Look for elastic data migration solutions that can dynamically scale to handle data migration workloads and adjust to your demands. 7 Tips for a Clean Cloud Data Migration: Define Sources and Targets Know the Rules & Regulations Proper Data Discovery Define Your Path Test, Test, Test Free Tools vs. Enterprise Establish a Communication Plan Komprise Smart Data Migration Strategy A “smart data migration” strategy for unstructured data, which is primarily file and object data, means: Analysis First: Analyze your data and your network topology so you know what to migrate, to where, the costs, and potential bottlenecks – before you migrate data. Tier Cold Data: A smart unstructured data migration tiers as it migrates to save costs by right-placing data. Archive cold data to lower-cost storage and migrate the hot data to high performance data storage to optimize data storage costs and performance. Komprise Elastic Data Migration makes cloud data migrations simple, fast and reliable with continuous data visibility and optimization. #### Cloud Data Storage Cloud data storage is a service for individuals or organizations to store data through a cloud computing provider such as AWS, Azure, Google Cloud, IBM or Wasabi. Storing data in a cloud service eliminates the need to purchase and maintain data storage infrastructure, since infrastructure resides within the data centers of the cloud IaaS provider and is owned/managed by the provider. Many organizations are increasing data storage investments in the cloud for a variety of purposes including: backup, data replication and data protection, data tiering and archiving, data lakes for artificial intelligence (AI) and business intelligence (BI) projects, and to reduce their physical data center footprint. As with on-premises storage, you have different levels of data storage available in the cloud. You can segment data based on access tiers: for instance, hot and cold data storage. Types of Cloud Data Storage Cloud data storage can either be designed for personal data and collaboration or for enterprise data storage in the cloud. Examples of personal data cloud storage are Google Drive, Box and DropBox. Increasingly, corporate data storage in the cloud is gaining prominence – particularly around taking enterprise file data that was traditionally stored on Network Attached Storage (NAS) and moving that to the cloud. Cloud file storage and object storage are gaining adoption as they can store petabytes of unstructured data for enterprises cost-effectively. Enterprise Cloud Data Storage for Unstructured Data (Cloud File Data Storage and Cloud Object Data Storage) Enterprise unstructured data growth is exploding – whether its genomics data, video and media content, or log files or IoT data.  Unstructured data can be stored as files on file data storage or as objects on cost-efficient object storage. Cloud storage providers are now offering a variety of file and object storage classes at different price points to accommodate unstructured data. Amazon EFS, FSX, Azure Files are examples of cloud data storage for enterprise file data, and Amazon S3, Azure Blob and Amazon Glacier are examples of object storage. Advantages of Cloud Data Storage There are many benefits of investing in cloud data storage, particularly for unstructured data in the enterprise. Organizations gain access to unlimited resources, so they can scale data volumes as needed and decommission instances at the end of a project or when data is deleted or moved to another storage resource. Enterprise IT teams can also reduce dependence on hardware and have a more predictable storage budget. However, without proper cloud data management, cloud egress costs and other cloud costs are often cited as challenges. In summary, cloud data storage allows: The opportunity to reduce capital expenses (CAPEX) of data center hardware along with savings in energy, facility space and staff hours spend maintaining and installing hardware. Deliver vastly improved agility and scalability to support rapidly changing business needs and initiatives. Develop an enterprise-wide data lake strategy that would otherwise be unaffordable. Lower risks from storing important data on aging physical hardware. Leverage cheaper cloud storage for archiving and tiering purposes, which can also reduce backup costs. Challenges and Considerations Cloud data storage can be costly if you need to frequently access the data for use outside of the cloud, due to egress fees charged by cloud storage providers. Using cloud tiering methodologies from on-premises storage vendors may result in unexpected costs, due to the need for restoring data back to the storage appliance prior to use. Read the white paper Cloud Tiering: Storage-Based vs. Gateways vs. File-Based Moving data between clouds is often difficult, because of data translation and data mobility issues with file objects. Each cloud provider uses different standards and formats for data storage. Security can be a concern, especially in some highly regulated sectors such as healthcare, financial services and e-commerce. IT organizations will need to fully understand the risks and methods of storing and protecting data in the cloud. The cloud creates another data silo for enterprise IT. When adding cloud storage to an organization’s storage ecosystem, IT will need to determine how to attain a central, holistic view of all storage and data assets. For these reasons, cloud optimization and cloud data management are essential components of an enterprise cloud data storage and overall data storage cost savings strategy. Komprise has strategic alliance partnerships with hybrid and cloud data storage technology leaders: Komprise for Microsoft Azure    Komprise for AWS    Komprise for Google Cloud Komprise for Qumulo    Komprise for Wasabi  Learn more about your options for migrating file workloads to the cloud: The Easy, Fast, No Lock-In Path to the Cloud. #### Cloud Migration Cloud migration refers to the movement of data, processes, and applications from on-premises data storage or legacy infrastructure to cloud-based infrastructure for storage, application processing, data archiving and ongoing data lifecycle management. Komprise offers an analytics-driven cloud migration software solution - Elastic Data Migration - that integrate with most leading cloud service providers, such as AWS, Microsoft Azure, Google Cloud, Wasabi, IBM Cloud and more. Benefits of Cloud Migration Migrating to the cloud can offer many advantages – lower operational costs, greater elasticity, and flexibility. Migrating data to the cloud in a native format also ensures you can leverage the computational capabilities of the cloud and not just use it as a cheap storage tier. When migrating to the cloud, you need to consider both the application as well as its data. While application footprints are generally small and relatively easier to migrate, cloud file data migrations need careful planning and execution as data footprints can be large. Cloud migration of file data workloads with Komprise allows you to: Plan a data migration strategy using analytics before migration. A pre-migration analysis helps you identify which files need to be migrated, plan how to organize the data to maximize the efficiency of the migration process. It’s important to know how data is used and to determine how large and how old files are throughout the storage system. Since data footprints often reach billions of files, planning a migration is critical. Improve scalability with Elastic Data Migration. Data migrations can be time consuming as they involve moving hundreds of terabytes to  petabytes of data.  Since storage that data is migrating from is usually still in use during the migration, the data migration solution needs to move data as fast as possible without slowing down user access to the source storage.  This requires a scalable architecture that can leverage the inherent parallelism of the data sets to migrate multiple data streams in parallel without overburdening any single source storage. Komprise uses a patented elastic data migration architecture that maximizes parallelism while throttling back as needed to preserve source data storage performance. Shrink cloud migration time. When compared to generic tools used across heterogeneous cloud and physical storage, Komprise cloud data migration is nearly 30x faster. Performance is maximized at every level with the auto parallelize feature, minimizing network usage and making migration over WAN more efficient. Reduce ongoing cloud data storage costs with smart migration, intelligent tiering and data lifecycle management in the cloud. Migrating to the cloud can reduce the amount spent on IT needs, storage maintenance, and hardware upgrades as these are typically handled by the cloud provider. Most clouds provide multiple storage classes at different price points – Komprise intelligently moves data to the right storage class in the cloud based on your policy and performs ongoing data lifecycle management in the cloud to reduce storage cost.  For example, for AWS, unlike cloud intelligent tiering classes, Komprise tiers across both S3 and Glacier storage classes so you get the best cost savings. Simplify storage management. With a Komprise cloud migration, you can use a single solution across your multivendor storage and multicloud architectures. All you have to do is connect via open standards - pick the SMB, NFS, and S3 sources along with the appropriate destinations and Komprise handles the rest. You also get a dashboard to monitor and manage all of your migrations from one place. No more sunk costs of point migration tools because Komprise provides ongoing data lifecycle management beyond the data migration. Greater resource availability. Moving your data to the cloud allows it to be accessed from wherever users may be, making your it easier for international businesses to store and access their data from around the world. Komprise delivers native data access so you can directly access objects and files in the cloud without getting locked in to your NAS vendor—or even to Komprise. Cloud Migration Process The cloud data migration process can differ widely based on a company’s storage needs, business model, environment of current storage, and goals for the new cloud-based system. Below are the main steps involved in migrating to the cloud. Step 1 – Analyze Current Storage Environment and Create Migration Strategy A smooth migration to the cloud requires proper planning to ensure that all bases are covered before the migration begins. It’s important to understand why the move is beneficial and how to get the most out of the new cloud-based features before the process continues. Step 2 – Choose Your Cloud Deployment Environment After taking a thorough look at the current resource requirements across your storage system, you can choose who will be your cloud storage provider(s). At this stage, it’s decided which type of hardware the system will use, whether it’s used in a single or multi-cloud solution, and if the cloud solution will be public or private. Step 3 – Migrate Data and Applications to the Cloud Application workload migration to the cloud can be done through generic tools.  However, since data migration involves moving petabytes of data and billions of files, you need a data management software solution that can migrate data efficiently in a number of ways including through a public internet connection, a private internet connection, (LAN or a WAN), etc. Step 4 – Validate Data After Migration Once the migration is complete, the data within the cloud can be validated and production access to the storage system can be swapped from on-premises to the cloud.  Data validation often requires MD5 checksum on every file to ensure the integrity of the data is intact after migration. Komprise Cloud Data Migration With Elastic Data Migration from Komprise, you can affordably run and manage hundreds of migrations across many different platforms simultaneously. Gain access to a full suite of high-speed cloud migration tools from a single dashboard that takes on the heavy lifting of migrations, and moves your data nearly 30x faster than traditional available services—all without any access disruption to users or apps. Our team of cloud migration professionals with over two decades of experience developing efficient IT solutions have helped businesses around the world provide faster and smoother unstructured data migrations with total confidence and none of the headaches. Contact us to learn more about our cloud data migration solutions or sign up for an assessment and demonstration to see the benefits beyond data migration with our analytics-driven Intelligent Data Management solution. Learn more about your options for migrating file workloads to the cloud: The Easy, Fast, No Lock-In Path to the Cloud. #### Cloud NAS What is Cloud NAS? Cloud NAS is a relatively new term – it refers to a cloud-based storage solution to store and manage files. Cloud NAS or cloud file storage is gaining prominence and several vendors have now released cloud NAS offerings. What is NAS? Network Attached Storage (NAS) refers to data storage that can be accessed from different devices over a network. NAS environments have gained prominence for file-based workloads because they provide a hierarchical structure of directories and folders that makes it easier to organize and find files. Many enterprise applications today are file-based, and use files stored in a NAS as their data repositories. Access Protocols Cloud NAS storage is accessed via the Server Message Block (SMB) and Network File System (NFS) protocols. On-premises NAS environments are also accessed via SMB and NFS. Why is Cloud NAS gaining in importance? While the cloud was initially used by DevOps teams for new cloud-native applications that were largely object-based, the cloud is now seen as a major destination for core enterprise applications. These enterprise workloads are largely file-based, and so moving them to the cloud without rewriting the application means file-based workloads need to be able to run in the cloud. To address this need, both cloud vendors and third-party storage providers are now creating cloud-based NAS offerings. Here are some examples of cloud NAS offerings: Amazon Elastic File System (EFS) – A file system by AWS that runs the NFS protocol. Amazon FSx – Windows File Server running on AWS, for SMB based workloads. Amazon FSx for NetApp ONTAP – Fully managed file services built on NetApp’s popular ONTAP, which makes it easy and cost-effective to launch, run, and scale feature-rich, high-performance file systems in the cloud. NetApp Cloud Volumes ONTAP (CVO) – NetApp ONTAP based file system on AWS, Azure and Google Qumulo Cloud NAS – Qumulo NFS and SMB running in the cloud Azure Files – Fully managed serverless file shares. Azure NetApp Files – High-performance NFS based file storage running NetApp in the Azure cloud. Cloud NAS Tiers Cloud NAS storage is often designed for high-performance file workloads and its high performance Flash tier can be very expensive. Many Cloud NAS offerings such as AWS EFS and NetApp CloudVolumes ONTAP do offer some less expensive file tiers – but putting data in these lower tiers requires some data management solution. As an example, the standard tier of AWS EFS is 10 times more expensive than the standard tier of AWS S3. Furthermore, when you use a Cloud NAS, you may also have to replicate and backup the data, which can often make it three times more expensive. As this data becomes inactive and cold data, it is very important to manage data lifecycle on Cloud NAS to ensure you are only paying for what you use and not for dormant cold data on expensive tiers. Intelligent Data Archiving and Intelligent Data Tiering for Cloud NAS An analytics-driven unstructured data management solution can help you get the right data onto your cloud NAS and keep your cloud NAS costs low by managing the data lifecycle with intelligent archiving and intelligent tiering. As an example, Komprise Intelligent Data Management for multi-cloud does the following: Analyzes your on-premises NAS data so you can pick the data sets you want to migrate to the cloud Migrates on-premises NAS data to your cloud NAS with speed, reliability and efficiency Analyzes data on your cloud NAS to show you how data is getting cold and inactive Enables policy-based automation so you can decide when data should be archived and tiered from expensive Cloud NAS tiers to lower cost file or object classes Monitors ongoing costs to ensure you avoid expensive retrieval fees when cold data becomes hot again Eliminates expensive backup and DR costs of cold data on cloud NAS Cloud NAS Migration There are man potential advantages to migrated your NAS device to the cloud. But the right approach to cloud data migration is essential. Some of the common cloud NAS migration challenges are outlined in this post: Eliminating the Roadblocks of Cloud Data Migrations for File and NAS Data. Avoid unstructured data migration challenges and pitfalls with an analytics-first approach to cloud data migration and unstructured data management. With Komprise Elastic Data Migration you will: Know before you migrate – analytics drive the most cost-effective plans Preserve data integrity – maintain metadata, run MD5 checksums Save time and costs – multi-level parallelism provides elastic scaling Be worry-free – built for petabyte-scale that ensures reliability Migrate NFS 27X faster and Migrate SMB data 25X faster – forget slow, free tools that need babysitting Get the fast, no lock-in path to the cloud with a unified platform for unstructured data migration. ---------- #### CloudPools What are CloudPools? Dell EMC Isilon CloudPools software provides policy-based automated tiering that allows for an additional storage tier for the Isilon cluster at your data center. CloudPools supports tiering data from Isilon to public, private or hybrid cloud options. This technology is a form of storage pools, which are collections of storage volumes exported to a shared storage environment. Read more about storage pools. Smart, fast proven Isilon migration. Read the blog post: What you need to know before jumping into the cloud tiering pool Download the white paper: Cloud Tiering: Storage-Based vs Gateways vs File-Based: Which is Better and Why? #### Cloud Storage Gateway A cloud storage gateway is a hardware or software appliance that serves as a bridge between local applications and remote cloud-based storage. A cloud storage gateway provides basic protocol translation and simple connectivity to allow incompatible technologies to communicate. The gateway may be hardware or a virtual machine (VM) image. The requirement for a gateway between cloud storage and enterprise applications became necessary because of the incompatibility between protocols used for public cloud technologies and legacy storage systems. Most public cloud providers rely on Internet protocols, usually a RESTful API over HTTP, rather than conventional storage area network (SAN) or network-attached storage (NAS) protocols. Gateways can also be used for archiving in the cloud. This pairs with automated storage tiering, in which data can be replicated between fast, local disk and cheaper cloud storage to balance space, cost, and data archiving requirements. The challenge with traditional cloud gateways which front the cloud with on-premise hardware and use the cloud like another storage silo is that the cloud is very expensive for hot data that tends to be frequently accessed, resulting in high retrieval costs. Read the blog post: Are Cloud Storage Gateways a Good Choice for Cloud Data Migrations? Cloud Storage Gateway versus File-Level Cloud Tiering Cloud storage gateways create a new appliance (virtual or physical) that acts as your storage at each site to cache data locally and put a golden copy in the cloud. They are useful when you are doing active file collaboration across multiple sites and do not have NAS at branch sites or do not want to use your existing NAS. But, they do not leverage existing data storage investments and require data to be moved to the gateway which creates additional infrastructure costs. Cloud storage gateways store data in the cloud in their proprietary format. Similar to storage-based cloud tiering, cloud storage gateways create proprietary lock-in and unnecessary cloud gateway costs in perpetuity. And they also typically create additional on-premises costs. Cloud Storage Gateways: Additional On-Premises Infrastructure Cloud storage gateways are typically hardware-based since they have to serve hot data from the cache. Many vendors also offer virtual appliance options for smaller deployments. Duplication of Data in the Cloud Cloud storage gateways typically put all the data in the cloud and then cache some data locally. So, if you are using a cloud storage gateway for 100TB, then all 100TB of data is in the cloud and a subset of it (maybe 20TB or 30TB) is also cached locally. This means you may need 130TB of infrastructure to house 100TB of data. Depending on the size of the local cache, this may be larger. Cloud Storage Gateways: A New Storage Silo A cloud storage gateway is a new storage infrastructure silo that caches some data locally and keeps all of the data in the cloud. It replaces your existing NAS. It does not work with it. It is a rip-and-replace approach. Cloud Storage Gateway Licensing Charges to Access Data in the Cloud Cloud storage gateways lock data in the cloud with their proprietary format. This means you cannot directly access your data in the cloud—data access needs to be through the gateway software in the cloud. Many customers are surprised to learn they have to pay gateway licensing costs even to access data in the cloud, and this cost continues as long as you need your data. This lock-in limits flexibility and creates unnecessary cloud expenses. It also limits your use of the cloud as you cannot natively access your data without the gateway software. Assuming $700/TB/yr. of cloud storage gateway licensing costs, cloud storage gateways have 287% higher annual costs than using a file-level data management solution with the cloud. This is a recurring cost that you pay for over the lifetime of your data! This table summarizes the common cloud data migration requirements and the differences between Komprise Elastic Data Migration and Cloud Storage Gateways. #### Cloud Tiering What is Cloud Tiering? Cloud tiering definition: Cloud tiering is increasingly becoming a critical capability in managing enterprise file workloads across the hybrid cloud. Cloud tiering (also referred to as cloud archiving or archive to the cloud) are techniques that offload less frequently used data, also known as cold data, from expensive on-premises file storage or Network Attached Storage (NAS) to cheaper levels of storage in the cloud, typically object storage classes such as Amazon S3. Cloud tiering is a variant of data tiering. The term “data tiering” arose from moving data around different tiers or classes of storage within a storage system, but has expanded now to mean tiering or archiving data from a storage system to other clouds and storage systems. Cloud Tiering Transparently Extends Enterprise File Storage to the Cloud Enterprises today are increasingly trying to move core file workloads to the cloud. Since file data can be voluminous, involving billions of files, migrating file data to the cloud can take months and create disruption. A simple solution to this is to gradually offload files to the cloud (cloud tiering) without changing the end user experience. Cloud tiering (or archiving to specific cloud tiers) enables this by moving infrequently used cold data to a cheaper cloud storage tier, while the data continues to remain accessible from the original location. This enables users to transparently extend on-premises capacity with the cloud. Cloud Tiering Can Yield Significant Savings If Done Correctly Cloud object storage is cost-efficient if used correctly. Most cloud providers charge not only for the storage, but also to retrieve data, and they charge egress fees if the data has to leave the cloud. Cloud retrieval fees are usually in the form of charges for “get” and “put” API calls and cloud egress costs are charged by the amount of data that is read from anywhere outside the cloud. So, to keep enterprise storage costs low, infrequently accessed data such as snapshots, logs, backups and cold data are best suited for tiering to the cloud. By tiering cold data to the cloud, the on-premises storage array needs to only keep hot data and the most recent logs and snapshots. Across Komprise customers, we have found that typically 60% to 80% of their actual data has not been accessed in over a year. By cloud tiering the cold data as well as older log files and snapshots, the capacity of the storage array, mirrored storage array (if mirroring/replication is being used) and backup storage is reduced dramatically. This is why tiering cold data can reduce the overall storage cost by as much as 70% to 80%. The many advantages of cloud tiering of cold data include: Reduced storage acquisition costs. Flash storage, used for fast access to hot data, is expensive. By tiering off infrequently used data you can purchase a much smaller amount of flash storage, thereby reducing acquisition costs. Cut backup footprint and costs. By continuously tiering off cold data that is not being accessed you can reduce your backup footprint, backup license costs, and backup storage costs if the cold data is placed in robust storage (such as that provided by the major CSPs). Increase disaster recovery speeds and lower disaster recovery (DR) costs. As with backup, by tiering off the cold data, the amount of data mirrored/replicated is dramatically reduced as well. Improved storage performance. By running storage at a lower capacity and by removing access to cold data to another storage device or service, you can increase the performance of your storage array. Leverage the cloud to run AI, ML, compliance checks and other applications on cold data. With cold data in the cloud, you can access, search and process your cold data without putting any load on your storage array. The cold data that is tiered off has value. Being able to process and feed your cold data into your AI/ML/BI engines is critical to staying competitive. By tiering you can extract value from your cold data without burdening your storage array. This also helps to extend the life of your storage array. Clearly, if cloud tiering is implemented correctly at the file level it will provide all of the above benefits whereas block tiering to the cloud will not. But not all cloud tiering choices are the same. To learn more about the differences between cloud tiering at the file level vs the block level, and why so-called cloud pools such as NetApp FabricPool or Dell EMC Isilon CloudPools are not the right approach for cloud tiering, read “What you need to know before jumping into the cloud tiering pool”. Also download the white paper: Cloud Tiering: Storage-Based vs Gateways vs. File-Based. #### Object Storage What is Object Storage? Object storage, also known as object-based storage, object data storage or cloud storage, is a way of addressing and manipulating data storage as objects. Objects are kept inside a single repository and are not nested in a folder inside other folders.  Each object has a distinct global identifier or key that is unique within its namespace. The access method for object is via URL, which allows object storage to abstract multiple regions, data centers and nodes, for essentially unlimited capacity behind a simple namespace.  Objects, unlike file, have no hierarchy or directories but are stored in a flat namespace. Another key difference versus file is the user or application metadata is in the form of key value pairs. An example of object metadata is when you take a picture with your phone and store to the cloud, it includes metadata such as “device=iphone.” Object storage can achieve extreme levels of durability by creating multiple copies or implementing erasure coding for data protection. Object storage is also cost-efficient and is a good option for cheap, deep, scale-on-demand storage. While many object storage APIs exist, Amazon’s Simple Storage Service or S3 has become the de-facto standard supported by other public and private cloud storage vendors. This excerpt from The New Stack explains the benefits in more detail: The Benefits of Cloud Object Storage for Unstructured Data Unlike cloud file storage, which delivers high performance for active data with scalability and pay-per-use pricing benefits, cloud object storage is a superb way to save dramatically on rarely used or “cold” data. Object storage was designed to be highly scalable and less costly to store large amounts of data. Unlike file storage, object storage is better for data that is read many times and written or modified rarely, thus making it ideal for use as an archive. Another benefit of migrating data to object storage is using new services, such as cloud AI and ML tools, which are mostly designed to work with objects. The point here is that your data is natively available to these services. Finally, if you move data into immutable storage such as AWS S3 Object Lock, no one can modify or delete it—thus creating an affordable ransomware defense tactic. Object Storage Solutions Popular object storage solutions include Amazon Simple Storage Service (S3), Google Cloud Storage, Microsoft Azure Blob Storage, IBM Cloud Object Storage. #### Network Attached Storage (NAS) What is Network Attached Storage? Network Attached Storage (NAS) definition: A NAS system is a storage device connected to a network that allows storage and retrieval of data from a centralized location for authorized network users and heterogeneous clients. These devices generally consist of an engine that implements the file services (NAS device), and one or more devices on which data is stored (NAS drives). The purpose of a NAS system is to provide a local area network (LAN) with file-based, shared storage in the form of an appliance optimized for quick data storage and retrieval. NAS is a relatively expensive storage option, so it should only be used for hot data that is accessed the most frequently. Many enterprise IT organizations today are looking to migrate NAS and Object data to the cloud to reduce costs improve agility and efficiency. NAS Storage Benefits Network attached storage devices are used to remove the responsibility of file serving from other servers on a network and allows for a convenient way to share files among multiple computers. Benefits of dedicated network attached storage include: Faster data access Easy to scale up and expand upon Remote data accessibility Easier administration OS-agnostic compatibility (works with Windows and Apple-based devices) Built-in data security with compatibility for redundant storage arrays Simple configuration and management (typically does not require an IT pro to operate) NAS File Access Protocols Network attached storage devices are often capable of communicating in a number of different file access protocols, such as: Network File System (NFS) Server Message Block (SMB) Apple Filing Protocol (AFP) Common Internet File System (CIFS) Most NAS devices have a flexible range of data storage systems that they’re compatible with, but you should always ensure that your intended device will work with your specific data storage system. Enterprise NAS Storage Applications In an enterprise, a NAS array can be used as primary storage for storing unstructured data and as backup for data archiving or disaster recovery (DR). It can also function as an email, media database or print server for a small business. Higher-end NAS devices can hold enough disks to support RAID, a storage technology that allows multiple hard disks into one unit to provide better performance times, redundancy, and high availability. Data on NAS systems (aka NAS device) is often mirrored (replicated) to another NAS system, and backups or snapshots of the footprint are kept on the NAS for weeks or months. This leads to at least three or more copies of the data being kept on expensive NAS storage. A NAS storage solution does not need to be used for disaster recovery and backup copies as this can be very costly. By finding and data tiering (or data archiving) cold data from NAS, you can eliminate the extra copies of cold data and cut cold data storage costs by over 70%. Check out our video on NAS storage savings to get a more detailed explanation of how this concept works in practice.   Network Attached Storage (NAS) Data Tiering and Data Archiving Since NAS storage is typically designed for higher performance and can be expensive, data on NAS is often tiered, archived and moved to less expensive storage classes. NAS vendors offer some basic data tiering at the block-level to provide limited savings on storage costs, but not on backup and DR costs. Unlike the proprietary block-level tiering, file-level tiering or archiving provides a standards-based, non-proprietary solution to maximize savings by moving cold data to cheaper storage solutions. This can be done transparently so users and applications do not see any difference when cold files are archived. Read this white paper to learn more about the differences between file tiering and block tiering. NAS Migration to the Cloud Cloud NAS is growing in popularity. But the right approach to migrating unstructured data to the cloud is essential. Unstructured data is everywhere. From genomics and medical imaging to streaming video, electric cars, and IoT products, all sectors generate unstructured file data. Data-heavy enterprises typically have petabytes of file data, which can consist of billions of files scattered across different storage vendors, architectures and locations. And while file data growth is exploding, IT budgets are not. That’s why enterprises’ IT organizations are looking to migrate file workloads to the cloud. However, they face many barriers, which can cause migrations to take weeks to months and require significant manual effort. Cloud NAS Migration Challenges Common unstructured data migration challenges include: Billions of files, mostly small: Unstructured data migrations often require moving billions of files, the vast majority of which are small files that have tremendous overhead, causing data transfers to be slow. Chatty protocols: Server message block (SMB) protocol workloads—which can be user data, electronic design automation (EDA) and other multimedia files or corporate shares—are often a challenge since the protocol requires many back-and-forth handshakes which increase traffic over the network. Large WAN latency: Network file protocols are extremely sensitive to high-latency network connections, which are essentially unavoidable in wide area network (WAN) migrations. Limited network bandwidth: Bandwidth is often limited or not always available, causing data transfers to become slow, unreliable and difficult to manage. Learn more about Komprise Smart Data Migration.   Network Attached Storage FAQ These are some of the most commonly asked questions we get about network attached storage systems. How are NAS drives different than typical data storage hardware? NAS drives are specifically designed for constant 24x7 use with high reliability, built-in vibration mitigation, and optimized for use in RAID setups. Network attached storage systems also benefit from an abundance of health management systems designed to keep them running smoothly for longer than a standard hard drive would. Which features are the most important ones to have in a NAS device? The ideal NAS devices have multiple (2+) drive bays, should have hardware-level encryption acceleration, offer support for widely used platforms such as AWS glacier and S3, and have moderately powerful multicore CPU’s with at least 2GB of ram to pair with it.If you’re looking for these types of features, Seagate and Western Digital are some of the most reputable brands in the NAS industry. Are there any downsides to using NAS storage? NAS storage systems can be quite expensive when they’re not optimized to contain the right data, but this can be remedied with an analytics-driven NAS data management software, like Komprise Intelligent Data Management. Using NAS Data Management Tools to Substantially Reduce Storage Costs One of the biggest issues organizations are facing with NAS systems is trouble understanding which data they should be storing on their NAS drives and which should be offloaded to more affordable types of storage. To keep data storage costs lower, an analytics-based NAS data management system can be implemented to give your organization more insight into your NAS data and where it should be optimally stored. For the thousands of data-centric companies we’ve worked with, most of them needed less than 20% of their total data stored on high-performance NAS drives. With a more thorough understanding of their NAS data, organizations are able to realize that their NAS storage needs may be much lower than they originally thought, leading to substantial storage savings, often greater than 50%, in the long run. Komprise makes it possible for customers to know their NAS and S3 data usage and growth before buying more storage. Explore your storage scenarios to get a forecast of how much could be saved with the right data management tools. This is what Komprise Dynamic Data Analytics provides.   NAS Fast Facts: Network-attached storage (NAS) is a type of file computer storage device that provides a local-area network with file-based shared storage. This typically comes in the form of a manufactured computer appliance specialized for this purpose, containing one or more storage devices. Network attached storage devices are used to remove the responsibility of file serving from other servers on a network, and allows for a convenient way to share files among multiple computers. Benefits of dedicated network attached storage include faster data access, easier administration, and simple configuration. In an enterprise, a network attached storage array can be used as primary storage for storing unstructured data, and as backup for archiving or disaster recovery. It can also function as an email, media database or print server for a small business. Higher end network attached storage devices can hold enough disks to support RAID, a storage technology that allows multiple hard disks into one unit to provide better performance times, redundancy, and high availability. Data on NAS systems is often mirrored (replicated) to another NAS system, and backups or snapshots of the footprint are kept on the NAS for weeks or months. This leads to at least three or more copies of the data being kept on expensive NAS devices. Read the white paper: How to Accelerate NAS Migrations and Cloud Data Migrations    Know the difference between NAS and Cloud Data Migration vs. Tiering and Archiving #### SMB protocol (Server Message Block) What is the SMB protocol? Server Message Block (SMB) protocol is network communication protocol for providing shared access to files, printers, and serial ports between nodes on a network. (SMB is also known as Common Internet File System (CIFS)). Cloud File Data Migration and SMB Unstructured data migrations to the cloud can be billions of (mostly small) files, which have significant overhead, causing data transfers to be slow. In addition, SMB protocol workloads, which can be user data, corporate shares, electronic design automation (EDA) and other multimedia files, etc., are bring even more challenges since the SMB protocol requires many back-and-forth handshakes, thereby increasing traffic over the network. Another challenge with SMB cloud data migrations is wide are network (WAN) latency. Network file protocols like SMB are extremely sensitive to high-latency network connections, which are unavoidable in WAN migrations. Bandwidth is also often limited or not always available, causing file data transfers to become slow, unreliable and difficult to manage. Komprise Hypertransfer for Faster SMB Migrations   Read the blog post: Turbo Charge Your SMB Cloud Migrations with Hypertransfer for Elastic Data Migration What is Server Message Block? Server Message Block (SMB) is a network protocol used for providing shared access to files, printers, and other communication between nodes on a network. It is a client-server communication protocol, where clients request services and servers respond to those requests. SMB operates at the application layer of the OSI model and facilitates communication between devices running different operating systems. With the introduction of SMB3 and improvements in subsequent versions, the protocol has become more efficient, secure, and feature-rich, making it suitable for various network file-sharing scenarios. Is Server Message Block (SMB) the same as CIFS? Common Internet File System (CIFS) is often used interchangeably with SMB. CIFS is a dialect of the SMB protocol and is widely used in Microsoft Windows environments. What is the role of SMB in data storage? Server Message Block (SMB) plays a crucial role in data storage and file sharing across networks, particularly in enterprise environments. SMB is a network protocol that enables shared access to files, printers, and other resources, and it has a significant impact on how data is stored, accessed, and managed. Here are some of the ways SMB is related to data storage: File Sharing: SMB is fundamental to file sharing within a network. It allows users to access and share files stored on servers or network-attached storage (NAS) devices. This capability is vital for collaborative work in organizations, enabling users to access shared documents and collaborate on projects. Network-Attached Storage (NAS): Many NAS devices support SMB for file sharing. These devices provide centralized and scalable storage solutions for businesses, allowing them to store, manage, and share data efficiently. SMB ensures that users across the network can access files on NAS devices seamlessly. Windows File Sharing: SMB is the foundation of Windows File Sharing, allowing Windows-based devices to share files and resources with each other. This is a common scenario in corporate environments where Windows servers and workstations are prevalent. Cross-Platform File Sharing: SMB’s cross-platform compatibility is crucial for heterogeneous environments where different operating systems coexist. It enables devices running Windows, macOS, Linux, and other operating systems to share files and collaborate across the network. Access Control and Permissions: SMB includes features for access control and permissions, allowing administrators to manage who can access specific files and directories. This ensures that sensitive data is protected and that users have appropriate levels of access based on their roles. Backup and Data Protection: SMB is often used in backup solutions to facilitate the storage and retrieval of backup data. Backup software can use SMB to connect to storage devices and perform backups of critical data, ensuring data protection and recovery capabilities. Remote Access and Work-from-Home Scenarios: With the rise of remote work, SMB has become essential for enabling secure remote access to shared files. VPNs or other secure methods are used to connect remote users to the corporate network, allowing them to access and collaborate on files using SMB. Distributed File Systems: In larger organizations, distributed file systems often use SMB to provide a unified and scalable approach to data storage. Distributed file systems allow organizations to manage and scale their storage infrastructure more efficiently. SMB3 Enhancements: The introduction of SMB3 and subsequent enhancements brought features such as improved performance, encryption, and support for larger files. These improvements contribute to better data storage and access capabilities over the SMB protocol. Cloud Storage Integration: SMB is sometimes used in conjunction with cloud data storage solutions to enable seamless integration between on-premises storage and cloud storage. This allows organizations to leverage cloud storage while maintaining compatibility with existing SMB-based infrastructure. SMB is a foundational protocol for data storage and file sharing in networked environments. Its versatility, cross-platform support, and features related to access control make it a critical component in the modern IT landscape. #### Archival Storage What is Archival Storage? Archival Storage is a source for data that is not needed for an organization's everyday operations, but may have to be accessed occasionally. By utilizing an archival storage, organizations can leverage to secondary sources, while still maintaining the protection of the data. Utilizing archival storage sources reduces primary storage costs required and allows an organization to maintain data that may be required for regulatory or other requirements. Data archiving, also known as data tiering, is intended to protect older information that is not needed for everyday operations, but may have to be accessed occasionally. Data Archival and Tiering storage is a tool for reducing your primary storage need and the related costs, rather than acting as a data recovery tool. Why Archival Storage? Some data archives allow data to be read-only to protect it from modification, while other data archiving products treat data as to allow users to modify it. The benefit of data archiving is that it reduces the cost of primary storage. Alternatively, archive storage costs less because it is typically based on a low-performance, high-capacity storage medium. Data archiving takes a number of different forms. Options can be online data storage, which places archive data onto disk systems where it is readily accessible. Archives are frequently file-based, but object storage is also growing in popularity. A key challenge when using object storage to archive file-based data is the impact it can have on users and applications. To avoid changing paradigms from file to object and breaking user and application access, use data management solutions that provide a file interface to data that is archived as objects. Another archival system uses offline data storage where archive data is written to tape or other removable media using data archiving software rather than being kept online. Data archiving on tape consumes less power than disk systems, translating to lower costs. A third option is using cloud data storage, such as those offered by Amazon and Microsoft Azure – this can be less expensive if done right, but requires ongoing investment. A Smart Data Migration strategy is essential. The data archiving process typically uses automated software, which will automatically move “cold” data via policies set by an administrator. Today, a popular approach to data archiving is to make the archive “transparent” – so the archived data is not only online but the archived data is fully accessed exactly as before by users and applications, so they experience no change in behavior. The patented Komprise Transparent Move Technology is designed to allow you to transparently archive and tier data. #### Amazon (AWS) S3 Intelligent Tiering S3 Intelligent Tiering is an Amazon storage class aimed at data with unknown or unpredictable data access patterns. See our S3 Intelligent Tiering glossary entry for further information. Learn more about AWS cloud tiering, cloud data migration and the Komprise AWS partnership. #### Azure Tiering What is Azure Tiering? Azure Storage offers several classes of cloud data storage for customers. However, to maximize savings and ROI from the cloud, IT directors need to consider tiering strategies. Cloud tiering moves less frequently used data, also known as cold data, from expensive on-premises file storage or Network Attached Storage (NAS) or cloud file storage such as Azure Files to cheaper levels of storage in the cloud, typically object storage classes aka Azure Blob storage.  Cloud tiering enables data to move across different storage tiers – and different cloud tiering solutions support different storage options. We will cover both the storage tiers in the Azure cloud and the options available to do cloud tiering for Azure. Azure Files and Azure Blob have different tiers of storage at different price points: Azure Files is Microsoft’s file storage solution for the cloud. As with all file storage solutions, it is more expensive than object storage solutions such as Azure Blob, especially when you add the required replication and data protection costs for files. Azure File Storage Hot tier is more than 1.9 times more expensive than Azure Blob Cool.  Azure Files supports two storage tiers: Standard and Premium. Standard file shares are created in general purpose (GPv1 or GPv2) storage accounts;  Premium file shares are created in FileStorage storage accounts. What is Azure Blob? Azure Blob is Microsoft's object storage solution for the cloud Azure Blob storage is optimized for storing massive amounts of unstructured data. It’s enabled for the following access tiers: Hot: storing data that is accessed frequently. Cool: storing data that is infrequently accessed and stored for at least 30 days. Archive: storing data that is rarely accessed and stored for at least 180 days with flexible latency requirements According to Microsoft: “You can upload data to your required access tier and change the blob access tier among the hot, cool, or archive tiers as usage patterns change, without having to move data between accounts. All tier change requests happen immediately and tier changes between hot and cool are instantaneous.” What is Azure File Sync? Azure Files has a service called Azure File Sync which enables an on-premises Windows Server to do cloud tiering to file storage in the cloud, not object storage.  Azure File Sync acts as a gateway that caches data locally and puts cold file objects in Azure File cloud storage. When enabled, Azure Files Sync stores hot files on the local Windows server while cool or cold files are split into namespace (file and folder structure) and file content. The namespace is stored locally, and the file content is stored in an Azure file share in the cloud. Azure will automatically tier cold data based on volume or age thresholds. See Microsoft Cloud Tiering overview. Considerations for Microsoft Azure Cloud Tiering Cloud tiering can save organizations up to 70% on on-premises storage costs when done correctly. But there are several limitations of Azure Cloud Tiering that you need to consider: Azure File Sync only tiers to Azure Files and leads to higher cloud costs. Azure Files is a file service in Azure and it is almost double the cost of the Azure Blob Cool tier. Since file storage is not resilient, data on Azure Files most commonly needs replication, snapshots and backups – leading to higher data management costs. An ideal cloud tiering solution should tier files from your NAS to an object storage environment to maximize savings. Otherwise, you are paying for higher costs in the cloud.   Azure File Sync only tiers blocks of data to the cloud and leads to 75% higher cloud egress costs. This means you cannot directly access your files in Azure; you have to go through the on-premises Windows Server to get your data. This leads to 75% higher cloud egress costs, and it limits the use of your data in the cloud. To learn more about the differences between block tiering and file tiering, read our block-level tiering vs file-level tiering white paper to learn more. For an analysis of the cloud egress costs of solutions like Azure File Sync Cloud Tiering, read the Cloud Tiering whitepaper. Azure File Sync is only available on Windows Server environments. Most organizations today have multiple file server and NAS environments. Using a different tiering strategy for each environment is tedious, error prone, and difficult to manage. Consider an unstructured data management solution that works across your multiple storage vendor environments and transparently tiers and archives data. Komprise enables enterprise IT organizations to quickly analyze data and make smart decisions on where data should live based on age, usage and other requirements. Komprise works across your multi-vendor NAS and object environments and clouds via standard protocols such as NFS, SMB and object. By using Komprise for cloud tiering to Azure, you can save not only on your on-premises storage but also on your cloud costs since you do not have to tier to Azure Files, you can tier directly to Azure Blob. Users get transparent access to the files moved by Komprise from the original location, and with Komprise moving data in native format, you can give users direct, cloud-native access to data in Azure while eliminating egress costs and data rehydration hassles.  Learn more about your Cloud Tiering choices  Learn more about Komprise for Microsoft Azure Komprise Smart Data Migration for Azure. Smarter. Faster. Proven. ### Data Security and Governance > Definitions for ransomware, sensitive data, immutable storage, shadow AI governance, and compliance concepts. #### Ransomware Protection Ransomware protection is the term used for the comprehensive set of tools, policies, and strategies designed to prevent, detect, respond to, and recover from ransomware attacks, specifically targeting malicious software that encrypts or threatens to expose data until a ransom is paid. The best ransomware protection strategy focuses not only on preventing the attack itself but also on minimizing damage and ensuring rapid recovery, especially for sensitive and unstructured data.  This data is often more vulnerable due to its dispersed and unorganized nature. Read: Protect Unstructured Data from Ransomware at 80% Lower Cost How to Protect Against Ransomware – Focus on Unstructured Data Unstructured data (emails, documents, videos, etc.) lacks a defined format and lives across endpoints, file shares, cloud storage, and collaboration platforms. It is frequently accessed and widely shared, making it a prime target for ransomware attacks. To protect against ransomware effectively, consider these strategies: Data Discovery & Data Classification: Identify where unstructured data lives and classify it based on sensitivity and value. This visibility is the first step toward applying the right level of protection. Access Control & Least Privilege: Restrict access to only those who need it. Implement role-based access and eliminate excessive permissions, reducing the potential spread if a user is compromised. Backup & Immutable Storage: Regularly back up unstructured data and store it in immutable object storage that cannot be altered or deleted by ransomware. Ensure backups are offline or air-gapped when possible. Endpoint & Email Protection: Since unstructured data often enters via endpoints or email, use advanced endpoint detection and anti-phishing tools to stop ransomware at the entry point. Behavioral Monitoring & Anomaly Detection: Use AI-driven monitoring tools to detect unusual access patterns, like bulk file modifications or encryption activities—hallmarks of ransomware in action. Zero Trust Architecture: Adopt a "never trust, always verify" approach. Every user and device must be continuously validated before accessing unstructured data. Employee Training & Awareness: Human error remains a key entry point. Educate employees on phishing, safe file sharing, and incident reporting. Rapid Response & Recovery Plans: Establish clear incident response protocols and test them regularly. Fast isolation and rollback are critical for the best ransomware protection. By implementing a layered approach to ransomware data protection, organizations can significantly protect against ransomware attacks, especially when managing vast volumes of unstructured data that are otherwise hard to secure. Komprise Unstructured Data Management for Ransomware Komprise delivers a ransomware protection strategy for unstructured data with intelligent data management and visibility across storage environments. Here's how Komprise contributes to a comprehensive defense against ransomware attacks: Data Visibility and Classification Komprise scans and analyzes unstructured data across multiple storage systems (on-prem and cloud), allowing organizations to: Identify where sensitive or valuable data resides. Understand usage patterns and data age. Highlight stale or orphaned data that could be risk-prone. You can't protect what you can't see. This visibility is the foundation of a strong ransomware protection strategy. Tiering and Archiving to Immutable Storage Komprise enables transparent tiering of infrequently accessed (cold) data from primary storage to cost-effective, immutable object storage (e.g., S3 with object lock, Azure Immutable Blob). Isolate and store backups or cold data in immutable formats, which Komprise automates: Data remains accessible without changing user workflows. Reduces the attack surface on primary storage systems. Archived/tiered data is protected from ransomware encryption.   Reducing the Ransomware Attack Surface By moving cold, inactive data out of high-risk, high-access storage: There's less data exposed to potential attack. It limits the spread of ransomware across file systems. This aligns directly with goals to protect against ransomware by minimizing vulnerable data volumes. Komprise Transparent Move Technology (TMT) reduces ransomware cost and liability by tiering cold files to the cloud so you can invest in high-end ransomware protection solutions for mission-critical data. Use Komprise TMT to tier cold data to immutable object storage in the cloud, which prevents ransomware actors from accessing or modifying your data. Set versioning on the immutable object storage to provide a pre-attack copy for restores. Audit Trails and Data Access Insights Komprise tracks file access, age, and data growth over time. This helps identify unusual activity patterns, such as sudden mass file access or changes—common signs of ransomware. A key ransomware protection strategy is the ability to monitor for suspicious behavior to detect attacks early and act quickly. Faster Recovery and Lower Downtime In the event of an attack: Data tiered to secure storage can be quickly recovered or restored. Komprise maintains metadata and file paths, helping organizations rebuild environments with minimal disruption. This minimizes downtime and reduces the impact of an attack. In conclusion, Komprise strengthens your ransomware protection strategy for unstructured data by: Providing deep data visibility and intelligent analytics. Reducing the attack surfaces through smart tiering of unstructured file and object data. Leveraging immutable cloud storage. Enabling rapid recovery and data resiliency. Together, these capabilities allow organizations to protect against ransomware more effectively and ensure long-term data security and business continuity. Watch the Data on the Move discussion with Komprise CEO and cofounder Kumar Goswami: Lower Cost Ransomware Data Protection. #### Sensitive Data Detection Sensitive data detection is the process of identifying and flagging (see data tagging) sensitive or confidential information within a system, document, or dataset. Sensitive data can include personally identifiable information (PII), financial data, healthcare records, proprietary information, and more. The detection process is critical for privacy, security, and compliance with data protection regulations like GDPR, HIPAA, and CCPA. As AI data governance and compliance concerns in the enterprise grow, sensitive data protection is growing in importance. What are the common types of sensitive data that must be protected? Personally Identifiable Information (PII): Name Social Security Number (SSN) Date of birth Address Phone number Email address Financial Data: Credit card numbers Bank account numbers Financial statements Healthcare Data: Medical records Health insurance details Prescription information Intellectual Property (IP): Trade secrets Patents Proprietary formulas or algorithms Authentication Data: Passwords Security tokens Encryption keys What are some methods of sensitive data detection? Pattern Matching (Regular Expressions): Detects patterns in text that match formats commonly used for sensitive data, such as credit card numbers (e.g., Luhn’s algorithm for validation), social security numbers, or email addresses. Data Classification: Systems use rules or machine learning algorithms to categorize data based on its content and context. This can be based on pre-set categories such as “confidential,” “public,” or “internal use only.” Natural Language Processing (NLP): NLP techniques are used to analyze and understand the context of text, which helps identify sensitive information that doesn’t follow a predictable pattern but can be inferred through the meaning of the text. Machine Learning: Machine learning models can be trained to recognize sensitive data by analyzing a large corpus of labeled data. Once trained, they can generalize from this data to detect sensitive information in new documents or datasets. Data Masking and Tokenization: Detecting and replacing sensitive information with anonymized values to protect it during storage or transmission. Contextual Analysis: A more advanced approach that looks at the surrounding text and metadata to understand whether the data could be sensitive, rather than just relying on pattern recognition. What are some tools for sensitive data detection? Today there are many categories of vendors and solution providers who provide elements of sensitive data protection, including data back-up and data storage vendors and increasingly sensitive data protection is becoming part of a holistic unstructured data management strategy. Examples of sensitive data tools include: DLP (Data Loss Prevention) solutions: Used to monitor and protect sensitive data in transit, at rest, and in use. Regular expression engines: For detecting simple patterns. Cloud services: Providers like AWS Macie and Azure Information Protection and Microsoft Purview offer sensitive data detection for data stored in their environments. Open-source tools: Like Octopii for detecting sensitive information in code repositories. What are some common sensitive data protection use cases? Compliance Audits: Ensuring that data handling adheres to regulations. Data Breach Prevention: Detecting and protecting sensitive data before unauthorized access occurs. Encryption Management: Identifying sensitive data that should be encrypted. AI Data Governance and Compliance: Ensure only the right data is being delivered to AI services in the enterprise. #### Immutable Storage What is immutable storage? Immutable storage is a feature of file storage, or more typically object storage, that protects data from modification or deletion for a set retention period. Immutable storage is often used in highly regulated industries such as finance and health care but is now gaining popularity across other industries as a defense against ransomware or insider threats. Implementations of immutable storage such as AWS S3 Object lock are certified by independent 3rd parties to ensure they comply with government regulations. Read the blog post: How to Protect File Data from Ransomware at 80% Lower Cost Since approximately 80% of data today is unstructured data, organizations cannot afford to leave file data unprotected from ransomware attacks. Early ransomware detection can deliver the best outcome, but as ransomware attacks are constantly evolving, detection is not always foolproof and can be difficult. Investing in ways to recover data if you do get attacked by ransomware is essential. An immutable copy of data in a separate location separate from your data storage and data backups gives you a way to recover data in the event of a potentially devastating ransomware attack. But keeping multiple copies of data can get prohibitively expensive. #### Ransomware What is ransomware? Ransomware is a form of malware or cyber-attack perpetrated by criminal organizations to hold a victim’s data for ransom. The attack is typically launched via a trojan that once clicked traverses the users network encrypting file data to deny user access and disrupt business operations. With users and application locked out, the criminals demand payment in exchange for decrypting the victim’s data. Ransomware Strategy for File Data Workloads In an eWeek article, Komprise co-founder and CEO Kumar Goswami reviewed the ransomware challenge for unstructured file data: The File Data Factor in Ransomware Defense: 3 Best Practices. To create a cost-effective layered ransomware strategy, he recommended the following: Prioritize visibility and audits Create a multi-layered data management defense Create Snapshots and Backups for Hot Data Establish Cloud Tiering and Immutable Storage for Cold Data Have a plan – and validate it Cost-Effective Ransomware Data Protection Komprise provides cost-effective protection and recovery of file data. Komprise transparently tiers cold data and archives it from expensive storage and backups into a resilient object-locked destination such as Amazon S3 IA with Object Lock. By putting the cold data in an object-locked storage and eliminating it from active storage and backups, you can create a logically isolated recovery copy while drastically cutting data storage costs and data backup costs. Komprise creates a logically-isolated copy of your file data with the following properties: Physical Separation on Immutable Storage File-level Isolation Prevent Deletion Instant access and recoverability in the cloud without expensive upfront investments Read the blog post: How to Protect File Data from Ransomware at 80 Lower Cost Learn more about Komprise for cyber resiliency, including optimizing your defenses against cyber incidents, system failure and file data. What is Ransomware? Ransomware is a type of malware that threatens to publish the victim’s personal data or perpetually block access to it unless a ransom is paid. According to Gartner, “Ransomware is one of the most common threats facing security and risk management leaders.” Most ransomware attacks target unstructured data on network shares, making centralized file data storage solutions a primary target. How to protect File data from Ransomware Data backup and disaster recovery (DR) solutions are where most enterprise IT organizations are investing in order to deliver better detection of and data protection against ransomware attacks. To protect file data from ransomware, the solution must: Be cost-effective Protect if backups and snapshots are infected Provide simple recovery without significant upfront investment Be verifiable Read the blog post How to Protect File Data from Ransomware at 80 Lower Cost Ransomware best practices In the eWeek article The File Data Factor in Ransomware Defense: 3 Best Practices, Komprise CEO and co-founder summarizes the following ransomware best practices: Prioritize visibility and audits Create a multi-layered data management defense Snapshots and Backups for Hot Data Cloud Tiering and Immutable Storage for Cold Data Have a plan – and validate it #### File Data Ransomware What is File Data Ransomware? This is a ransomware attack targeting file data.  File data can be generated from users as well as machines. From genomics and medical imaging, streaming video, electric car data, and IoT products, all industries are generating vast amounts of unstructured file data, and increasingly enterprises are migrating file workloads to the cloud. File data can be petabytes of data and billions of files, so migrating this much unstructured data to the cloud takes time and can be disruptive. Cloud data migrations require proper planning to ensure minimal disruption and unintended costs. There is a growing recognition in the importance of having a layered protection strategy in place against potential file data ransomware attacks. Upwards of 80% of data today is unstructured file data, so IT organizations cannot afford to leave file data unprotected from ransomware. Early detection of ransomware will deliver the best outcome, but ransomware attacks are constantly evolving. Detection is not always foolproof and can be difficult. Investing in ways to recover data if you do get attacked by ransomware and establishing an immutable copy of data in a separate location separate from data storage and backups is the best way to recover data in the event of a ransomware attack.  But keeping multiple copies of data can get prohibitively expensive. Read the blog: How to Protect File Data from Ransomware at 80% Lower Cost Learn more about Komprise for cyber resiliency, including optimizing your defenses against cyber incidents, system failure and file data. What is File Data Ransomware? Ransomware is an attack by malware that holds your data files hostage by encrypting your systems and making your data inaccessible to you.  The majority of enterprise data in the enterprise is unstructured file data, which means organizations cannot afford to leave file data unprotected from ransomware. While the primary target for ransomware is file data, as the attacks grow more sophisticated hackers are seeking to defeat backups and snapshots. How to recover your ransomware encrypted data files The way to recover from a ransomware attack is to establish an immutable copy of your data in a separate location, ensuring it is separate from your data storage. Immutable storage can be physically “air gapped” with offline media such as tape or virtually air gapped with technologies such as AWS S3 object lock that prevent any modification of data even by administrators for a set retention period. How long does it take to recover from a ransomware attack? A critical component often overlooked is how long the ransomware recovery can take – if your business can’t resume until data is restored, every minute adds to the cost of the ransomware attack. Recovery from a ransomware attack is equivalent to a disaster where potentially 100% of your data must be restored. Having a tested recovery plan in place is essential to a successful recovery. How do you protect file data from ransomware? There are two components of ransomware protection: detection and recovery. Early detection of ransomware will deliver the best outcome, but this is not always foolproof and can be difficult. Organizations should also invest in data recovery strategies and create an immutable copy of data in a separate location data storage and backups in the event of a ransomware attack. But keeping multiple copies of data can get prohibitively expensive. To protect file data from ransomware, the solution must: – Be cost-effective – Protect if backups and snapshots are infected – Provide simple recovery without significant upfront investment – Be verifiable. ### General Data Management Terms > Additional terminology relevant to enterprise storage, cloud, and data management. #### Komprise Data Experience (KDX) The Komprise Data Experience (KDX) draws upon a storage-agnostic unstructured data management solution that prioritizes analytics and visibility, scalability, data mobility and flexibility. This experience means that Komprise Intelligent Data Management customers can meet the needs of various stakeholders with the best cost economics, the lowest risk, and the best pathway to leverage unstructured data for long-term value and AI initiatives. According to IDC, unstructured data is 90% of the data created in the enterprise today, and data-heavy organization depend on IT infrastructure and operations teams to effectively manage it for fast access, simple search, flexible data movement, long-term value and to feed AI data pipelines. Yet, vast unstructured data estates can incur undue security and compliance risks, high data storage costs and poor user experiences if not managed intelligently and holistically. No enterprise IT director wants to see their organization’s name in the headlines because something went awry with proprietary or customer data. Data management today transcends managing storage and involves right-placing data across the hybrid cloud to optimize both storage and backups, while shrinking ransomware exposure and providing data governance for AI data workflows. Storage-based data management is too silo-centric to fit the bill and point solutions for analysis or migration are too clunky to perform at scale. Read the Komprise Data Experience paper: 5 ways Komprise delivers unstructured data value.   STOP Making Storage Decisions in the Dark Unstructured data is too diverse to be managed through each vendor silo. Right-placing unstructured data requires choosing the best storage through its lifecycle and rightsizing backups and ransomware defense. To do this well, you need global visibility into your data across silos with comprehensive analytics on the data, its costs, its usage, and even your network environment. With the Komprise Data Experience (KDX) you get: Analytics across all your storage and AI silos that dynamically refreshes with new context in a global metadatabase. Comprehensive reports on costs, usage, and departmental breakdowns without impeding user performance. Scale-out elastic parallelism to handle the modern scale of data with local execution to prevent unnecessary data movement. The Komprise Data Experience (KDX) provides full visibility across silos to optimize storage, backup, ransomware and cloud costs. Search and curate the right data for AI with governance. Learn more about Komprise Analysis and Komprise Deep Analytics. STOP Overspending on Data Migrations Unstructured data migrations involve moving billions of files, which is time-consuming, cumbersome, complex, and error-prone if done manually. Most organizations report cost overruns on unstructured data migrations. Komprise Elastic Data Migration has many features for resiliency, data integrity and auditing to ensure a smooth transition for users and applications. The Komprise Data Experience (KDX) delivers: Analytics and tiering to right-place data as it migrates, reducing costs and achieving faster ROI. The ability to handle brownouts and edge conditions inevitable in complex migrations and migrates file data 25x+ faster. An assessment of your environment to anticipate and address network and other issues that can derail a migration. No more labor-intensive, disruptive and dreaded data migrations. Whether migrating to the cloud, cloud NAS or to a NAS in your data center, with Komprise Elastic Data Migration you get the fastest, most predictable and cost-efficient data migration for file and object data. Review the Unstructured Data Migration Best Practices Guide.   STOP Paying the Data Rehydration Penalty The rehydration penalty is a nice way to say you are locked in for good. When you decide you want to modernize and move from one storage platform to another, and you have tiered data using the current storage vendor’s tiering solution, you are stuck. You must rehydrate all the data back onto the source, migrate that data to the new storage unit and then tier it again. For instance, if you have tiered 5PB over three years, you must now find 5PB of extra storage. The Komprise Data Experience (KDX) delivers: Tier data from any file storage without the traditional lock-in so you can switch vendors with no penalty. Set granular policies for different departments and use cases and manage showback and data lineage. Tier with no application and user disruption while Komprise stays out of the hot data path. Go beyond storage-based tiering to analyze, migrate, archive and replicate data across multi-vendor storage and clouds while enabling native use of the data at each layer. Don’t lock away data. Unlock cost savings and greater value with Komprise. Learn about Transparent Move Technology (TMT)   STOP Exposing Your Unstructured Data to Ransomware Cybersecurity is now a critical ingredient to proper unstructured data management. Yet, it is not financially viable for most organizations to protect all their data. Inactive unstructured data can be your weakest link when it comes to a potential ransomware attack because of its sheer volume, the number of users who have access to it and the long latency before a breach is detected. Storage-based tiering solutions do not shield the cold data from ransomware attacks since the tiered files are still managed by the storage file system. The Komprise Data Experience (KDX) delivers: Shrink the ransomware attack surface and cut the liability from your weakest link, the cold unstructured data that constitutes on average 70% of your footprint. Reduce your ransomware exposure without disturbing tamperproof snapshots. Get an extra layer of data protection with Intelligent Data Management. Dramatically reduce the cost of file data ransomware protection and gain confidence by securing and validating your data, which is always-available in native format. Global Law Firm Saves $900K on Data Storage and Achieves Resilient Ransomware Protection in Azure with Komprise. STOP Sharing Sensitive Data with AI AI data governance is a top priority for enterprise IT organizations, since employees are already using generative AI tools as part of their everyday work processes. Enriching, segmenting, and classifying data to include or exclude data sets for AI data pipelines is a growing requirement for unstructured data management. Image The Komprise Data Experience (KDX) delivers: Build data pipelines by searching across your entire data estate, connecting to any AI processor and enriching metadata. Find and tag sensitive data such as PII to exclude from AI and classify your data so it is readily searchable for AI. Automate AI ingestion with proper data governance using Komprise to search, curate, and feed AI. The Komprise Smart Data Workflow Manager is a simple point-and-click UI wizard to search across on-premises, edge and cloud data storage silos, find the data you need, execute an AI function on a subset of data and tag the data with additional metadata. Move only the data you need, build custom AI data workflows, and manage the lifecycle of unstructured data intelligently. Learn more about Komprise Smart Data Workflows. #### ETL ETL stands for Extract, Transform, Load. ETL is a process used to move and prepare data from one system (often raw or messy) into another system (usually for analysis or reporting). It's a foundational method in data integration and data warehousing. ETL is a term primarily used for structured data management, pioneered by companies like Informatica. A good resource to learn core principles of ETL and data warehousing is The Data Warehouse Institute (TDWI). ETL Steps Defined Extract This is where data is pulled from various sources. Sources can include databases, files, APIs, cloud storage, logs, or unstructured formats like emails or PDFs. The goal is to gather raw data from disparate sources. Transform This is where the data is cleaned, formatted, or enriched. This might involve converting dates, removing duplicates, categorizing content, or deriving new fields (e.g., calculating age from birthdate). The goal is to make data usable and consistent. Load The transformed data is moved into a destination system, like a data warehouse, data lake, analytics platform, or application. The goal is to store data where it can be accessed, queried, and analyzed by data engineers, data scientist and other data analyst titles. Source: https://learn.microsoft.com/en-us/azure/architecture/data-guide/relational-data/etl Why Use ETL? ETL tools combine data from multiple sources. They are designed to makes data clean, accurate, and analysis-ready. ETL tools typically powers dashboards and business intelligence / analytics tools. A new, cloud-centric approach to data and application integration emerged in the past 10 years that Gartner calls integration platform as a service (iPaaS). Common ETL Tools and Example: Traditional: Informatica, Talend, Microsoft SSIS Modern cloud-native: Fivetran, Airbyte, dbt (Transform only), AWS Glue, Azure Data Factory, SnapLogic For unstructured data: Apache Nifi, Apache Tika, custom Python pipelines (or alternative approaches like Komprise Intelligent Data Management) Here is a common ETL example - a company wants to analyze sales across multiple stores: Extract sales data from each store's system Transform it to a common format and currency Load it into a central data warehouse for reporting ETL Challenges for Unstructured Data Traditional ETL tools were designed for structured data (for example, data in relational databases), where schemas and rules are clearly defined. Unstructured data — such as PDFs, images, audio, and raw text — doesn’t conform to those rules and needs more flexible, intelligent processing. The core problem with unstructured data is its lack of a common schema. You can't take a video file, an audio file, or even three video files from three different applications and place them in a tabular format, because they have different contexts and semantics.  Other challenges for ETL tools and unstructured data include: Complexity of content AI needs semantics, not just structure; ETL often doesn’t understand meaning. AI models often need to work with streaming or near-real-time data whereas ETL is typically batch-based. Context AI services (e.g. LLMs or computer vision) require contextual, often multi-modal understanding. ETL doesn’t offer that. That said, ETL-like processes are useful, especially at the preprocessing and data wrangling stage: Extract: Ingest unstructured content from various systems (e.g., files, images, emails). Transform: Clean and normalize formats (e.g., convert audio to text, extract text from PDFs). Extract metadata (e.g., timestamps, entities, classification). Enrich or annotate data (e.g., labeling for training ML models). Load: Push processed data to a vector store (e.g., Pinecone, FAISS) or cloud AI service (e.g., OpenAI, Azure AI). Store in a data lakehouse (e.g., Databricks, Snowflake) for further analysis or fine-tuning. Modern ETL Alternative: AI Data Workflows In many AI data workflows or pipelines, especially for unstructured data, the pattern shifts from ETL to ELT (Extract → Load → Transform) using modern data pipelines. Komprise provides unstructured AI data ingestion capabilities that are a better approach than using traditional ETL tools to address this use case. Learn more about AI Data Workflows. In this scenario, the Transform step is done after data has been copied/migrated/moved to the source where the data is stored, which increasingly is in an Object Storage system like Amazon S3 or Azure Blob. Workflows are iterative and prompt engineering RAG (retrieval-augmented generation) and model training / fine-tuning are part of the process. Example pipeline for AI and unstructured data Ingest PDFs, audio, video → extract with OCR/speech-to-text tools Store in object storage or a document DB Run AI services (e.g., OpenAI for summaries, classification, embeddings) Index in a Global File Index or metadatabase for fast retrieval Use in downstream AI apps (chatbots, recommendation systems, etc.)   To make unstructured data usable and searchable for AI, organizations must enrich metadata beyond the basic attributes storage systems provide. This requires creating a global index across storage environments to gain visibility and then applying tags—either manually by knowledgeable users or automatically using AI tools. These enriched tags help identify sensitive data and classify information for specific use cases. Ensuring metadata stays with data during movement is critical, as transferring large unstructured datasets is costly and time-consuming. Therefore, precise metadata-driven classification enables efficient, secure AI data pipelines. To conclude, traditional ETL tools are not well suited for feeding unstructured data to AI. Ideal use cases for ETL tools include: Preprocessing raw files with custom transformations Feeding structured data sources to defined targets Use cases not well suited for traditional ETL tools include: Real-time AI data pipelines, which require a data streaming / data lake architecture Semantic understanding & embedding, which require AI-native tooling RAG, LLMs, semantic search. ETL tools could play a role for AI data workflows, especially with unstructured data, they should be combined with modern AI services, flexible storage, and unstructured data management solutions. Data on the Move Discussion: Agentic AI and Unstructured Data Preparation #### Exabyte An exabyte (EB) is a unit of digital information storage equal to 1 billion gigabytes (GB) or 1 million terabytes (TB). Specifically, it is: 1 exabyte = 1,000 petabytes (PB) 1 exabyte = 1,000,000 terabytes (TB) 1 exabyte = 1,000,000,000 gigabytes (GB) 1 exabyte = 1,000,000,000,000 megabytes (MB) 1 exabyte = 10/18 bytes (in decimal, SI units) Real-world exabyte comparisons The total amount of data transmitted over the internet daily is estimated to be in the exabyte range. World Economic Forum. All words ever spoken by humans (if digitized) would take up roughly 5 exabytes. Source. Modern data centers of major tech companies store data measured in exabytes. Until recently, enterprise data growth has been measured in petabytes. #### PII Detection PII detection is the process of identifying and managing personally identifiable information within vast and dispersed data environments, such as file shares, emails, collaboration platforms, and cloud storage. Unlike structured databases, where PII is neatly categorized, unstructured data presents a challenge due to its free-form nature and proliferation across hybrid storage environments. Enterprise IT directors and storage IT managers need ways to automatically scan, tag, and classify sensitive data so that it’s properly stored and protected. This capability is essential for ensuring regulatory compliance, preventing inadvertent exposure, and securing customer trust in an era where data breaches are increasingly severe and costly. PII detection is also crucial for AI data governance. Organizations need controls and systems that can prevent the ingestion of sensitive data into generative AI models where the data can be exposed into the public domain. Storage administrators and PII detection PII and sensitive data detection is no longer solely a concern for cybersecurity teams; storage IT managers now play a pivotal role as data stewards, given that sensitive data can be easily copied and inadvertently stored in places where it doesn’t belong. Traditional security tools may identify PII but often lack integration with storage management platforms, limiting IT teams' ability to act on the findings. As storage environments become more distributed—spanning on-premises systems, cloud repositories, and edge locations—scalable solutions that allow real-time visibility and remediation of PII and sensitive data are critical. These solutions should not only detect PII but also provide tools to act on the findings: confine it for review, securely move the PII to compliant environments and/or exclude it from AI data pipelines. Since unstructured data is the lifeblood for AI and business intelligence, enterprise IT leaders must ensure that only appropriate datasets are integrated into these workflows. The ability to filter and segregate PII from business-critical data ensures organizations can leverage unstructured data without creating legal, financial, or reputational risks. By adopting integrated PII detection and unstructured data management tools, storage and infrastructure teams can help close the gaps for enterprise IT security and privacy goals. Learn more about Komprise capabilities for PII detection and sensitive data management. #### PII PII (Personally Identifiable Information) is any data that can be used to identify an individual either directly or indirectly. Examples of PII include: Full name Social Security number Email address Phone number Date of birth Passport number Physical address Financial account numbers IP address (in some contexts) If you're handling PII, it's important to follow applicable privacy laws and regulations (e.g., GDPR, CCPA) to protect it and ensure it is used responsibly. PII Detection in the Enterprise Once the domain of data security and data privacy teams, increasingly PII detection and mitigation capabilities are being offered as part of a broader unstructured data management solution. Detecting PII in an enterprise is a critical step in maintaining data security, compliance, and data governance. Here's are some of the ways enterprises handle PII detection today. Identify PII Sources Data Repositories: Databases, file servers, email systems, and cloud storage. Data Flows: API interactions, third-party integrations, and data pipelines. Endpoints: User devices, internal systems, and web applications. Use Automated Tools A variety of tools and technologies can detect PII across the enterprise: Data Discovery Tools: Tools like Microsoft Purview, Varonis, and Spirion scan structured and unstructured data repositories to find PII. These tools have historically been used to discover PII and other sensitive data but they have often not been part of a broader data management and data mobility strategy. DLP Solutions: Data Loss Prevention (DLP) tools monitor and prevent unauthorized transfer of PII outside the organization. Machine Learning: Advanced systems use natural language processing (NLP) and pattern recognition to identify PII in complex datasets. Cloud-Native Services: AWS Macie, Google DLP, and Azure Information Protection provide PII detection for cloud environments. What are some common techniques for PII detection? Pattern Matching: Regular expressions to detect common PII formats (e.g., email regex, SSN patterns). Keyword Matching: Identifying sensitive terms associated with PII (e.g., "social security," "passport number"). Metadata Analysis: Analyzing file names, tags, or attributes. Contextual Analysis: Understanding context to distinguish between sensitive data and non-sensitive similar patterns. AI/ML-Based Detection: Identifying nuanced PII (e.g., names in free text). Implementing PII detection policies As part of a broader data management strategy, it's important to be able to define policies to classify and handle detected PII: Data Classification: Tagging data as sensitive, restricted, or public. Access Control: Restrict access to PII based on roles and need-to-know principles. Retention Policies: Delete PII when it is no longer needed to minimize exposure risk. PII Compliance and Data Governance Ensure PII detection aligns with legal and regulatory requirements: GDPR (EU): Requires organizations to identify and protect personal data. CCPA (California): Mandates disclosures about collected personal data. HIPAA (USA): Enforces protections for health-related PII. Common PII Data Detection Challenges Historically PII detection has been narrowly defined and technologies have not been part of a broader unstructured data management strategy. Some of the common PII detection challenges include: Data Volume: Enterprises often have petabytes of data scattered across silos. False Positives: Pattern-based detection can misidentify non-sensitive data as PII. Evolving Data Types: New formats or types of PII may require constant updates to detection mechanisms. Hybrid Environments: Monitoring PII in both on-premises, cloud and edge environments. #### Retrieval Augmented Generation (RAG) Retrieval-Augmented Generation (RAG) is an advanced machine learning framework that enhances the performance of generative models by combining retrieval mechanisms with generation capabilities. It is useful for tasks requiring access to external knowledge or handling large-scale, domain-specific information. Key Components of RAG Retrieval Module: A component (e.g., a dense vector search engine like FAISS or Elasticsearch) that retrieves relevant information or documents from an external knowledge base or corpus. It searches based on a query generated by the user or the system itself. Generative Model: A natural language generation (NLG) model, such as OpenAI's GPT or other large language models (LLMs), which generates context-aware responses or outputs. It uses the retrieved information as a supplementary knowledge source for crafting its response. Integration: The retrieved data is used as additional input or context for the generative AI model, enabling it to produce more accurate, informed, and grounded outputs. Workflow of RAG Query Formation: The system receives a user query (e.g., a question or a request for information). Document Retrieval: The query is sent to the retrieval module, which fetches the most relevant documents or passages from a knowledge base. Augmented Input: The retrieved documents are concatenated with the user query or reformulated as context for the generative model. Response Generation: The generative model processes the augmented input and generates a coherent, contextually rich response. Benefits of RAG Access to External Knowledge: RAG enables generative models to reference up-to-date or domain-specific information without embedding all the knowledge within the model itself. Scalability: Large-scale knowledge bases can be integrated, reducing the need for retraining the generative model on static datasets. Improved Accuracy: By grounding responses in retrieved factual data, RAG reduces hallucinations (fabricated information) common in LLMs. Domain Adaptability: Easily adapts to specific industries (e.g., legal, healthcare) by connecting to specialized corpora. Applications of RAG Question Answering Systems: Enhanced customer support, technical troubleshooting, and search tools. Knowledge Management: Retrieval and synthesis of enterprise knowledge for employees. Legal and Compliance Analysis: Summarizing and interpreting documents while citing original sources. Content Generation: Writing reports, creating articles, or summarizing documents with reference material. Challenges of RAG Retrieval Quality: The quality of generated responses depends heavily on the accuracy and relevance of the retrieved documents. Latency: Combining retrieval and generation can increase response time, especially with large-scale datasets. Knowledge Base Maintenance: Keeping the retrieval module's knowledge base updated is crucial for the system's reliability. RAG bridges the gap between generative AI and external knowledge, making it a powerful tool for knowledge-intensive applications. #### ROT Data ROT data is Redundant, Obsolete, and Trivial data, which includes data stored within an organization that no longer has value or relevance. This type of data can clutter systems, increase storage costs, and pose compliance or security risks. File data (see File) is where a lot of ROT lives in the enterprise, which is why file data management is a growing category of software solutions. High Performance Computing (HPC), research labs and engineering teams are a common culprit for ROT. Types of ROT Data Redundant Data: Duplicate files or records (e.g., multiple copies of the same document). Overlapping datasets that provide no additional insights. Obsolete Data: Outdated information (e.g., old project files, expired contracts). Legacy system data that is no longer used or supported. Trivial Data: Non-business-related content (e.g., personal files, memes, or irrelevant emails). Temporary files or drafts that are no longer needed. Challenges of ROT Data Data Storage Costs: Storing ROT data increases hardware, cloud storage, and maintenance expenses unnecessarily. Visibility into cold data and building a plan to tier, archive, migrate this inactive data to lower cost storage is part of an overall unstructured data management strategy. Performance Issues: Excessive data can slow down system performance and make finding relevant information harder. Compliance Risks: Retaining outdated or unnecessary data can result in non-compliance with regulations like GDPR or HIPAA. See Data Governance. Security Risks: ROT data increases the attack surface for cyber threats or leaks of sensitive information. Learn more about ransomware data protection at 80% less cost: Cybersecurity and ransomware data protection. Managing ROT Data Data Audit: Conduct regular data audits to identify redundant, obsolete, and trivial files. Having visibility into your file and object data across storage silos is a start. Learn more about Komprise Analysis. Data Classification: Use automated tools to classify and tag data for better visibility and management. Read the blog post: Why unstructured data classification matters. Retention Policies: Implement policies to define the lifecycle of data, specifying when data should be archived or deleted. Data Cleanup Tools: For structured and semi-structured data, use data management software or scripts to de-duplicate and remove ROT data. User Education: Train employees on proper data storage and retention practices to minimize ROT data creation. Showback: Establish reporting strategies to ensure departmental research teamshave visibility into their data storage costs. Read: The Rise of Data Services for Unstructured Data Management. Archiving Solutions: Archive important historical data and securely delete (or confine) the rest to free up space. The term data archiving is often used interchangeably with data tiering. The point is to ensure ROT data is identified and there is a plan to ensure teams are working with the right data at the right time and inactive data is moved to lower cost storage (or removed). By actively managing ROT data, organizations can improve efficiency, reduce costs, and enhance data governance. Also see Zombie Data. #### Data Storage Optimization Data storage optimization is the process of improving how data is stored to maximize efficiency, reduce storage costs, and enhance performance. Historically data storage optimization has focused on storage management strategies and technologies designed to minimize the amount of physical storage used while ensuring quick access to data and maintaining data integrity. In the AI era, there is been a greater focus on unstructured data management and delivering data storage management services (DSMS) that separate the storage from the data to deliver both infrastructure optimization and unlock data value. Strategies for Data Storage Optimization Data Deduplication: Eliminating duplicate copies of data can significantly reduce the amount of data stored, especially in backup systems where the same data might be saved multiple times, but also email storage and cloud storage. Compression: Reducing the size of data by using algorithms that eliminate redundant information. Compressing files (e.g., zip or gzip) reduces the storage footprint, which can lead to significant space savings, especially for large or repetitive datasets such as log files, media files, and archival data storage. Thin Provisioning: Allocating storage on demand rather than pre-allocating it allows you to allocate more storage to applications than what is physically available, assuming that not all of it will be used immediately. This reduces wasted storage and is commonly used in virtual environments and cloud services. Tiered Storage (see Data Tiering): Storing data on different types of storage based on its importance or access frequency. Frequently accessed (hot) data can be stored on high-performance, expensive storage (like SSDs), while less accessed (cold) data can be moved to slower, cheaper storage (like HDDs or cloud archives). Tiered block storage is common for databases and large-scale enterprise data storage. Read: Block Level Tiering versus File Level Tiering. Data Archiving: Moving infrequently used data to long-term, cost-efficient storage. Regularly archiving old data to low-cost storage (such as tape drives or cold cloud storage) is a well known strategy to free up space in more expensive, high-performance systems. Legal records, research data, historical logs are common examples of data that is archived as it often has to be retained for regulatory and compliance reasons. Storage Virtualization: Pooling physical storage from multiple devices and managing it as a single resource. Virtualization simplifies storage management, increases utilization rates, and enables more flexible data distribution across storage devices. Cloud storage environments and data centers often deploy storage virtualization strategies. Use of Solid-State Drives (SSDs): Replacing traditional hard disk drives (HDDs) with SSDs, which are faster and more reliable. SSDs significantly improve read/write speeds, reducing latency, and boosting overall system performance. High-performance applications and virtual machines often rely upon SSDs. Erasure Coding: A data protection method that breaks data into fragments, expands and encodes it with redundant data pieces, and stores it across different locations. This provides more efficient data protection than traditional RAID setups, reducing storage overhead while ensuring that data can be recovered even if part of the storage system fails.Use cases include: Distributed storage systems and cloud storage. Cloud Storage Optimization: Using cloud-based services to store data, with intelligent policies to manage what data is stored locally vs. in the cloud. Cloud storage can offer elastic scalability, allowing enterprises to store massive amounts of data at a fraction of the cost of physical storage. Some cloud providers offer tiered storage solutions that automatically move data to lower-cost storage as it ages. Data backups, disaster recovery and business applications increasingly rely upon cloud cost optimizations solutions. Storage Efficiency Tools: Using software tools to analyze and optimize data storage. These tools can identify underutilized storage, provide automated tiering, and offer analytics to forecast future storage needs. These tools often integrate with virtualization platforms and cloud providers to deliver insights. Use cases include storage monitoring and proactive capacity planning. Automated Data Management and Policies: This means you're able to automate the movement of data based on usage patterns and business rules, which allows organizations to automate data lifecycle management, like archiving or deleting old files, ensures that only active or important data consumes valuable storage resources. Enterprise file systems and cloud storage providers work with software solution partners like Komprise to deliver analytics-driven unstructured data management solutions. What are the Benefits of Data Storage Optimization Services? Cost Savings: Reducing the amount of physical storage needed, especially in large environments. Improved Performance: Faster access to data by eliminating bottlenecks caused by inefficiencies. Simplified Management: Easier storage management due to intelligent policies and reduced data footprint. Data Security: Improved data protection, backup efficiency, and recovery capabilities. Budget Available for AI: The 2024 State of Unstructured Data Report found that “preparing for AI” remained a top data storage and data management priority for IT leaders. Yet leaders said that cost optimization is an even higher priority this year as they are try to fit AI into existing IT budgets. Only 30% say they will increase their IT budgets to support AI projects. Read the press release. Effective data storage optimization balances performance, costs, and data accessibility, ensuring that businesses can scale efficiently. When it comes to growing file and object data in the enterprise, both It and line of business teams are increasingly looking to unstructured data management solutions like Komprise to deliver cost savings and to unlock greater data value. Get Started with Data Storage Optimization Komprise Intelligent Data Management is focused on delivering data storage agnostic data storage optimization and data mobility software solutions. Why is data storage optimization important? Optimizing data storage is essential for enterprises to prevent excessive costs, improve data accessibility, reduce risks associated with unmanaged data, and support compliance and security requirements. Why are storage optimization strategies so important for unstructured data? Managing Uncontrolled Growth: Unstructured data, such as documents, images, emails, and multimedia, does not fit traditional relational databases. Its volume grows much faster than structured data, making it easy for costs to escalate out of control. Redundancy and Complexity: Unstructured data is often duplicated or left unmanaged, leading to cloud and on-premises silos, inefficiency, and high storage expenses minimizing potential business value. Security, Compliance, and Risk: Poorly managed unstructured data increases the risk of breaches and loss, as sensitive information may be improperly stored and accessed. Robust management (classification, security controls, and periodic audits) helps protect data and minimize compliance-related costs and risks. Learn more about Sensitive Data Management. Challenges in Analytics and Retrieval: Without proper organization, valuable insights from unstructured data are harder to extract. Structured management supports faster search, analytics, and more agile business decision-making, justifying spending and preventing wastage, but for most enterprise organizations petabytes of file data are locked in NAS devices. Operational and Backup Costs: Unnecessary retention of excessive unstructured data drives up costs for backups, disaster recovery, and datacenter footprint, often without benefits to business objectives. Prioritizing cost-efficient management of unstructured data is essential for optimizing IT budgets, maximizing resource utilization, ensuring security and compliance, and unlocking hidden business value from massive datasets. Enterprises that adopt these strategies control (and avoid) data storage costs while enhancing operational agility and competitiveness. What is Data Storage Management Services (DSMS) Data Storage Management Services (DSMS) is  set of software solutions, tools, and practices that help organizations efficiently manage, optimize, and protect their data storage infrastructure. DSMS solutions enable enterprises to organize, monitor, and automate the handling of vast amounts of unstructured data, regardless of where it is stored, on-premises, in the cloud, or at the edge. These services typically include features for storage optimization, lifecycle management, governance, reporting, data classification, data migration, data tiering, and security protection. The right DSMS strategy helps businesses reduce storage, cloud, ransomware, and backup costs, improves access and performance while maintaining compliance and ensuring reliable and secure data storage across all environments. Learn more. #### Data Storage Management Services (DSMS) Data Storage Management Services (DSMS) is a term used by Gartner to refer to storage agnostic unstructured data management software solutions like Komprise. IDC estimates that 90% of the data generated by organizations today is unstructured. And Gartner predicts (subscription required) that by 2028, large enterprises will triple their unstructured data capacity across their on- premises, edge and public cloud locations, compared to mid-2024. Common DSMS Use Cases Storage Optimization: Read 8 Ways to Save on Storage and Backup Costs Data Lifecycle Management: Read How Lummus Technologies Saves 80% and Improves Data Lifecycle Management Data Governance and Reporting: Read New Reports on Unstructured Data and Storage Costs Data Classification: Read Why Data Classification Matters Data Migration: Read the Unstructured Data Migration Guide Data Tiering: Read the Unstructured Data Tiering Guide Ransomware Protection: Read How to Protect File Data from Ransomware at 80% Lower Costs Why Storage-Agnostic Unstructured Data Management Services? Enterprise data will outlive your data storage, so increasingly enterprise organizations are looking for data storage agnostic approaches to managing and delivering data services. This is only going to become more essential as organizations not only see to optimize costs but also to harness the potential of their unstructured data as part of a broader AI strategy. According to Komprise cofounder and CEO Kumar Goswami: "Komprise is on a mission to change the way the world manages unstructured data, which is growing exponentially in the enterprise." He noted when reviewing the 2024 State of Unstructured Data Management report: “Our latest survey reveals a pivotal moment in enterprise IT as organizations grapple with the transformative potential of AI while balancing fiscal responsibility. IT leaders will also need to factor in critical data governance and security capabilities. Managing unstructured data strategically to optimize costs and use data workflows to enrich metadata is a great place to start an AI initiative.” #### Storage Refresh A storage refresh involves upgrading or replacing existing data storage infrastructure with newer, more efficient, or higher-capacity storage solutions. A data storage refresh can involve hardware upgrades, such as installing new storage arrays, servers, or disk drives, as well as software upgrades for managing and optimizing storage resources, or cloud migrations as more of your data storage moves to cloud storage and infrastructure platforms such as AWS, Azure and Google Cloud. Optimizing data storage costs is also a primary driver of a storage refresh as data volumes continue to grow and IT budgets do not. Common reasons for a storage refresh Performance Improvement: Upgrading to faster storage technology can improve overall system performance, reducing latency and improving data access times. Capacity Expansion: As data storage needs grow, organizations may need to add more storage capacity to accommodate increasing amounts of data. Cost Reduction: Newer storage solutions may offer better cost efficiencies, such as lower power consumption or more efficient use of storage space. Enhanced Data Protection: Modern storage systems often come with improved data protection features, such as better redundancy and disaster recovery capabilities. Compatibility with New Technologies: Upgrading storage infrastructure can ensure compatibility with new technologies or software applications that may require specific storage requirements. Compliance Requirements: Organizations may need to refresh storage infrastructure to comply with changing regulatory requirements related to data storage and security. A storage refresh process typically involves assessing current storage needs, evaluating available storage solutions, planning the migration or deployment process, and implementing the new storage infrastructure while minimizing disruption to ongoing operations. It's important to carefully plan and execute a storage refresh to ensure a smooth transition and avoid data loss,  and end-user or application disruption. Read the eWeek article: 5 Mistakes to Avoid in a Storage Refresh Mistake 1: Making Decisions without Holistic Data Visibility Mistake 2: Choosing One-Size-Fits-All Storage Mistake 3: Becoming Locked into One Vendor Mistake 4: Moving Too Fast Mistake 5: Ignoring Future Storage Needs Learn more about the Komprise Storage Refresh Assessment. #### NetApp BlueXP Data Management In December 2022, NetApp announced it would be changing the name of their Cloud Manager product to NetAppt BlueXP. NetApp has a blue logo and the idea is that with a single console you can get the full “Blue Experience” (aka BlueXP). According to the website, NetApp BlueXP lets you “build and operate an efficient, resilient, secure, and performant hybrid multicloud data estate through a single control plane: Storage, Mobility, Protection, Analysis and Control.” Blocks & Files covered the launch, summarizing BlueXP as, “a software control plane to manage a customer’s data estate in a hybrid on-premises and multi-cloud environment with extensibility beyond NetApp tech.” NetApp customers should be thrilled that the company is seeking to unify the management of their hybrid solutions. BlueXP is a cloud console for managing both on-premises and cloud-based NetApp products (OnTap, Data Sense, CloudSync, FabricPools, etc.), and each underlying product supports a different set of platforms and options. It is not a storage-agnostic unstructured data management solution. 5 Requirements of a Unified Data Control Plane for Unstructured Data Here are five requirements you must ensure are in place if you are looking for a modern approach to unstructured data visibility and mobility which achieves maximum data storage price/performance optimization and data value: Ease of set up and administration.  How many admin guides are there? Agentless architecture. Can the solution scale across environments without complexity? Visibility + mobility. Can the solution provide actionable data and storage insights? Native data access. Is the solution in the hot data path or outside? Unlock data value. Does the solution lock you in or free your data? Why unstructured data management should be data storage agnostic. #### Pure Storage FlashBlade Pure Storage FlashBlade ® is a consolidated storage platform for unstructured data, be it file or object, that is built for unlimited scale. FlashBlade //S™ is designed to deliver the efficiency, density, and top performance that modern unstructured data needs at scale. FlashBlade //E™ is designed to deliver the environmental, ease of use, and reliability benefits of all-flash storage for unstructured data workloads, but at a cost competitive with disk-based storage solutions. FlashBlade was built with better engineering in mind. The chassis itself was built for long-life, or multiple generations of hardware, in mind while including maximum rack density along with power efficiency. It incorporated unified fast file and object storage with always-on encryption and data compression. Komprise Intelligent Tiering for Pure Storage FlashBlade While performance-optimized FlashBlade //S and capacity-optimized FlashBlade //E can scale-out to petabytes of data, organization can be most cost-effective by keeping data at the most relevant FlashBlade based on where data is in its lifecycle. Komprise, a SaaS for unstructured data management and mobility, was designed with the understanding that data is always in motion and should not be treated the same. Komprise gives enterprises the ability to intelligently manage their data by identifying rarely accessed data from FlashBlade//S and transparently tiering it to FlashBlade//E without any changes to the user or application access. With Komprise Transparent Move Technology™ (TMT), users and applications access data in the same location as before with Komprise use of Dynamic Links. The combination of Komprise Intelligent Data Management with the FlashBlade line of high-performance, resilient storage ensures the optimal cost/performance ROI in the industry. Komprise Intelligent Data Management software complements the FlashBlade portfolio of products by providing transparent data tiering. Komprise software can intelligently identify data across FlashBlade//S and transparently move infrequently accessed data to more cost-efficient FlashBlade//E without disruptions to user or application access. Komprise also provides Pure Storage customers with analytics across data silos, powerful data migration and ongoing data lifecycle management. Read this white paper to further understand the need for transparent data tiering, suggested architecture, solution validations and the benefits. Learn more about Komprise for Pure Storage. #### BlueXP NetApp BlueXP is a management console designed to unify a disparate set of hybrid cloud NetApp products.  The BlueXP interface provides a set of data management capabilities, that are licensed separately. The capabilities are: Data Tiering Copy/Sync Data Classification / PII OnTap to S3 Backup BlueXP was designed by NetApp to provide a control plane for managing multiple NetApp tools. It is important to note, however, that BlueXP is NetApp storage-centric and has key limitations and restrictions that impact the applicability and simplicity of the solution. With the huge influx of unstructured data, customers want a centralized solution they can use across their storage platforms and in a multi-cloud and hybrid environment. It is imperative that broader unstructured data management requirements and the benefits of a storage-agnostic solution are considered before embarking on this effort. Read the 5 requirements of unified data control plane for unstructured data management. NetApp BlueXP Tiering Data tiering is one component of BlueXP. According to NetApp, BlueXP is able to: Tier to cloud object Set flexible policies Reduce data storage costs First of all, it's important to understand the differences and benefits of file-level tiering vs block-level tiering used by NetApp. Read the white paper. Secondly, it's important to understand the tiering requirements. As Komprise co-founder and CEO Kumar Goswami wrote in this post, block-level tiering has benefits for tiering snapshots, certain log files and other data that is proprietary and deleted in short order. But block-level tiering has significant shortcomings, including: Limited policies result in more data accessed, increasing increasing cloud egress costs. Defragmentation of blocks leads to higher cloud storage costs. Sequential reads lead to higher cloud costs and lower performance. Data tiered to the cloud cannot be accessed from the cloud without licensing a storage file system. Tiering blocks impacts performance of the storage array. Data access results in re–hydration, thereby reducing potential cost savings. Block tiering does not reduce backup costs or the backup window. Block tiering locks you into your storage vendor and requires rehydration of all data you tier when switching to a new system. Read: 5 Mistakes to Avoid in a Data Storage Refresh.  Proprietary lock-in and cloud file storage licensing costs. NetApp BlueXP Tiering Feature Comparison with Komprise Intelligent Data Management Komprise is a storage-agnostic control plane across all your hybrid data estate that optimizes data storage costs and puts enterprise IT organizations in control of their data at all times with no lock-in. Komprise uses a superior file-based tiering solution. Below are questions you should ask and a table comparing NetApp BlueXP functionality versus Komprise Intelligent Data Management: Can you tier data that is more than 183 days old? Can you tier directly to Amazon S3 IA or Azure Blob Cool? Do you require a cooling period on rehydration? Do you have flexible data management policies at the share, directory and file levels? Can you access tiered files without additional licensing? Can you migrate data or move to another system without rehydrating everything you've tiered? Do you tier files or blocks? Learn more about Komprise Data Management for NetApp. Webinar: NetApp + Komprise - Right Data, Right Place, Right Time Watch a demo of Komprise Storage Insights. What is Unstructured Data Management? Unstructured data management has emerged as a new category that encompasses elements of data classification, mobility, governance, and cost optimization. While data storage and backup vendors each have some elements of data management included by design, they are primarily focused on optimizing their own devices and deployments, not providing comprehensive data visibility, mobility and value across heterogenous environments. GigaOm notes in their 2024 Unstructured Data Management Radar Report: “As data ecosystems flourish, sophisticated unstructured data management (UDM) tools are emerging, poised to unlock the vast potential of dormant data and propel organizations into a data-driven future.” The report also advises: “Strategic deployment of UDM solutions grants organizations full visibility into their data, informing the development of cost-effective roadmaps that maximize ROI on data storage.” Gartner uses these terms: Data storage management services – “By 2027, at least 40 percent of organizations will deploy data storage management solutions for classification, insights, and optimization, up from 15 percent in early 2023.” Hybrid cloud file data services – “By 2027, 60 percent of infrastructure and operations leaders will implement hybrid cloud file deployments, up from 20 percent in early 2023. This will consolidate unstructured data to a single copy, enabling centralized management around the protection and security of the underlying data, thereby simplifying operations while consolidating use cases. Typical outcomes include cost optimization to align the cost of the storage with the value of the data; data governance to ensure sensitive data has the right protection and retention policies applied; data security to enable the right level of permission and access level controls; and enhanced analytics workflows that leverage data classification and optional tag the data with custom metadata.” Understand the benefits of a storage-agnostic unstructured data management to deliver a unified data control plane for visibility and mobility across storage silos. What are the Five Key Requirements of a Unified Data Control Plane for Unstructured Data Management? Unstructured data management requires a cohesive product vision, product architecture and long-term commitment from a vendor to deliver enterprise scale. At Komprise, we believe that data management functionality is a layer independent of storage. This means it is possible to manage data holistically across vendors and across technologies, be they files or objects. This approach has allowed Komprise to create a data management solution that is vendor agnostic and integrates tightly with on-premises and cloud storage to create a hybrid data management platform, without lock-in. Here are five requirements you must ensure are in place if you are looking for a modern approach to unstructured data visibility, mobility 1. Ease of Set Up and Administration 2. Agentless Architecture 3. Visibility + Mobility 4. Native Data Access without Vendor Lock-In 5. Unlock Data Value #### Solid State Drives (SSDs) Solid-State Drives (SSDs) are a type of non-volatile storage device that stores persistent data on solid-state flash memory. Unlike traditional Hard Disk Drives (HDDs), which use spinning disks and magnetic heads to read and write data, SSDs have no moving parts. This lack of mechanical components makes SSDs faster, more reliable, and more energy-efficient than HDDs. Key characteristics and advantages of solid-state drives Speed: SSDs provide faster data access and transfer speeds compared to HDDs. This is due to the absence of mechanical parts, resulting in virtually instant data access. Durability: Because there are no moving parts, SSDs are more durable and less prone to mechanical failure than HDDs. They are also more resistant to physical shock and temperature variations. Energy Efficiency: SSDs consume less power than traditional HDDs. This can be particularly beneficial for laptops and other battery-powered devices, as it contributes to longer battery life. Quiet Operation: Since there are no moving parts, SSDs operate silently, providing a quiet computing experience compared to the audible noise generated by spinning HDD disks. Compact Form Factor: SSDs are available in smaller form factors, which is advantageous for devices with limited space, such as ultrabooks, tablets, and certain server configurations. Reliability: SSDs are generally more reliable due to their solid-state nature. They are less susceptible to mechanical failures and wear and tear over time. Lower Latency: SSDs exhibit lower latency in accessing and retrieving data, contributing to improved system responsiveness and faster application loading times. While SSDs offer numerous benefits, they are often more expensive on a per-gigabyte basis compared to traditional HDDs. As a result, data storage solutions often involve a combination of both types, with frequently accessed data stored on SSDs for speed and performance, and less frequently accessed data stored on HDDs or other slower, more cost-effective storage media. #### Storage Efficiency Storage efficiency is the optimization of data storage resources to ensure that data is stored in a manner that maximizes capacity utilization, reduces data storage costs, and maintains or improves performance. Efficient storage practices are crucial for enterprise IT organizations dealing with growing data volumes, the majority of which is unstructured data. What are some strategies to maximize data storage efficiency? In addition to the right approach to unstructured data management, some of the common ways to ensure storage efficiency include: Data Deduplication Deduplication involves identifying and eliminating duplicate copies of data. By storing only one instance of duplicate data, organizations can save storage space and reduce redundancy. Watch the demo of the Komprise Potential Duplicates Report. Compression Compression techniques reduce the size of data by encoding it in a more compact form. Compressed data requires less storage space and can lead to more efficient storage utilization. Download the white paper Tiered Storage Implementing tiered storage involves categorizing data based on access frequency and importance. Frequently accessed or critical data can be stored on high-performance, more expensive storage, while less critical or infrequently accessed data can be moved to lower-cost, slower storage tiers. Thin Provisioning Thin provisioning allows organizations to allocate storage space on an as-needed basis rather than allocating the full amount upfront. This helps prevent over-provisioning and ensures that storage resources are used efficiently. Automated Storage Management Implementing automated storage management tools and processes allows for dynamic adjustment of storage resources based on changing demands. Automation helps optimize storage allocations and reduces the need for manual intervention. Snapshots and Backup Efficiency Efficient storage systems use snapshot technologies to create point-in-time copies of data. This allows for quick and efficient backups, reducing the impact on primary storage and simplifying data recovery processes. Archiving and Data Lifecycle Management Archiving infrequently accessed or older data to lower-cost storage solutions can free up space on primary storage. Implementing effective data lifecycle management ensures that data is stored on the most suitable storage tier throughout its lifecycle. Storage Virtualization Storage virtualization abstracts physical storage resources, allowing for centralized management and optimization of storage across heterogeneous environments. It simplifies storage administration and enables more efficient resource utilization. Cloud Storage Integration Integrating cloud storage into the storage infrastructure allows organizations to leverage scalable and cost-effective cloud resources for storing data. Cloud storage can be used for archival, backup, and offloading infrequently accessed data. Efficient File Systems Choosing file systems optimized for storage efficiency can make a significant difference. Some file systems are designed to handle large amounts of data efficiently, with features such as fast indexing and snapshot capabilities. Monitoring and Analytics Implementing monitoring tools and analytics helps organizations understand storage usage patterns, identify potential bottlenecks, and make informed decisions about optimizing storage configurations. Read the blog post: File Data Metrics to Live By Regular Maintenance and Cleanup Periodic reviews and cleanup of obsolete or redundant data, as well as reclaiming unused storage, contribute to maintaining an efficient storage environment. Efficient storage management is an ongoing process that requires a combination of technologies, best practices, and strategic decision-making. By adopting these strategies, organizations can optimize storage resources, reduce data storage costs, and ensure that their storage infrastructure aligns with business objectives. #### Dynamic Links Komprise takes the native advantages of symbolic links and innovated further, dynamically binding them to the file at runtime, akin to a DNS router. This makes the links themselves disposable – if a link is accidentally deleted, Komprise can restore it. When Komprise tiers data from a file system, it replaces the original file with a Dynamic Link address: resilient, always available and flexible. There are several benefits to the Dynamic Link approach: The file can be moved again through its data lifecycle and the link is unchanged. Allows Komprise to not sit in the hot data and metadata paths because it uses standard file system constructs. The link is resilient when coupled with the high-availability architecture of Komprise and has no single point of failure. Here is a summary of these advantages in an unstructured data migration use case. Once Komprise moves a file and replaces it with a Dynamic Link, if the file is moved again – say, for example, after the first archive of a file to an object store and another later to the cloud – the Dynamic Link address does not need to be changed. This eliminates the challenges of managing links. Users and applications continue to access the moved data transparently from the original location even as the data is moved throughout its lifecycle, without any changes. Learn More about Komprise TMT. Before and After Migration: SMB (Windows) Systems Before and After Migration: NFS (Linux) Systems By leveraging a standard protocol construct whenever possible (in more than 95% of all cases), Komprise is able to deliver non-proprietary, open, transparent data access without getting in front of the hot data or metadata. If a user accidentally deletes the links on the source, Komprise can repopulate the links since the link itself does not contain the context of the moved file. Data can be moved from one destination to another (e.g. for ongoing unstructured data management) and there are no changes to the link. To the user, this means no disruption. Users’ storage teams won’t get bothered with help desk tickets from employees unable to find their data, and their applications will be able to keep access to their data. Users and applications that rely on the data that has been moved by Komprise are unaffected. Stubs versus Dynamic Symbolic Links at a Glance Comparing Stubs and Komprise Dynamic Links While it’s clear that symlinks offer superior resilience and flexibility than static stubs, it’s not that stubs are never useful or never used by Komprise. While most file servers support symbolic links, there are a few situations where the file servers do not support symbolic links. For such file servers, Komprise uses stubs that are dynamic. Dynamic stubs point to Komprise, which redirects them to actual files in the target. This ensures that even if the stub is lost, the corresponding file on the target can be accessed via Komprise and the stub can be restored. Komprise’s dynamic stubs can be made similar in size and appearance to the original file. Watch a TMT Chalk Talk presentation with Komprise CTO Mike Peercy. #### Orphaned Data Orphaned data refers to data that is no longer associated with a corresponding record or entity in a database, data storage or other information system. This situation typically arises when a record, file, or object is deleted, but the associated data remains in the system without a proper link to a parent entity. Orphaned data can lead to various issues, including data inconsistency, inefficiency in storage usage, and potential challenges in data maintenance and retrieval. One of the most popular Komprise prebuilt reports, the Orphaned Data report shows metrics on data from ex-employees - sometimes referred to as “zombie data” or “unowned data.” Most organizations have no idea how much orphaned data they have nor how much it is costing them, which is both a cost liability and a potential compliance issue if the organization has policies on deleting ex-employee data. The Komprise Orphaned Data report shows the amount and cost of orphaned data and lists the top 10 shares with orphaned data by size. The report also recommends actionable steps to reduce these costs. Watch the Reporting Best Practices Komprise customer success webinar. What are some of the characteristics and considerations related to orphaned data? Deletion of Parent Records: Orphaned data often occurs when a parent record or entity is deleted from a database, but the associated child data is not properly removed or updated. Incomplete Data Relationships: Orphaned data indicates incomplete or broken relationships between data elements within a database or system. Database Integrity Issues: Orphaned data can compromise database integrity, as it may violate referential integrity constraints that define relationships between tables. Storage Inefficiency: Orphaned data occupies data storage space without contributing to the meaningful content or structure of the database or data storage device, leading to inefficient use of storage resources and high data storage costs. Data Cleanup Challenges: Identifying and cleaning up orphaned data can be challenging, especially in large and complex databases. Automated tools and careful database maintenance practices are often necessary. Impact on Data Quality: Orphaned data can contribute to data quality issues, as it may lead to inconsistencies and inaccuracies when querying or analyzing information. Data Retrieval Difficulties: Retrieving relevant information from a database with orphaned data can be problematic, as the disconnected data (often trapped in data silos) may not be readily accessible or associated with the desired context. Prevention and Cleanup Strategies: Database administrators often implement strategies to prevent orphaned data, such as using cascading delete operations or triggers to ensure that child records are appropriately handled when parent records are deleted. Data storage administrators are increasingly replying upon unstructured data management solutions to provide visibility and actionable insights. Regular data audits and cleanup processes are essential to identify and address orphaned data. Application Development Considerations: When designing database schemas and developing applications, it's crucial to implement robust data management practices to avoid orphaned data scenarios. Addressing orphaned data challenges Addressing orphaned data requires a combination of proactive prevention measures during system development and ongoing maintenance practices to identify and resolve any existing orphaned data issues. Database administrators play a key role in implementing and enforcing data integrity constraints and regularly auditing the database for potential orphaned data situations. Data storage administrators with hybrid, multi-cloud and multi-storage vendor environments are increasingly looking to data storage agnostic solutions like Komprise Intelligent Data Management to provide analytics driven unstructured data management and ongoing data lifecycle management solutions for cost savings and to harness greater value from their growing volumes of unstructured data. #### Data Lineage Data lineage is the tracking and visualization of the flow of data as it moves through various stages of a business process or analytical pipeline. A common function for extract, transform and load (ETL) and other structured data management tools, data lineage provides a detailed view of how data is sourced, transformed, and consumed within an organization. The primary purpose of data lineage is to enhance transparency, traceability, and understanding of data movement, helping organizations to meet compliance requirements, ensure data quality, and troubleshoot issues. While data lineage is becoming part of a unstructured data management strategy, it is more commonly considered to be part of traditional data management. What are some of the components of data lineage? Source Systems: Identification of the original sources of data, which could include databases, applications, external data feeds, or manual data entry. Transformation Processes: Documentation of the steps and processes involved in transforming raw data into a more usable format. This may include data cleansing, aggregation, enrichment, and other transformations. Storage Locations: Tracking where data is stored at various stages of processing, such as databases, data warehouses, or data lakes. Consumers: Identification of downstream processes, applications, or users that consume the data for reporting, analytics, or other purposes. Dependencies: Understanding the relationships and dependencies between different data elements and datasets. This includes understanding how changes in one dataset may impact others. Timestamps and Versions: Recording the timing of data movements and transformations, as well as the versioning of datasets to understand when data was updated and how it has changed over time. What are the benefits of knowing data lineage? Considered to be a core component of a data integration and data lifecycle management strategy, the right data lineage approach assists with the following: Data Quality Assurance: Data lineage helps organizations identify and rectify issues related to data quality by providing a clear view of the transformations and processes applied to the data. Compliance and Auditing: For industries with regulatory requirements, data lineage is essential for compliance and auditing purposes. It enables organizations to demonstrate the traceability and integrity of their data. Issue Resolution: When errors or discrepancies are identified in the data, data lineage allows for efficient issue resolution by tracing the problem back to its source and understanding the steps involved in data processing. Impact Analysis: Data lineage facilitates impact analysis by showing how changes to source data or processes may affect downstream systems and reports. Data Governance: Data lineage is a critical component of effective data governance, providing insights into how data is managed, used, and shared across the organization. Metadata and data lineage Tools and technologies for implementing data lineage often include metadata management solutions, data cataloging and data classification tools, and data integration platforms. These tools help automate the documentation and visualization of data lineage, making it more manageable in complex data ecosystems. #### Data Silos Data silos refer to isolated pockets of data within an organization that are not easily accessible or shared with other parts of the organization. Data silos are a common challenge for enterprise IT organizations over time as data becomes confined to a specific department, team, or system, and there is limited integration, communication or collaboration with other parts of the organization. This lack of data access and data integration can lead to inefficiencies, redundancies, and challenges in obtaining a holistic view of the organization's data. What are some common characteristics of data silos? Data Isolation: Data within a silo is typically isolated from the rest of the organization. Different departments or teams may have their own databases, systems, or tools, and data is stored separately. Limited Data Access: Access to data in a silo is often restricted to the individuals or teams that own and manage that particular data. This can hinder collaboration and decision-making across the organization. Data Redundancy: Multiple copies of similar or identical data may exist across different silos. This redundancy can lead to inconsistencies, as updates or changes made in one silo may not be reflected in others. Inefficiencies: Working with data silos can result in duplicated efforts and increased manual labor. For example, if different departments maintain their own customer databases, it may be challenging to get a unified view of all customer interactions. Lack of Data Integration: Data in silos often lacks integration with other parts of the organization. This lack of integration can make it difficult to derive meaningful insights or value from data making informed decisions based on a comprehensive understanding of the data challenging. Data Quality Issues: Siloed data may suffer from data quality issues, as there might be variations in data standards, formats, and definitions across different silos. Barriers to Innovation: Siloed data can impede innovation and hinder the adoption of advanced analytics, machine learning, or other data-driven technologies that benefit from a unified and comprehensive dataset. What are some strategies to address data silos in the enterprise? For structured and semi-structured data, addressing data silos involves implementing strategies and technologies to break down barriers and promote data integration and collaboration. This may include: Data Integration Solutions: Implementing tools and processes to integrate data from different sources and systems. Master Data Management (MDM): Establishing centralized management of core data entities (e.g., customers, products) to ensure consistency across the organization. Data Governance: Implementing policies and practices to ensure data quality, security, and compliance across the organization. Cross-Functional Collaboration: Encouraging collaboration and communication between different departments and teams to break down silos and promote a more holistic approach to data management. Unstructured data management solutions that are data storage agnostic have emerged to address the challenges of data storage silos in the enterprises, designed to optimize data storage costs and unlock value from the majority of data in the enterprise, which is unstructured. By addressing data silos, organizations can unlock the full potential of their data, improve decision-making, and foster a more agile and data-driven culture. #### Data Orchestration Data orchestration is a general term primarily used by data management vendors. It refers to the process of coordinating and managing the flow of data within an organization or between different systems (often referred to as data silos) and platforms. It involves the arrangement, coordination, and optimization of data workflows to ensure that data is efficiently and effectively moved, processed, and utilized. The term data orchestration can refer to unstructured data and structured / semi-structured data. Key Aspects of Data Orchestration Data Integration: Bringing together data from various sources, such as databases, applications, and external APIs, to create a unified and cohesive view of the information. Increasingly data integration vendors focus on data automation and data orchestration. Modern examples include Boomi and SnapLogic. Data Movement: Transferring data between different systems, platforms, or storage locations. This may involve tasks such as ETL (Extract, Transform, Load) processes or real-time data streaming. Increasingly data orchestration is a term used for unstructured data management solution providers. Komprise Intelligent Data Management is an example. Workflow Automation: Designing and automating data workflows to streamline processes and reduce manual intervention. This can include scheduling, triggering, and monitoring data tasks. Learn more about Komprise Smart Data Workflows. Data Protection: As it relates to unstructured data, a data orchestration strategy should automate the movement of critical data to online and offline storage and include a comprehensive strategy for valuing, classifying, and protecting these data assets from user errors, malware and viruses, machine failure, or facility outages/disruptions, in addition to reducing data storage costs. See Data Protection. Data Transformation: Modifying the structure, format, or content of data to meet the requirements of the target system or application. Data Quality: Ensuring the accuracy, completeness, and consistency of data by implementing validation checks, cleansing processes, and error handling. Metadata Management: Managing metadata to provide context, lineage, and documentation for better understanding and governance of the data. Scalability and Performance Optimization: Optimizing data processes for performance, scalability, and resource efficiency, especially in large-scale data environments. Security and Compliance: Implementing measures to ensure data security, privacy, and compliance with relevant regulations and policies. Monitoring and Logging: Implementing tools and processes to monitor the health and performance of data workflows, detect issues, and log relevant information for troubleshooting. Collaboration and Governance: Facilitating collaboration among different teams and stakeholders involved in data management. Establishing governance policies to ensure data is handled responsibly and in accordance with organizational standards. Data orchestration is broad term used by many different types of technology vendors. It is not a term that has been embraced by enterprise IT teams and there is not a Gartner Market Guide or Magic Quadrant focused on Data Orchestration because it is such a broad term. That said, it is increasingly important as part of a data management and data lifecycle management strategy, where organizations deal with diverse data sources, formats, and volumes. The right approach to data orchestration helps private and public sector organizations derive value from their data by making it more accessible, reliable, and actionable. Various tools and platforms, including data integration tools, workflow automation tools, unstructured data management platforms, are used to implement and manage data orchestration processes. #### Storage Metrics Storage metrics (or data storage metrics) are measurements and indicators used to assess various aspects of a storage system's performance, capacity, and efficiency. These metrics provide valuable insights into how data storage resources are utilized, helping organizations optimize their storage infrastructure, plan for future needs, and troubleshoot issues. Read the blog post: File Data Metrics to Live By. Common data storage metrics Capacity Utilization: Used Capacity: The amount of storage space currently in use. Free Capacity: The remaining storage space available for use. Throughput: IOPS (Input/Output Operations Per Second): The number of read and write operations that a storage system can perform in one second. Throughput: The amount of data (in bytes) transferred per unit of time. Latency: Read Latency: The time it takes for a storage system to respond to a read request. Write Latency: The time it takes for a storage system to acknowledge the completion of a write operation. Availability: Uptime/Downtime: The percentage of time the storage system is available versus the time it is unavailable. Reliability: Error Rate: The frequency of errors or data corruption within the storage system. Data Protection: Backup Success/Failure: The success or failure rate of backup operations. Snapshot Usage: The utilization of snapshot technology for data protection. Storage Efficiency: Deduplication Ratio: The ratio of data reduction achieved through deduplication. Compression Ratio: The ratio of data reduction achieved through compression. Data Lifecycle Management: Data Age: The age of data in the storage system, helping in the management of data lifecycle. Resource Utilization: CPU and Memory Usage: The utilization of CPU and memory resources on storage devices. Network Performance: Bandwidth: The amount of data that can be transmitted over the network in a given time. Queue Length: Storage Queue Length: The number of I/O operations waiting to be processed by the storage system. Monitoring these storage metrics provides data storage administrators and IT teams with the information needed to make informed decisions about storage provisioning, performance optimization, and overall system health. Many storage management tools and platforms offer dashboards and reports that display these metrics for easy analysis and troubleshooting. Unified Data and Storage Insights Komprise Storage Insights gives administrators the ability to drill down into file shares and object stores across locations and sites, including relevant metrics by department, division or business unit, such as: Which shares have the greatest amount of cold data? Which shares have the highest recent growth in new data? Which shares have the highest recent growth overall? Which file servers have the least free space available? Which shares have tiered the most data? One Komprise customer put it this way: “It’s a single interface that will show us important metrics like capacity usage in every storage location, which will save us a lot of time and ensure we make the right decisions for our departments and users.” #### High Performance Computing High-Performance Computing (HPC) is the term used to define supercomputer and computer clusters that solve complex computational problems and require significant processing power. Learn more about the annual HPC "SuperCompute" conference: The International Conference for High Performance Computing, Networking, Storage, and Analysis. What are HPC Systems? HPC systems are designed to deliver much higher performance than traditional computing systems, making them suitable for tasks such as scientific simulations, weather modeling, financial modeling, and other applications that demand intensive numerical calculations. Key characteristics of HPC systems include: Parallel Processing: HPC systems often rely on parallel processing, where multiple processors or cores work simultaneously to perform computations. This allows for the efficient handling of large datasets and complex algorithms. Specialized Hardware: HPC systems may use specialized hardware components, such as GPUs (Graphics Processing Units) or accelerators, to enhance computational speed for specific types of calculations. High-Speed Interconnects: The components of an HPC cluster need to communicate rapidly with each other. High-speed interconnects, such as InfiniBand or other high-performance networking technologies, are used to minimize communication delays. Scalability: HPC systems are designed to scale horizontally, meaning that additional computing nodes can be added to the system to increase overall performance. High-Performance Storage: Efficient storage systems are crucial for handling the large amounts of data generated and processed by HPC applications. High-performance file systems are often used to ensure fast access to data. Distributed Computing: HPC applications are typically designed to run in a distributed computing environment, where tasks are divided among multiple processors or nodes. HPC is used in various fields, including scientific research, engineering simulations, climate modeling, bioinformatics, and more. It plays a crucial role in advancing our understanding of complex phenomena and solving problems that would be infeasible with conventional computing resources. Data Management and HPC High-Performance Computing data management involves handling and organizing large volumes of data (mostly unstructured data) efficiently within an HPC environment. As HPC systems are designed for parallel processing and high-speed computation, managing data becomes a critical aspect to ensure optimal performance. Here are key considerations for HPC data management: High-Performance Storage Systems HPC environments often use high-performance parallel file systems to ensure fast and reliable access to data. Distributed and parallel file systems, such as Lustre or GPFS (IBM Spectrum Scale), are commonly employed to provide scalable and high-throughput storage. Data Locality Optimizing data locality is crucial for minimizing data transfer times between storage and compute nodes. Data is often distributed across the storage system in a way that minimizes the need for long-distance data transfers during computation. Parallel I/O HPC applications generate and consume large amounts of data in parallel. Efficient input/output (I/O) mechanisms are essential for maintaining high performance. Parallel I/O libraries, like HDF5 or MPI-IO, are used to enable concurrent data access from multiple nodes. Data Compression and Storage Formats Compression techniques may be employed to reduce the amount of data stored and transferred, especially when dealing with massive datasets. Choosing appropriate storage formats that are optimized for the specific characteristics of the data can also impact performance. Data Movement Efficient data movement between different components of the HPC system is critical. This includes moving data between storage and compute nodes and potentially between different levels of storage (e.g., from high-speed scratch storage to longer-term storage). Data movement strategies should minimize the impact on overall system performance. Metadata Management Effective management of metadata (information about the data, such as file attributes) is crucial for quick and accurate data retrieval. Metadata servers and databases are often employed to index and organize metadata efficiently. Data Replication and Data Backup Ensuring data reliability and availability is important. HPC systems often implement data replication and backup strategies to prevent data loss due to hardware failures. Data Lifecycle Management Implementing data lifecycle management policies helps determine how data is stored, moved, and eventually archived or deleted based on its relevance and usage patterns over time. Security and Access Control Implementing robust security measures, including access controls and encryption, is crucial to protect sensitive data in an HPC environment. Monitoring and Analytics Monitoring tools are used to track data usage, performance, and potential issues within the HPC data management infrastructure. Analytics tools can provide insights into data patterns and trends, helping optimize data storage and retrieval strategies. Efficient data management, and more specifically unstructured data management, in HPC environments is essential for maximizing the performance of computational workflows and ensuring that data-intensive applications can scale effectively. #### Department Showback Department showback is a financial management practice that involves tracking and reporting on the costs associated with specific departments or business units within an organization. Also see Showback. It is a way to allocate and show the IT or operational costs incurred by various departments or units to help them understand their resource consumption and budget utilization. Department showback is often used as a transparency and accountability tool to foster cost-awareness and responsible resource usage. Key aspects of department showback Cost Attribution: Department showback allocates or attributes the costs of IT services, infrastructure, or other shared resources to individual departments or business units based on their actual usage or consumption. This helps departments understand their financial responsibilities. Reporting and Visualization: The results of department showback are typically presented in reports or dashboards that clearly outline the costs incurred by each department. Visualization tools can make it easier for department heads and executives to understand the cost breakdown. Transparency: By providing departments with detailed information on their costs, department showback promotes transparency and accountability in resource consumption. It allows departments to see the financial impact of their decisions. Budgeting and Planning: Armed with cost data, departments can better plan and budget for their future resource needs. They can make more informed decisions about IT or operational expenditures. Chargeback vs. Showback Department showback is different from chargeback. In chargeback, departments are billed for the actual costs they incur. In showback, departments are informed of their costs, but no actual billing takes place. Showback is often used for educational and cost-awareness purposes, while chargeback is a financial transaction. Both models are popular with data storage and becoming more popular with broader adoption of storage-agnostic unstructured data management software. Cost Optimization: Armed with cost information, departments can identify opportunities for cost optimization. This might involve reducing unnecessary resource usage or finding more cost-effective alternatives. Resource Allocation: Departments can use the cost data to justify resource allocation requests, ensuring that they have the resources needed to meet their objectives. Data-Driven Decision-Making: Department showback promotes data-driven decision-making by providing departments with financial data that can guide their choices and strategies. Benchmarking: Comparing the costs of similar departments or units can help identify best practices and opportunities for improvement. Department showback is particularly valuable in organizations with complex IT infrastructures, cloud services, or shared resources. It helps ensure that resources are used efficiently, aligns costs with departmental priorities, and fosters a culture of financial responsibility and accountability. It's important to note that department showback should be implemented with clear communication and collaboration between the finance department, IT, and department heads to ensure that cost allocation methods are fair and accurate. Additionally, the success of department showback depends on the organization's commitment to using cost data to inform decision-making and drive cost optimization efforts. #### Storage Insights Komprise announced Storage Insights to unify storage management and unstructured data management with the release of Komprise Intelligent Data Management 5.0. What is Storage Insights? Storage Insights is a console that is included in all editions of Komprise including Komprise Analysis and Komprise Elastic Data Management. Komprise has always provided visibility across heterogeneous storage on data, including how data is being used, how fast it’s growing, who is using it, what data is hot and cold, and where it lives. Storage Insights delivers a Data Stores console that adds storage metrics to these data metrics to further simplify file and object data management. See all your storage organized by data center locations and clouds and view the available capacity, data size, data growth, growth percentage, amount of cold data, department the share belongs to, vendor-specific metrics and more. Storage Insights is a management console to quickly understand both data usage and storage consumption and where you add new data stores for analysis and data management activities. A single view for unified data and storage management. Customize your view and track and manage what matters most. Why Storage Insights? Increasingly distributed and multi-storage enterprise IT organizations are frustrated that each storage vendor tends to report free space or storage consumption differently and they must search across different places for this information. By adding storage metrics and a customizable interface to Komprise Intelligent Data Management, customers don't have to look in multiple places.  They get one consistent definition and view of their storage metrics and data metrics. Komprise unifies both data and storage insights in a single console with Storage Insights. Customers can easily spot trends like which shares have the most anomalous activity or which shares are filling up the fastest or which shares are owned by a particular department that have a lot of cold data. Learn more about Komprise Intelligent Data Management 5.0. #### Global File System A global file system, often referred to as a global distributed file system or a global namespace file system, is a type of file system that allows for the unified management and access of files and data across a distributed or networked environment. The goal is to abstract the physical location of files and provides a single, logical view of data regardless of where it is stored or how the storage is distributed. The concept of a global file system is commonly discussed in enterprise environments and cloud computing as a way to simplify data management, primarily unstructured data management, and improve accessibility. Common features and characteristics of global file systems Unified Namespace: A global file system provides a single, unified namespace that abstracts the underlying storage infrastructure. Users and applications access files and data using a consistent naming convention, irrespective of the physical storage location. Distributed Data: Data within a global file system can be distributed across multiple storage devices, servers, data centers, or cloud services. This distribution can improve data availability, scalability, and fault tolerance. Access Transparency: Users and applications can access files and data without needing to know the physical location or storage details. This access transparency simplifies data access and management. Data Replication: Global file systems often support data replication to enhance data availability and redundancy. Copies of data can be stored in multiple locations for failover and disaster recovery purposes. Scalability: These file systems are designed to scale horizontally, allowing for the addition of storage devices or nodes to accommodate growing data requirements. A key issue with most so-called global file system or global namespace solution is that they sit in front of the hot data and become a data access and data performance bottleneck. Load Balancing: Load balancing mechanisms distribute data access requests across multiple servers or storage devices to optimize performance and prevent bottlenecks. Security: Security features, such as access controls, encryption, and authentication, are typically implemented to protect data within the global file system. Caching: Caching mechanisms can be employed to improve read and write performance by temporarily storing frequently accessed data in memory. Metadata Management: Metadata about files, such as file attributes, permissions, and access control lists, is managed centrally to ensure consistency. Versioning: Some global file systems support versioning, allowing users to access and restore previous versions of files. File Locking: File locking mechanisms may be implemented to prevent conflicts when multiple users or applications access the same file simultaneously. Compatibility: Global file systems are often designed to be compatible with various operating systems, file protocols, and APIs, making them versatile in heterogeneous environments. Examples of global file systems and distributed file systems NFS (Network File System): NFSv4 and NFSv4.1 support a global namespace, enabling clients to access files across a network as if they were on a local file system. Ceph: Ceph is an open-source distributed storage platform that provides a global file system called CephFS, offering a unified namespace for object storage and block storage. GlusterFS: GlusterFS is a distributed file system that creates a single global namespace from multiple underlying storage servers. Amazon Elastic File System (EFS): EFS is a cloud-based global file system service provided by Amazon Web Services (AWS) that allows multiple Amazon EC2 instances to access shared file storage. The promise of a global file system is to simplify data management in modern, distributed computing environments, making it easier for organizations to store, access, and manage their data resources efficiently and consistently across the network. Global File System: Always in the Hot Data Path A global file system provides a consistent way to access the data or metadata residing in that file system from many locations, and where multiple users in different locations may be working on copies of the same file. It also provides a consistent way to access, configure and administer the file system. The two types of global file systems are: Storage-centric: Stores the data and provides access to it using a single mount that fronts all data requests and is always in the hot data path. By “fronts all data” we mean that all data and metadata request are channeled through this mount. Some vendors extend this notion to keep the bulk of the data as proprietary blocks in the cloud. In this case of “cloud storage” GFS, you need to recognize that access to your data always requires licensing the GFS even when the bulk of your data may be in the cloud, which may unnecessarily add costs. A storage-centric GFS does not provide a truly global namespace. It can only provide visibility into data residing on that vendor's storage system. Metadata-based: Also known as a virtual global file system, this approach fronts data sitting on other storage systems. All data and metadata access is channeled through this virtual global file system, which runs in front of existing storage file systems. The benefit of this approach is that it works across multiple storage vendors. However, there is a heavy price for this as all access must pass through the metadata-based controller, which slows down performance if it is implemented fully in software or increases costs substantially if it requires dedicated hardware. This is because it is in the hot data path and manages data access even though it is not storing any data blocks. A metadata-centric GFS can provide a global namespace across multi-vendor storage systems, but it must do so by fronting all data access, which will negatively impact performance and scalability. A global file system enhances the inherent value of a storage solution when employees need to actively collaborate in use cases such as engineering collaboration and design. But since 80% of data is cold and not actively accessed, and since typically less than 5% of data requires active collaboration, for unstructured data management, data tiering and feeding data to AI/ML, a global namespace that is not in the hot data path gives truly heterogeneous visibility with the best performance. Learn more about the Komprise Deep Analytics, the Global File Index and the Komprise Intelligent Data Management platform architecture. #### Warm Cutover A so-called "warm cutover" is a strategy used during the migration or deployment of a new system, application, or infrastructure, including data storage. It involves transitioning from the old system to the new one with minimal downtime or service interruption. Unlike a "cold cutover," where the old system is completely shut down before the new one is brought online, a warm cutover allows for some overlap where both systems run concurrently for a period. With the release of Komprise Intelligent Data Management 5.0, Komprise Elastic Data Migration supports warm cutover to enable a zero-downtime data migration. More Information about a Warm Cutover Migration Common characteristics / features include: Parallel Operation: In a warm cutover, the new system is brought online and begins operating in parallel with the old system. This means that both systems are running and serving users simultaneously for a period. Data Synchronization: During the warm cutover, data is continuously synchronized or copied from the old system to the new one to ensure that both systems have the same data. This may involve real-time data replication or periodic data transfers. Testing and Validation: Extensive testing and validation of the new system occur during the overlap period. This is crucial to ensure that the new system functions correctly, meets performance expectations, and is free of critical issues. User Transition: Users are gradually transitioned from the old system to the new one. This can be done in stages or based on user groups, and users are typically provided with guidance and training to adapt to the new system. Monitoring and Rollback Plan: Close monitoring of both systems is essential during the warm cutover to detect any issues promptly. Additionally, a rollback plan is prepared in case critical problems arise, allowing for a safe return to the old system if necessary. Final Transition: Once the new system is confirmed to be stable and meets all requirements, the final transition occurs. This involves redirecting all users and traffic to the new system while shutting down the old system. Benefits of a Warm Cutover Reduced Downtime: Compared to a cold cutover, which involves a complete system shutdown, a warm cutover minimizes downtime. Users experience less interruption in service. Risk Mitigation: The ability to test and validate the new system extensively during the overlap period reduces the risk of unexpected issues or failures when transitioning. Smooth Transition: Users and staff have time to adapt to the new system gradually, reducing the potential for confusion and errors during the transition. Fallback Option: The presence of a rollback plan provides a safety net in case issues arise, allowing for a return to the old system without major disruptions. Warm cutover data migration strategies are commonly employed in scenarios where system availability and data integrity are critical, such as in business-critical applications, e-commerce platforms, and other situations where continuous service is essential. They require careful planning, coordination, and testing to ensure a seamless transition from the old system to the new one. Learn more about warm cut-over and other use cases supported by Komprise Elastic Data Migration Learn more about Komprise Elastic Data Migration Watch the webinar: Migration Best Practices #### Common Internet File System (CIFS) The Common Internet File System (CIFS) is a network file-sharing protocol that allows applications to read and write to files and request services from file servers over a network. CIFS is also known as Server Message Block (SMB), which is the name of the protocol's predecessor. CIFS/SMB is commonly used in Windows-based environments for sharing files, printers, and other resources over a network. It's also widely supported on other operating systems, making it a cross-platform protocol. Common Internet File System (CIFS) features typically: File Sharing: CIFS allows multiple users and applications to access files and directories on a remote server as if they were on a local file system. This enables efficient file sharing and collaboration within a network. Authentication and Authorization: CIFS provides authentication mechanisms to ensure that only authorized users can access shared resources. It supports user-level permissions and access control lists (ACLs) to define who can read, write, or modify files and directories. Naming and Path Resolution: CIFS uses a hierarchical naming system for files and directories, similar to the file systems on local devices. It supports both absolute and relative paths to locate and access resources on the network. Session Management: CIFS establishes and manages sessions between client and server for resource access. Sessions help maintain the connection state and security context during file operations. Transaction Support: CIFS allows clients to perform multiple file operations as part of a single transaction, ensuring consistency and data integrity. Printing: CIFS supports print services, allowing users to send print jobs to remote printers connected to CIFS-enabled servers. Browser and Discovery Services: The protocol includes a mechanism for network clients to discover available resources and servers on the network, making it easier to locate shared resources. Transport Layer: CIFS can run over various transport protocols, including TCP/IP, NetBEUI, and NetBIOS over TCP/IP. TCP/IP is the most common transport used for CIFS over modern networks. Versions: Over the years, CIFS has seen several versions and enhancements. SMB1, SMB2, SMB3, and SMB3.1 are the major versions, each introducing improvements in performance, security, and features. Cross-Platform Compatibility: While originally developed for Windows, CIFS/SMB is supported on various operating systems, including Linux, macOS, and even some network-attached storage (NAS) devices. This cross-platform support makes it a popular choice for heterogeneous network environments. CIFS / SMB has become the de facto standard for file sharing in Windows-based networks and is widely used in corporate environments, home networks, and cloud-based storage services. It allows users to access and share files and resources seamlessly, making it a foundational technology for networked file systems and collaborative computing. Naming: CIFS or SMB? The terms "Common Internet File System" (CIFS) and "SMB" (Server Message Block) are often used interchangeably because they refer to essentially the same network file-sharing protocol. However, there is some historical context and nuance to these terms: SMB (Server Message Block) Origin: SMB was originally developed by IBM in the early 1980s as a network protocol for file and printer sharing in local area networks (LANs). Microsoft later adopted and extended the SMB protocol for use in its Windows operating systems. Versions: Over the years, SMB has seen several versions, including SMB1, SMB2, SMB3, and SMB3.1. Each version introduced enhancements in terms of performance, security, and features. Naming: The term "SMB" is often used to refer to the protocol in general, regardless of the specific version. CIFS (Common Internet File System) Origin: CIFS is essentially an extension or enhancement of SMB. It emerged in the late 1990s as a set of improvements and additions to SMB to make it more suitable for internet-based file sharing. CIFS was intended to provide better support for wide-area networks (WANs) and the internet. Enhancements: CIFS includes additional features like support for long file names, better security mechanisms, and improved performance over WAN connections. Naming: "CIFS" is often used to refer to a specific version or dialect of the SMB protocol that includes these enhancements. While SMB and CIFS are often used interchangeably, SMB typically refers to the family of protocols, including various versions like SMB1, SMB2, SMB3, etc. CIFS, on the other hand, can be thought of as a specific version or dialect of SMB with additional features and improvements aimed at better internet-based file sharing. However, in practice, the term "SMB" is commonly used to encompass both the earlier SMB versions and the later enhancements found in CIFS. It's worth noting that in recent years, there has been a shift away from using older SMB1 due to security vulnerabilities, and organizations and systems have been encouraged to upgrade to more secure and feature-rich versions like SMB2 or SMB3. How to detect, enable and disable SMBv1, SMBv2, and SMBv3 in Windows #### Global Namespace A global namespace is a concept used in many fields of computer science and IT to describe a unified and consistent naming system for resources that can be accessed from multiple locations or contexts within a distributed computing environment. While the idea makes sense, too often the technology solutions available on the market today sit directly in the hot data path, causing performance bottlenecks. It's important to step back and assess the goals for a global namespace and choose your technology solution carefully. The primary purpose of a global namespace is to provide a way to access and manage resources, such as files, directories, objects, or services, in a manner that abstracts their physical location or distribution across a network. This abstraction should simplify resource management and allows for scalability and flexibility in distributed systems. A Global Namespace does not need a Global File System. Read the whitepaper. Learn more about the Komprise architecture and how the Intelligent Data Management platform never sits in front of the hot data path. Key aspects of a global namespace: Be clear on the objective before embarking on this journey towards a global namespace, also known as a universal file system. Also be sure to learn more about the Komprise Global File Index as an alternative that ensures there is no user or application disruption and does not sit in front of the hot data and impact performance. You can think of Komprise as a federated global namepace - visibility, mobility, value. Key aspects of a the global namespace strategy have historically included: Resource Abstraction: A global namespace abstracts the physical or logical location of resources, making them appear as if they are part of a single, unified namespace. This abstraction allows users and applications to access resources without needing to know where they are physically located. Learn more about Komprise Transparent Move Technology. Scalability: Global namespaces musb be able to scale as the number of resources and the size of the distributed system grow. New resources can be added to the namespace without disrupting existing operations. Consistency: A global namespace enforces naming conventions and consistency across the distributed environment. This ensures that resources have unique names and that naming conflicts are minimized. Access Transparency: Users and applications can access resources in the global namespace using a consistent naming convention, regardless of whether the resource is located on the local system or a remote system. This transparency simplifies resource access. Location Transparency: Location transparency means that users and applications don't need to be aware of the physical location of resources. The global namespace is meant to provide a level of indirection that allows the system to route requests to the appropriate location. Distribution: Resources in a global namespace should be distributed across multiple servers, data centers, or cloud environments. The namespace management system handles resource distribution and location details. Security: Global namespace systems often include access control and authentication mechanisms to ensure that only authorized users or applications can access resources. Examples of global namespaces in different contexts: File Systems: Distributed file systems like the Server Message Block (SMB) Common Internet File System (CIFS) and the Network File System (NFS) provide a global namespace for accessing files and directories across a network. Object Storage: Cloud-based object storage services like Amazon S3 and Azure Blob Storage offer a global namespace for storing and accessing objects (e.g., images, documents) via unique object keys. Distributed Databases: Distributed databases may use global namespaces to abstract the location and naming of data tables and records across multiple database nodes. Service Discovery: In microservices architectures, global namespaces can be used for service discovery, allowing applications to locate and communicate with services across a distributed environment. Global namespaces are a core concept in the design of distributed and scalable computing systems. They goal is to simplify resource management and access in complex distributed environments, making it easier for users and applications to interact with resources across the network seamlessly. Some of the known challenges of the concept of a global namespace include: Scalability: As the number of resources and the size of the distributed system grow, managing a global namespace becomes increasingly complex. Ensuring that namespace operations remain efficient and do not become a bottleneck can be challenging. Consistency: Maintaining consistency across a global namespace can be difficult, especially in distributed systems where multiple copies of data or resources may exist. Ensuring that all clients see a consistent view of the namespace, even in the presence of concurrent updates, is a challenge. Concurrency Control: Dealing with concurrent access and updates to the global namespace can lead to conflicts and synchronization issues. Implementing effective concurrency control mechanisms is essential to prevent data corruption and maintain data integrity. Security: Ensuring the security of resources and access control in a global namespace can be complex. Controlling who can access and modify resources, especially in a distributed and potentially untrusted environment, requires robust security measures. Data Distribution: In distributed systems, resources may be distributed across various physical locations or data centers. Ensuring that data is distributed optimally for performance and availability while maintaining a consistent namespace view is a challenge. Data Migration: Moving data or resources within a global namespace, especially in response to changes in the system's topology or resource allocation, can be challenging. Data migration needs to be seamless and transparent to users and applications. Fault Tolerance: Global namespaces must be designed to be fault-tolerant. When network failures, server crashes, or other issues occur, the namespace should continue to function correctly and without data loss. Network Latency: In distributed environments, network latency can impact the performance of namespace operations. Minimizing the impact of latency on user experience is a challenge. Naming Conflicts: Ensuring that resource names within a global namespace are unique and avoiding naming conflicts can be challenging, especially in large-scale distributed systems with many users and applications. Versioning and Compatibility: Managing versioning and ensuring backward compatibility of the global namespace protocol as it evolves over time can be complex, particularly in heterogeneous environments where various protocol versions may coexist. Monitoring and Diagnostics: Debugging issues in a global namespace, monitoring its health and performance, and diagnosing problems can be challenging due to the distributed and abstract nature of the namespace. Compliance and Regulations: Ensuring that the global namespace complies with legal and regulatory requirements, such as data privacy and data retention policies, can be complex and may require specific features or controls. To address these challenges, organizations often rely on advanced distributed file systems, object storage systems, namespace management solutions and unstructured data management solutions. These solutions must be designed to provide scalability, consistency, security, and fault tolerance in global namespaces, making them suitable for various use cases, including cloud storage, content delivery, and data sharing in distributed environments. Benefits of a Global Namespace without a Global File System As unstructured data continues to pile up across both data center and cloud silos, it’s easy to see the appeal of a single way to access and manage data no matter where it lives. Imagine having one place to get visibility into data across all your silos, identify hot and cold data, and plan and execute data migrations and data tiering across all your storage and cloud locations? And what if this same system allowed your users to search for relevant data across storage silos and feed AI/ML pipelines and create automated data workflows? These are the many advantages of a global namespace for enterprise data storage. As unstructured data volumes continue to expand exponentially, data silos proliferate and IT budgets remain relatively flat, many organizations are interested in the data management benefits of a global namespace. However, it’s important to note that a global namespace does not require a global file system (GFS), despite vendors often claiming this to be the case. A global file system sits in front of the data and serves the appropriate files, thus acting as a controller. While a GFS is useful in some collaboration scenarios, using it to achieve the management benefits of a global namespace creates unnecessary overhead that results in loss of data control, loss of flexibility, poor visibility, poor performance and high costs. It is important to understand the different approaches and goals and ideal use cases for each. Komprise provides the data visibility, access and cost management benefits of a global namespace - all without the overhead of a global file system. Questions to Help You Determine What's Best: Global Namespace and/or Global File System 1. Do you want to: A) Replace your existing NAS with something new. B) Leverage our existing investments and modernize our infrastructure. If the answer is A) a storage-centric global file system might be the solution. Be sure to not only focus on switching costs, but also the long-term costs and implications of having a new storage technology platform sitting in front of (and hosting) all your data. If the answer is B) Komprise can help with analytics-driven data migration, management and mobility 2. Are you trying to: A) Collaborate across teams and locations? B) Improve the ability to view and manage data across systems? If the answer is A) a metadata-based GFS might be the solution. Be sure to determine the importance of collaboration use cases and the ongoing costs of fronting all of your data storage. If the answer is B) Komprise can help provide the benefits of a global namespace without sitting in the hot data path. 3. Do you anticipate needing multiple users to collaborate on large files across multiple locations, requiring local caching? If the answer is YES, you will need a GFS. If the answer is NO, you want the visibility benefits of a global namespace and do not want or need the overhead of a global file system. Komprise Intelligent Data Management might be the right solution. Learn more about the Komprise Global File Index - the benefits of a federated global namespace that never sits in front of the hot data path. #### Chain of Custody The NIST definition of Chain of Custody is: "A process that tracks the movement of evidence through its collection, safeguarding, and analysis lifecycle by documenting each person who handled the evidence, the date/time it was collected or transferred, and the purpose for the transfer." Also, a process that tracks the movement of evidence through its collection, safeguarding, and analysis lifecycle by documenting each person who handled the evidence, the date/time it was collected or transferred, and the purpose for any transfers. With Komprise Elastic Data Migration you can manage chain of custody reporting with checksums and integrity reporting per file. Komprise logs any files that can’t be copied due to permission, file locking, or other issues. While some issues such as file locking will typically be resolved in later iterations, other issues such as permissions will require administrator intervention. Komprise maintains an advanced audit log to identify and help in resolving issues. Learn more. Chain of Custody Data Management In data management, chain of custody is the systematic and organized process of recording, tracking, and managing the custody and movement of physical or digital evidence, documents, or samples throughout their lifecycle. The need to track chain of custody in data management is crucial in various industries and contexts, including law enforcement, forensics, healthcare, environmental testing, legal proceedings, and supply chain management. It ensures the integrity, security, and traceability of items as they move from one entity or location to another. Chain of custody is a term in data management that can be related to: Data Collection: The process begins with the collection of detailed information about the evidence or items. This information includes a unique identifier, description, date and time of collection, location, and the names and contact information of individuals involved in the collection process. Secure Storage: Secure storage of both physical and digital items - physical evidence may be stored in controlled environments, while digital evidence or documents may be stored in secure servers or repositories with access controls. Data Recording: All relevant information about the custody and handling of items is recorded in a structured manner. This includes any transfers of custody, changes in location, inspections, tests, or analyses conducted. Access Control: Access to chain of custody data should be restricted to authorized personnel only. This helps prevent unauthorized modifications or tampering of records. Timestamps: Timely and accurate timestamps are essential to establish a clear chronological history of an item's movement and custody. This ensures that any gaps or irregularities can be easily identified and addressed. Documentation Continuity: Any actions or changes made to the item or its data must be documented, including who performed the action, when it was done, and the reason for the action. This documentation preserves the integrity of the chain of custody. Verification and Authentication: Chain of custody data should be periodically verified to ensure its accuracy and completeness. This can involve reconciling physical items with their corresponding records or using digital signatures and encryption to verify the integrity of digital data. Reporting: In data management, chain of custody often includes the generation of reports or documentation that summarize the history of an item's custody and handling. These reports can be used in legal proceedings or audits. Compliance: Depending on the industry and context, chain of custody in data management may need to adhere to specific regulations and standards, such as ISO 17025 for laboratories or legal requirements. Integration: Chain of custody systems can be integrated with other systems, such as laboratory information management systems (LIMS) or electronic health record (EHR) systems, to streamline data capture and reporting. Chain of custody plays a crucial role in ensuring the traceability and credibility of evidence and information, especially in legal and regulatory contexts. It helps establish that items have been handled and maintained in a manner that preserves their integrity and prevents tampering, contamination, or loss. Accurate and well-maintained chain of custody records are vital for legal defensibility, accountability, and the protection of individuals' rights in various processes and industries. #### File Analysis (File Storage Analysis) File analysis or file storage analysis is the process of evaluating and managing the storage of digital files within an organization or on a computer system. The goal of storage analysis is to optimize file storage resources, improve data accessibility, and ensure efficient use of data storage infrastructure. Gartner Peer Insights defines File Analysis (FA) products this way: "File analysis (FA) products analyze, index, search, track and report on file metadata and file content, enabling organizations to take action on files according to what was identified. FA provides detailed metadata and contextual information to enable better information governance and organizational efficiency for unstructured data management. FA is an emerging solution, made of disparate technologies, that assists organizations in understanding the ever-growing volume of unstructured data, including file shares, email databases, enterprise file sync and share, records management, enterprise content management, Microsoft SharePoint and data archives." Read: Komprise Names Top File Analysis Software Vendor by Gartner Why File Data Analysis? File storage analysis is the process of evaluating and managing the storage of digital files within an organization. The goal of storage analysis is typically to optimize file storage resources and cost, improve data accessibility, and ensure efficient use of storage infrastructure. Some common file storage analysis use cases include: Storage Capacity Assessment: Determine the total storage capacity available, both in terms of physical storage devices (e.g., hard drives, SSDs) and cloud storage services (e.g., AWS S3, Azure Blob Storage). This assessment helps in understanding how much storage is currently being used and how much is available for future use. Storage Usage Analysis: Analyze how storage space is being utilized, including the types and sizes of files stored, the distribution of data across different file types, and the storage consumption patterns over time. File Data Lifecycle Management: Implement file lifecycle policies to identify and manage files based on their age, usage, and importance. This includes data archiving, data deletion (See: Data Hoarding), or file data migration to different storage tiers as they age or become less frequently accessed. Duplicate File Identification: Identify and eliminate duplicate files to free up storage space. Duplicate files are common in many organizations and can waste valuable storage resources. Watch a demonstration of the Komprise Potential Duplicates Report. Performance Optimization: Analyze storage performance to ensure that data retrieval and storage operations meet performance expectations. This may involve optimizing file placement on storage devices, load balancing, and caching strategies. Cost Optimization (including Cloud Cost Optimization): Evaluate the costs associated with different storage solutions, including on-premises storage, cloud storage, and hybrid storage configurations. Optimize storage costs by selecting the most cost-effective storage options based on data usage patterns. Backup and Disaster Recovery Analysis: Ensure that files are properly backed up and that disaster recovery plans are in place. Regularly test data recovery processes to verify their effectiveness. It's important to analyze your data before backup to optimize data storage and backup costs. Data Retention Policy Compliance: Ensure that data retention policies are adhered to, particularly in industries subject to strict data compliance regulations (e.g., healthcare, finance). This involves safely deleting files that are no longer needed and retaining data as required by law. Storage Tiering and Optimization: Implement data storage tiering strategies to allocate data to the most suitable storage class based on access frequency and performance requirements. This can include the use of high-performance SSDs for frequently accessed data and slower, less expensive storage for archival purposes. Read the white paper: File-level Tiering vs. Block Level Tiering. Forecasting and Capacity Planning: Predict future storage needs based on historical data and growth trends. This helps organizations prepare for increased storage requirements and avoid unexpected storage shortages. See FinOps. The right approach to file storage analysis involves the use of specialized data management and storage management software and tools. Read more about the benefits of storage-agnostic unstructured data management. The goal is to deliver insights into storage usage, performance metrics, and compliance with storage policies in order to make informed decisions about storage investments and ensure that file storage is efficient, cost-effective, and aligned with business needs. Komprise Analysis: Make the Right File Data Storage Investments Komprise Analysis allows customers with petabyte-scale unstructured data volumes to quickly gain visibility across storage silos and the cloud and make data-driven decisions. Plan what to migrate, what to tier, and understand the financial impact with an analytics-driven approach to unstructured data management and mobility. Komprise Analysis is available as a standalone SaaS solution included with Komprise Elastic Data Migration and the full Komprise Intelligent Data Management Platform. Read: What Can Komprise Analysis Do For You? #### Active Storage Active storage is a data storage approach where frequently accessed or frequently changing data is stored on high-performance storage systems that are readily available for immediate use. This method is commonly used in the context of computer systems, databases, and cloud computing. The purpose of active storage is to ensure that critical and frequently needed data is quickly accessible, allowing applications and users to retrieve and modify the data with minimal latency. High-speed access to active storage is essential for real-time processing, interactive applications, and any use cases where data needs to be rapidly read or updated. In contrast, less frequently accessed or less critical data may be moved to less expensive and slower (aka lower-cost) storage tiers, such as archival storage or cold data storage, where retrieval times are not as critical. This tiered storage approach, often referred to as hierarchical storage management, optimizes data storage costs by storing data on the most suitable and cost-effective storage based on its usage patterns and access requirements. Active storage can be implemented using various technologies, including high-performance disk arrays, solid-state drives (SSDs), in-memory databases, and cloud-based storage solutions designed for low-latency access. The concept of active storage is closely related to the idea of "hot data" - data that is currently being actively used and requires immediate access. As data usage patterns change over time, data may transition between active storage and other storage tiers based on access frequency, age, or other criteria defined by the data lifecycle management strategy of an organization. Case study: Pfizer is saving 75% on storage by using Komprise to analyze and continuously move cold data to Amazon S3 as it ages. #### Data Lifecycle Management Data Lifecycle Management (DLM) is the process of managing data throughout its entire lifecycle - from creation or acquisition to its deletion or archiving. As the name suggests, Data Lifecycle Management involves various stages and activities to ensure that data is effectively and securely managed throughout its existence. With unprecedented data growth in the enterprise, particularly of unstructured data, data hoarding has become a significant challenge to address. The right approach to unstructured data management and the recognition that all data cannot be treated the same has led to an increased focus on data governance and data lifecycle management, which typically includes: Data Creation/Acquisition: This is the initial stage where data is generated or acquired by an organization through various sources such as data entry, sensor devices, APIs, data feeds, or third-party vendors. Data Storage: After data is created or acquired, it needs to be stored in appropriate data repositories, such as databases, data warehouses, data lakes, or cloud storage systems. The storage infrastructure must be designed to accommodate the volume, velocity, and variety of the data being managed. Data Processing and Analysis: Once the data is stored, it can be processed, transformed, and analyzed to derive insights and valuable information. This stage involves data cleansing, data integration, aggregation, and applying analytical techniques to extract meaningful patterns and trends. (Related areas: Data science, data lakes, data preparation, data warehousing.) Data Usage and Presentation: After the data has been analyzed, it is utilized to make informed decisions, generate reports, create dashboards, or feed into applications for various business purposes. Increasingly feeding AI and ML is a use case here. Data Archiving: As data ages or becomes less frequently used, it may be moved from active storage to long-term archival storage for compliance purposes or to free up resources on primary storage systems. (See hot data, cold data.) Data Retention and Deletion: Organizations need to establish data retention policies that dictate how long data should be kept based on regulatory requirements or business needs. At the end of its useful life, data should be securely and permanently deleted to avoid any data privacy or security risks. (See Data Hoarding) Data Security: Throughout the entire data lifecycle, data security measures must be implemented to protect data from unauthorized access, breaches, or other cybersecurity threats. (See Data Protection.) Data Governance and Compliance: Data governance policies and procedures are put in place to ensure data quality, integrity, and compliance with relevant regulations and standards. Data Backup and Disaster Recovery: Regular data backups and disaster recovery plans are essential to safeguard against data loss due to hardware failures, natural disasters, or cyber incidents. The right data lifecycle management (see also Information Lifecycle Management) strategy can help organizations maximize the value of their data, reduce data storage costs, ensure data integrity, comply with regulations, and maintain good data hygiene practices. It is particularly crucial in the context of artificial intelligence (AI), big data, data privacy, and data protection considerations. #### Komprise Deep Analytics Komprise Deep Analytics delivers granular, flexible search and indexes data in-place across file, object and cloud data storage to build a comprehensive Global File Index (GFI) spanning petabytes of unstructured data. Komprise Deep Analytics Actions: Add Deep Analytics queries to a plan and operationalize your ability to search and find what you need and when you need it. Smart Data Workflows: Leverage the GFI metadata catalog for systematic, policy-driven data management actions that can feed your data pipelines. #### Komprise Intelligent Data Management Komprise Intelligent Data Management is the full platform suite from Komprise, which delivers instant insight into data across NAS and Object data storage silos—from on-prem to the edge and across multi-cloud data storage. Identify savings, systemically move cold data transparently without any disruption to optimize costs, get an easier, faster path to the cloud and deliver greater unstructured data value. With Komprise Intelligent Data Management as a service you can analyze, migrate, tier, archive, replicate, and manage data at scale simply and reliably. Read the solution brief: Why Komprise Intelligent Data Management. Komprise Intelligent Data Management includes Komprise Analysis, Elastic Data Migration and Deep Analytics. Many enterprise organizations start with Komprise Analysis to know first and then determine the right data mobility and ongoing data management strategy. #### Komprise Analysis Komprise Analysis provides strategic insights into unstructured file and object data across your on-premises and cloud enterprise IT infrastructure: Analyze across all your NAS, NFS, SMB, dual shares, as well as cloud storage. See how much data you have, how fast it is growing, what is hot/cold. Quickly understand file data types, top users, top groups, top directories. Perform cost/benefit modeling and capacity planning for tiering and data management.   With Komprise Analysis, you quihttps://www.komprise.com/resource/komprise-analysis-overview/ckly gain visibility across storage silos and the cloud to make data-driven decisions. Plan what to migrate, what to tier, and understand the financial impact with an analytics-driven approach to data management and mobility. What if you could significantly reduce your data costs by transparently moving/tiering infrequently used data to less expensive storage? What if you could tier data without disrupting users or applications and feed select data to AI and ML analysis tools to help generate revenue? With Komprise you can know first, move smart, extract value and take control of your unstructured data growth and costs. That’s the power of Intelligent Data Management. Komprise Analysis is available as a standalone SaaS solution included with Komprise Elastic Data Migration and the full Komprise Intelligent Data Management Platform. #### Dark Data What is Dark Data? Dark data is the term used to describe the vast amount of data (primarily unstructured data) that organizations collect, generate, and store but do not actively use, analyze, or leverage for decision-making, business intelligence, analytics, AI or other purposes. This data typically remains untapped or unexplored due to various reasons, such as lack of awareness, inadequate data management processes, or technical challenges. Gartner defines Dark Data as: The information assets organizations collect, process and store during regular business activities, but generally fail to use for other purposes (for example, analytics, business relationships and direct monetizing). Similar to dark matter in physics, dark data often comprises most organizations’ universe of information assets. Thus, organizations often retain dark data for compliance purposes only. Storing and securing data typically incurs more expense (and sometimes greater risk) than value. In the article: 5 Steps for Minimizing Dark Data Risk, the first step to protecting dark data is visibility. (See Komprise Analysis.) Examples of Dark Data Unstructured data: This includes text documents, images, videos, audio files, and other forms of data that are not organized in a structured format like databases. Log files: Many systems generate log files to record events, errors, and other activities, but these logs may not be regularly reviewed or analyzed. Historical data: Older datasets that were collected for specific projects or purposes might no longer be actively used or considered valuable. Redundant or duplicated data: Copies of data that were created for backup or replication purposes but are not actively used. (Sometimes known as Redundant, Outdated, Trivial or ROT data.) Siloed data: Data that is isolated in different departments or systems, making it challenging to access and integrate with other data sources. IoT-generated data: With the proliferation of Internet of Things (IoT) devices, there's an increasing amount of data being generated, but not all of it is fully utilized. Dark Data Challenges Some of the known challenges for the accumulation of so-called Dark Data include: Data storage costs: Storing large amounts of unused data can be costly, both in terms of hardware and cloud storage expenses. Security and privacy risks: Dark data may contain sensitive information that isn't adequately protected, increasing the risk of data breaches. Missed insights: Valuable insights and opportunities for improvement may be hidden within the dark data, preventing organizations from making data-driven decisions. Compliance and legal challenges: Regulatory requirements may demand proper data management and disposal practices, which dark data may violate. To address dark data challenges, organizations need to implement better data governance practices, invest in data management tools and infrastructure, particularly unstructured data management, and establish processes to identify, classify, and leverage relevant data both efficiently and effectively. By doing so, they can ensure strong data protection is established while unlocking the potential hidden within their dark data and turn it into valuable insights for better decision-making, strategic planning and the growing opportunity presented by artificial intelligence in the enterprise. #### Wide Area Network (WAN) A Wide Area Network (WAN) is a type of computer network that spans a large geographic area, typically covering multiple cities, countries, or even continents. WANs are designed to connect devices and networks that are located far apart, allowing them to communicate and share resources. Compared to Local Area Networks (LANs), which are typically confined to a single location like an office building, WANs provide connectivity over longer distances. They enable organizations to establish communication links between their various branch offices, data centers, and remote locations. WANs often rely on public or private telecommunication networks, such as leased lines, MPLS (Multiprotocol Label Switching), or the Internet itself. They can use various networking technologies, including routers, switches, and other network equipment, to facilitate data transmission and routing across the network. Common WAN use cases Connecting branch offices: WANs allow organizations to interconnect their branch offices, enabling seamless communication and sharing of resources between different locations. Data center connectivity: WANs provide connectivity between geographically distributed data centers, enabling data replication, disaster recovery, and efficient resource utilization. Remote access: WANs enable remote workers to securely connect to the organization's network and access resources as if they were on-site, using technologies like VPN (Virtual Private Network) or remote desktop services. Cloud connectivity: WANs facilitate connectivity to cloud service providers, allowing organizations to access and utilize cloud-based resources and services. WANs play a crucial role in modern networking, enabling efficient and reliable communication across vast distances. They are essential for businesses and organizations that require connectivity and data exchange between multiple locations. Read the blog post: Tips for a Clean Cloud Migration Learn more about Komprise Hyperstransfer for faster cloud file migrations. #### Water Usage Effectiveness (WUE) Water Usage Effectiveness (WUE) is a metric used to assess the water efficiency of data centers. It measures the amount of water consumed per unit of IT equipment output or computing work performed in a data center. WUE is derived from the concept of Power Usage Effectiveness (PUE) and Carbon Usage Effectiveness (CUE), which focus on energy efficiency and carbon emissions, respectively. Just as PUE and CUE aim to minimize energy consumption and carbon footprint, WUE aims to minimize water consumption and promote sustainable water management in data centers. To calculate WUE, the total water consumption of a data center is divided by the amount of computing work or IT equipment output. Water consumption includes both direct water usage, such as for cooling systems, and indirect water usage associated with electricity generation. WUE = Data center water consumption (L) ÷ IT equipment energy usage (kWh) By optimizing cooling systems, adopting water-efficient technologies, and implementing best practices, data centers can reduce their WUE and minimize their impact on water resources. Strategies for improving WUE may include using water-efficient cooling methods, recycling and reusing water, implementing advanced cooling technologies like evaporative cooling, and optimizing facility design for reduced water usage. Efficient water management in data centers is becoming increasingly important as water scarcity and conservation efforts gain attention worldwide. By monitoring and improving WUE, data center operators can contribute to sustainable water use and reduce the environmental impact of their operations. Komprise has written about the opportunity for sustainable data management as part of an overall sustainability and data center emission, data center optimization and data center consolidation strategy. #### Carbon Usage Effectiveness Carbon Usage Effectiveness (CUE) is a metric used to evaluate the energy efficiency and environmental impact of data centers. It measures the amount of carbon emissions produced per unit of computing work performed in a data center. CUE is an extension of the Power Usage Effectiveness (PUE) metric, which measures the energy efficiency of a data center. The concept of CUE recognizes that not all energy sources used by data centers have the same carbon footprint. Some energy sources, such as fossil fuels, have a higher carbon intensity and contribute more to greenhouse gas emissions compared to cleaner sources like renewable energy. Calculating CUE CUE is calculated by dividing the total carbon emissions from all sources associated with a data center (including the emissions from electricity generation) by the amount of computing work performed in the data center. The computing work is typically measured in terms of the data center's IT load or the number of computations performed. Carbon dioxide emission equivalents caused by data center energy use (CO2eq) ÷ IT equipment energy usage (kWh) A lower CUE value indicates a more energy-efficient and environmentally friendly data center, as it means less carbon emissions are produced per u nit of computing work. Data center operators strive to reduce their CUE by adopting energy-efficient technologies, optimizing cooling systems, implementing renewable energy sources, and improving overall operational efficiency. It's worth noting that while CUE is a useful metric for evaluating the environmental impact of data centers, it is just one aspect of sustainability. Other factors such as water usage, electronic waste management, and overall lifecycle assessment should also be considered to have a comprehensive understanding of a data center's environmental footprint. Komprise has written about the opportunity for sustainable data management as part of an overall sustainability and data center emission, data center optimization and data center consolidation strategy. #### Storage Assessment A storage assessment is a process of evaluating an organization's data storage infrastructure to gain insights into its performance, capacity, efficiency, and overall effectiveness. The goal of a storage assessment is typically to identify any bottlenecks, inefficiencies, or areas for improvement in the storage environment. Whether delivered by a service provider or the storage vendor, traditional storage assessments have focused on: Storage Performance: The assessment examines the performance of the storage infrastructure, including storage arrays, network connectivity, and storage protocols. It measures factors such as IOPS (Input/Output Operations Per Second), latency, throughput, and response times to identify any performance limitations or areas for optimization. Capacity Planning: The assessment analyzes the current storage capacity utilization and predicts future storage requirements based on data growth trends and business needs. It helps identify potential capacity constraints and ensures adequate storage resources are available to meet future demands. Storage Efficiency: The assessment evaluates the efficiency of storage utilization and identifies opportunities for optimization. This may include analyzing data deduplication, compression, thin provisioning, and other techniques to reduce storage footprint and improve storage efficiency. Data Protection and Disaster Recovery: The assessment reviews the data protection and disaster recovery strategies in place, including backup and recovery processes, replication, snapshots, and data redundancy. It ensures that appropriate data protection measures are in place to minimize the risk of data loss and to achieve desired recovery objectives. Storage Management and Monitoring: The assessment examines the storage management practices, including storage provisioning, data lifecycle management, storage tiering, and data classification. It assesses the effectiveness of storage management tools and processes and identifies areas for improvement. Storage Security: The assessment assesses the security measures implemented within the storage infrastructure, including access controls, encryption, data privacy, and compliance with industry standards and regulations. It helps ensure the security of sensitive data stored in the infrastructure. Cost Optimization: The assessment examines the data storage costs and identifies opportunities for cost optimization. This may include evaluating storage utilization, identifying unused or underutilized storage resources, and recommending strategies to optimize storage spending. Based on the findings of the storage assessment, organizations can develop a roadmap for improving their storage infrastructure, addressing performance bottlenecks, enhancing data protection, optimizing storage efficiency, and aligning storage resources with business requirements. This helps ensure a robust and well-managed data storage environment that supports the organization's data storage and unstructured management needs effectively. Analyzing Data Silos Across Vendors: Hybrid Cloud Storage Assessments Komprise Intelligent Data Management is an unstructured data management solution that helps organizations gain visibility, control, and cost optimization over their file and object data across on-premises and cloud storage environments. It offers a range of features and capabilities to simplify data management processes and improve storage efficiency. Komprise is used by customers and partners to deliver a data-centric, storage agnostic assessment of unstructured data growth and potential data storage cost savings. It helps organizations optimize storage resources, reduce costs, and improve data management efficiency based on real-time analysis of data usage patterns. Common Komprise Use Cases In addition to storage assessments, common use cases for Komprise include: Data Visibility and Analytics: Komprise Analysis provides comprehensive visibility into data usage, access patterns, and storage costs across heterogeneous storage systems. It offers detailed analytics and reporting, allowing organizations to understand their data landscape and make informed decisions. Transparent File Archiving: Komprise identifies and archives infrequently accessed data to lower-cost storage tiers without disrupting user access thanks to patented Transparent Move Technology (TMT). It provides a transparent file system view, allowing users to access archived files seamlessly and retrieve them on-demand when needed. It identifies cold or inactive data and migrates it to more cost-effective storage, without disrupting user access or requiring changes to existing applications or file systems. Cloud Data Management: Komprise extends its data management capabilities to cloud storage environments, including major cloud providers such as Amazon S3, Microsoft Azure Blob Storage, and Google Cloud Storage. It enables organizations to manage data across hybrid and multi-cloud environments with consistent policies and visibility. Data Migration: Komprise Elastic Data Migration is a SaaS solution available with the Komprise Intelligent Data Management platform or standalone. Designed to be fast, easy and reliable with elastic scale-out parallelism and an analytics-driven approach, it is the market leader in file and object data migrations, routinely migrating petabytes of data (SMB, NFS, Dual) for customers in many complex scenarios. Komprise Elastic Data Migration ensures data integrity is fully preserved by propagating access control and maintaining file-level data integrity checks such as SHA-1 and MD5 checks with audit logging. As outlined in the white paper How To Accelerate NAS and Cloud Data Migrations, Komprise Elastic Data Migration is a highly parallelized, multi-processing, multi-threaded approach that improves performance at many levels. And with Hypertransfer, Komprise Elastic Data Migration is 27x faster than other migration tools. Data Lifecycle Management: Komprise helps organizations automate the movement and placement of data based on data management policies. It enables the seamless transition of data between storage tiers, such as high-performance storage and lower-cost archival storage, to optimize performance and reduce storage costs. Komprise Intelligent Data Management helps organizations optimize their storage infrastructure, reduce storage costs, improve data management efficiency, and gain better control and insights into their unstructured data. It simplifies complex data management processes and empowers organizations to make informed decisions about their data storage and utilization. #### Carbon footprint Carbon footprint is the total amount of greenhouse gases (GHGs) emitted directly or indirectly by an individual, organization, product, or activity. It measures the impact of human activities on climate change by quantifying the amount of carbon dioxide (CO2) and other GHGs emitted into the atmosphere. Data centers consume significant amounts of energy for powering servers, cooling systems, networking equipment, and other infrastructure, which often leads to the generation of carbon dioxide (CO2) and other GHG emissions. Sustainable data management is increasingly part of an overall enterprise IT strategy to reduce the carbon footprint, with a new set of unstructured data management metrics being recommended. See File Metrics to Live By. Increasingly, unstructured data management solution providers are delivering dashboards across data storage silos that show metrics such as: Co2 Emissions Savings per TB year Power Utilization Efficiency (PUE) savings Measuring the carbon footprint The carbon footprint is typically measured in metric tons of carbon dioxide equivalent (CO2e), which includes the warming potential of other GHGs such as methane (CH4) and nitrous oxide (N2O). These emissions arise from various sources, including energy consumption, transportation, industrial processes, agriculture, and waste management. Scope of Emissions Carbon footprints can be categorized into three scopes: Scope 1: Direct emissions from sources that are owned or controlled by the entity, such as onsite fuel combustion or company-owned vehicles. Scope 2: Indirect emissions from the generation of purchased electricity, heat, or steam consumed by the entity. Scope 3: Indirect emissions from sources not owned or controlled by the entity but associated with its activities, such as supply chain emissions, business travel, and product use. Calculating the Carbon Footprint To determine the carbon footprint, emissions from various sources are measured or estimated and converted into CO2e using specific global warming potential factors. This data is then aggregated to provide a comprehensive assessment of the total emissions associated with the entity or activity. Carbon Footprint Reduction Strategies Once the carbon footprint is calculated, organizations and individuals can implement strategies to reduce their emissions. These may include energy efficiency improvements, transitioning to renewable energy sources, optimizing transportation systems, adopting sustainable practices in agriculture and manufacturing, and promoting waste reduction and recycling. Carbon offsetting involves investing in projects that help remove or reduce CO2e emissions from the atmosphere. Offsetting initiatives may include reforestation, renewable energy projects, methane capture from landfills, or investing in carbon credits. Offsetting can be used to balance or compensate for the remaining emissions that cannot be eliminated. According to modern science, understanding and reducing carbon footprints are crucial for mitigating climate change. In March, 2023 the United Nations warned of catastrophic global warming due to climate change. By measuring and managing emissions, individuals and organizations can contribute to a more sustainable future, reduce energy costs, enhance reputation, and comply with regulatory requirements. It's important to note that calculating carbon footprints can be complex due to the diverse sources and factors involved. Precise measurements and accurate data collection are essential for obtaining reliable results. Various tools and standards are available to assist organizations in calculating and managing their carbon footprints, such as the Greenhouse Gas Protocol and ISO 14064. Here are 105 ways to reduce your carbon footprint. #### OneFS FilePolicy OneFS FilePolicy is a feature of Dell EMC's PowerScale Isilon OneFS operating system. It enables organizations to automate and enforce policies for managing files within the Isilon cluster based on specified criteria. With OneFS FilePolicy, administrators can define rules and conditions that determine how files are organized, protected, and managed within the file system. These policies can be based on file attributes such as file type, file size, creation date, access patterns, or any other metadata associated with the files. OneFS FilePolicy Features Automated File Management: FilePolicy allows administrators to automate file management tasks, such as moving, copying, or deleting files based on predefined policies within Isilon environments. For example, files older than a certain date can be automatically moved to a lower-tier storage tier or archived to a separate storage system. Storage Tiering: OneFS FilePolicy enables storage tiering by automatically moving files between different storage tiers based on policies. This helps optimize storage utilization and performance by placing frequently accessed or high-priority files on faster storage tiers, while less frequently accessed or lower-priority files can be moved to lower-cost, slower storage tiers. Data Protection and Replication: FilePolicy can be used to define policies for data protection and replication. For instance, it can automatically replicate critical files to remote locations or create snapshots at regular intervals to ensure data durability and availability. Data Retention and Compliance: FilePolicy enables organizations to enforce data retention and compliance requirements by automatically applying policies for file retention, archival, and deletion. This helps ensure that files are retained for the required period and are disposed of properly when no longer needed. Customizable Policies: OneFS FilePolicy offers flexibility in defining policies based on specific business requirements. Administrators can set up multiple policies with different criteria and actions to accommodate varying file management needs. By leveraging OneFS FilePolicy, organizations can automate and streamline file data management tasks within their Isilon cluster. It helps optimize storage utilization, improve data protection and compliance, and reduce manual intervention for routine file management operations. Komprise Intelligent Data Management integrates with FilePolicy across hybrid, multi-cloud and multi-storage environments and delivers analytics-driven unstructured data management. While OneFS FilePolicy enables Dell EMC customers to manage data inside an Isilon cluster, moving data between disks for Isilon tiering, with Komprise you are able to analyze across storage vendors formats, storage types and clouds Know more. Move smart. Save more. Komprise for Dell/EMC. Isilon Migration #### SmartPools SmartPools is the tiering engine if the OneFS operating system from Dell/EMC. According to the Dell documentation, SmartPools enables you to define subgroups of nodes, called disk pools, within a single OneFS cluster. SmartPools includes an optional license that unlocks additional features including the ability to associate individual files and directories to specific disk pools. See also CloudPools Learn more about the benefits of storage agnostic unstructured data management. #### OneFS OneFS is a distributed file system developed by Dell EMC for its Isilon scale-out network-attached storage (NAS) platform. Now called the Dell PowerScale OneFS Operating System, it is designed to provide a highly scalable and resilient storage solution for managing large volumes of unstructured data. What makes OneFS different? According to Dell: The most important design choice and fundamental difference of Dell PowerScale scale out NAS is that with OneFS the storage system does not rely on hardware as a critical part of the storage architecture. Rather, OneFS combines the three functions of traditional storage architectures—file system, volume manager, and data protection—into one unified software layer. This combination creates a single, intelligent file system that spans all nodes within a storage system. OneFS features Scalability High Performance Resiliency and Data Protection Simplified Management Multi-Protocol Support OneFS is a powerful and scalable distributed file system that forms the foundation of Dell EMC's PowerScale Isilon NAS platform. It enables organizations to efficiently store, manage, and protect large amounts of unstructured data while providing high performance and scalability to meet evolving storage needs. Learn more about Komprise Intelligent Data Management for Dell. Learn more about Komprise Migration for Isilon. #### Dell PowerScale SmartPools Dell PowerScale SmartPools is the name of the feature of Dell EMC network attached storage (NAS) used for storage tiering. See Storage Pools and CloudPools. This technology was originally built for Isilon Tiering to extend Isilon storage to the cloud, optimize storage costs, handle fluctuating workloads and leverage the benefits of cloud storage while maintaining the performance and features of on-premises Isilon storage. Read: What you need to know before jumping into the cloud pool. #### Data Center Emissions Data center emissions are the greenhouse gas (GHG) emissions produced by data centers during their operations. Data centers consume significant amounts of energy to power and cool their IT infrastructure, and this energy consumption often leads to the generation of carbon dioxide (CO2) and other GHG emissions. See Data Center Consolidation and Data Storage Costs.) What contributes to data center emissions? Traditionally the factors contributing to data center emissions have focused on the IT operations management of the physical location(s) such as: Electricity Consumption: The primary source of emissions in data centers is the electricity consumed to power the IT equipment, cooling systems, lighting, and other supporting infrastructure. The majority of data centers rely on electricity generated from fossil fuel sources such as coal, natural gas, or oil, which results in the release of CO2 and other GHGs. Cooling Systems: Data centers require cooling systems to maintain optimal operating temperatures for their IT equipment. Traditional cooling methods, such as air conditioning and refrigeration, consume significant amounts of energy, contributing to emissions. However, more energy-efficient cooling technologies, such as free cooling or liquid cooling, can help reduce emissions associated with cooling. Backup Power: Data centers often rely on backup power systems, such as diesel generators, to ensure continuous operations in case of a power outage. The use of backup power systems can contribute to emissions, especially if they run on fossil fuels. Infrastructure Efficiency: The energy efficiency of data center infrastructure plays a crucial role in emissions. Inefficient equipment, power distribution systems, and cooling mechanisms result in higher energy consumption and emissions. Implementing energy-efficient technologies and optimizing infrastructure can help reduce emissions. Strategies to mitigate data center emissions Again, with the focus on the physical operations of the data center, traditional strategies to reduce data center emissions include: Energy Efficiency: Improving energy efficiency within data centers can significantly reduce emissions. This includes using energy-efficient IT equipment, optimizing cooling systems, implementing advanced power management techniques, and adopting server virtualization to maximize resource utilization. Renewable Energy: Transitioning to renewable energy sources, such as solar, wind, or hydroelectric power, can help reduce the carbon footprint of data centers. Many organizations are investing in renewable energy projects or purchasing renewable energy credits to offset their electricity consumption. Data Center Design: Implementing energy-efficient data center designs, including proper airflow management, efficient equipment layout, and insulation, can optimize energy usage and reduce emissions. Lifecycle Management: Proper lifecycle management of IT equipment, including responsible disposal and recycling, can help minimize the environmental impact and emissions associated with data center operations. Carbon Offsetting: Some organizations choose to offset their emissions by investing in carbon offset projects. These projects aim to reduce or remove CO2 from the atmosphere, such as through reforestation or renewable energy projects. Despite the shift to the cloud and increasingly hybrid and consolidated data centers, emissions continue to be a major a concern as the demand for lower cost data storage in the face of massive unstructured data growth as well as the demand processing power and high performance continues to grow. And while the industry continues to focus on sustainability by adopting energy-efficient practices, leveraging renewable energy sources, and seeking new ways to reduce emissions, the environmental impact of data center emissions cannot be denied. Data Management and Data Center Emissions It is only recently that enterprise IT organizations have been to focus on unstructured data management, as opposed to storage management, as a means to reduce data center emissions. In a post on the Azure Storage blog, the point is made about the true cost of traditional file data. The post points out that storage is only 25% of file data costs: When looking at the storage cost of file data, you need to consider that the cost of file data is at least three to four times higher than the cost of the file storage itself. The reason is that beyond storage, IT teams must also protect it with backups and replicate it for disaster recovery. The point is that with better data management practices, data growth will be managed, emissions (and costs) will be reduced. Read the eBook: 8 Ways to Reduce File Storage and Backup Costs. #### EMC PowerScale EMC PowerScale (see Dell PowerScale). PowerScale is the name of Dell Technologies scale-out network-attached storage (NAS) solution. Learn more about Komprise for Dell EMC. Learn more about Smart Migration from PowerScale Isilon. #### EMC EMC (formerly known as EMC Corporation and now known as Dell EMC) was a multinational technology company that specialized in data storage. The company was founded in 1979 and played a significant role in the development of the modern data storage industry. In 2016, Dell Technologies acquired EMC Corporation, forming Dell EMC, which is now a subsidiary of Dell Technologies. Dell EMC continues to provide a wide range of storage solutions, leveraging the technologies and expertise of both Dell and EMC. EMC offers a wide range of products and services, including storage systems, software-defined storage, data protection solutions, content management, and information governance solutions. Notable EMC products and technologies Symmetrix: EMC Symmetrix is a high-end enterprise storage platform that offers scalability, high availability, and advanced data protection features. It has been widely used in mission-critical environments. VMAX: EMC VMAX is a family of enterprise storage arrays designed to deliver high performance, scalability, and availability. It provides features like dynamic virtualization, automated tiering, and replication capabilities. Isilon (now part of the Dell PowerScale product line: Acquired by EMC, Isilon is a scale-out network-attached storage (NAS) platform that allows organizations to efficiently store, manage, and analyze large amounts of unstructured data. It is commonly used in industries such as media and entertainment, life sciences, and research. Data Domain: Also acquired by EMC, Data Domain is a deduplication storage system that reduces storage requirements by eliminating redundant data. It is used for backup and recovery purposes, providing efficient and cost-effective data protection. XtremIO: XtremIO is an all-flash storage array designed to deliver high performance and low latency for demanding workloads. It leverages inline data deduplication and compression to optimize storage efficiency. RSA Security: RSA Security, a division of EMC (now part of the Dell family of brands), focuses on providing security solutions and products, including identity and access management, encryption, and cybersecurity solutions. For an up to date history of EMC, be sure to check out Wikipedia. Learn more at Dell Storage. Komprise and Dell EMC. Komprise for Isilon migrations. #### Power Usage Effectiveness (PUE) Power Usage Effectiveness (PUE) is the metric used to measure the energy efficiency of a data center or computing facility. It is calculated by dividing the total amount of energy consumed by the data center (including IT equipment and supporting infrastructure) by the energy consumed by the IT equipment alone. (See Data Center Consolidation.) The formula for calculating PUE PUE = Total Facility Energy Consumption / IT Equipment Energy Consumption "Total Facility Energy Consumption" refers to the combined energy consumed by the entire data center, including cooling systems, lighting, power distribution, backup generators, and other supporting infrastructure. "IT Equipment Energy Consumption" represents the energy used specifically by the IT servers, storage devices, networking equipment, and other computing hardware. The purpose of the Power Usage Effectiveness metric The purpose of PUE is to provide insight into the efficiency of a data center's power usage. A lower PUE value indicates higher energy efficiency because it means a larger proportion of the total energy consumption is used directly by the IT equipment rather than being allocated to supporting infrastructure. A PUE of 1.0 represents a hypothetical ideal state where all the energy consumed is used exclusively by the IT equipment, with no additional energy needed for cooling or other infrastructure. In practice, achieving a PUE of exactly 1.0 is extremely challenging, and most data centers typically have PUE values above 1.0. Data center strategies to reduce PUE and improve efficiency Efficient Cooling Systems: Implementing energy-efficient cooling technologies, such as hot and cold aisle containment, precision cooling, or free cooling, to optimize cooling efficiency and reduce energy consumption. Virtualization and Consolidation: Using virtualization technologies to consolidate servers and optimize resource utilization, thereby reducing the overall power requirements of the IT equipment. Energy Management and Monitoring: Implementing energy management systems and monitoring tools to track and optimize energy usage, identify areas of inefficiency, and make data-driven decisions for improvement. Efficient Power Distribution: Employing efficient power distribution systems, such as uninterruptible power supplies (UPS) with high-efficiency ratings, to minimize power losses and increase energy efficiency. Renewable Energy Sources: Incorporating renewable energy sources, such as solar or wind power, into the data center's energy mix to reduce reliance on fossil fuels and lower the environmental impact. PUE is just one metric to evaluate data center energy efficiency. Additional factors like water usage efficiency (WUE) and carbon usage effectiveness (CUE) may also be considered for a comprehensive assessment of environmental impact and resource efficiency. Data Management and Sustainability In early 2022 supply chain challenges and sustainability were grabbing headlines: See this post for coverage. The focus on improving energy efficiency and reducing PUE has become increasingly important as pressures mount to consolidate data centers, accelerate cloud migration and reduce data storage costs, but to reduce the overall carbon footprint and contribute to sustainable IT operations. Komprise cofounder and COO Krishna Subramanian published this article: Sustainable data management and the future of green business. Here is how she summarized the importance of unstructured data management to sustainability in the enterprise: A lesser-known concept relates to managing data itself more efficiently. Most organizations have hundreds of terabytes of data, if not petabytes, which can be managed more efficiently and even deleted but are hidden and/or not understood well enough to manage appropriately. In most businesses, 70% of the cost of data is not in storage but in data protection and management. Creating multiple backup and DR copies of rarely used cold data is inefficient and costly, not to mention its environmental impact. Furthermore, storing obsolete “zombie data” on expensive on-premises hardware (or even, cloud file storage, which is the highest cost tier for cloud storage), doesn’t make sage economic sense and consumes the most energy resources. The recommendations for achieving sustainable data management in the article are: Understand your unstructured data Automate data actions by policy Work with data owners and key stakeholders Read the article. #### Elastic Block Store (EBS) Elastic Block Store (EBS) is a block-level storage service provided by Amazon Web Services (AWS) that is designed to be used with Amazon Elastic Compute Cloud (EC2) instances. EBS provides durable, persistent, and high-performance block storage volumes that can be attached to EC2 instances as virtual disks. Amazon EBS characteristics Block-Level Storage: EBS provides block-level storage volumes that can be formatted and used as virtual disks by EC2 instances. These volumes can be used for a wide range of applications and databases that require persistent and reliable storage. Performance Options: EBS offers different volume types to cater to various performance requirements. These include General Purpose SSD (gp2), Provisioned IOPS SSD (io1/io2), Throughput Optimized HDD (st1), and Cold HDD (sc1). Each volume type is optimized for specific use cases in terms of performance, capacity, and cost. Elasticity and Scalability: EBS volumes can be created and attached to EC2 instances on the fly, providing elasticity and flexibility in storage provisioning. Volumes can be easily resized to meet changing capacity needs without requiring downtime or data migration. Data Durability and Availability: EBS volumes are designed for durability and availability. Data stored in EBS volumes is automatically replicated within an Availability Zone (AZ) to protect against hardware failures. For additional data protection, EBS snapshots can be created and stored in Amazon Simple Storage Service (S3). Snapshots and Backup: EBS allows you to create point-in-time snapshots of your volumes, which are stored in Amazon S3. These snapshots serve as backups and can be used to restore data or create new volumes. Snapshots are incremental, capturing only the changed data, which helps reduce backup costs and storage requirements. Encryption and Security: EBS volumes support encryption at rest using AWS Key Management Service (KMS) keys. This helps protect data stored on EBS volumes and ensures compliance with data security requirements. Performance Monitoring: AWS provides tools and metrics to monitor the performance of EBS volumes, including metrics for throughput, latency, and IOPS. This allows you to optimize performance and troubleshoot any performance-related issues. Common EBS scenarios Common EBS scenarios include hosting databases, running applications, storing data files, and building scalable and highly available architectures on AWS. It integrates seamlessly with other AWS services, making it a versatile and integral part of the AWS ecosystem. EBS features, including performance characteristics, and pricing details are regularly being updated so refer to the official AWS documentation or consult with AWS for the most up-to-date information and guidelines. Learn more about Komprise for AWS. #### IOPS IOPS stands for Input/Output Operations Per Second. It is a commonly used metric to measure the performance or throughput of storage devices, such as hard disk drives (HDDs), solid-state drives (SSDs), or data storage systems. IOPS represents the number of read and write operations a storage device or system can perform in one second. It is an important metric for determining the responsiveness and efficiency of storage solutions, especially in high-performance or latency-sensitive environments. The IOPS value can vary significantly depending on factors such as the storage technology, disk capacity, disk speed, queue depth, block size, and workload characteristics. Key points about IOPS: Random IOPS: Random IOPS refers to the number of random read or write operations a storage device can handle per second. It is a measure of how quickly the storage device can handle small, random data access patterns typically seen in databases or virtualized environments. Sequential IOPS: Sequential IOPS represents the number of sequential read or write operations a storage device can perform per second. It measures the storage device's ability to handle large, sequential data access patterns, which are common in tasks such as streaming or large file transfers. Queue Depth: The queue depth represents the number of I/O requests that can be queued or outstanding at a given time. A higher queue depth allows for more simultaneous I/O operations, which can increase IOPS performance. Block Size: The block size refers to the size of the data transferred in each I/O operation. Smaller block sizes typically result in higher IOPS values, as more operations can be performed in a given time period. However, larger block sizes can improve throughput and efficiency for certain workloads. IOPS is just one metric to consider when evaluating storage performance. Other factors like latency, bandwidth, and throughput also play a significant role. Workload characteristics, including read-to-write ratios, access patterns, and the number of concurrent users or applications, should be taken into account to determine the appropriate storage solution for specific use cases. When comparing storage devices or systems, it is recommended to consider multiple performance metrics, including IOPS, to gain a comprehensive understanding of their capabilities and suitability for a given workload. Historically, hardware-oriented metrics was how data storage was measured, including: Latency, IOPS and network throughput Uptime and downtime per year RTO: Recovery point objective (time-based measurement of the maximum amount of data loss that is tolerable to an organization) RPO: Recovery time objective (time to restore services after downtime) Backup window: Average time to perform a backup Read more about the file metrics that matter. What are the top reports or metrics that data storage people need today to help keep up with these trends? Read: The Critical Role of Reporting in Trimming Storage Costs. #### Dell PowerScale Dell PowerScale is the name of Dell Technologies scale-out network-attached storage (NAS) solution. According to Dell, PowerScale is designed to provide high-performance storage for unstructured data workloads and is well-suited for demanding file and object storage requirements. In 2020, Dell rebranded many of the acquired EMC technologies such as EMC Isilon to PowerScale. PowerScale is used in a variety of industries, including media and entertainment, healthcare, research, and financial services, where large-scale data storage, high performance, and data-intensive workloads are critical. Whether your use case is cloud tiering, cloud data migration or optimizing performance and reducing storage costs, with Komprise for Dell PowerScale technologies you are able to: Identify data that can move to Dell PowerScale targets Forecast Dell PowerScale (Isilon) storage savings and ROI Access data from Dell PowerScale ECS storage without lock-in Migrate object  data into Dell PowerScale ECS Deliver a Smart Data Migration Manage unstructured data costs more effectively Scale on demand Ensure you have the easy, fast, no-lock-in path to the cloud for on-premises file and object data Learn more about Komprise for Dell EMC. Learn more about Smart Migration from PowerScale Isilon. #### Storage Costs Storage costs are the price you pay for data storage. With the exponential growth and variety of cloud storage tiers to choose from, it is important to regularly evaluate your storage costs, which will vary depending on the storage solution, type and provider you choose. See Data Storage Costs. Read the interview with Komprise Field CTO: Is there any relief for data storage costs? Read the Komprise eBook: 8 Ways to Save on File Storage and Backup Costs. Consolidate storage and data management solutions. Adopt a data services mindset: Adopt new data management metrics. Introduce an analytics approach for departments and users: Become a cloud cost optimization expert. Develop best practices for data lifecycle management. Develop a ransomware strategy that also cuts costs. Don’t get locked in. Factors that can impact storage costs: Storage Type: Different storage types have varying costs. For example, solid-state drives (SSD) generally cost more than traditional hard disk drives (HDD) due to their higher performance and faster access times. Additionally, specialized storage options like archival storage or object storage may have different pricing structures based on the intended use cases. Capacity: The amount of storage space you require directly impacts the cost. Providers typically charge based on the amount of data you store, usually measured in gigabytes (GB), terabytes (TB), or petabytes (PB). As you scale up your storage capacity, the costs will increase accordingly. See Capacity Planning. Redundancy and Data Replication: If you require data redundancy or replication for increased data durability and availability, additional costs may be involved. Providers may charge for creating and maintaining multiple copies of your data across different locations or availability zones. Data Access and Retrieval: The frequency and speed of data access can influence storage costs. Some storage services offer different retrieval tiers with varying costs, such as faster access options for immediate retrieval (which can be more expensive) or lower-cost options for infrequent access. Data Transfer: Uploading and downloading data from storage solutions often incurs data transfer costs. These charges may apply when moving data into or out of the storage service or transferring data between regions or availability zones. Service Level Agreements (SLAs): Certain storage solutions may come with service-level agreements that guarantee a certain level of performance, availability, or support. These enhanced SLAs may have higher associated costs. Cloud Provider and Pricing Models: Different cloud providers have their own pricing structures, and costs can vary between them. It's important to carefully compare the pricing details, including storage rates, data transfer costs, and any additional charges specific to each provider. Read: Cloud Storage Pricing in 2023: Everything You Need to Know. To get accurate and up-to-date pricing information, it is recommended to visit the websites of cloud storage providers like Amazon Web Services (AWS), Google Cloud Platform (GCP), or Microsoft Azure. They typically provide detailed pricing calculators and documentation that can help estimate the costs based on your specific storage requirements. #### Data Transfer Data transfer is the term used to describe the movement of data from one location or system to another. It involves transmitting data over a network or transferring it from one data storage device to another. Data transfer can occur within a local network, between different networks, or across the internet. See Komprise Hypertransfer for an example of high-speed file migration transfer. Common Data Transfer Methods Local Data Transfer: This involves transferring data within a local network or between devices connected to the same network. Local data transfer can be accomplished through wired connections like Ethernet or USB cables, or wirelessly using technologies like Wi-Fi or Bluetooth. File Transfer Protocol (FTP): FTP is a standard network protocol used for transferring files between a client and a server on a computer network. It enables the exchange of files over the internet using dedicated FTP clients or through web browsers with built-in FTP capabilities. Cloud Data Transfer: Cloud data transfer refers to the movement of data to and from cloud storage services like Amazon S3, Google Cloud Storage, or Microsoft Azure. It involves uploading data from local storage to the cloud or downloading data from the cloud to local storage. Cloud providers offer various methods, such as APIs, SDKs, command-line tools, and web interfaces, to facilitate data transfer to and from their platforms. Data Replication and Synchronization: Data replication involves creating and maintaining duplicate copies of data across multiple systems or storage locations. It ensures data redundancy and availability. Synchronization, on the other hand, involves keeping data consistent and up-to-date across different devices or storage locations by transferring only the changed or modified portions of the data. Data Transfer over the Internet: Transferring data over the internet involves transmitting data packets between devices or networks using standard internet protocols like TCP/IP. This can include methods like email attachments, cloud storage services, peer-to-peer file sharing, or direct data transfers between client and server applications. In our Tips for a Clean Cloud File Migration series of webinars we discussed the importance of performance tuning, topology and network to a successful cloud migration initiative. When transferring data, factors such as the size of the data, network bandwidth, latency, security considerations, and the transfer method or protocol being used can impact the speed and efficiency of the transfer. Security measures, such as encrypting sensitive data during transfer and verifying the integrity of transferred data to prevent unauthorized access or data corruption are also important considerations. Komprise Hypertransfer Komprise Hypertransfer for Elastic Data Migration creates dedicated virtual channels across the WAN to accelerate cloud data migrations. By establishing dedicated channels to send data, Komprise Hypertransfer minimizes the WAN roundtrips, which mitigates SMB protocol chattiness and dramatically improves data transfer rates.Tests done using a dataset dominated by small files shows Komprise accelerates cloud data migration 25x faster than other alternatives. #### AWS DataSync AWS DataSync is an online service that moves data between on premises and AWS Storage services. According to AWS, DataSync can copy data between Network File System (NFS) shares, Server Message Block (SMB) shares, Hadoop Distributed File Systems (HDFS), self-managed object storage, AWS Snowcone, Amazon Simple Storage Service (Amazon S3) buckets, Amazon Elastic File System (Amazon EFS) file systems, Amazon FSx for Windows File Server file systems, Amazon FSx for Lustre file systems, Amazon FSz for OpenZFS file systems, and Amazon FSx for NetApp ONTAP file systems. Point tools vs. platform Cloud migration of file data can be complex, labor-intensive, costly and time-consuming. Understanding your migration options is essential. Generally they are as follows: Free Tools: Good for tactical use cases, but often require a lot of hand-holding. Data migration reliability and performance are concerns. Point Data Migration Solutions: Usually older vendors who have a professional-services-centric approach. Watch out for difficult to set up and use technologies with legacy architectures, which will present user disruption and scalability challenges. Komprise Elastic Data Migration: Makes cloud data migrations simple, fast, reliable and eliminates sunk costs since you continue to use Komprise after the migration. Komprise is the only solution that gives you the option to cut 70%+ cloud storage costs by placing cold data in Object classes while maintaining file metadata so it can be promoted in the cloud as files when needed. Learn more about Komprise for AWS. #### Amazon S3 Glacier Instant Retrieval Amazon S3 Glacier Instant Retrieval is an archive storage class that was introduced in November, 2021. According to Amazon, it delivers the lowest-cost archive storage with milliseconds retrieval for rarely accessed data. Komprise works closely with AWS to ensure enterprise customers have visibility into data across storage environments. With analytics-driven unstructured data management, Komprise right places data to the right storage class: Hot data on high performance managed file services in AWS and cold data on lower cost Amazon S3 Glacier object storage such as Amazon S3 Glacier Instant Retrieval and Amazon S3 Infrequent Access. Learn more about Amazon S3 Storage Classes. Learn more about Komprise for AWS. #### Data Management for AI Data Management for AI (artificial intelligence) is the process of gathering and storing data in a way that can be used by AI and machine learning models to generate insights, make predictions and drive research and innovation initiatives. AI models require significant amounts of data to train and improve their accuracy, most of which is unstructured data. However, this data is not simple rows and columns. It is files, objects, semi-structured and structured data, all of which can be messy and difficult to manage. In late 2022, Komprise cofounder and CEO Kumar Goswami noted: “Enterprises need to be ready for this wave of change and it starts by getting unstructured data prepped, as this data is the critical ingredient for AI/ML.” He published this post in early 2023: The AI/ML Revolution: Data Management Needs to Evolve, making the following recommendations: Get full visibility so you can optimize and leverage your data If you aren’t indexing your data today, that’s a problem Make new uses of data while still being cost-efficient Collaborate with departments on data needs SPOG: Data Management Requirements for AI With so much discussion about ChatGPT, generative AI, AI regulations and the opportunities and threats posed by rapid AI innovation, Komprise cofounder and COO Krishna Subramanian tied the discussion back to data management for AI summarizing the need for strategies and policies focused on data security, data privacy, data ownership, data lineage and data governance. Read: SPLOG: The Data Management Issues with Generative AI Komprise’s Krishna Subramanian on Generative AI and Data Management AI needs unstructured data #### Storage as a Service (STaaS) Storage as a Service (STaaS) is a model where data storage resources are provided and managed by a service provider or where IT resources are set up as a service provider delivering data services to departments, divisions and data consumers. With the STaaS model, instead of organizations managing their own physical storage infrastructure, they can outsource their storage infrastructure needs to a third-party provider, who offers storage resources on-demand, typically over the internet. In addition to the public cloud vendors who deliver data storage as a service (AWS, Azure, Google), in recent years traditionally on-premises data storage vendors like HPE (GreenLake), NetApp (Keystone), Pure Storage (Evergreen/One), Dell (APEX) and others have introduced services-based options to customers. Watch Now: STaaS Best Practices with Komprise Intelligent Data Management Why Storage as a Service (STaaS)? The STaaS model offers flexibility, scalability, and data storage cost efficiency to organizations, allowing them to focus on their core business while outsourcing storage infrastructure management. In addition to OPex vs. CAPex data storage benefits, here are some of the key value proposition of storage as a service: Scalable Storage: STaaS allows organizations to scale their storage resources up or down based on their changing needs. The service provider typically offers flexible storage capacity options, allowing customers to adjust their storage allocations as required. Pay-as-You-Go Model: With STaaS, organizations pay for the storage resources they consume on a usage-based model. This can be advantageous as it eliminates the need for large upfront investments in hardware and infrastructure. Customers only pay for the storage they actually use, which can help optimize costs. Maintenance and Management: The service provider is responsible for the maintenance and management of the storage infrastructure. This includes tasks such as data backups, data replication for redundancy, security measures, software updates, and hardware maintenance. Customers can offload these operational responsibilities to the service provider, freeing up their own IT resources. Accessibility and Availability: STaaS typically offers high availability and accessibility of data. The storage resources are accessible over the internet, allowing users to retrieve and store data from anywhere, at any time. Service-level agreements (SLAs) often specify the level of data availability and performance guarantees. Data Security and Compliance: Storage service providers implement security measures to protect customer data. This may include encryption, access controls, data integrity checks, and compliance with industry and regulatory standards. Customers should ensure that the service provider meets their security requirements and complies with relevant data protection regulations. Data Transfer and Migration: STaaS providers offer mechanisms for transferring data to and from their storage infrastructure. They may provide tools, APIs, or dedicated network connections to facilitate data transfer and migration. Data transfer can be done over the internet or through direct transfer methods, depending on the provider's offerings. Integration and APIs: STaaS solutions often provide APIs (Application Programming Interfaces) that allow integration with other applications and systems. This enables seamless integration of storage services into existing workflows, applications, or cloud environments. Data Redundancy and Disaster Recovery: STaaS providers typically offer data redundancy and disaster recovery mechanisms to ensure data durability and protection against data loss. They may replicate data across multiple geographic locations or employ other backup and recovery strategies to safeguard customer data. Of course it's important for organizations to carefully evaluate service providers, understand their offerings, security measures, and SLAs to ensure that they meet their specific storage requirements and compliance needs. Showback: A STaaS Tool for Better Unstructured Data Management Read the blog post for an example of what's possible with Komprise Analysis. Also read the whitepaper: Getting Departments to Care About Storage Savings: #### Yottabyte A yottabyte is a unit of digital information storage capacity and it represents an extremely large amount of data. The prefix "yotta" denotes a factor of 10^24, which means that a yottabyte is equal to 1 septillion bytes or 1 trillion terabytes. A yottabyte's size in perspective: 1 yottabyte is equivalent to 1,000 zettabytes, which = 1 million exabytes (EB) = 1 billion petabytes (PB) = 1 trillion terabytes (TB). 1 yottabyte can hold approximately 250 trillion DVDs, each with a standard capacity of 4.7 gigabytes. It would take billions of years to transfer a yottabyte of data using a typical home internet connection. The concept of yottabytes is often used when discussing data storage capacities on a global scale, such as the estimated amount of data generated and stored worldwide. However, it is worth noting that yottabyte-scale storage is currently not practically achievable using existing data storage technologies. The term is mainly used to conceptualize and illustrate the vastness of data that can be generated in the digital age, the majority of which by far is unstructured data. #### Tape Tape or tape storage (or magnetic tape storage) is used for long-term data storage and archiving. It involves storing data on magnetic tape cartridges that can be sequentially read or written using tape drives. Tape storage is commonly used in industries such as media and entertainment, healthcare, research, finance, and government, where long-term data retention and cost-effective archiving are critical. It offers advantages in terms of capacity, durability, and cost, particularly for organizations with large data volumes and compliance requirements. However, it is important to consider factors such as access speed, data migration efforts, and ongoing maintenance and monitoring when incorporating tape storage into an overall data management strategy. Tape-Based Storage Historical Benefits Storage Capacity Tape storage offers high storage capacity, with tape cartridges now capable of storing multiple terabytes or even petabytes of data. The capacity varies depending on the specific tape technology and generation. Long-Term Tape Archival Tape is often used for long-term data archival purposes. It provides a cost-effective solution for storing large amounts of data that may not need frequent access but require long-term data retention for compliance, regulatory, or historical purposes. Data Durability Magnetic tape has good durability and can typically withstand physical wear and environmental conditions. Tape cartridges are designed to provide data integrity over extended periods, often ranging from several decades to even longer under proper storage conditions. Offline Storage Tape storage is commonly used as an offline or offline-to-online backup solution. The tapes are disconnected from the network or storage infrastructure, providing an "air gap" that helps protect against cyber threats, ransomware attacks, and unauthorized access. Sequential Access Tape drives read and write data sequentially, meaning that accessing specific data requires reading through the tape from the beginning or searching based on tape markers. This makes tape storage well suited for large-scale sequential data processing rather than random access patterns. Lower Cost Tape storage is generally more cost-effective compared to other storage technologies, such as disk-based systems or solid-state drives (SSDs). It offers a lower cost per terabyte of storage, making it an attractive option for organizations with large data volumes and budget constraints. Tape Storage Challenges Tape technology evolves over time, with new generations of tape drives and cartridges providing higher capacities and improved performance. Data migration from older tape formats to newer ones may require periodic data migration efforts to ensure compatibility and longevity. So while tape storage may have benefits for some types of data, it also comes with considerable challenges, especially for more modern enterprise IT organizations. Common tape storage challenges include: Access Speed: Tape storage is slower compared to disk-based or solid-state storage systems when it comes to data retrieval. Due to its sequential access nature, locating and accessing specific data on tapes can be time-consuming, especially when compared to random access storage mediums. Latency: Tape drives have higher latency compared to disk or flash-based storage systems. The time it takes for the tape drive to locate the desired data and position the tape heads accordingly can result in longer access times, particularly for small or scattered data requests. Limited Concurrent Access: Tape drives typically support limited concurrent access. While multiple tape drives can be used simultaneously, each drive usually operates independently, and accessing multiple tapes concurrently may require additional infrastructure and coordination. Physical Handling: Tape storage involves physical handling of tape cartridges. This can be challenging when dealing with large tape libraries or when there is a need to retrieve specific tapes from storage. Proper storage, cataloging, and tracking mechanisms are necessary to ensure efficient tape management. Reliability and Maintenance: Tape drives and cartridges require regular maintenance and monitoring. The mechanical nature of tape drives can lead to wear and tear over time, necessitating periodic cleaning and maintenance. Tape cartridges may also encounter issues such as read/write errors or physical damage, requiring proper handling and care. Compatibility and Obsolescence: Tape technology evolves over time, with new generations of tape drives and cartridges being introduced. This can lead to challenges with compatibility and data migration when transitioning from older tape formats to newer ones. Obsolescence of tape drives or media formats may also pose challenges in accessing or recovering data stored on outdated tapes. Environmental Considerations: Tape storage is sensitive to environmental conditions. Factors such as temperature, humidity, dust, and magnetic fields can affect the integrity and longevity of tape cartridges. Proper storage conditions, including temperature and humidity controls, are necessary to ensure data durability and reduce the risk of data loss or corruption. Limited Random Access: Tape storage is optimized for sequential data access rather than random access patterns. While tape libraries may provide some mechanisms for indexing and searching data, the overall random access performance is typically lower compared to disk-based storage systems. Despite these challenges, tape storage remains a viable option for long-term data retention and data archiving, particularly for organizations with large data volumes and compliance requirements. Proper planning, unstructured data management, and maintenance strategies can help mitigate these challenges and ensure the reliability and accessibility of data stored on tapes. #### Logs Logs refer to records or entries that capture events, activities, or messages generated by software applications, operating systems, servers, or network devices. Logs provide a chronological and detailed account of various system activities, which can be helpful for troubleshooting, analysis, auditing, security monitoring, and performance optimization. Log Types Logs can vary in their format and content depending on the system or application generating them. Common types of logs include system logs, application logs, security logs, event logs, error logs, access logs, audit logs, and debug logs. Logging Frameworks Software applications and systems often employ logging frameworks or libraries to generate logs systematically. These frameworks provide APIs or functions that developers can use to log specific events or messages at different levels of severity, such as debug, info, warning, error, or critical. Log Entries Each log entry typically contains specific information, including timestamps, log levels, event descriptions, error codes, source IP addresses, user actions, system configurations, stack traces, or any other relevant data related to the event being logged. Log Analysis and Monitoring Logs are frequently collected and stored centrally, making them accessible for analysis and monitoring. Log analysis involves parsing, filtering, aggregating, and correlating log data to identify patterns, anomalies, errors, or security incidents. Log monitoring involves real-time tracking and alerting based on predefined conditions or thresholds. There are many monitoring and observability vendors who focus on log analysis and monitoring. Log Retention and Archiving Organizations often establish log retention policies to determine how long logs should be retained for compliance, auditing, or forensic purposes. Logs may be archived or backed up periodically to ensure their long-term availability and integrity. Log Management Systems To effectively handle and analyze large volumes of logs, organizations may employ log management systems or log analytics platforms. These tools automate log collection, storage, indexing, search, visualization, and analysis, enabling efficient log management and insights. Security and Compliance Logs play a crucial role in security and compliance efforts. They can provide valuable information for detecting and investigating security incidents, tracking user activities, identifying vulnerabilities, and meeting regulatory requirements. Log Rotation To manage log file sizes and prevent excessive storage usage, log rotation is often implemented. Log rotation involves periodically renaming or compressing log files and starting new log files to ensure continuous logging without overwhelming storage resources. Logs are an essential component of system and application management, offering valuable insights into the operation, performance, security, and troubleshooting of computing environments. Effective log management practices and log analysis techniques can help organizations maintain the health, security, and reliability of their systems. Unstructured data management and logs: Read the whitepaper: Komprise Architecture Overview. #### File A file is a named collection of data that is stored on a computer or other storage device. It represents a unit of information, such as a document, image, video, audio recording, program, or any other type of digital content. Files are organized within a file system, which provides a hierarchical structure for storing and retrieving data. File Formats Files are typically associated with specific file formats that define the structure and organization of the data they contain. Common file formats include .txt (plain text), .docx (Microsoft Word document), .jpg (JPEG image), .mp3 (MP3 audio), .mp4 (MP4 video), and many more. Each file format has its own specifications and is designed to be interpreted or processed by specific software or applications. File Extensions File extensions are a part of the file name that indicates the file format or type. They usually consist of a period (.) followed by a few letters or a combination of letters and numbers. For example, a file named "document.txt" has a ".txt" extension, indicating that it is a plain text file. File Properties and Metadata Files can have associated properties and metadata that provide additional information about the file. This may include attributes such as file size, creation date, modification date, author, permissions, and more. File properties and metadata help users and operating systems manage and organize files effectively. File Operations Files can be manipulated through various file operations, such as creating, opening, reading, writing, modifying, moving, copying, and deleting. These operations are typically performed using file management functions or commands provided by the operating system or specific software applications. File Systems Files are stored within a file system, which is responsible for managing and organizing the storage of files on a storage device, such as a hard drive, solid-state drive, or network attached storage (NAS). File systems provide a directory structure to organize files into folders or directories and enable efficient retrieval and storage of data. File Compression Files can be compressed to reduce their size, making them occupy less storage space and facilitating faster file transfers. Compression algorithms, such as ZIP or GZIP, are used to compress files by eliminating redundancy or encoding data more efficiently. Compressed files need to be decompressed or extracted to restore them to their original form. Files are fundamental units of data in computing and are essential for storing and accessing various types of digital content. They enable the creation, sharing, and management of information in a structured and organized manner. File Tiering File Data Management. Read the white paper: Block-Level versus File-Level Tiering File Protocols There are many standards and protocols that define how files are transferred, shared, and accessed. File protocol examples include: File Transfer Protocol (FTP): FTP is one of the earliest and most widely used protocols for transferring files between computers over a network. It provides a simple way to upload, download, and manage files on a remote server. Secure File Transfer Protocol (SFTP): SFTP is an extension of the SSH protocol and provides a secure method for transferring files over a network. It offers encryption and authentication, ensuring that data is protected during transit. File Transfer Protocol over Secure Shell (FTP over SSH or FTPS): FTPS combines the FTP protocol with SSL or TLS encryption to provide secure file transfers. It adds a layer of security to the traditional FTP protocol. Hypertext Transfer Protocol (HTTP): While primarily used for transferring web pages, HTTP can also be used to transfer files. When files are accessed via HTTP, they can be downloaded directly from a web server using a web browser or other HTTP client. Hypertext Transfer Protocol Secure (HTTPS): HTTPS is the secure version of HTTP. It uses SSL or TLS encryption to secure the communication between a web server and a client, ensuring that files transferred over HTTPS are protected from eavesdropping and tampering. Network File System (NFS): NFS is a distributed file system protocol that allows files to be accessed and shared among multiple computers in a network. It enables clients to mount remote file systems and access them as if they were local. Server Message Block (SMB) / Common Internet File System (CIFS): SMB, also known as CIFS, is a network file sharing protocol commonly used in Windows environments. It allows computers to share files, printers, and other resources over a network. Web Distributed Authoring and Versioning (WebDAV): WebDAV extends the HTTP protocol to support remote file management. It enables users to collaboratively edit and manage files stored on a remote server, providing features like file locking, versioning, and metadata management. These are just a few examples of file protocols used for transferring, sharing, and accessing files over networks. Each protocol has its own specifications and features, catering to specific use cases and requirements for secure and efficient file operations. Komprise Intelligent Data Management is built on open standards. In a 2021 interview, CEO and cofounder Kumar Goswami noted: We built the product on open standards, so the customer is not locked into our solution. This was risky, because it meant that a customer could kick us out at any time. This is contradictory in the data storage industry where the popular mindset is: “own the data, own the customer.” Our approach forces us to deliver white glove treatment to ensure we’re really solving a customer’s problem. In the process, this has made Komprise stickier with our customers. The way I see it is, if you have data you need Komprise. #### Deduplication Deduplication, also known as data deduplication, is a technique used to eliminate redundant or duplicate data within a dataset or data storage system. It is primarily employed to optimize storage space, reduce data backup sizes, and improve storage efficiency. Deduplication identifies and removes duplicate data chunks, storing only a single instance of each unique data segment, and references the duplicate instances to the single stored copy. Duplicate Data Identification Deduplication algorithms analyze data at a block or chunk level to identify redundant patterns. The algorithm compares incoming data chunks with existing stored chunks to determine if they are duplicates. Chunking and Fingerprinting Data is typically divided into fixed-size or variable-sized chunks for deduplication purposes. Each chunk is assigned a unique identifier or fingerprint, which can be computed using hash functions like SHA-1 or SHA-256. Fingerprinting enables quick identification of duplicate chunks without needing to compare the actual data contents. Inline and Post-Process Deduplication Deduplication can be performed inline, as data is being written or ingested into a system, or as a post-process after data is stored. Inline deduplication reduces storage requirements at the time of data ingestion, while post-process deduplication analyzes existing data periodically to remove duplicates. Deduplication Methods There are different deduplication methods based on the scope and granularity of duplicate detection. These include file-level deduplication (eliminating duplicates across entire files), block-level deduplication (eliminating duplicates at a smaller block level), and variable-size chunking deduplication (eliminating duplicates at a variable-sized chunk level). Deduplication Ratios Deduplication ratios indicate the level of space savings achieved through deduplication. Higher ratios signify more redundant or duplicate data within the dataset. The deduplication ratio is calculated by dividing the original data size by the size of the deduplicated data. Backup and Storage Optimization Deduplication is commonly used in backup and storage systems to reduce storage requirements and optimize data transfer and backup times. By removing duplicate data, only unique data chunks need to be stored or transferred, resulting in significant storage and bandwidth savings. Deduplication Challenges and Considerations Deduplication algorithms should be efficient to handle large datasets without excessive computational overhead. Data integrity and reliability are critical, ensuring that deduplicated data can be accurately reconstructed. Additionally, deduplication requires careful consideration of security, privacy, and legal compliance when handling sensitive or regulated data. Deduplication is widely used in various storage systems, backup solutions, and cloud storage environments. It helps organizations save storage costs, improve data transfer efficiency, and streamline data management processes by eliminating redundant copies of data. Deduplication History Companies such as Data Domain (acquired by EMC) and their Data Domain Deduplication Storage Systems, introduced commercial deduplication products in the mid-2000s, which gained significant attention and adoption. These systems played a crucial role in popularizing deduplication as a key technology for data storage optimization and backup solutions. Since then, numerous vendors and researchers have contributed to the development and improvement of deduplication techniques, including variations such as inline deduplication, post-process deduplication, and source-based deduplication. Deduplication has become a standard feature in many storage systems, backup solutions, and data management platforms, providing significant benefits in terms of storage efficiency and data optimization. #### Generative AI Generative AI is a branch of artificial intelligence (AI) that focuses on creating models or systems capable of generating new content, such as images, text, music, or even video, that is original and realistic. Generative AI models learn patterns and structures from existing data and then use that knowledge to produce new, unique outputs. Generative Models Generative AI models are designed to learn and understand the underlying patterns in a given dataset and generate new samples that resemble the original data. These models aim to capture the distribution of the training data and generate outputs that are consistent with that distribution. Varieties of Generative Models There are several types of generative models, each with its own approach and architecture. Some common types include Generative Adversarial Networks (GANs), Variational Auto-encoders (VAEs), and autoregressive models like Recurrent Neural Networks (RNNs) and Transformers. Generative Adversarial Networks (GANs) consist of two neural networks: a generator and a discriminator. The generator creates new samples, while the discriminator evaluates the generated samples and distinguishes them from real samples. The two networks are trained in competition with each other, with the goal of improving the quality of the generated outputs. Variational Auto-encoders (VAEs) are generative models that learn the underlying distribution of the input data and generate new samples by sampling from that distribution. VAEs typically consist of an encoder that maps input data to a lower-dimensional latent space and a decoder that reconstructs the original input from the latent space. Applications of Generative AI Generative AI has seen a growing number of practical applications - from generating realistic images, synthesizing human-like speech, creating music, to generating natural language text, to enhancing and transforming existing content, and even to generating virtual environments for simulations and gaming. Challenges and Ethical Considerations Generative AI poses challenges and ethical considerations. Ensuring that generated outputs are diverse, realistic, and unbiased is a challenge that researchers and developers strive to address. There are concerns about potential misuse of generative AI, such as generating deepfake images or spreading disinformation. Video: The role of data management and governance in AI Generative AI Advancements and Research Generative AI technology innovation is moving very fast and is an active area of research and development. New architectures, techniques, and approaches are constantly being explored to improve the quality and diversity of generated outputs. Researchers are also working on methods to control the generation process and incorporate user preferences or constraints. Generative AI has gained significant attention and has found applications in various domains, including art, entertainment, design, and data augmentation. It offers exciting possibilities for creating new content and expanding the capabilities of AI systems beyond traditional problem-solving and pattern recognition tasks. ChatGPT and Google Bard are examples of Generative AI tools. #### Compression Compression is the process of reducing the size of a file or data set to occupy less storage space or transmit more efficiently. It involves encoding data in a more compact representation, which can be restored to its original form when needed. Compression techniques are widely used in data storage, data transmission, and multimedia applications. All about compression: Lossless Compression: Lossless compression algorithms reduce the file size without losing any data. The compressed file can be fully restored to its original form. This is commonly used for text files, databases, and other data where data integrity is crucial. Lossy Compression: Lossy compression algorithms achieve higher compression ratios by selectively discarding some data that is considered less perceptually important. This results in some loss of information, which may not be noticeable in certain types of data, such as images, audio, or video. Lossy compression is often used in multimedia applications to reduce file sizes while maintaining acceptable quality. Compression Algorithms: Various compression algorithms and techniques are employed, each with its own advantages and limitations. Some well-known compression algorithms include ZIP, GZIP, Lempel-Ziv-Welch (LZW), Huffman coding, and MPEG for video compression. Application-Specific Compression: Different types of data may benefit from specialized compression techniques tailored to their characteristics. For example, images can be compressed using techniques like JPEG, while audio can use formats like MP3 or AAC. Each format optimizes the compression based on the unique properties of the data. Compression Ratio: The compression ratio represents the reduction in file size achieved by the compression process. It is calculated by dividing the original file size by the compressed file size. Higher compression ratios indicate more efficient compression techniques. Decompression: Decompression is the reverse process of compression, where the compressed file is restored to its original form. Decompression algorithms reconstruct the compressed data based on the compression method used. Compression Performance Considerations Compression and decompression processes require computational resources, including processing power and memory. The performance impact depends on the complexity of the compression algorithm and the size of the data being compressed or decompressed. Compression is widely used to optimize storage space, reduce data transfer times, and improve bandwidth utilization. It enables efficient data storage, faster data transmission over networks, and better utilization of resources in various applications, ranging from file compression on personal computers to multimedia streaming and archival data compression. #### Data Retention Data retention is the term used for storing and keeping data for a specific period of time based on legal, regulatory, business, or operational requirements. While for many organizations there is overlap with the term data hoarding, data retention involves defining policies and procedures to determine how long different types of data (the majority of which is unstructured data) should be retained, as well as ensuring compliance with applicable laws and regulations regarding data storage and privacy. Key points about data retention: Legal and Regulatory Requirements: Many industries and jurisdictions have specific regulations or laws that dictate how long certain types of data must be retained. These requirements aim to ensure compliance, support legal obligations, facilitate audits, or provide evidence in case of disputes or investigations. Examples include financial records, healthcare data, customer information, and communication records. Business and Operational Needs: Organizations establish data retention policies to address their internal needs, such as operational efficiency, historical analysis, reporting, or knowledge management. Retaining data for a certain period allows organizations to reference past information, track trends, support decision-making, or fulfill business requirements. Retention Periods: The duration for which data should be retained varies depending on factors such as data type, industry regulations, legal requirements, business practices, and risk considerations. Some data may only need to be retained for a short period, while other data, especially for compliance-related purposes, may need to be retained for several years or even indefinitely. Data Lifecycle: Data retention is part of the broader data lifecycle management process. It involves stages such as data creation, storage, usage, archival, and ultimately disposal. Retention policies define how long data should be kept at each stage and provide guidelines for when and how data should be archived or deleted. Data Security and Privacy: During the retention period, it is essential to ensure the security and privacy of the stored data. Adequate security measures, access controls, and data protection mechanisms should be in place to protect the data from unauthorized access, loss, or breach. Disposal and Data Destruction: At the end of the retention period, data should be disposed of properly. Secure data disposal methods, including data destruction techniques like shredding or data wiping, should be employed to ensure that sensitive or confidential information cannot be recovered or accessed. Legal Holds and Exceptions: In some cases, legal holds or litigation may require data retention beyond the initially defined periods. Legal holds suspend the regular data disposal practices to preserve relevant data for legal proceedings or investigations. Learn more about Smart Data Workflow use cases, including legal hold. It is crucial for organizations to establish clear data retention policies, regularly review and update them to align with changing requirements, and ensure compliance with applicable laws and regulations. Consulting legal and compliance professionals can help organizations determine the appropriate retention periods and develop robust data retention practices. Policy-based unstructured data management and mobility should be a core component of your enterprise data retention strategy. #### Backup Backup (also see Data Backup) is the process of creating copies of data to protect against loss or damage. It involves making duplicate copies of important files, databases, applications, or entire systems, which can be used to restore the data in the event of a disaster, hardware failure, human error, or other unforeseen circumstances. Key points about backup storage: Data Protection: The primary purpose of backups is to safeguard data and ensure its availability even in the face of data loss incidents. Backups serve as a safety net, allowing organizations and individuals to recover lost or corrupted data and resume normal operations. Backup Frequency: The frequency of backups depends on various factors, such as the criticality of the data, the rate of data change, and the desired recovery point objective (RPO). RPO determines the maximum acceptable amount of data loss in the event of a failure. Organizations may choose to perform backups daily, weekly, or in more frequent intervals based on their needs. Full and Incremental Backups: Different backup strategies can be employed, such as full and incremental backups. A full backup involves copying all data from the source to the backup storage. Incremental backups only copy the changes made since the last backup, resulting in smaller backup sizes and faster backups. A combination of full and incremental backups can provide a balance between data protection and storage efficiency. Backup Storage: Backups are stored on separate storage devices or media from the original data. This ensures that if the primary storage fails or becomes inaccessible, the backups remain unaffected. Common backup storage options include external hard drives, network-attached storage (NAS), tape drives, cloud storage, or off-site backup facilities. Data Recovery: When data loss occurs, backups are used to restore the lost or corrupted data. The recovery process involves retrieving the backup data and copying it back to the original or alternative locations. Depending on the backup strategy employed, recovery may involve restoring the latest full backup followed by incremental backups or directly restoring the most recent backup. Testing and Verification: It is important to regularly test backups and verify their integrity to ensure they are usable when needed. Regular restore tests help identify any issues or discrepancies in the backup data or the recovery process. Verification involves performing integrity checks on the backup files to ensure they are not corrupted or damaged. Backup practices will vary depending on the scale of data, business requirements, and compliance regulations. Be sure to follow best practices, including having multiple copies of backups, storing backups off-site or in the cloud for disaster recovery, and regularly reviewing and updating backup strategies to align with changing data needs and technologies. Many backup vendors talk about data management for the data they are backing up. Komprise is a data agnostic unstructured data management solution. Komprise partners with backup vendors and allow customers to know first, move smart and take control of file and object data with an analytics-driven Intelligent Data Management platform as a service. #### Air Gap An air gap, in the context of computer security, refers to a physical or logical separation between a computer or network and any external or untrusted networks or systems. It is a security measure used to protect sensitive or critical information from unauthorized access or cyber threats. The concept behind an air gap is to create a physical or logical barrier that prevents direct communication or data transfer between the protected system and external networks. This isolation helps reduce the risk of malicious actors or malware infiltrating the system and compromising its security. Physical and Logical Air Gap Physical air gap: The isolated system is physically disconnected from any external networks, typically by physically unplugging network cables or using dedicated networks that are not connected to the internet or other networks. This is commonly seen in high-security environments or critical infrastructure systems where data protection is of utmost importance. Logical air gap (or virtual air gap): Using network configurations, firewalls, or security controls to create a virtual separation between the protected system and external networks. While the system may still be physically connected to a network, it is isolated in such a way that communication with external systems is restricted or highly regulated. Air gaps are commonly employed in situations where highly sensitive or classified data is involved, such as government or military networks, financial systems, or critical infrastructure control systems. However, it is important to note that air gaps are not foolproof and additional security measures should be implemented to address potential risks like insider threats or physical access breaches. In the blog post How to Protect File Data from Ransomware at 80 percent Lower Cost, there an overview of how to create affordable cloud ransomware recovery copy that is logically air-gapped. If you want to use Komprise for both hot and cold data, Komprise can create an affordable logically isolated recovery copy of all data in an object-locked destination such as Amazon S3 IA, so data is protected even if the backups and primary storage are attacked. #### Artificial Intelligence (AI) Artificial Intelligence (AI) is the simulation of human intelligence in machines that are programmed to perform tasks that would typically require human intelligence such as visual perception, speech recognition, decision-making, and language translation. AI involves the development of computer systems capable of performing these tasks. AI subfields AI subfields employ different techniques and algorithms to enable machines to learn from data, recognize patterns, make predictions, and solve complex problems. Examples include: Machine learning: a prominent branch of AI, focuses on enabling machines to learn from and adapt to data without explicit programming. It involves the development of algorithms that allow computers to analyze and interpret large volumes of data, identify patterns, and make informed decisions or predictions. Natural language processing (NLP): Deals with enabling machines to understand, interpret, and generate human language. NLP plays a crucial role in applications such as speech recognition, language translation, chatbots, and text analysis. Computer vision: Involves enabling machines to interpret and understand visual information from images or videos. It enables systems to perceive and analyze visual data, such as object recognition, image classification, and autonomous driving. Robotics, expert systems and more. AI has a wide range of applications across various industries, including finance, healthcare, transportation, manufacturing and entertainment. It has the potential to revolutionize industries, improve efficiency, automate processes, and solve complex problems. AI is still an evolving field, and while it has made significant advancements, it is not yet capable of replicating the full spectrum of human intelligence. Researchers and developers continue to explore and push the boundaries of AI, striving to create more advanced and sophisticated systems. There is an ongoing discussion about the important role of regulation and governance, especially as they relate to generative AI. The leaders of OpenAI have proposed an international regulatory body. AI needs unstructured data At the end of 2022, Komprise CEO Kumar Goswami wrote about the importance of unstructured data and unstructured data management to AI and machine learning. He wrote: Enterprises need to be ready for this wave of change and it starts by getting unstructured data prepped, as this data is the critical ingredient for AI/ML. This entails new data management strategies which create automated ways to index, segment, curate, tag and move unstructured data continuously to feed AI and ML tools. Unforeseen changes to society, fueled by AI, are coming soon and you don’t want to be caught flat-footed. In 2023 he wrote an article entitled: The AI/ML Revolution: Data Management Must Evolve. #### Sharding Sharding, or storage sharding, is the technique of partitioning data in a data storage system into multiple subsets or "shards" to improve performance, scalability, and availability. In a storage system, sharding is used to distribute the workload of storing and retrieving data across multiple nodes or servers. Benefits of Sharding Improved performance: By distributing the workload across multiple nodes, storage sharding can improve the performance of the storage system. This is because each node is responsible for storing and retrieving a smaller subset of data, which can reduce the amount of data that needs to be processed in any given operation. Improved scalability: Storage sharding can also improve the scalability of a storage system. As the amount of data being stored grows, more nodes can be added to the system to handle the increased workload. This allows the storage system to scale up to handle large amounts of data. Improved availability: By storing data across multiple nodes, storage sharding can improve the availability of the storage system. If one node fails, the data can still be accessed from the other nodes in the system. Sharding Challenges Data consistency: As with any sharding technique, ensuring data consistency can be a challenge. When data is partitioned across multiple nodes, it can be difficult to ensure that all nodes have the same version of the data at all times. Query complexity: Queries may need to be executed across multiple nodes, which can make querying more complex and impact query performance. Shard rebalancing: When data is added or removed from the storage system, the shards may need to be rebalanced to maintain performance. This can be a complex and time-consuming process. Overall, sharding can be a powerful technique for improving the performance, scalability, and availability of a storage system, but it requires careful planning and management to ensure its success. Sharding Vendors Some examples of vendors that use sharding include: Amazon Web Services (AWS): AWS offers a service called Amazon S3 (Simple Storage Service), which is a highly scalable and durable object storage service that uses storage sharding to distribute data across multiple storage nodes. Google Cloud Platform (GCP): GCP offers a similar service to Amazon S3 called Google Cloud Storage, which also uses storage sharding to distribute data across multiple nodes. Microsoft Azure: Microsoft Azure offers a service called Azure Blob Storage, which is a highly scalable object storage service that uses storage sharding to distribute data across multiple nodes. MongoDB: MongoDB is a popular NoSQL database that uses storage sharding to distribute data across multiple nodes in a cluster. This allows MongoDB to scale horizontally to handle large amounts of data. Apache Cassandra is another NoSQL database that uses storage sharding to distribute data across multiple nodes in a cluster. Cassandra is designed to be highly scalable and can handle large amounts of data. These are just a few examples of vendors that use storage sharding in their products. There are many other vendors that offer distributed storage systems that use storage sharding or similar techniques to improve performance, scalability, and availability. Alternatives to Sharding There are other techniques and approaches that can be used in distributed systems, depending on the specific needs of the system. For example, replication can be used to improve data availability and reduce the risk of data loss in the event of a node failure. Load balancing can be used to distribute workloads across multiple nodes, improving performance and reducing the risk of bottlenecks. Other techniques that can be used in distributed systems include caching, data partitioning, and distributed locking. The choice of technique will depend on factors such as the specific use case, the size and complexity of the system, and the performance and availability requirements. Ultimately, the key to achieving the best performance and availability in a distributed system is to carefully evaluate the needs of the system and select the appropriate techniques and approaches to meet those needs. There is no one-size-fits-all solution, and the choice of technique will depend on the specific requirements of the system in question. The Difference Between Sharding and Chunking Sharding and chunking are two different techniques used in different contexts. Sharding is a technique used in distributed systems to divide data into smaller subsets, or "shards," which are then distributed across multiple nodes in a network. Sharding is commonly used to improve scalability and availability in large-scale databases and storage systems. Chunking is a technique used to break down larger pieces of information or data into smaller, more manageable chunks. Chunking is used in many different contexts, such as memory and learning, data storage and transmission, content creation, and user interface design. While both sharding and chunking involve breaking down larger units into smaller pieces, they are used in different contexts and serve different purposes. Sharding is used in distributed systems to improve scalability and availability, while chunking is used to make information or data easier to process, remember, and communicate. #### Petabyte What is a petabyte? A petabyte (PB) is a unit of data storage that represents 1,000,000,000,000,000 bytes or 10^15 bytes. It is 1000x larger than a terabyte (TB) and one million times larger than a gigabyte (GB). Petabytes are commonly used to describe the capacity of large-scale data storage systems, run by data heavy industries such as those used in scientific research, big data analytics, and cloud computing. For example, a single petabyte could store over 200 million 5 MB photos, or about 13.3 years' worth of HD video content. There are 1,000 petabytes (PB) in a zettabyte (ZB). In other words, 1 zettabyte is equal to 1,000,000 petabytes, or 10^21 bytes. In recent years, with the exponential growth of data generation and the need for high performance, yet cost effective data storage, the term "zettabyte" has become increasingly relevant in discussions around big data and data management. It's worth noting that even larger units of storage exist, including yottabytes (10^24 bytes) and brontobytes (10^27 bytes), but these are not yet commonly used. From TechTarget: What is a petabyte? How big is a petabyte? According to Teradata, one petabyte is equal to one quadrillion bytes, which is 1 million gigabytes, or 1,000 terabytes. Some estimates hold that a Petabyte is the equivalent of 20 million tall filing cabinets or 500 billion pages of standard printed text. Here is a breakdown of the size of a petabyte: Bytes: A petabyte is equal to 1,024 terabytes (TB) Equivalent Measurements: 1 PB = 1,024 TB 1 PB = 1,048,576 gigabytes (GB) 1 PB = 1,073,741,824 megabytes (MB) 1 PB = 1,099,511,627,776 kilobytes (KB) 1 PB = 1,125,899,906,842,624 bytes Petabyte Comparison Examples A typical HD movie is about 4-5 GB in size. A petabyte could store around 200,000 HD movies. An average MP3 song is about 5 MB. A petabyte could hold approximately 210 million songs. A 1 terabyte hard drive can store around 250,000 photos. A petabyte could hold about 256 million photos. The bottom line is that a petabyte is an enormous amount of data storage, most of which is unstructured data. This volume of data is typically suitable for large-scale data centers, cloud storage providers, and organizations that handle massive amounts of information. Healthcare organizations, which produce more data than most other sectors, has on average 50PB of data (per hospital). Petabyte-Scale Unstructured Data Management and Data Migration At Komprise we talk about petabyte-scale unstructured data management and data migrations. For example, Komprise Analysis analyzes across hundreds of petabytes without impacting performance. Read the press release. Komprise executes petabyte-scale file data migrations across many NAS and cloud storage technologies. Komprise enterprise customers are distributed across healthcare, life sciences, biotech, media and entertainment, public sector, higher education, financial services, legal, energy, high-tech and other industries managing petabyte-scale unstructured data environments. Learn more about Komprise Intelligent Data Management. #### Replication What is replication? Replication, or data replication, is the process of creating and maintaining one or more copies of data, files, or information in multiple locations or systems in order to increase data availability, reliability, and performance. The purpose of replication is to ensure that data is always available, even if one of the copies becomes unavailable due to hardware failure, network issues, or other disruptions. Replication can be done in different ways, depending on the specific requirements of the data and the systems involved. For example, replication can be done in real-time or near real-time, and the copies can be stored locally or remotely. Replication can also be done synchronously or asynchronously, with synchronous replication ensuring that all copies are identical at all times, and asynchronous replication allowing some lag time between updates to the different copies. Replication is used in various systems and technologies, including databases, file systems, cloud storage services, and content delivery networks (CDNs). It is often an essential part of high-availability and disaster recovery (DR) strategies, as it can help ensure that data is always accessible even in the face of unexpected events. Cloud Replication Cloud replication is the process of replicating data or services from a primary cloud environment to one or more secondary cloud environments typically to improve the availability, reliability, and durability of data and services in the cloud, and to provide DR capabilities. In a cloud replication scenario, data is automatically copied and synchronized between different cloud regions, data centers, or cloud providers, depending on the specific requirements of the system and the data. Cloud replication can be done in real-time or near real-time, and the copies can be stored locally or remotely. Some cloud providers offer automatic replication features, which enable customers to easily configure and manage replication of their data and services across multiple regions or providers. Cloud replication can also be used to ensure compliance with data sovereignty and privacy regulations, as it allows data to be stored in multiple locations, each subject to different laws and regulations. It can also improve performance by enabling users to access data and services from the nearest available location. Overall, cloud replication is an important part of any cloud-based disaster recovery and business continuity plan. It can help organizations minimize the impact of unexpected events, such as natural disasters or cyber attacks, and ensure that critical data and services are always available to users. Watch the webinar: Komprise for cloud-to-cloud replication use cases. Komprise Elastic Replication Cuts Disaster Recovery Costs for Unstructured Data by 70% #### Microsoft Azure What is Microsoft Azure? Microsoft Azure is a cloud computing platform and set of on-demand services provided by Microsoft, including virtual machines, cloud storage, databases, cloud analytics, and more. From the Microsoft website: The Azure cloud platform is more than 200 products and cloud services designed to help you bring new solutions to life—to solve today’s challenges and create the future. Build, run, and manage applications across multiple clouds, on-premises, and at the edge, with the tools and frameworks of your choice. Azure supports a variety of programming languages, tools, and frameworks, including Microsoft-specific and third-party software and systems. It is designed to be flexible and scalable, allowing users to pay only for the services they use and adjust their resources as needed. With trends like digital transformation, data center consolidation and DevOps adoption in the enterprise, a growing number of organizations are planning to (or in the midst of) migrate on-premises applications and infrastructure to the cloud platforms like Microsoft Azure to create and deploy web applications and APIs and store and analyze data. Cloud service provider platforms like Microsoft Azure are also popular among developers who use it to build and test applications, collaborate with other developers, and deploy code to the cloud. Azure Storage Azure Storage is a cloud-based storage service provided by Microsoft. It is designed to provide scalable and highly-available storage for data, files, and unstructured data, such as images, videos, and documents. Azure Storage provides four types of storage services: Blob Storage: Blob storage is designed to store large unstructured data such as documents, images, videos, and logs. File Storage: File storage is a fully managed file share service that enables customers to migrate their on-premises file shares to the cloud, but requires the right path to the cloud. Queue Storage: Queue storage provides messaging capabilities between different components of a distributed application. Table Storage: Table storage is a NoSQL key-value store that can be used to store massive amounts of structured data. Azure Storage provides features such as automatic replication, backup and restore, disaster recovery, and access control to ensure the security and availability of data. It can be accessed through REST APIs or SDKs for various programming languages. Azure Storage also integrates with other Azure services, such as Azure Virtual Machines, Azure Web Apps, and Azure Functions. Learn more about Komprise for Microsoft Azure. #### File Storage What is File Storage? File storage, or file-based storage, is the process of storing digital files, such as documents, photos, videos, and other types of data, primarily unstructured data, in a secure and accessible location. There are several options available for file storage, including: Local storage: This involves storing files on a physical device, such as a hard drive, USB drive, or memory card. Local storage can provide a high level of control over the files, but there is a risk of data loss if the device fails or is lost or stolen. Cloud storage: This involves storing files on remote servers that are accessed through the internet. Cloud storage providers offer varying levels of security, accessibility, and storage capacity, and can be a convenient and cost-effective option for storing and accessing files. Network-attached storage (NAS): This is a type of storage device that is connected to a network and allows multiple users to store and access files. NAS devices can provide a high level of control and security over the files, but can be more complex and expensive to set up than other options. When choosing a file storage solution, it is important to consider factors such as security, accessibility, reliability, and cost. It may also be helpful to assess the specific needs of your organization or personal use case, such as the volume and type of files that need to be stored, and the number of users who will need to access them. File Storage Cost Savings File storage cost savings can be achieved by optimizing your storage strategy to reduce the amount of data that needs to be stored, and by leveraging cost-effective storage solutions. Here are some tips file data storage cost savings suggestions: Know your data: Conduct an audit of your files to determine which files are necessary and which can be deleted or archived. By reducing the amount of data you need to store, you can save on storage costs. Learn more about Komprise Analysis. Use compression: Compressing files can reduce their size, allowing you to store more files in the same amount of storage space. Many file types, such as images and videos, can be compressed without losing quality. Leverage cloud storage: Cloud storage providers offer a range of options with varying levels of storage capacity and pricing. By choosing a provider that meets your needs, you can save on the cost of physical storage devices and maintenance. Consider tiered storage: Use different types of storage for different types of files, such as high-performance storage for frequently accessed files and lower-performance storage for archival files. This can help you optimize storage costs while still ensuring accessibility and performance. Implement data deduplication: Data deduplication is a process that eliminates redundant data, such as duplicate files or multiple versions of the same file. By reducing the amount of duplicate data, you can save on storage costs. File data is growing exponentially. Budgets are not. Reducing file storage costs, while gaining data value is a top enterprise IT priority. Read the white paper: Know your file tiering options: Storage-based vs. Gateways vs. File-based. Read the white paper: Block-level vs. File-level tiering. #### Storage as a Service Storage as a Service (STaaS) is a subscription service model for enterprise storage providers. Dell, HPE, NetApp, Pure Storage and others all offer SaaS subscriptions, which shifts IT spending from capital expenses (CAPEX) to operating expenses (OPEX), where you pay for what you need. Storage as a Service can also be used to describe cloud-based storage solutions that allow users to store and access their data over the internet through a third-party service provider. STaaS providers typically offer scalable, on-demand storage capacity that can be easily provisioned and accessed via a web-based interface or an application programming interface (API). The benefits of cloud-based STaaS include: Scalability: STaaS providers typically offer scalable storage capacity that can be easily adjusted based on the user's needs. Cost-effectiveness: Users only pay for the storage capacity they need, without the need for upfront capital expenditures on storage infrastructure. Flexibility: STaaS providers offer a range of storage options, including object storage, file storage, and block storage, allowing users to choose the most appropriate storage solution for their needs. Data security: STaaS providers typically offer robust security features, including encryption, backup, and disaster recovery capabilities, to ensure data is protected against loss or unauthorized access. Accessibility: STaaS providers allow users to access their data from anywhere, at any time, via an internet connection, making it easier to collaborate with colleagues and access data while on the go. Examples of cloud native STaaS providers include Amazon S3, Microsoft Azure Blob Storage, and Google Cloud Storage. STaaS can be particularly beneficial for organizations with large or rapidly growing storage needs or for users who need to store and access data from multiple locations or devices. Read the white paper: Getting Departments to Care About Data Storage Cost Savings. #### Chargeback What is Chargeback? Chargeback is a cost allocation strategy used by enterprise IT organizations to charge business units or departments for the IT resources / services they consume. This strategy allows organizations to assign costs to the departments that are responsible for them, which can help to improve accountability, cost management and cost optimization. Under a chargeback model, IT resources such as hardware, software, and services are assigned a cost and allocated to the business units or departments that use them. The costs may be based on factors such as usage, capacity, or complexity. The business units or departments are then billed for the IT resources they consume based on these costs. The chargeback model can provide several benefits for organizations. It can help to promote transparency and accountability, as departments are charged for the IT resources they use. This can help to encourage departments to use IT resources more efficiently and reduce overall costs. Chargeback can also help to align IT spending with business goals, as departments are more likely to prioritize spending on IT resources that directly support their business objectives. Implementing an IT chargeback model requires careful planning and communication to ensure that it is implemented effectively. It is important to establish clear policies and guidelines for how IT resources are assigned costs and billed to business units or departments, and to provide regular reporting and analysis to help departments understand their IT costs and usage. Showback and Storage as a Service Many enterprise have adopted a Storage-as-aService (STaaS) approach to centralize IT’s efforts for each department. But convincing department heads to care about storage savings is a tough task without the right tools. Storage-agnostic data management, tiering and archiving are viewed by users as an extraneous hassle and potential disruption that fails to answer “What’s in it for me?” This white paper explains how to make STaaS successful by telling a compelling data story department heads can’t ignore. This coupled with transparent data tiering techniques that do not change the user experience are critical to successful systematic archiving and significant savings. Learn how using analytics-driven showback can help secure the buy-in needed to archive more data more often. Once they understand their data—how much is cold and how much they could be saving—the conversation quickly changes. Read the blog post: How Storage Teams Use Deep Analytics. #### Block Storage What is Block Storage? Block storage is a type of data storage technology used to store data in blocks, each with a unique address. Each block can be accessed independently and typically has a fixed size, ranging from a few bytes to several terabytes, depending on the specific storage system. Block storage is commonly used in enterprise IT environments for storing data that requires high performance and low latency, such as databases and virtual machine disk images. It provides direct access to storage volumes at the block level, allowing applications to read and write data with high throughput and low latency. One of the key advantages of block storage is its flexibility. It can be used with a variety of operating systems and applications, and it allows storage volumes to be resized and partitioned as needed. This makes it a popular choice for cloud-based storage solutions, where customers can purchase and provision storage volumes on-demand, and only pay for the storage they actually use. Examples of block storage solutions include: Amazon Elastic Block Store (EBS), Google Cloud Persistent Disk, and Microsoft Azure Disk Storage. Block Storage vs. File Storage File storage is a storage system where data is organized into files and directories. File storage systems typically use protocols such as NFS and SMB to access and manage files. File storage is commonly used for storing and sharing unstructured data files such as documents, images, videos, and audio files. The key difference between block storage and file storage is that block storage provides direct access to storage volumes at the block level, while file storage provides access to files and directories. File storage is well suited for applications that require shared access to files and directories, such as file servers, web servers, and content management systems. Block storage and file storage are both important storage technologies, but they are designed for different use cases. Block storage is optimized for high performance storage and low latency, while file storage is optimized for shared access to files and directories. Download the white paper Block-level Tiering vs File-Level Tiering Block-based tiering is typically used by data storage vendors. Storage tiering, aka pools solutions, use block-based tiering. Only the operating system of the NAS knows exactly what blocks were moved, so you can only access the file through the original source. If you decide to end-of-life the device, you must re-hydrate all of the archived data. Given that there will likely not be enough space on the device, this can be a painful, slow, iterative approach. Secondary storage vendors also starting to tier data to their device which is moving the storage concerns from Tier 1 to Tier 2 storage. You are now tied to that secondary storage vendor and lose the same flexibility on secondary storage based on need, costs and the direction of your company’s infrastructure initiatives as you had on Tier 1 storage. Ultimately, unstructured data management is not something that should be left to storage devices. You should be able to freely move from one storage device to another. Komprise is an unstructured data management software solution that tiers and archives data at the file-level and fully preserves file fidelity and standards-based access to your data at each tier. Data-storage agnostic, Komprise enables you to freely move data across different vendor storage and clouds without lock-in to either the storage or to Komprise. The solution is analytics driven, so you can choose what you move, when, and how. Read more about Komprise file-level tiering. Read the white paper: Block-Level vs. File-Level Tiering. #### Azure NetApp Files What is Azure NetApp Files? Azure NetApp Files is a cloud-based file storage service offered by Microsoft Azure that enables enterprise-grade file shares to be created and managed in the cloud. The service is built on NetApp's technology and is designed to meet the high-performance, availability, and scalability requirements of enterprise file data workloads. Azure NetApp Files provides a fully managed service that allows customers to deploy and manage high-performance file shares in Azure. It offers features such as NFS and SMB protocol support, file share snapshots, and data replication across Azure regions. Customers can also choose from different performance tiers and capacity sizes to optimize the cost and performance of their file shares. Azure NetApp Files is commonly used for use cases such as database file shares, big data analytics, media and entertainment workloads, and high-performance computing. It provides a scalable, high-performance, and highly available solution for enterprise customers who need to store and manage large amounts of file data in the cloud. Azure NetApp Files Data Management Komprise first announced support for Azure NetApp Files in 2020: By using Komprise Intelligent Data Management, customers can migrate file workloads to the cloud more than 27 times faster than with other solutions. They can also reduce cloud NAS by 70 percent by transparently archiving cold data from Azure NetApp Files to various Azure Blob storage classes. Komprise’s Transparent Move Technology™ (TMT) enables archived data to be viewed as files, native objects, or both. These new capabilities now allow Komprise to deliver the same on-premises NAS data management features to cloud-enabled NAS. Read the white paper: Accelerate Cloud and NAS Migrations to NetApp CVO and Azure NetApp Files (ANF) Learn more about Komprise for Azure. Learn more about Komprise for NetApp. #### AWS Lambda What is AWS Lambda? AWS Lambda is a serverless, event-driven compute service provided by Amazon Web Services (AWS) that allows developers to run code without managing servers or infrastructure. AWS Lambda provides a scalable, flexible, and cost-effective way to run code in response to events, such as changes to data in an Amazon S3 bucket or an update to a DynamoDB table. With AWS Lambda, developers can write code in a variety of programming languages, including Python, Java, C#, and Node.js. They can then upload this code to AWS Lambda, where it is executed in response to events triggered by other AWS services, such as Amazon S3, DynamoDB, or API Gateway. AWS Lambda automatically scales the number of instances needed to handle incoming requests, and developers only pay for the compute time they consume, which makes it a cost-effective option for many use cases. AWS Lambda also provides built-in monitoring and logging capabilities, making it easy for developers to monitor the performance and behavior of their functions. One of the key benefits of AWS Lambda is its serverless architecture, which eliminates the need for developers to manage infrastructure, allowing them to focus on writing code and building applications. This makes it easier and faster to develop and deploy new applications, as well as reducing the cost and complexity of managing infrastructure. AWS Lambda is a powerful and flexible tool for building serverless applications and integrating them with other AWS services. Its scalability, cost-effectiveness, and ease of use make it a popular choice for developers looking to build modern, cloud-native applications. AWS Lambda Data Management AWS Lambda provides several options for managing data within your functions, including: Environment Variables: Environment variables can be used to store configuration data, such as API keys or database connection strings, that are used by your function. These variables can be set and managed within the AWS Lambda console or via the AWS CLI. Local File Storage: AWS Lambda provides a temporary storage area for your function to read and write files during execution. This storage area is deleted when the function completes, so it should not be used for permanent data storage. AWS Services: AWS Lambda can interact with various AWS services, including Amazon S3, Amazon DynamoDB, and Amazon RDS. These services provide persistent storage options for your data that can be accessed by multiple functions. External Services: AWS Lambda functions can also interact with external services, such as third-party APIs or databases. These services can be accessed via the internet or through a virtual private network (VPN) connection. Learn more about AWS Lambda. Learn more about Komprise and AWS unstructured data migration and AWS data management. #### Application Programming Interface (API) What is an API? An Application Programming Interface (API) is a set of protocols, routines, and tools for building software applications. APIs define how software components should interact with each other, providing a standard way for developers to create programs that can access services or data provided by other software components or systems. APIs allow developers to access services or data without needing to understand how those services or data are implemented. Instead, they can use the API's predefined set of functions and methods to interact with the service or data. This makes it easier and faster for developers to create new applications that can leverage existing services and data sources. APIs are often used to connect different software components or systems, such as web applications or mobile apps to backend servers or databases. They can also be used to integrate different software tools, enabling them to work together seamlessly. APIs can be public or private, depending on whether they are available for external developers to use or are restricted to use within a specific organization or system. Many public APIs are available from companies such as Google, Amazon, and Twitter, which provide access to their services and data for developers to build applications on top of. APIs are an essential tool for modern software development, enabling developers to build complex and powerful applications quickly and efficiently by leveraging existing services and data sources. API-Driven Data Management and Data Migration Komprise Smart Data Workflows can enrich data by allowing the execution of external functions or cloud services either at the edge, datacenter or cloud and then tagging data with metadata. Examples include: Snowflake, Amazon Macie, Azure machine learning. Read the blog post Read the AWS blog: Using Amazon Macie with Komprise for Detecting Sensitive Content in On-Premises Data   Komprise Elastic Data Migration is both UI and API driven. Here are is an example of a hospital group who used the Komprise API to migrate petabytes of SMB files from EMC Isilon access zones to Qumulo. Komprise set up 400+ migration jobs via scripting using the APIs and migrated 278 million SMB files spanning nearly 1500 shares. Because of the number of shares and folders in the environment it was unrealistic to set up migrations one at a time via the UI, which led to Komprise recommending the API approach. Read the blog post: 5 Industry Data Migration Use Case #### Alternate Data Streams (ADS) Alternate Data Streams (ADS) is a feature in the Windows operating system that allows data to be associated and hidden within files. An ADS can be used to store additional information about a file, such as metadata or comments, without changing the file itself. Read ADS overview here. ADS is a feature that was introduced in the New Technology File System (NTFS) used by Windows, and it allows users to attach a second data stream to a file, which is invisible to most applications and users. The ADS is named using a colon, for example, "myfile.txt:ads.txt". ADS Spyware? ADS can be used for legitimate purposes, such as adding metadata to a file, but it can also be used for malicious purposes, such as hiding malware or other sensitive information within a file. As a result, ADS has been used in some types of cyberattacks, such as those involving stealthy data exfiltration or command-and-control communications. To view and manage ADS, you can use the Windows command prompt or third-party tools such as ADS Spy or ADS Scanner. It is important to be aware of the existence of ADS and to take appropriate security measures to protect against malicious use of this feature. Komprise Intelligent Data Management for Microsoft #### Bucket Sprawl Bucket sprawl refers to the problem of having a large number of data storage buckets, also known as an object storage bucket, often in cloud data storage environments, that are created and left unused or forgotten over time. This can happen when individuals or teams create buckets for specific projects or tasks, but fail to properly manage and delete them once they are no longer needed. What is a Cloud Bucket? A cloud bucket is a container for storing data objects in cloud storage services such as Amazon S3, Google Cloud Storage, or Microsoft Azure Storage. Cloud buckets can hold a variety of data types including images, videos, documents, and other files. Cloud buckets are typically accessed and managed through an API or web-based interface provided by the cloud storage provider. They offer a scalable and cost-effective way to store and retrieve large amounts of data, and can be used for a variety of applications including backup and disaster recovery, content delivery, and web hosting. Cloud buckets provide a number of benefits over traditional on-premises data storage solutions, including ease of use, cost-effectiveness, scalability, and availability. However, it is important to properly manage and secure cloud buckets to ensure that sensitive data is protected and costs are kept under control. The Problem with Cloud Bucket Sprawl Cloud bucket sprawl can lead to a number of issues, including increased data storage costs, decreased efficiency in accessing necessary data, and potential security risks if sensitive information is stored in forgotten or unsecured buckets. To avoid bucket sprawl, it is important to have a system in place for regularly reviewing and managing storage buckets, including identifying and deleting those that are no longer necessary. Cloud Data Management for Bucket Sprawl In the blog post: Making Smarter Moves in a Multicloud World, Komprise CEO and cofounder Kumar Goswami introduced Komprise cloud data management capabilities this way: It gives customers a better way to manage their cloud data as it grows, (combat “bucket sprawl”), gives visibility into their cloud costs, and provides a simple way to manage data both on premises and in the cloud. Komprise now provides enterprises with actionable analytics to not only understand their cloud data costs but also optimize them with data lifecycle management. Learn more about Komprise cloud data management. Infographic: How to Maximize Cloud Cost Savings #### Data Center Consolidation Data center consolidation is the process of merging or reducing the number of data centers that an organization operates. The consolidation is typically done in order to reduce costs, increase efficiency, and simplify management of the data center infrastructure. There are several steps involved in data center consolidation, including: Assessing the current state of the data center environment, including the number and locations of data centers, the types of systems and applications being used, and the costs associated with operating and maintaining the infrastructure. Developing a consolidation plan that outlines the goals, timelines, and resources needed for the project. This plan should include an analysis of the potential benefits and risks of consolidation, as well as a detailed roadmap for migrating applications and data to the new infrastructure. Migrating applications and data migration to the consolidated data center(s). This may involve re-architecting applications to run in a virtualized environment or on cloud infrastructure. Decommissioning or repurposing the legacy data center(s), including disposing of any equipment that is no longer needed. Continuously monitoring and optimizing the consolidated data center infrastructure to ensure it remains efficient and cost-effective. Overall, data center consolidation can be a complex process that requires careful planning and execution. However, the benefits of consolidation can be significant, including lower costs, improved performance, and increased agility and flexibility for the organization. In 2023, Komprise summarized the following customers trends in unstructured data management and storage. Simplifying infrastructure, getting rid of legacy apps and software and data center consolidation to support business growth and IT modernization. Reducing IT spending by pivoting to more of an OPEX environment and by deleting data that is no longer needed to reduce data storage costs and complexity. Managing research workflows and the full lifecycle of data: Examples include, from a major university: enabling users to share data between labs and send some data to the cloud for processing, then bring it back on-premises. Using industry standards to move data easily between platforms. Externalize (tier) data off NAS: IT and storage managers want to tier cold data from across the business to cheaper, secondary storage to save money and free up primary storage capacity. Learn more about Komprise Elastic Data Migration and read 5 Industry Case Studies. #### Data Services Data services describes a range of services typically provided by enterprise IT operations teams or shared services teams, such as: data processing, data integration, data security, data reduction, data protection, data storage, and unstructured data management. Data services is a broad term that can overlap with analytics services, cloud services or professional services, but is tied to financial operations (FinOps) goals. Data services are essential for data-heavy enterprise organizations that need to manage, process, and analyze large amounts of (mostly unstructured) data to gain insights and make better business decisions. Examples of data services include: Data storage services: The storage of data in various forms, including files, databases, and cloud storage. The shift to Storage as a Service (STaaS) is part of a data services strategy. Read the white paper: Getting Departments to Care About Storage Savings. Data management services: The management of data throughout its lifecycle, including data quality management, data governance, data classification is critical to lower costs and grow data value. Data management services include analysis and line of business reporting into data storage usage and costs for showback along with  data migration, data tiering, data replication and deletion. Data processing services: This entails the processing of data through various algorithms and techniques, including data analytics, machine learning, and artificial intelligence. Data integration services: This is the integration of data from multiple sources (ETL/ELT) to create a single, unified view of the data (usually for analytics) as well as real-time application of data between systems (EAI, ESB, streaming).   From Storage Services to Data Services In VMblog predictions post: Unstructured Data Management Predictions for 2023: Data Insights and Automation take Center Stage, Komprise cofounder and COO Krishna Subramanian noted that enterprises are moving away from managing storage to managing data services: “Storage teams have traditionally measured infrastructure metrics for capacity and performance such as latency, I/O operations per second (IOPS) and throughput. But given the massive  growth of unstructured data, data-centric metrics are becoming paramount as enterprises move away from managing storage to managing data services in hybrid cloud infrastructure. New data management metrics look at usage indicators such as top data owners, percentage of “cold” files which haven’t been accessed in over a year, most common file size and type, and financial operations metrics such as storage costs per department, storage costs per vendor per TB, percentage of backups reduced, rate of data growth, chargeback metrics and more.” In the same post she highlighted the changing role of storage administrators: The storage architect/engineer will evolve to incorporate data services "We'll see more experienced individuals in these roles move on to cloud architect and other engineering roles while IT generalists/junior cloud engineers inherit their responsibilities. This is a challenging time for IT organizations in a hybrid model as there is still significant NAS expertise needed. Either way, the IT employees managing the storage function will need new skills beyond managing the storage hardware. These individuals must understand the concept of data services-including facilitating secure, reliable governance and access to data and making data searchable and available to business stakeholders for applications such as cloud-based machine learning and data lakes. The new storage architect will frequently analyze and interpret data characteristics, developing data management plans which factor in cost savings strategies and business demands to create new value from data. This individual will interact regularly with departments to create and execute ongoing data management processes and plans." In a Solutions Review post: 2023 Expert Data Management Best Practices & Predictions, Komprise cofounder and CEO Kumar Goswami noted: "IT organizations must better understand data to improve migrations and gain maximum ROI from cloud, meet compliance requirements, deliver data services to departments, and to facilitate new value generation from data." He went on to say: "To keep up with ever-changing data services demands from the business, IT will implement collaborative processes with stakeholders across many different departments such as finance, marketing, legal, research, HR. Data workflow automation will support a variety of use cases from governance and compliance to cost savings to big data analytics." In the 2022 Strategic Roadmap for Storage, Gartner noted (subscription required): I&O leaders must implement intelligent data services infrastructure powered by software-defined storage and hybrid cloud IT operations....Integration of data services to the hybrid cloud platform is among the top enterprise challenges to address the need for seamless data services across the edge, the core data center and public clouds. Read the article: Unstructured Data Growth and AI Give Rise to Data Services #### Hierarchical Storage Management (HSM) software, also known as tiered storage, was designed for distributed server environments to automate the process of identifying cold data sets and automatically migrating them from primary disk to less expensive optical and tape storage devices. Going back to the era of the mainframe, HSM was also supposed to handle file recall requests automatically whenever a user clicked on a stub file. Unfortunately, these early HSM products (see Wikipedia for a history) suffered from a number of deficiencies such as: They were custom designed for specific proprietary storage systems, which limited hardware choices and resulted in vendor lock-in. Many required file server agents that required substantial memory and compute resources, and operated in the direct data path, impacting performance. They used static stub files left in place of the moved data. These static stub files could be corrupted, deleted, and orphaned making it difficult if not impossible to locate the original source file. The early HSM solutions did not scale well. As file counts increased, HSM performance deteriorated significantly since they were traditional database-driven architectures. The solutions would disrupt storage s ystem performance, interrupting active usage. File recalls could take a long time, especially if the requested file was stored on tape. So bad were these deficiencies, that HSM became a “bad word” amongst IT professionals. Many of those IT pros believed that the only viable way to manage storage was to just keep adding more capacity to the primary tier. As the data center landscape has changed, with organizations having a wide range of data storage options available. Flash memory devices have replaced high performance physical disk drives as Tier-1 storage. High performance and commodity physical hard disks now function as secondary and tertiary storage tiers. Cloud file storage and object storage options are available to handle large bulk, long-term storage requirements. All of these options are needed to combat the unstructured data onslaught (and data sprawl and high data storage costs) that most organizations are facing. However, the main problem remains; how to automatically detect “warm” and “cold” data sets then continuously migrate them to the most cost-effective storage tier while also managing the entire file life cycle. As outlined in this early review of Komprise: In short, we have more storage options than ever but less intelligence about how and when to move our increasing data to which storage platform. In a 2022 Blocks and Files review, Komprise Intelligent Data Management is referred to as an HSM or Information Lifecycle Management solution. The new category of software is now known as unstructured data management as well as the broader term: data services. #### Treesize What is Treesize? Treesize is a free disk space analysis utility written by Jam Software. According to Wikipedia, the first version of TreeSize was programmed by Joachim Marder in 1996. Treesize has become a term used to describe the amount of storage space that a storage directory or folder (and its contents) take up on a computer's hard drive. Treesize is a measure of the overall size of all files and subfolders within a particular directory. Knowing this information his been useful for storage administrators and IT teams managing hard drive space and identifying which files or folders are taking up the most space. Going Beyond Basic Disk Analysis As enterprise data storage is increasingly hybrid and multi-cloud, IT operations and storage teams need visibility and analysis across all unstructured data so they always know what data they have, how much is hot, how much is cold, how fast data is growing and data storage costs across silos. This is why storage-agnostic unstructured data management solutions that provide aggregate metrics in addition to information by share or directory and the ability to move data by policy has become essential to enterprise IT and line of business teams. It's also important to have a tool that can scale to the needs of the enterprise. That's where Komprise comes in. Check out the Komprise Directory Explorer. Learn more about Komprise Analysis. Learn more about Komprise Deep Analytics.   ---------- #### Digital Pathology Data Management According to the Digital Pathology Association:  "Digital pathology is a dynamic, image-based environment that enables the acquisition, management and interpretation of pathology information generated from a digitized glass slide." Healthcare organizations have shifted to digital media for medical imaging. Digital pathology, digital PACS and VNA systems are all generating and now storing petabytes of medical imaging data—lab slides, X-rays, MRIs, CT scans and more. These ever-expanding datasets are pushing the limitations of data storage systems and challenging IT department’s ability to effectively manage data. And with increasing regulations, healthcare providers typically must retain medical imaging files for many years. In addition to compliance requirements, clinical researchers may also need access to the data indefinitely. They also typically need access to the unstructured data immediately. The potential future value of this ever-expanding data repository must be weighed against the growing financial and overall unstructured data management costs. The Digital Pathology Data Management Challenge Data center storage for large image files is expensive - typically costing millions a year for some organizations on expensive NAS devices. Not only is NAS expensive, but its data must also be secured, replicated and backed up, which typically triples the costs. Meanwhile, in most cases, imaging data is rarely accessed after a few days or weeks. To get greater flexibility and manage data storage costs, healthcare organizations are adopting unstructured data management software to tier cold medical imaging data out of expensive storage to cost-effective environments such as the cloud. Data management decisions can be difficult internally with politics, vendor relationships and long-standing institutional perspectives. Health systems are handling sensitive patient information and tolerance for downtime is usually quite low. There are many benefits from augmenting medical imaging solutions with data management software that transparently tiers cold data from your data storage and backups.                       Komprise has many customers in the healthcare industry dealing with multiple petabytes of file and object data. Learn more. #### Data Storage Costs Data storage costs are the expenses associated with storing and maintaining data in various forms of storage media, such as hard drives, solid-state drives (SSDs), cloud storage, and tape storage. These costs can be influenced by a variety of factors, including the size of the data, the type of storage media used, the frequency of data access, and the level of redundancy required. As the amount of unstructured data generated continues to grow, the cost of storing it remains a significant consideration for many organizations. In fact, according to the Komprise 2023 State of Unstructured Data Management Report, the majority of enterprise IT organizations are spending over 30% of their budget on data storage, backups and disaster recovery. This is why shifting from storage management to storage-agnostic data management continues to be a topic of conversation for enterprise IT leaders. In the 2024 report the top priorities for data storage include cost optimization (54%), preparing for AI (51%) and investing in data management and data mobility (41%). Cloud Data Storage Costs Cloud data storage costs refer to the expenses incurred for storing data on cloud storage platforms provided by companies like Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform (GCP). In addition to the points above about data storage costs (amount of data stored and frequency of data access) in the cloud the level of durability and availability required are also factors when it comes to cloud storage costs. Cloud data storage providers typically charge based on the amount of data stored per unit of time, and additional fees may be incurred for data retrieval, data transfer, and data processing. Many cloud storage providers offer different storage tiers with varying levels of performance and cost, allowing customers to choose the option that best fits their budget and performance needs. With the right cloud data management strategy, cloud storage can be more cost-effective than traditional hardware-centric on-premises storage, especially for organizations with large amounts of data and high storage needs. Managing Data Storage Costs Managing data storage costs involves making informed decisions (and the right investment strategies) about how to store, access, and use data in a cost-effective manner. Read the interview with Komprise Field CTO Benjamin Henry: Is there any relief from data storage costs? Here are some strategies for managing data storage costs: Data archiving: Archiving infrequently accessed data to lower cost storage options, such as object storage or tape, can help reduce storage costs. Data tiering: Using different storage tiers for different types of data based on their access frequency and importance can help optimize costs. Compression and deduplication: A well known data storage technique, compressing data and deduplicating redundant data can help reduce the amount of storage needed and lower costs. Cloud file storage: Using cloud storage can be more cost-effective than traditional on-premises storage, especially for organizations with large amounts of data and high storage needs. Data lifecycle management (aka Information Lifecycle Management): Regularly reviewing and purging unneeded data can help control storage costs over time. Cost monitoring and optimization (see cloud cost optimization): Regularly monitoring and analyzing data storage costs and usage patterns can help identify opportunities for cost optimization. By using a combination of these strategies, organizations can effectively manage their data storage costs and ensure that they are using their data storage resources efficiently. Additionally, organizations can negotiate with data storage providers to secure better pricing and take advantage of cost-saving opportunities like bulk purchasing or long-term contracts. Stop Overspending on Data Storage with Komprise The blog post How Storage Teams Use Komprise Deep Analytics summarizes a number of strategies storage teams use Komprise Intelligent Data Management to deliver greater data storage cost savings and unstructured data value to the business, including: Business unit metrics with interactive dashboards Business-unit data tiering, retention and deletion Identifying and deleting duplicates Mobilizing specific data sets for third-party tools Using data tags from on-premises sources in the cloud In the blog post Quantifying the Business Value of Komprise Intelligent Data Management, we review a storage cost savings analysis that saves customers an average 57% of overall data storage costs and over $2.6M+ annually. In addition to cost savings, benefits include: Plan Future Data Storage Purchases with Visibility and Insight With an analytics-first approach, Komprise delivers visibility into how data is growing and being used across a customer’s data storage silos - on-premises and in the cloud. Data storage administrators no longer have to make critical storage capacity planning decisions in the dark and now can understand how much more storage will be needed, when and how to streamline purchases during planning. Optimize Data Storage, Backup, and DR Footprint Komprise reduces the amount of data stored on Tier 1 NAS, as well as the amount of actively managed data—so customers can shrink backups, reduce backup licensing costs, and reduce DR costs. Faster Cloud Data Migrations Auto parallelize at every level to maximize performance, minimize network usage to migrate efficiently over WANs, and migrate more than 25 times faster than generic tools across heterogeneous cloud and storage with Elastic Data Migration. Reduced Datacenter Footprint Komprise moves and copies data to secondary storage to help reduce on-premises data center costs, based on customizable data management policies. Risk Mitigation Since Komprise works across storage vendors and technologies to provide native access without lock-in, organizations reduce the risk of reliance on any one storage vendor. Deliver the Right Data to AI AI outcomes depend on the quality and relevance of the data. Komprise identifies, classifies, and curates the right unstructured datasets across hybrid storage without requiring data migration or disruption. Eliminate the bottlenecks of traditional ETL and moves only the data you need, directly in native format, so you can feed AI/ML pipelines faster and avoid vendor lock-in. Learn more about Smart Data Workflows. #### Hypertransfer Hypertransfer for Komprise Elastic Data Migration migrates file data to the cloud 25x faster. Announced in December 2022, Komprise Hypertransfer for Elastic Data Migration creates dedicated virtual channels across the WAN to accelerate cloud data migrations. By establishing dedicated channels to send data, Komprise Hypertransfer minimizes the WAN roundtrips, which mitigates SMB protocol chattiness and dramatically improves data transfer rates. Tests done using a dataset dominated by small files shows Komprise accelerates cloud data migration 25x faster than other alternatives. Read the Hypertransfer white paper. #### Data Retrieval Data retrieval refers to the process of accessing and retrieving data from a database or data storage system. Data retrieval is possible using various techniques and tools, such as database querying, data mining, and data warehousing. The specific techniques and tools used will depend on the type of data being retrieved, along with the requirements and goals of the organization. Some benefits of effective data retrieval include: Improved data access: By providing quick and easy access to data, organizations can improve their overall data management processes and make better use of their existing data. Better decision making: By providing access to up-to-date and accurate information, data retrieval can help organizations to make better decisions and improve their overall performance. Better customer insights: By retrieving and analyzing customer data, organizations can gain valuable insights into customer behavior and preferences, so they can improve customer relationships and drive business growth. Cloud Data Retrieval There are several challenges associated with retrieving data from the cloud, including: Network Latency: Retrieving data from a remote server can result in significant latency, especially if the data is large or the network is congested. Bandwidth Limitations: Bandwidth limitations can limit the speed at which data can be retrieved from the cloud. Data Security: Ensuring the security and privacy of data stored in the cloud can be challenging, especially for sensitive data. Data Compliance: Organizations must ensure that their data retrieval practices comply with relevant regulations and standards, such as data privacy laws and industry standards. Data Availability: In some cases, cloud data may not be available due to network outages, server downtime, or other technical issues. Cloud Costs: Retrieving large amounts of data from the cloud can be expensive, especially if the data is stored in a high-performance tier. Complexity: Interacting with cloud data storage systems can be complex and requires a certain level of technical expertise. Cloud Data Retrieval and Egress Costs Egress fees refer to the costs associated with transferring data from a cloud storage service to an external location or to another cloud provider. Many cloud service providers charge fees for data egress, as transferring large amounts of data can put a strain on their network and infrastructure. The cost of egress is usually based on the amount of data transferred, the distance of the transfer, and the speed of the transfer. It is important for organizations to understand their cloud service provider's data egress policies and fees, as well as their data transfer needs, to avoid unexpected costs. Organizations can minimize egress costs by compressing data, reducing the amount of data transferred, or storing data in the same geographic region as their computing resources. The Benefits of Smart File Data Migration A smart data migration strategy for enterprise file data means an analytics-first approach ensuring you know which data can migrate, to which class and tier, and which data should stay on-premises in your hybrid cloud storage infrastructure. With Komprise, you always have native data access, which not only removes end-user disruption, but also reduces egress costs and the need for rehydration and accelerates innovation in the cloud. #### Storage Array A storage array is a type of data storage system that provides centralized, scalable storage for multiple computer systems. It typically consists of multiple disk drives, along with the hardware and software required to manage and control the disk storage. Storage arrays can be used for a variety of purposes, including data backup and recovery (DR), data archiving, and as a shared storage resource for virtualized environments. They offer several benefits over traditional direct-attached storage (DAS) systems, including increased reliability, scalability, and performance. Types of Storage Arrays There are several types of storage arrays, including: Network Attached Storage (NAS): A NAS storage array provides file-level access to storage over a network. Storage Area Network (SAN): A SAN storage array provides block-level access to storage over a high-speed network. Hybrid Storage Array: A hybrid storage array combines the features of both NAS and SAN storage arrays and delivers a balance of file-level and block-level access to storage. All-Flash Storage Array: This type of storage array uses only solid-state drives (SSDs) for storage, rather than traditional hard disk drives (HDDs). It provides extremely high performance and low latency, making it suitable for demanding applications such as database and virtualization environments. Cloud Storage Arrays The trend of storage arrays moving to the cloud has been growing in recent years, as organizations seek to leverage the benefits of cloud computing for their storage needs. Cloud storage arrays offer several advantages over traditional on-premise storage arrays, including: Scalability: Cloud storage arrays can easily scale up or down as storage needs change, without the need for physical hardware upgrades. Cost savings: Cloud storage arrays can be less expensive than on-premise storage arrays, especially for organizations with rapidly changing storage needs. Flexibility: Cloud storage arrays are accessible from anywhere with an internet connection. Disaster recovery: Cloud storage arrays can be used to recover data quickly and easily in the event of a disaster or outage, without the need for physical hardware or tapes. #### Information Lifecycle Management Information Lifecycle Management (ILM) is a data management strategy that focuses on managing the flow of data from creation to deletion. The goal of ILM is to optimize the use of storage resources and improve data management efficiency and cost-effectiveness. Gartner defines ILM this way: Information Lifecycle Management (ILM) is approach to data and storage management that recognizes that the value of information changes over time and that it must be managed accordingly. ILM seeks to classify data according to its business value and establish policies to migrate and store data on the appropriate storage tier and, ultimately, remove it altogether. ILM has evolved to include upfront initiatives like master data management and compliance. Source TechTarget Defines ILM this way: Information lifecycle management (ILM) is a comprehensive approach to managing an organization's data and associated metadata, starting with its creation and acquisition through when it becomes obsolete and is deleted. Source ILM involves a series of activities that are performed at different stages of the data lifecycle, such as data creation, data storage, data protection, data archiving, and data deletion. At each stage, the data is managed and stored according to its value, importance, and frequency of use. ILM typically involves the use of data classification, data retention, data archiving policies, and data management tools and technologies. These policies and technologies help to manage the flow of data throughout its lifecycle and ensure that it is stored in the most appropriate location and format for its current needs. Benefits of implementing ILM Improved storage utilization and cost savings By managing data throughout its lifecycle, ILM helps organizations ensure that the most valuable and important data is stored on high-performance storage systems, while less important data is stored on lower-cost storage systems. Increased data protection and security By managing the flow of data and applying appropriate data protection and security measures, ILM helps reduce the risk of data loss or corruption. Better compliance ILM helps organizations meet regulatory and compliance requirements by ensuring that data is managed and stored in accordance with the organization's policies and best practices. Overall, Information Lifecycle Management is an essential aspect of modern data management and is critical to effectively manage and store data securely and with cost savings in mind. ILM Challenges Complexity: In organizations with large and complex data environments it can be difficult to effectively manage and store data throughout its lifecycle. This can lead to data sprawl, increased data storage costs, and increased security and compliance risks. Cost: Implementing ILM requires investment in the right data management tools and technologies, and structured and unstructured data management policies and processes. This can be a significant cost for organizations, especially those with limited budgets. Data protection and security: ILM can introduce new security and privacy risks, especially if sensitive data is stored on low-cost or low-security storage systems. Organizations should ensure that they have appropriate data protection and security measures in place to mitigate these risks. By carefully planning and executing your ILM strategies, organizations can manage and store your data throughout its lifecycle, cutting costs while ensuring that data is protected, secure, and compliant with regulatory requirements. On-going Unstructured Data Management as part of an ILM Strategy As we noted when we launched Smart Data Workflows, with billions of files and objects, analytics plus continuous mobilization is essential because data has a lifecycle and data management is not a one-time thing. Whether the use case is data analytics, data migration, data tiering, data replication, data search or anything related to the data lifecycle, it is important to look for an unstructured data management solution that delivers on-going data management. Learn more about Komprise Intelligent Data Management. #### Data Lakehouse Data Lakehouse is a term first coined by the co-founder and then CTO of Pentaho, James Dixon. And while both Amazon and Snowflake had already started using the term "lakehouse," it wasn't until Databricks really endorsed it in a January 30, 2020 blog post entitled "What is a Data Lakehouse?" that it received more mainstream attention (amongst data practitioners at least). You've heard of a Data Lake. You've heard of a Data Warehouse. Enter the Data Lakehouse. A data lakehouse is a modern data architecture that combines the benefits of data lakes and data warehouses. A data lake is a centralized repository that stores vast amounts of raw, unstructured, and semi-structured data, making it ideal for big data analytics and machine learning. A data warehouse, on the other hand, is designed to store structured data that has been organized for querying and analysis. A data lakehouse builds on key elements of these two approaches by providing a centralized platform for storing and processing large volumes of structured and unstructured data, while supporting real-time data analytics. It allows organizations to store all of their data in one place and perform interactive and ad-hoc analysis at scale, making it easier to derive insights from complex data sets. A data lakehouse typically uses modern (and often open source) technologies such as Apache Spark, Apache Arrow, to provide high-performance, scalable data processing. Who are the data lakehouse vendors? There are several vendors that offer data lakehouse solutions, including: Amazon Web Services (AWS) with Amazon Lake Formation Microsoft with Azure Synapse Analytics Google with Google BigQuery Omni Snowflake Databricks Cloudera with Cloudera Data Platform Oracle with Oracle Autonomous Data Warehouse Cloud IBM with IBM Cloud Pak for Data These vendors provide a range of services, from cloud-based data lakehouse solutions to on-premises solutions that can be deployed in an organization's own data center. The choice of vendor will depend on the specific needs and requirements of the organization, such as: the size of the data sets, the required performance and scalability, the level of security and compliance needed and the overall budget. Komprise Smart Data Workflows is an automated process for all the steps required to find the right unstructured data across your data storage assets, tag and enrich the data, and send it to external tools such as a data lakehouse for analysis. Komprise makes it easier and more streamlined to find and prepare the right file and object data for analytics, AI, ML projects. #### Sustainable Data Management What is Sustainable Data Management? Sustainable data management refers to the practice of collecting, storing, and using data in a way that is environmentally friendly, economically feasible, and socially responsible. This involves reducing the carbon footprint of data centers, leveraging renewable energy sources, and following ethical principles in the collection, storage, and use of data (regardless of source, structure or location). It also involves implementing data management strategies that ensure the long-term preservation and accessibility of valuable data, while reducing waste and avoiding data hoarding. The goal of sustainable data management is to balance the economic, environmental, and social impacts of data operations and ensure that data is managed in a way that supports the well-being of both current and future generations In an an article for Sustainability magazine, Komprise co-founder and COO Krishna Subramanian noted: Most organizations have hundreds of terabytes of data, if not petabytes, which can be managed more efficiently and even deleted but are hidden and/or not understood well enough to manage appropriately. In most businesses, 70% of the cost of data is not in storage but in data protection and management. Creating multiple backup and DR copies of rarely used cold data is inefficient and costly, not to mention its environmental impact. Furthermore, storing obsolete “zombie data” on expensive on-premises hardware (or even, cloud file storage, which is the highest cost tier for cloud storage), doesn’t make sage economic sense and consumes the most energy resources. She summarized the steps to sustainable unstructured data management as: Understand your unstructured data (analyze your unstructured data) Automate data actions by policy (data management policy) Work with data owners and key stakeholders (getting departments to care about data storage savings) Her sustainable data management conclusion: Sustainable data center and data management practices are no longer nice to have – but in many respects, a need to have. The world is storing too much data and without smart strategies for managing it, the price is becoming too high: significantly higher IT infrastructure costs, lack of opportunity to participate in government incentives, potential customer attrition, and long-term potential brand damage by ignoring the sustainability movement. Read the full article here. ---------- #### Storage Area Network (SAN) What is a Storage Area Network (SAN)? A Storage Area Network (SAN) is a dedicated high-speed network (usually Fibre Channel) that provides block-level access to storage devices such as disk arrays and tape libraries. The goal of a SAN is to provide centralized data storage and data management that can be easily accessed by multiple servers. SANs can increase storage utilization, improve data security, and speed access times compared to using direct-attached storage (DAS). Storage Area Networks (SANs) are still widely used in modern data centers. They provide centralized data storage and management of data, which allows for improved data availability, performance, and security compared to traditional direct-attached storage (DAS) solutions. SANs also typically provide advanced features such as storage virtualization, disaster recovery, and basic data tiering. In recent years, cloud computing adoption has led to greater use of network-attached storage (NAS) and object storage solutions, but SANs remain a popular choice for many organizations due to their performance, reliability, and compatibility with existing infrastructure. Komprise Intelligent Data Management is a storage-agnostic solution that works across NAS technologies, from the data center to the cloud, to deliver visibility and mobility of unstructured data. Komprise helps customers with petabyte-scale data environments be more efficient in managing unstructured data and also proactively (and intelligently) moves file and object data to the right location at the right time for cost savings and value. #### Smart Data Workflows What are Komprise Smart Data Workflows? Smart Data Workflows, part of the Komprise Intelligent Data Management platform, is a systematic process to discover relevant file and object data across cloud, edge and on-premises datacenters and feed data in native format to AI and machine learning (ML) tools, data lakes and cloud file storage or cloud object storage. Smart Data Workflows solve common problems in unstructured data management: finding and moving the right unstructured data into data lakes, analytics platforms and cloud storage. Most of the work in finding and categorizing unstructured data to feed machine learning pipelines has been manual, delaying time to value and impeding the results of machine learning and AI projects. Users can create automated workflows for all the steps required to find the right data across your storage assets, tag and enrich the data, and send it to external tools for analysis. The Komprise Global File Index and Smart Data Workflows together reduce the time it takes to find, enrich and move the right unstructured data by up to 80%. The components of Smart Data Workflows: Search: Define and execute a custom query across on-prem, edge and cloud data silos to find the data you need.​ Execute & Enrich: Execute an external function on a subset of data and tag it with additional metadata. ​ Cull & Mobilize: Move only tagged data to the cloud.​ Manage Data Lifecycle: Move the data to a lower storage tier for cost savings once the analysis is complete.​ Watch the Smart Data Workflows chalk-talk. #### Amazon FSx What is Amazon FSx? Amazon FSx is a fully managed service, high-performance file systems in the cloud that runs on AWS. Customers can choose between four file systems: NetApp ONTAP OpenZFS Windows File Server Lustre Komprise supports for Amazon FSx for NetApp ONTAP with a focus on Smart Data Migration. As an Advanced AWS partner, the Komprise cloud data migration solution is able to "right place" data to reduce costs and increase data value. Read the AWS partner press release and blog post Komprise and AWS FSx for Netapp ONTAP. For more information on Komprise File Data Migration to the Cloud be sure to check out our Path to the Cloud section of the website and download the Smart Data Migration for AWS white paper. Other Resources: Komprise Smart Data Migration Komprise for AWS Komprise for NetApp #### Data Hoarding What is Data Hoarding? Data hoarding is now being recognized as a growing challenge in the technology world. Many IT teams are caught in an endless cycle of buying more data storage. Unstructured data is growing at record rates and this data is increasingly being stored across hybrid cloud infrastructure. This massive data growth and increased data mobility has only created more disconnected data silos. Just like hoarding has been recognized as a real problem in the real-world (see reality TV shows like Hoarders and Storage Wars), data hoarding refers to the practice of retaining large amounts of data that is no longer needed or is rarely used, for extended periods of time. This is a common problem in many organizations, where employees tend to save data out of habit, fear of losing it, or simply because they don't know what to do with it. What is the impact of data hoarding? The impact of data hoarding is more significant than most people / organizations realize, including: Increased costs: Storing large amounts of unnecessary data can be expensive, especially if the organization is using expensive storage solutions, such as high-end disk arrays or tape libraries. Reduced efficiency: Hoarded data can slow down systems and applications, as well as increase the time required to complete backups and other data management tasks. Compliance risks: Hoarded data can pose a risk to organizations in terms of compliance, as they may contain sensitive information that is subject to data privacy regulations. Cybersecurity risks: Hoarded data can also pose a security risk, as it may contain sensitive information that could be targeted by cybercriminals or hackers. Stop Treating All Data the Same Sound familiar? Cold data sits on expensive storage. Everything gets replicated. Everything gets backed up and backup windows are getting longer. Costs are spiraling out of control. The IDC report, How to Manage Your Data Growth Smarter with Data Literacy noted: 60% of the storage budget is not really spent on storage. It’s spent on secondary copies of data for data protection – backups, backup software licenses, replication, and disaster recovery. 1/3 of IT organizations are spending most of their IT storage on secondary data. And with ransomware attacks on the rise, which increasingly target unstructured data, it’s increasingly important to find ways to manage, tier, migrate, replicate file data within tight IT budgets. Read the blog post: How to Protect File Data from Ransomware at 80% Lower Cost. Dealing with Data Hoarding To address the data hoarding challenge and establish an Intelligent Data Management strategy, IDC recommends the following: Focus less on finding alternatives to store data better/faster and focus more on finding intelligent alternatives to unstructured data management. Use modern, next-generation cloud data management technologies that are lightweight and non-intrusive, and that demonstrate powerful return on investment. Aim to deliver continuous insights as a service to business and achieve speed of intelligence for a competitive edge. Establish a Cold Data Storage Strategy One obvious strategy to deal with data hoarding is to define a cold data storage strategy and establish unstructured data management policies. Read this post to learn how to quantify the business value impact of Komprise Intelligent Data Management. #### Zettabyte What is a zettabyte? A zettabyte is a measure used to describe a computer or other device's data storage capacity which equals a thousand exabytes, a billion terabytes, or a trillion gigabytes. One zettabyte is the equivalent of 250 billion DVDs worth of data. In 2020, IDC reported that 59 zettabytes of data would be consumed over the course of the year, the majority of which data is unstructured data. By 2025, IDC says worldwide data will grow 61% to 175 zettabytes, with as much of the data residing in the cloud as in data centers. Read more about intelligent data management. The 2022 State of Unstructured Data Management Report found that more than 50% of enterprise IT are managing at least 5 PB of data today. Unstructured Data Management #### FabricPool What is NetApp FabricPool? FabricPool is a NetApp storage technology that enables automated tiering of data from an all-flash appliance to low-cost object storage tiers either on or off premises. This technology is a form of storage pools which are collections of storage volumes exported to a shared storage environment. Read more about storage pools. Enterprise data storage vendors have had a complicated relationship with data tiering. While some storage vendors continue to make claims about needing no tiers, most data storage vendors now acknowledge that 70%+ of unstructured file data is cold data and provide options for tiering to lower-cost storage. While lower cost drives were initially the target for the cold tier, many vendors now also offer tiering through their file system to object storage on-premises or in the cloud. Storage-based data tiering and pools-based techniques to move data within a single architecture or operating system can deliver cost savings for system data such as snapshots. However, storage tiering introduces many limitations when it comes to flexible data policies, storage cost savings, ransomware defense, broader data access and unstructured data management needs. Read the blog post: What you need to know before jumping into the cloud tiering pool Download the white paper: Cloud Tiering: Storage-Based vs Gateways vs File-Based: Which is Better and Why? Learn more about the Komprise path to the cloud for file and object data. #### Zombie Data Data that is considered dead in a company but that still lurks around somewhere, often generated from ex-employees. At Komprise, we refer to ZOMBIE DATA as network attached storage (NAS) file data that is owned by users that are no longer with the organization. This unstructured data resides on a company’s expensive NAS array taking up not only expensive primary storage space, impacting performance and resulting in higher data storage costs, but is typically being replicated and maybe even backed up. Storing this ZOMBIE DATA comes at great expense to enterprise IT organizations. The Komprise 2022 State of Unstructured Data Management Report found that enterprises are spending over 30% of their IT budget on data storage, backups and disaster recovery. Do you know how much of this data is Zombie Data? Read the blog post, "Is Zombie Data Haunting You" and watch the TechKrunch demonstration, "Using Komprise Data Analytics and Data Modeling" to learn more. #### Virtual Data Lakes A virtual data lake, for Komprise called the Global File Index, is a granular, flexible and searchable index across file, object and cloud data storage spanning petabytes of unstructured data. A virtual data lake has been called a metadata lake, allowing organizations to find and execute Smart Data Workflows that enable Big Data, AI, and ML projects. Research has shown that with Big Data projects, up to 80% or more time is spent on finding the right data and getting it out of data centers and cloud infrastructure. With Komprise, powerful metadata-based search and indexing technology automates the process of finding unstructured data based on your specific criteria. This capability allows organizations to dynamically build virtual data lakes across storage silos on the fly so they can better manage and reuse your data for AI and ML. Komprise Deep Analytics lets you build specific queries to find the files you need, tag it to build real-time virtual data lakes that the entire organization can use, without having to first move the data. Learn more about Komprise Deep Analytics. #### S3 The S3 protocol is used in a URL that specifies the location of an Amazon S3 (Simple Storage Service) bucket and a prefix to use for reading or writing files in the bucket. See S3 Intelligent Tiering. Smart Data Migration to Amazon S3 with Komprise Komprise Elastic Data Migration makes cloud data migrations simple, fast and reliable. It eliminates sunk costs with continual data visibility and optimization even after the migration. Komprise has received the AWS Migration and Modernization Competency Certification, verifying the solution’s technical strengths in file data migration. A “smart data migration” strategy for enterprise file data means you take an analytics-first approach to ensure you know which data can migrate, to which AWS class and tier, and which data should stay on-premises to maximize performance. You can create execution plans based on policy and Komprise will continually move data to the right location. Most importantly, Komprise tiers data to the cloud in such a way that you can use all the native AWS storage tiers and data services—without any proprietary lock-in. The analytics-first approach helps our customers understand: “What data do I have, what data is hot, what data is cold, how is data being used, how fast is it growing?” Answers to these questions are provided by Komprise analytics across all your file storage and Network Attached Storage (NAS) environments. Knowing which data is hot and which is cold helps you understand what data requires the performance of Amazon FSx classes and what data should go to S3 or elsewhere. Learn more about Komprise for AWS. #### Rehydration What is rehydration? Rehydration is the process to fully reconstitute files so the transferred data can be accessed and used. Block-level tiering requires rehydrating tiered archived data before it can be used, migrated or backed up. No rehydration is needed with Komprise, which uses file-based tiering. Rehydration and the Cloud In this post, Komprise CEO Kumar Goswami answers the question: "Will I lose storage efficiencies such as de-dupe by not using a storage tiering solution in the cloud?" He notes: The overhead of keeping blocks in the cloud due to high egress costs, high data rehydration costs and high defragmentation costs significantly overshadows any potential de-dupe savings. When data is moved at the block level to the cloud, you are really not saving on any third-party backups and other applications because block tiering is a proprietary solution – read this white paper for more background on block-level vs file-based data tiering and cloud tiering. So if you consider all the additional backup licensing costs, cloud egress costs, cloud retrieval costs plus the fact that you are now locked-in and have to pay file system costs forever in the cloud to access your data (learn more about the benefits of cloud native unstructured data access), then the small savings you may get from dedupe are significantly overshadowed by overall costs and the loss of flexibility. Komprise provides a custom data rehydration policy that the user can configure to meet their needs. Data need not be re-hydrated on the first access. Komprise also provides a bulk recall feature if needed. Learn more about file-based cloud tiering with Komprise. #### Native Data Access Native Data Access: Having direct access to tiered or archived data without needing rehydration because files are accessed as objects from the target storage. The Benefits of Cloud Native Data Access Gartner estimates that by 2025 more than 95% of new digital workloads will be deployed on cloud-native platforms, up from 30% in 2021. According to the 2022 State of Unstructured Data Management report, enterprise IT organizations are looking to optimize data storage efficiency by moving more data to the cloud. As a result cloud NAS file data storage options are attracting attention. In fact, cloud NAS topped the list for storage investments in the 2023 (47%), followed closely by cloud object storage (44%). Enterprise data storage vendors such as NetApp have popular cloud NAS offerings alongside cloud-native offerings such as Amazon FSx and Azure Files. These services are ideal for active or “hot” data requiring high performance and response times; rarely-accessed or “cold” data can live on object storage which delivers significant cost savings for long-term storage. Read the Blog Post: Why Cloud Native Data Access Matters As you migrate file workloads to the cloud, it's important to not limit the potential of your data by locking data into a proprietary format. Cloud native data access is essential to unleash the potential of the cloud. Cloud native is a way to move data to the cloud without lock in, which means that your data is no longer tied to the file system from which it was originally served. Watch the TechKrunch session: How to Access Tiered Data in the Cloud This short webinar demonstrates how Komprise allows you to access your stored data wherever it’s stored, whenever you want, without rehydration. Because moved data are always intact, you can extract data value with both file and native access - and without penalty. Read the Komprise Architecture Overview for more information on Native Access. #### Native File Format Native File Format or Native Data Format. The file structure in which a document is created and maintained by the original creating application. Komprise provides transparent data tiering from the source storage array with native access to the cold data on the target, without getting in front of hot data on the source. Native data access is especially powerful when you are moving data to the cloud. You can move data to the cloud and always enable direct access to it because your data is no longer tied to the file system from which it was originally served. Why is native data access important? Maximize Data Efficiency When data is retained in native file format at the target, your organization can benefit from the full array of cloud-native technologies and service and further, leverage all tiers of cloud storage on your data. For example, Amazon FSx versus Glacier Instant Retrieval: there are significant cost differences between the higher-cost file storage and the low-cost object storage. When you move your data to the cloud with cloud tiering, ensure you can take advantage of all of the efficiencies the cloud has to offer by moving data as it ages to lower-cost storage. No-Lock-In Data Access When you move data in cloud native format, users should be able to access the data not only as a file, but also as a native object. That's useful to apply cloud-native analytics and other services on your data. You want to avoid going through your file storage layer to access data, which results in increased licensing fees and  capacity. Maximize Data Services Native data access makes it easy for your users to search and find the data they need and send it to data lakes and analytics services, most of which operate at the object layer. Cloud-native access ensures that your data can leverage these services without delay and hassle.     Learn more about the benefits of Native Data Access and Komprise Transparent Move Technology. #### File Server A file server is the central server in a computer network that provides a central storage place for files on internal data media to connected clients. A file server allows multiple users or client devices to access, share, and manage files over a network. File servers are commonly used in businesses, organizations, and homes to centralize file storage, making it easier to collaborate and back up data. What are the main roles of a file server in the enterprise? File Storage: It stores various types of files (documents, images, videos, etc.) that can be accessed by authorized users. (See Network Attached Storage) File Sharing: It allows users on the same network to share files with one another without having to manually transfer files via external storage (USB drives, etc.). Security and Access Control: File servers often include security features to control which users or groups have permission to view, modify, or delete files. Backup and Recovery: Many file servers support automatic backups and recovery tools to prevent data loss. Read the paper: Block-level Tiering Vs File-Level Tiering #### Egress Costs Egress costs are the network fees most cloud providers charge to move your data out of the cloud. Most allow you to move your data into the cloud for free (ingress). It's important to understand ingress and egress fees when moving data to the cloud. If you have moved data to cold storage in the cloud for archiving purposes but users recall it more than expected, you may incur hefty egress costs. Egress fees also happen when data is pulled out of cloud storage for use in analytics applications and to transfer data to another cloud region or cloud service. In the post 5 Tips to Optimize Your Unstructured Data, a key benefit of embracing open, standards-based unstructured data management is that organizations can do whatever they need to do with their file and object data data without paying licensing penalties and costs, such as for a third-party cloud file system or unnecessary cloud-egress fees. Komprise moves and manages unstructured data in native format in each tier, which means you can directly access the data and use all the cloud data services on your data without having to pay a data management or storage vendor. Avoiding these costs, including egress costs, is a priority for IT leaders surveyed by Komprise. Read the report: State of Unstructured Data Management. To learn more about Egress Costs read the New Stack article: Why Data Egress in the Cloud is Expensive. To learn more about right approach to cloud data migrations and data management visit: Smart Data Migration. The Benefits of Cloud Native Access Cloud native is a way to move data to the cloud without lock in, which means that your data is no longer tied to the file system from which it was originally served. In this webinar, Komprise leaders review the importance of cloud native data access and maximizing the potential of your data in terms of access, efficiency and data services. When you move data in cloud native format, your users should be able to access the data not only as a file, but also as a native object—which is necessary for leveraging cloud-native analytics and other services. Access to your data should not have to go through your file storage layer, as this incurs licensing fees and requires adequate capacity. Read the blog post: Why Cloud Native Unstructured Data Access Matters #### Data Analytics Data analytics refers to the process used to enhance productivity and business improvement by extracting and categorizing data to identify and analyze behavioral patterns. Techniques vary according to organizational requirements. The primary goal of data analytics is to help organizations make more informed business decisions by enabling analytics professionals to evaluate large volumes of transactional and other forms of data. Data analytics can be pulled from anything from Web server logs to social media comments. Potential issues with data analytics initiatives include a lack of analytics professionals and the cost of hiring qualified candidates. The amount of information that can be involved and the variety of data analytics data can also cause data analytics issues, including the quality and consistency of the data. In addition, integrating technologies and data warehouses can be a challenge, although various vendors offer data integration tools with big data capabilities. Big data has drastically changed the requirements for extracting data analytics from business data. With relational databases, administrators can easily generate reports for business use, but they lack the broader intelligence data warehouses can provide. However, the challenge for data analytics from data warehouses is the costs associated. Unstructured Data Analytics There is also the challenge of pulling the relevant data sets to enable data analytics from cold data. This requires intelligent data management solutions that track what unstructured data is kept and where, and enable you to easily search and find relevant data sets for big-data analytics. Deliver the right data to the right place at right time with Komprise and bring unstructured data to you your analytics projects.   Learn more Komprise unstructured data analysis and insight. #### Data Backup Why Data Backup? Data loss can occur from a variety of causes, including computer viruses, hardware failure, file corruption, fire, flood, or theft, etc. Data loss may involve critical financial, customer, and company data, so a solid data backup plan is critical for every organization. Data backup plan considerations: What data (files and folders) to backup How often to run your backups Where to store the backup data What compression method to use What type of backups to run What kind of media on which to store the backups In general, you should back up any data that can't be replaced easily. Some examples are structured data like databases, and unstructured data such as word processing documents, spreadsheets, photos, videos, emails, etc. Typically, programs or system folders are not part of a data backup program. Installation discs, operating system discs, and registration information should be stored in a safe place. Data backup frequency depends on how often your organizational data changes. Frequently changing data may need daily or hourly backups Data that changes every few days might require a weekly or even monthly backup For some data, a backup may need to be created each time it changes The challenge with unstructured data is that backing up unstructured data is not only time consuming but also very complex, with millions to billions of files of various sizes and types and growing at an astronomical rate, leaving enterprises to struggle with long backup windows, overlapping backup cycles, backup footprint sprawl, spiraling costs, and above all, vulnerable in the case of a disaster. Read the white paper: Rein in Storage and Backup Costs. Read the post: 5 Ways to Get to the Cloud Smarter and Faster Backing Up Unstructured Data First (Before Analysis) is Backwards Don't backup data first. Know your data first to make smarter, cost-saving decisions. Start with the Komprise TCO calculator. Learn more about Komprise Analysis. #### Data Governance What is data governance? Data governance refers to the management of the availability, security, usability, and integrity of data used in an enterprise. Data governance in an organization typically includes a governing council, a defined set of procedures, and a plan to execute those procedures. Data governance is not about allowing access to a few privileged users; instead, it should allow broad groups of users access with appropriate controls. Business and IT users have different needs; business users need secure access to shared data and IT needs to set policies around security and business practices. When done right, data governance allows any user access to data anytime, so the organization can run more efficiently, and users can manage their workload in a self-service manner. 3 things to consider when developing a data governance strategy: Selecting a Data Governance Team Balance IT and business leaders to get a broad view of the data and service needs Start small – choose a small group to review existing data analytics Data Quality Strategy Audit existing data to discover data types and how they are used Define a process for new data sources to ensure quality and availability standards are met Data Security Make sure data is classified so data requiring protection for legal or regulatory reasons meets those requirements Implement policies that allow for different levels of access based on user privileges Komprise is not a data governance solution but we are part of an overall governance strategy as it relates to unstructured data management. With the Deep Analytics user profile, you can provide secure data access to specific users to search and tag file and object data so that it can then be incorporated into smart data migration and data mobility use cases, including Smart Data Workflows. #### Data Lake A data lake is data stored in its natural state. The term typically refers to unstructured data that is sitting on different storage environments and clouds. The data lake supports data of all types – for example, you may have videos, blogs, log files, seismic files and genomics data in a single data lake. You can think of each of your Network Attached Storage (NAS) devices as a data lake. One big challenge with data lakes is to comb through them and find the relevant data you need. With unstructured data, you may have billions of files strewn across different data lakes, and finding data that fits specific criteria can be like finding a needle in a haystack A virtual data lake is a collection of data that fits certain criteria – and as the name implies, it is virtual because the data is not moved. The data continues to reside in its original location, but the virtual data lake gives a discrete handle to manipulate that entire data set. The Komprise Global File Index can be considered to be a virtual data lake for file and object metadata. Some key aspects of data lakes – both physical and virtual: Data Lakes Support a Variety of Data Formats: Data lakes are not restricted to data of any particular type. Data Lakes Retain All Data: Even if you do a search and find some data that does not fit your criteria, the data is not deleted from the data lake. A virtual data lake provides a discrete handle to the subset of data across different storage silos that fits specific criteria, but nothing is moved or deleted. Virtual Data Lakes Do Not Physically Move Data: Virtual data lakes do not physically move the data, but provide a virtual aggregation of all data that fits certain criteria. Deep Analytics can be used to specify criteria. #### Data Literacy The ability to derive meaningful information from data. Komprise Data Analytics provides data literacy by showing how much data, what kind, who’s using it, how often—across all storage silos. Read the IDC InfoBrief: How to Manage Your Data Growth Smarter with Data Literacy. #### Data Management Data management is officially defined by DAMA International, the professional organization data management professionals, is: "Data Resource Management is the development and execution of architectures, policies, practices and procedures that properly manage the full data lifecycle needs of an enterprise." Data management is the process of developing policies and procedures in order to effectively manage the information lifecycle needs of an enterprise. This includes identifying how data is acquired, validated, stored, protected, and processed. Data management policies should cover the entire lifecycle of the data, from creation to deletion. Due to the sheer volume of unstructured data, an unstructured data management plan is necessary for every organization. The numbers are staggering – for example, more data has been created in the past two years than in the entire previous history of the human race. Cloud data management is also a growing area of investment in the enterprise. #### Data Management Policy What is a Data Management Policy? A data management policy addresses the operating policy that focuses on the management and governance of data assets, and is a cornerstone of governing enterprise data assets. This policy should be managed by a team within the organization that identifies how the policy is accessed and used, who enforces the data management policy, and how it is communicated to employees. It is recommended that an effective data management policy team include top executives to lead in order for governance and accountability to be enforced. In many organizations, the Chief Information Officer (CIO) and other senior management can demonstrate their understanding of the importance of data management by either authoring or supporting directives that will be used to govern and enforce data standards. Considerations for a data management policy Enterprise data is not owned by any individual or business unit, but is owned by the enterprise Enterprise data must be safe Enterprise data must be accessible to individuals within the organization Metadata should be developed and utilized for all structured and unstructured data Data owners should be accountable for enterprise data Users should not have to worry about where data lives Data should be accessible to users no matter where it resides Ultimately, a data management policy should guide your organization’s philosophy toward managing data as a valued enterprise asset. With automation, IT can "set and forget" the policy to ensure continuous adherence to policies. For instance, a policy could dictate that all data over one year of age is tiered to cold storage, that research data from a department is moved to secondary storage upon completion of the project, or that all ex-employee data is deleted 30 days after the employee's last day. Watch the video: Intelligent Data Management: Policy-Based Automation  Developing an unstructured data management policy It is important to develop enterprise-wide data management policies using a flexible governance framework that can adapt to unique business scenarios and requirements. Identify the right technologies following a proof of concept approach that supports specific risk management and compliance use cases. Tool proliferation is always a problem so look to consolidate and set standards that address end-to-end scenarios. Unstructured data management policies must address data storage, data migration, data tiering, data replication, data archiving and data lifecycle management of unstructured data (block, file, and object data stores) in addition to the semi-structured and structured data lakes, data warehouses and other so-called big-data repositories. Read the VentureBeat article: How to create data management policies for unstructured data. What is a Data Management Policy? A data management policy addresses the operating policy that focuses on the management and governance of data assets. The data management policy should contain all the guidelines and information necessary for governing enterprise data assets and should address the management of structured, semi-structured and unstructured data. What does a Data Management Policy contain? A comprehensive Data Management Policy should contain the following: An inventory of the organization’s data assets A strategy of effective management of the organization’s data assets An appropriate level of security and protection for the data including details of which roles can access with data elements Categorization of the different sensitivity and confidentiality levels of the data The objectives for measuring expectations and success Details of the laws and regulations that must be adhered to regarding the data program Data Management policy and procedures Firstly the business much select who should be part of the policy-making process. This should include legal, compliance and risk executives, security and IT leaders, business unit heads and the chief data officer or relevant alternative. Once the committee is selected, they should identify the risks associated with the organizations data and create a data management policy. #### Data Protection Data protection is used to describe both data backup and disaster recovery. A quality data protection strategy should automate the movement of critical data to online and offline storage and include a comprehensive strategy for valuing, classifying, and protecting data as to protect these assets from user errors, malware and viruses, machine failure, or facility outages/disruptions. Data protection storage technologies include tape backup, which copies data to a physical tape cartridge, or cloud backup, which copies data to the cloud, and mirroring, which replicates a website or files to a secondary location. These processes can be automated and policies assigned to the data, allowing for accurate, faster data recovery. Data protection should always be applied to all forms of data within an organization, in order to protect the integrity of the data, protect from corruption or errors, and ensuring privacy of the data. When classifying data, policies should be established to identify different levels of security, from least secure (data that anyone can see) to most secure (data that if released, would put the organization at risk). #### Data Sprawl What is Data Sprawl? Data sprawl describes the staggering amount of unstructured data produced by enterprises worldwide every day; with new devices, including enterprise and mobile applications added to a network, it is estimated data sprawl to be 40% year over year, into the next decade. Given this growth in data sprawl, data security is imperative, as it can lead to enormous problems for organizations, as well as its employees and customers. In today’s fast-paced world, organizations must carefully consider how to best manage the precious information it holds. Organizations experiencing unstructured data sprawl need to secure all of their endpoints. Security is critical. Addressing data security as well as remote physical devices ensure organizations are in compliance with internal and external regulations. As the amount of security threats mount, it is critical that data sprawl is addressed. Taking the right steps to ensure data sprawl is controlled, via policies and procedures within an organization, means safeguarding not only internal data, but also critical customer data. Organizations should develop solid practices that may have been dismissed in the past. Left unchecked, control of an organization’s unstructured data will continue to manifest itself in hidden costs and limited options. With a little evaluation and planning, it is an aspect of your network that can be improved significantly and will pay off long term. Analyzing and Managing Unstructured Data: Getting Sprawl (and Costs) Under Control According to this Geekwire article, Gartner estimates that unstructured data represents an astounding 80 to 90% of all new enterprise data, and it’s growing 3X faster than structured data. Komprise Intelligent Data Management rapidly analyzes file ad object unstructured data in-place across multi-vendor storage to provide aggregate analytics (e.g., how much data, how much is hot, how much is cold, what types, top users, etc.) as well as a Global File Index across cloud and on-prem environments. The Komprise Global File Index is highly efficient and scalable to handle billions of files, exabytes of data without the scalability issues of using a central database or any other centralized architectures. Customers can build queries using Komprise Deep Analytics to find the precise subset of data they need through any combination of metadata and tags, and then move, copy and tier that data using Deep Analytics Actions. Komprise combines in-place analytics with data movement and on-going data management to provide a closed-loop system that is intelligent and adapts to a customer's unique needs. The functionality is also available via API. Tackling Data Sprawl with Komprise Analysis Komprise Analysis provides consistent unified insights into unstructured data across many vendors’ storage and cloud platforms. Key metrics include data volume, data growth rates, where data is stored, top owners, top file types/sizes and time of last access. Komprise can create cost models based on different storage targets and tiering plan that will show. #### Data Virtualization Data virtualization delivers a unified, simplified view of an organization’s data that can be accessed anytime. It integrates data from multiple sources, to create a single data layer to support multiple layers and users. The result is faster access to this data, providing instant access, any way you want it. Data virtualization involves abstracting, transforming, federating and delivering data from disparate sources. This allows users to access the applications without having to know their exact location. Advantages to data virtualization: An organization can gain business insights by leveraging all data They can become aware of analytics and business intelligence Data virtualization can streamline an organization’s data management approach, which reduces complexity and saves money Data virtualization involves three key steps. First, data virtualization software is installed on-premise or in the cloud, which collects data from production sources and stays synchronized as those sources change over time. Next, administrators are able to secure, archive, replicate, and transform data using the data virtualization platform as a single point of control. Last, it allows users to provision virtual copies of the data that consume significantly less storage than physical copies. Data virtualization use cases: Application development Backup and disaster recovery Datacenter migration Test data management Packaged application projects #### Deep Analytics What is Deep Analytics? Deep analytics is the process of applying data mining and data processing techniques to analyze and find large amounts of data in a form that is useful and beneficial for new applications. Deep analytics can apply to both structured and unstructured data. In the context of unstructured data and unstructured data management, Komprise Deep Analytics is the process of examining file and object metadata (both standard and extended) across billions of files to find data that fits specific criteria. A petabyte of unstructured data can be a few billion files. Analyzing petabytes of data typically involves analyzing tens to hundreds of billions of files. Because analysis of such large workloads can require distribution over a farm of processing units, deep analytics is often associated with scale-out distributed computing, cloud computing, distributed search, and metadata analytics. Deep analytics of unstructured file and object data requires efficient indexing and search of files and objects across a distributed farm. Financial services, genomics, research and exploration, biomedical, and pharmaceutical are some of the early adopters of Komprise Deep Analytics, which is powered by a Global File Index medata catalog. In recent years, enterprises have started to show interest in deep analytics as the amount of corporate unstructured data has increased, and with it, the desire to extract value from the data. Deep analytics enables additional use cases such as Big Data Analytics, Artificial Intelligence and Machine Learning. When the result of a deep analytics query is a virtual data lake, which we call the Global File Index, data does not have to be moved or disrupted from its original destination to enable reuse. This is an ideal scenario to rapidly leverage deep analytics without disruption since data can be pretty heavy to move. Learn more about Komprise Deep Analytics. Learn more about Deep Analytics with Actions. Read the blog post: How Storage Teams Use Deep Analytics #### Digital Business A digital business is one that uses technology as an advantage in its internal and external operations. Information technology has changed the infrastructure and operation of businesses from the time the Internet became widely available to businesses and individuals. This transformation has profoundly changed the way businesses conduct their day-to-day operations. This has maximized the benefits of data assets and technology-focused initiatives. This digital transformation has had a profound impact on businesses; accelerating business activities and processes to fully leverage opportunities in a strategic way. A digital business takes advantage of this fully so to not be disrupted and to thrive in this era. C-Level staff needs to help their organizations seize opportunities while mitigating risks. This technology mindset has become standard in even the most traditional of industries, making a digital business strategy imperative for storing and analyzing data to gain a competitive advantage over the competition. The introduction of cloud computing and SaaS delivery models means that internal processes can be easily managed through a wide choice of applications, giving organizations the flexibility to chose, and change software as the businesses grows and changes. A digital business also has seen a shift in purchasing power; individual departments now push for the applications that will best suit their needs, rather than relying on IT to drive change. Unstructured Data Management is a Digital Business Priority The Komprise 2022 State of Unstructured Data Management Report found that data storage costs comprise over 30% of enterprise IT budgets. This is why the right unstructured data management strategy has become an essential component of a digital business strategy. Unstructured Data Management                 Read the latest Unstructured Data Management Report Unstructured data management is about being able to realize business outcomes from analytics through data movement, extraction, and value. Komprise provides a storage-independent way to manage data no matter where it lives so a digital business can get value from unstructured data from every tier. Unlike storage tiering or data backup solutions that move blocks of data and lock customers into proprietary file systems, Komprise Intelligent Data Management moves the entire file intact and enables customers to directly leverage native services at every tier without going through Komprise or their primary file system. This is key to a seamless user experience because users in a modern digital enterprise transparently access data from their original file system while also being able to build new applications in the cloud. Furthermore, user transparency, powered by patented Transparent Move Technology, makes it possible for IT teams to deploy transparent tiering company wide. Without this transparency, IT would need user and/or departmental approval and this essentially is a major roadblock that prevents any large scale tiering. #### Direct Data Access Direct data access is the ability to directly access your data whether on-premises, in the cloud, or a hybrid environment without needing to rehydrate. The patented Komprise Transparent Move Technology™ (TMT) tiers file data workloads to a target without using any agents or stubs, allowing users to still access files natively from the original source as if they had never moved. Known as file and object duality, with Komprise users access files as native objects without getting in front of hot, mission-critical data. Native Data Access definition. #### Director (Komprise Director) The Komprise Director is the administrative console of the Komprise distributed architecture that runs as a cloud service. Read the white paper: Komprise Intelligent Data Management Architecture Overview or one of the Komprise TechKrunch videos to learn more. Komprise simplifies data management by creating a lightweight management plane across all your data silos without getting in the path of data access. Storage and backup vendors that offer analytics require your data to be on their hardware first before they’ll give you these insights. This forces you to make costly investment decisions and move or copy data before you’ve had a chance to first understand it. (If you could, you probably wouldn’t make the same decisions.) Komprise lets you know first, then move smart and take control by providing analytics in place, across your hybrid, multi-cloud storage environments. Analyze all your data before investing, copying, or moving. Komprise lets you analyze data across your storage environments without first requiring a move or copy. We do this by connecting to your storage and analyzing your data via standard protocols, such as NFS, SMB or S3. This allows Komprise to analyze your data in-place without needing to import it into a proprietary format. Komprise has no proprietary interfaces, agents, or clients on your storage. Our standards-based approach gives you critical insights into your data—wherever it resides—so you can make the best, most-informed decisions. Analyze unstructured data usage metrics in hours, not weeks Most unstructured data management solutions take weeks to crawl and index all your data before they can provide insights on billions of files and petabytes of data. Komprise delivers analytics in hours—even on billions of files—using patented data analytics and aggregation techniques. It simplifies building your data management plan by allowing you to: Find files by various criteria such as file types, sizes, owners, top groups, last access time, etc; Run “what if” scenarios and get subsequent capacity needs and cost savings in seconds; Manage and move your data the way you need, to save the most. Want to know what would happen if you moved all data untouched in over a year to the cloud? Komprise provides instant analysis based on your data, your costs, and historical data growth patterns. Learn more about the Komprise architecture. #### Disaster Recovery Disaster recovery (DR) refers to security planning to protect an organization from the effects of a disaster – such as a cyber attack or equipment failure. A properly constructed disaster recovery plan will allow an organization to maintain or quickly resume mission critical functions following a disaster. The disaster recovery plan includes policies and testing, and may involve a separate physical site for restoring operations. This preparation needs to be taken very seriously, and will involve a significant investment of time and money to ensure minimal losses in the event of a disaster. Control measures are steps that can reduce or eliminate various threats for organizations. Different types of measures can be included in disaster recovery plan. There are three types of disaster recovery control measures that should be considered: Preventive measures – Intended to prevent a disaster from occurring Detective measures – Intended to detect unwanted events Corrective measures – The plan to restore systems after a disaster has occurred. A quality disaster recovery plan requires these policies be documented and tested regularly. In some cases, organizations outsource disaster recovery to an outsourced provider instead of using their own remote facility, which can save time and money. This solution has become increasingly more popular with the rise in cloud computing. According to the 2023 Komprise State of Unstructured Data Management report, more than 50% of enterprise IT organizations are managing at least 5 PB of data today and 73% are spending more than 30% of their IT budget on data storage, backups and disaster recovery. Read the eBook: 8 Ways to Save on File Data Storage. Enterprise data protection schemes for most businesses continue to follow traditional lines based on backups and well-established disaster recovery (DR) strategies, such as having remote sites to replicate data to on a scheduled basis. This is no longer enough. Today, the higher frequency and likelihood of disasters, the greater impact they have on our digital society, and the uncontrolled rate of data have created the perfect storm. Read this FastCompany article by Komprise COO Krishna Subramanian, which goes into new tactics such as installing data deletion and replication policies and replicating low-priority data to cloud object storage for a more affordable DR strategy.   Read the case study: Leading Idaho Health System Selects Komprise to Right-Place Data and Bolster Disaster Recovery #### Dynamic Data Analytics Komprise unstructured data analytics allows organizations to analyze data across all storage to know how much exists, what kind, who’s using it, and how fast it’s growing. “What if” data scenarios can be run based on various policies to instantly see capacity and data storage cost savings, enabling informed, optimal unstructured data management planning decisions without risk. Learn more about Komprise Analysis. Learn more about Komprise Deep Analytics. #### Data Storage What is Data Storage? Data storage refers both to the methods of transferring digital information from the source (users, applications, sensors) via protocols or APIs and to the destination; it consists of physical storage media such as magnetic or solid-state disks, tape, or optical, or cloud-based file and object storage. Data storage is pervasive and  implemented in enterprise data centers, cloud providers, and consumer technology such as laptops, and phones.  From genomics and medical imaging to streaming video, electric cars, IoT at the edge and user generated data, unstructured data growth is exploding. Enterprise IT organizations are looking to new cloud and hybrid cloud strategies to manage costs and investing in unstructured data management and cloud data migration and cloud data management technologies and strategies to reduce data storage costs and while maximizing data value. What are the different types of data storage protocols? File Data Storage: File storage records data to files that are organized in folders, and the folders are organized under a hierarchy of directories and subdirectories. For example, a text file stored to your home directory on your laptop. File data is typically used for collaboration and shared access. Examples of File Storage NAS Network Attached Storage, Network File System NFS, and Server Message Block SMB File Storage Vendor Solutions NetApp ONTAP, Dell/EMC PowerScale (Isilon), Qumulo, Microsoft Windows Server, Pure FlashBlade, Amazon FSx, Azure Files Block Data Storage Typically used in servers and workstations where data is being written directly to physical media (HDD or SSD) in chunks or blocks. In contrast to file, block data is typically dedicated for access by a single application. Block storage is often used for the most performance intensive applications. Examples of Block Data Storage: Direct Attached Storage DAS, Storage Attached Network SAN, iSCSI, NVME Block Storage Vendor Solutions: Pure FlashArray, Dell/EMC VMAX, NetApp ONTAP and E-series, HDS Object Storage Also known as object-based storage or cloud storage, is a way of addressing and manipulating data storage as objects. In contrast to file storage, object data is stored in a flat namespace. Object storage was designed for use in massive repositories and is accessed over the HTTP protocol as a REST API. Examples of Object Storage: AWS S3, Azure Blob, Google Cloud Storage, Cloud Data Management Interface (CDMI) Object Storage Vendors: AWS, Azure, Google, Wasabi, Cloudian, NetApp, Dell/EMC, Scality NDMP (Network Data Management Protocol) Storage protocol that allows file servers and backup applications to communicate directly to a network-attached tape device for backup or recovery operations. What are types of physical storage media? Hard Disk Drive (HDD): Disk based storage, used for high density data storage. Data is written to a magnetic layer of spinning disk. Solid State Drive (SSD): Also known as flash. Silicone replaces the spinning disk component of HDD to achieve higher performance and smaller form factor. Tape: Data is written to a ribbon of magnetic material in a cartridge. Used strictly for backup and archive, tape’s slow performance is off set by low cost, high levels of density, and the ability to be stored offline.  Optical Storage: In contrast to magnetic storage data is recorded optically to media such as CD and DVD disks. Optical storage is used for durable, long term, off-line, archival storage.  What is Primary Storage? Primary storage is used for active read and write data sets where high performance is critical. SSD or flash media with the highest level of performance is the ideal storage media for primary storage. While less typical HDD is also used as primary storage where lower cost and storage density is the key factor. What is Secondary Storage? Also referred to as active archive, secondary storage is used for less frequently accessed data sets. While any protocol and media can be used for secondary storage HDD with NAS and Object are the most common choices. Use cases for secondary storage is data tiering and backup / data protection applications. Read the white paper: Block-Level vs. File Level Tiering What is Data Storage? Data storage refers both to the methods of transferring digital information from the source (users, applications, sensors) via protocols or APIs and to the destination; physical storage media such as magnetic or solid-state disks, tape, or optical. What is Block Level Data Storage? Mainly used in servers and workstations where data is being written directly to physical media (HDD or SSD) in chunks or blocks. As opposed to file level data storage, block level data storage is mostly dedicated for access by a single application. Block storage uses either direct attached storage (DAS), or data transfer protocols Fiber Channel (FC) or iSCSI (Internet Small Computer Systems Interface) via a storage area network (SAN). What is Data Lake Storage in Azure? Data Lake Storage in Azure from Microsoft is a fully managed scalable system based on a secure cloud platform that provides industry-standard, cost-effective storage for big data analytics. #### Intelligent Data Management What is Intelligent Data Management? Intelligent Data Management is the process of managing unstructured data throughout its lifecycle with analytics and intelligence. It is also the name of the Komprise platform as a service: Intelligent Data Management. The criteria for a solution to be considered as Intelligent Data Management includes: Analytics-Driven Data Management Is the solution able to leverage analysis of the data to inform its behavior? Is it able to deliver analysis of the data to guide the data management planning and policies? Learn more about Komprise Analysis. Storage-Agnostic Data Management Is the data management solution able to work across different vendor and different storage platforms? Adaptive Data Management Based on the network, storage, usage, and other conditions, is the data management solution able to intelligently adapt its behavior? For instance, does it throttle back when the load gets higher, does it move bigger files first, does it recognize when metadata does not translate properly across environments, does it retry when the network fails? Closed Loop Unstructured Data Management Analytics feeds the data management which in turn provides additional analytics. A closed loop system is a self-learning system that uses machine learning techniques to learn and adapt progressively in an environment. Will Intelligent Data Management Help with Data Storage Eficiency and Cost? An intelligent data management solution should be able to scale out efficiently to handle the load, and to be resilient and fault tolerant to errors.It should also ensure you're able to achieve data storage cost savings. Intelligent data management solutions typically address the following use cases: Analysis: Find the what, who, when of how data is growing and being used Planning: Understand the impact of different policies on costs, and on data footprint Data Tiering or Data Archiving: Support various forms of managing cold data and offloading it from primary storage and backups without impacting user access. Includes: Tier and archive data by policy – move data with links for seamless access, Archive project data – archive data that belongs to a project as a collection, Archive without links – move data without leaving a link behind when data needs to be moved out of an environment Data Replication: Create a copy of data on another location. Data Migration: Move data from one storage environment to another Deep Analytics: Search and query data at scale across storage #### Capacity Planning Capacity planning is the estimation of space, hardware, software, and connection infrastructure resources that will be needed a period of time. In reference to the enterprise environment, there is a common concern over whether or not there will be enough resources in place to handle an increasing number of users or interactions. The purpose of capacity planning is to have enough resources available to meet the anticipated need, at the right time, without accumulating unused resources. The goal is to match the resource of availability to the forecasted need, in the most cost-efficient manner for maximum data storage cost savings. True data capacity planning means being able to look into the future and estimate future IT needs and efficiently plan where data is stored and how it is managed based on the SLA of the data. Not only must you meet the future business needs of fast-growing unstructured data, you must also stay within the organization’s tight IT budgets. And, as organizations are looking to reduce operational costs with the cloud (see cloud cost optimization), deciding what data can migrate to the cloud, and how to leverage the cloud without disrupting existing file-based users and applications becomes critical. Data storage never shrinks, it just relentlessly gets bigger. Regardless of industry, organization size, or “software-defined” ecosystem, it is a constant stress-inducing challenge to stay ahead of the storage consumption rate. That challenge is not made any easier considering that typically organizations waste a staggering amount of data storage capacity, much of which can be attributed to improper capacity management. Are you making capacity planning decisions without insight? Komprise enables you to intelligently plan storage capacity, offset additional purchase of expensive storage, and extend the life of your existing data storage by providing visibility across your storage with key analytics on how data is growing and being used, and interactive what-if analysis on the ROI of using different data management objectives. Komprise moves data based on your objectives to secondary storage, object storage or cloud storage, of your choice while providing a file gateway for users and applications to transparently access the data exactly as before. With an analytics-first approach, Komprise provides visibility into how data is growing and being used across storage silos. Storage administrators and IT leaders no longer have to make storage capacity planning decisions without insight. With Komprise Intelligent Data Management, you'll understand how much more storage will be needed, when and how to streamline purchases during planning. #### Checksum Checksum is a calculated value that’s used in NAS data analytics to determine the integrity of data. The most commonly used checksum is MD5, which Komprise uses to manage chain of custody and integrity reporting per file. Learn more about Komprise Elastic Data Migration for smart, fast and proven file and object data migrations. Learn tips on a clean cloud data migration on the Komprise blog. #### NetApp FabricPool What is NetApp FabricPool? Is it the Right Choice for NetApp Data Tiering? FabricPool (now called NetApp Cloud Tiering) is a NetApp storage technology that enables automated data tiering at the block level from flash storage to low-cost object storage tiers, in the cloud or on premises. FabricPool is a form of storage pools which are collections of storage volumes that often blend different tiers of storage into a logical pool or shared storage environment. Originally developed to tier “snapshot” or backup data, the functionality has been extended to infrequently accessed blocks of the active file system. Tiered data is stored in a proprietary format in object storage and as a result can only be read via the original NetApp array. File data access from the object storage is not possible, eliminating the use of cloud-based tools for AI/ML. Additionally functions such as backup by external application or migration to new storage array require full rehydration of data, leading to egress fees from cloud storage and the need to retain sufficient storage capacity on-premises. Read the white paper, Cloud Tiering: Storage-Based vs Gateways vs File-Based, for more discussion on storage pools. Array block-level tiering is a mismatch for the cloud. NetApp cloud tiering blocks rather than entire files has the following ramifications: Limited policies result in more data access from the cloud. Defragmentation of blocks leads to higher cloud costs. Sequential reads lead to higher cloud costs and lower performance. Tiering blocks impacts performance of the storage array. Read the blog post: What you need to know before jumping into the cloud tiering pool Learn more about FabricPool technology. When it comes to considering NetApp data tiering and NetApp cloud tiering, it's important to understand your cloud tiering choices. Cloud tiering and archiving can save you millions by offloading infrequently accessed cold data to cost-efficient cloud data storage. But, the approach you take can either create an easy path to the cloud for file data with full use of data in the cloud or it can create costly cloud egress and lock-in. Also, what about cloud data migration and cloud tiering for other storage systems (i.e. Isilon cloud tiering) if you are a multi-storage enterprise IT organization? And what about tiering data from older versions of NetApp? This is why increasingly the market is moving to storage-agnostic unstructured data management. Learn about Komprise’s native integration with NetApp and why Komprise is the right choice for NetApp cloud data tiering. #### General Data Protection Regulation (GDPR) The General Data Protection Regulation (GDPR) (Regulation (EU) 2016/679) is a regulation by the European Union that aims to strengthen and unify data protection for all individuals within the European Union (EU). It also addresses the export of personal data outside the EU. GDPR becomes enforceable from 25 May 2018. Businesses transacting with countries in the EU will have to comply with GDPR laws. The GDPR regulation applies to personal data collected by organizations including cloud providers and businesses. Article 17 of GDPR is often called the “Right to be Forgotten” or “Right to Erasure”. The full text of the article is found below. To comply with GDPR, you need to use an intelligent data management solution to identify data belonging to a particular user and confine it outside the visible namespace before deleting the data. This two-step deletion ensures there are no dangling references to the data from users and applications and enables an orderly deletion of data.   Art. 17 GDPR Right to erasure (‘right to be forgotten’) 1) The data subject shall have the right to obtain from the controller the erasure of personal data concerning him or her without undue delay and the controller shall have the obligation to erase personal data without undue delay where one of the following grounds applies: the personal data are no longer necessary in relation to the purposes for which they were collected or otherwise processed; 2 the data subject withdraws consent on which the processing is based according to point (a) of Article 6(1), or point (a) of Article 9(2), and where there is no other legal ground for the processing; the data subject objects to the processing pursuant to Article 21(1) and there are no overriding legitimate grounds for the processing, or the data subject objects to the processing pursuant to Article 21(2); the personal data have been unlawfully processed; the personal data have to be erased for compliance with a legal obligation in Union or Member State law to which the controller is subject; the personal data have been collected in relation to the offer of information society services referred to in Article 8(1). 2) Where the controller has made the personal data public and is obliged pursuant to paragraph 1 to erase the personal data, the controller, taking account of available technology and the cost of implementation, shall take reasonable steps, including technical measures, to inform controllers which are processing the personal data that the data subject has requested the erasure by such controllers of any links to, or copy or replication of, those personal data. 3) Paragraphs 1 and 2 shall not apply to the extent that processing is necessary: for exercising the right of freedom of expression and information; for compliance with a legal obligation which requires processing by Union or Member State law to which the controller is subject or for the performance of a task carried out in the public interest or in the exercise of official authority vested in the controller; for reasons of public interest in the area of public health in accordance with points (h) and (i) of Article 9(2) as well as Article 9(3); for archiving purposes in the public interest, scientific or historical research purposes or statistical purposes in accordance with Article 89(1) in so far as the right referred to in paragraph 1 is likely to render impossible or seriously impair the achievement of the objectives of that processing; or for the establishment, exercise or defense of legal claims. #### Scale-Out Grid Komprise is a scale-out grid architecture. Traditional approaches to managing data have relied on a centralized architecture – using either a central database to store information, or requiring a primary-replica architecture with a central primary server to manage the system. These approaches do not scale to address the modern scale of data because they have a central bottleneck that limits scaling. A scale-out architecture delivers unprecedented scale because it has no central bottlenecks. Instead, multiple servers work together as a grid without any central database or master and more servers can be added or removed on-demand. Scale-out grid architectures are harder to build because they need to be designed from the ground up to not only distribute the workload across a set of processes but also need to provide fault-tolerance so if any of the processes fails the overall system is not impaired. Below is a screenshot of the Komprise elastic grid architecture. Read the Komprise Architecture Overview white paper to learn more. Learn more about the Komprise architecture. #### REST (Representational State Transfer) REST (Representational State Transfer) is a software architectural style for distributed hypermedia systems, used in the development of Web services. Distributed file systems send and receive data via REST. Web services using REST are called RESTful APIs or REST APIs. There are several benefits to using REST APIs: it is a uniform interface so you don't have to know the inner workings of an application to use the interface, it's operations are well defined and so data in different storage formats can be acted upon by the same REST APIs, and it is stateless, so each interaction does not interfere with the next. Because of these benefits, REST APIs are fast, easy to implement with, and easy to use. As a result, REST has gained wide adoption. 6 guiding principles for REST: Client–server – Separate user interface from data storage improves portability and scalability. Stateless – Each information request is wholly self contained so session state is kept entirely on the client. Cacheable – A client cache is given the right to reuse that response data for later, equivalent requests. Uniform interface – The overall REST system architecture is simplified and uniform due to the following constraints: identification of resources; manipulation of resources through representations; self-descriptive messages; and, hypermedia as the engine of application state. Layered system – The layered system is composed of hierarchical layers and each component cannot “see” beyond the immediate layer with which they are interacting. Code on demand (optional) – REST allows client functionality to be extended by downloading and executing code in the form of applets or scripts. The REST architecture and lighter weight communications between producer and consumer make REST popular for use in cloud-based APIs such as those authored by Amazon, Microsoft, and Google. REST is often used in social media sites, mobile applications and automated business processes. REST provides advantages over leveraging SOAP REST is often preferred over SOAP (Simple Object Access Protocol) because REST uses less bandwidth, making it preferable for use over the Internet. SOAP also requires writing or using a server program and a client program. RESTful Web services are easily leveraged using most tools, including those that are free or inexpensive. REST is also much easier to scale than SOAP services. Thus, REST is often chosen as the architecture for services available via the Internet, such as Facebook and most public cloud providers. Also, development time is usually reduced using REST over SOAP. The downside to REST is it has no direct support for generating a client from server-side-generated metadata whereas SOAP supports this with Web Service Description Language (WSDL). Unstructured data management software using REST APIs Open-APIs and a REST-based architecture are the keys to Komprise integrations. Using REST APIs gives customers the greatest amount of flexibility and here are some things customers can do with the Komprise Intelligent Data Management software via its REST API: Get analysis results and reports on all their data Run data migrations, data archiving and data replication operations Search for data across all their storage by any metadata and tags Build virtual data lakes to export to AI and Big Data applications A REST API is a very powerful, lightweight and fast way to interact with data management software. Here is an example of the Komprise API in action: Automated Data Tagging with Komprise. #### High Performance Storage What is High Performance Storage? High performance storage is a type of storage management system designed for moving large files and large amounts of data around a network. High performance storage is especially valuable for moving around large amounts of complex data or unstructured data like large video files across the network. Used with both direct-connected and network-attached storage, high performance storage supports data transfer rates greater than one gigabyte per second and is designed for enterprises handling large quantities of data - in the petabyte range. High performance storage supports a variety of methods for accessing and creating data, including FTP, parallel FTP, VFS (Linux), as well as a robust client API with support for parallel I/O. High performance storage is useful to manage hot or active data, but can be very expensive for cold/inactive data. Since over 60 to 90% of data in an organization is typically inactive/cold within months of creation, this data should be moved off high performance storage to get the best TCO of storage without sacrificing performance. Is Cold Data Impacting Data Storage Performance? Unstructured data management policies ensures that data is always stored in the appropriate environment according to its usage, age, value and business priority to maximize data storage performance and data storage costs. Read: The Need for Policies to Corral Your Unstructured Data #### Scale-Out Storage Scale-out storage is a type of storage architecture in which devices in connected arrays add to the storage architecture to expand disk storage space. This allows for the storage capacity to increase only as the need arises. Scale-out storage architectures adds flexibility to the overall data storage environment while simultaneously lowering the initial storage set up costs. With data growing at exponential rates, enterprises will need to purchase additional storage space to keep up. This data growth comes largely from unstructured data, like photos, videos, PowerPoints, and Excel files. Another factor adding to the expansion of data is that the rate of data deletion is slowing, resulting in longer data retention policies. For example, many organizations are now implementing “delete nothing" data management policies for all kinds of data. With data storage demands skyrocketing and budgets shrinking, scale-out storage can help manage these growing costs. Whether it's NetApp, Pure Storage, Dell EMC, Qumulo or other enterprise scale-out storage technology, including cloud services from AWS, Azure or Google, Komprise Intelligent Data Management ensures you get maximum cost savings and value from your unstructured data. Read the white paper: Why Data Growth is Not a Storage Problem #### Transparent Move Technology Transparent Move Technology refers to an approach for Data Tiering, Data Archiving and Data Management that moves cold files transparently such that: The archived files can still be viewed and opened from the original location so users and applications do not need to change their data access. The archived files can be accessed via the original file protocols even if they are archived on an object repository. There is no change to the data path for the hot data that is not archived.  So there are no server or client side agents or static stubs. Accessing archived files does not cause the data to be brought back or rehydrated.  The approach is transparent to backup software and other applications. Read the white paper: Transparent Move Technology - Leverage the Full Power of the Cloud without Disrupting Users and Applications with Komprise TMT #### Secondary Storage What is Secondary Storage? Secondary storage devices are storage devices that operate alongside the computer’s primary storage, RAM, and cache memory. Secondary storage is for any amount of data, from a few megabytes to petabytes. These devices store almost all types of programs and applications. This can consist of items like the operating system, device drivers, applications, and user data. For example, internal secondary storage devices include the hard disk drive, the tape disk drive, and compact disk drive. Some key facts about secondary storage: It is typically designed for long-term storage – its non-volatile media such as solid state devices, optical or magnetic storage devices such as tape. It is typically orders of magnitude cheaper than primary storage – it is designed for more capacity storage than performance. It can either be hosted on-premises at data centers or in the cloud. It can use file (Network Attached Storage NAS via NFS and SMB/CIFS protocols) or block-based storage-area-network (SAN) or object formats. Object-based secondary storage is extremely popular today especially in the cloud. Examples include: Amazon Simple Storage Service (S3), Amazon Glacier, Azure Blob, Google Cloud ColdLine Storage, and on-premises object stores such as IBM Cloud Object Storage. Use cases include: Cold data storage, Cold data tiering or data archiving, data backup and Disaster Recovery storage. Komprise data management software is used to find the right data to place on secondary storage and move data to secondary storage without user disruption. Secondary Storage Data Tiering Secondary storage typically tiers or archives inactive cold data and backs up primary storage through data replication or other data backup methods. This replication or data backup process, ensures there is a second copy of the data. In an enterprise environment, the storage of secondary data can be in the form of a network-attached storage (NAS) box, storage-area network (SAN), or tape. In addition, to lessen the demand on primary storage, object storage devices may also be used for secondary storage. The growth of organizational unstructured data has prompted storage managers to move data to lower tiers of storage, increasingly cloud data storage, to reduce the impact on primary storage systems. Furthermore, in moving data from more expensive primary storage to less expensive tiers of storage, knowns as cloud tiering, storage managers are able to save money. This keeps the data easily accessible in order to satisfy both business and compliance requirements. When data tiering and archiving cold data to secondary storage, it is important that the archiving / tiering solution does not disrupt users by requiring them to rewrite applications to find the data on the secondary storage. Transparent archiving is key to ensuring that data moved to secondary storage still appears to reside on the primary storage and continues to be accessed from the primary storage without any changes to users or applications. Transparent move technology solutions that use file-level tiering to accomplish this. Learn More: Why Komprise is the Easy, Fast, No Lock-In Path to the Cloud for file and object data. What is Secondary Storage? Secondary storage, sometimes called auxiliary storage, is non-volatile and is used to store data and programs for later retrieval. It is also known as a backup storage device, tier 2 storage, external memory, secondary memory or external storage. It is a non-volatile device that holds data until it is deleted or overwritten. Secondary Storage Devices Here are some examples of secondary storage devices: Hard drive Solid-state drive USB thumb drive SD card CD DVD Floppy Diskette Tape Drive What is the difference between Primary and Secondary Storage? Primary storage is the main memory where the operating system resides and is likely to be temporary, more expensive, smaller and faster and is used for data that needs to be frequently accessed. Secondary storage can be hosted on premises, in an external device, or in the cloud. It is more likely to be permanent, cheaper, larger and slower and is typically used for long term storage for cold data. #### Shadow IT Shadow IT is a term used in information technology describing systems and solutions not compliant with internal organizational approval. This can mean typical internal complacence is not followed, such as documentation, security, reliability, etc. However, shadow IT can be an important source of innovation, and can also be in compliance, even when not under the control of an IT organization. An example of shadow IT is when business subject matter experts can use shadow IT systems and the cloud to manipulate complex datasets without having to request work from the IT department. IT departments must recognize this in order to improve the technical control environment, or select enterprise-class data analysis and management tools that can be implemented across the organization, while not stifling business experts from innovation. Ways to IT teams can cope with shadow IT are: Reducing IT evaluation times for new applications Consider cloud applications Provide ways to safely identify and move relevant data to the cloud Clearly document and inform business controls Approve Shadow IT in the short term Get involved with teams across your organization to help stay informed of upcoming needs Read the white paper: Getting Departments to Care About Storage Savings. #### Policy-Based Data Management Policy-based data management is data management based on metrics such as data growth rates, data locations and file types, which data users regularly access and which they do not, which data has protection or not, and more. The trend to place strict policies on the preservation and dissemination of data has been escalating in recent years. This allows rules to be defined for each property required for preservation and dissemination that ensure compliance over time. For instance, to ensure accurate, reliable, and authentic data, a policy-based data management system should generate a list of rules to be enforced, define the data storage locations, storage procedures that generate data tiering and archival information packages, and manage replication. Policy-based data management is becoming critical as the amount of unstructured data continues to grow while IT budgets remain flat. By automating movement of data to cheaper storage such as cloud data storage or private object storage, IT organizations can rein in data sprawl and cut costs. Other things to consider are how to secure data from loss and degradation by assigning an owner to each file, defining access controls, verifying the number of replicas to ensure integrity of the data, as well as tracking the chain of custody. In addition, rules help to ensure compliance with legal obligations, ethical responsibilities, generating reports, tracking staff expertise, and tracking management approval and enforcement of the rules. As data footprint grows, managing billions and billions of files manually becomes untenable. Using analytics-driven data management to define governing policies for when data should move, to where and having data management solutions that automate based on these policies becomes critical. Policy-based data management systems rely on consensus. Validation of these policies is typically done through automatic execution – these should be periodically evaluated to ensure continued integrity of your data. #### Hosted Data Management With hosted data management, a service provider administers IT services, including infrastructure, hardware, operating systems, and system software, as well as the equipment used to support operations, including data storage, hardware, servers, and networking components. The managed service provider (MSP) typically sets up and configures hardware, installs and configures software, provides support and software patches, maintenance, and monitoring. Services may also include disaster recovery, security, DDoS (distributed denial of service) mitigation, and more. Hosted data management may be provided on a dedicated or shared-service model. In dedicated hosting, the service provider sets aside servers and infrastructure for each client; in shared hosting, pooled resources and charged for on a per-use basis. Hosted data management can also be referred to as cloud services. With cloud hosting, resources are dispersed between and across multiple servers, so load spikes, downtime, and hardware dependencies are spread across multiple servers working together. In this arrangement, the client usually has administrative access through a Web-based interface. Another popular model is hybrid cloud hosted data management – where the administrative console resides in the cloud but all the data management (analyzing data, moving data, accessing data) is done on premise. Komprise Intelligent Data Management uses this hybrid approach as it offers the best of both worlds – a fully managed service that reduces operating costs without compromising the security of data. #### Flash Storage Flash storage is storage media intended to electronically secure data, which can be electronically erased and reprogrammed. The other advantage is it responds faster than a traditional disc, increasing performance. With the increasing volume of stored unstructured data from the growth of mobility and Internet of Things (IoT), organizations are challenged with both storing data and the opportunities it brings. Disk drives can be too slow, due to the speed limitations. For stored data to have real value, businesses must be able to quickly access and process that data to extract actionable information. Flash storage has a number of advantages over alternative storage technologies Greater performance. This leads to agility, innovation, and improved experience for the users accessing the data - delivering real insight to an organization Reliability. With no moving parts, Flash has higher uptime due to no moving parts. A well-built all-flash array can last between 7-10 years. While Flash storage can offer a great improvement for organizations, it is still too expensive as a place to store all data. Flash storage has been about twenty times more expensive per gigabyte than spinning disk storage over the past seven years. Many enterprises are looking at a tiered model with high-performance flash for hot data and cheap, deep object or cloud storage for cold data. #### Cold Data What is cold data? Cold data refers to data that is infrequently accessed, as compared to hot data that is frequently accessed. As unstructured data grows at unprecedented rates, organizations are realizing the advantages of utilizing cold data storage devices instead of high-performance primary storage as they are much more economical, simple to set up & use, and are less prone to suffering from drive failure. For many organizations, the real difficulty with cold data is figuring out when data should be considered hot and kept on primary storage or it can be labeled as cold and moved off to a secondary storage device. For this reason, it’s important to understand the difference between data types to develop a solution for managing cold data that is most cost effective for your organization. Types of Data That Cold Storage is Typically Used For Examples of data types for which cold storage may be suitable include information a business is required to keep for regulatory compliance, video, photographs, and data that is saved for backup, archival, big-data analytics or disaster recovery purposes. As this data ages and is less frequently accessed, it can generally be moved to cold storage. A policy-based data management approach allows organizations to optimize storage resources and reduce data storage costs by moving inactive data to more economical cold data storage. Advantages of Developing a Cold Data Storage Solution Prevent primary storage solutions from becoming overburdened with unused data Reduce overall resource costs of data storage Simplify data storage solution and optimize the management of its data Efficiently meet governance and compliance requirements Make use of more affordable & reliable mechanical storage drives for lesser used data Reduce Strain on Primary Storage by Moving Cold Data to Secondary Storage Affordable Costs of Cold Storage When comparing costs for enterprise-level storage drives, the mechanical drives used in many cold data storage systems are just over 20% of the price that high-end solid-state drives (SSD) can cost on average. For SSD’s at the top tier of performance, storage still costs close to 10 centers per gigabyte whereas NAS-level mechanical drives cost only around 2 centers per gigabyte on average. Simplify Your Unstructured Data Storage Solution A well-optimized cold data storage system can make your local storage infrastructure much less cluttered & easier to maintain. As the storage tools which help us automatically determine which data is hot and cold continue to improve, managing the movement of data between solutions or tiers is becoming easier every year. Some cold data storage solutions are even starting to automate the entirety of the unstructured data management process based on rules that the business establishes. Meet Regulatory or Compliance Requirements Many organizations in the healthcare industry are required to hold onto their data for extended periods of time, if not forever. With the possibility of facing litigation somewhere down the line based on having this data intact, corporations are opting to use a cold data storage solution which can effectively store critically important, unused data under conditions in which it cannot be tampered with or altered. Increase Data Durability with Cold Data Storage Reliability is one of the most important factors when choosing a data storage solution to house data for extended periods of time or indefinitely. Mechanical drives can be somewhat slower than SSD’s in providing file access, but they are still quick to be able to pull files and offer much more budget room for creating additional backup or parity within your storage system. When considering storage hardware for cold data solutions, consider low cost, high-capacity options with a high degree of data durability so your data can remain intact for as long as it needs to be stored for. Learn more about the your options when it comes to migrating file workloads to the cloud. How Pfizer Saved Millions with a Cold Data Management Strategy Pfizer needed to change the way it was managing petabytes of unstructured data to cut data storage costs and reinvest in areas with patients at the center. Read the blog. #### New Technology File System (NTFS) Extended Attributes Properties organized in (name, value) pairs, optionally set to New Technology File System (NTFS) files or directories to record information that can’t be stored in the file itself. #### Hot Data Hot data is business-critical data that needs to be accessed frequently and resides on primary storage (NAS). Hot data is considered to be of high value and importance. This type of data is typically stored in fast memory, such as RAM, to ensure quick and efficient access. Examples of hot data include frequently used databases, in-memory caches, and real-time data streams. The term Hot Data is used in data management to refer to data that is frequently accessed or in high demand. The term Cold Data or Cold Data Storage refers to data that is infrequently accessed. The distinction between hot and cold data is essential for storage efficiency, especially when it comes to optimizing storage and retrieval processes in various systems as well as optimizing data storage costs. Hot data is usually stored in high-performance storage systems, such as solid-state drives (SSDs) or in-memory databases, to ensure quick access and response times. This is especially important for applications that require rapid access to frequently used information, such as transactional databases or real-time analytics. In contrast, less frequently accessed or "cold" data may be stored on slower and more cost-effective storage solutions, such as traditional hard disk drives (HDDs) or archival systems. This tiered storage approach helps organizations balance performance requirements with cost considerations. Managing hot and cold data effectively is part of data lifecycle management, where data is classified based on its importance, access frequency, and other factors to optimize storage resources and overall system performance. Unstructured data management solutions like Komprise that are storage agnostic and do not get in the hot data path are increasingly popular for maximum data storage cost savings and ongoing data value. #### Shared-Nothing Architecture A shared-nothing architecture is a distributed-computing architecture in which each update request is handled by a single node, which eliminates single points of failure, allowing continuous overall system operation despite individual node failure. Komprise Intelligent Data Management is based on a shared-nothing architecture. Learn more about the Komprise shared-nothing architecture. #### POSIX ACLS POSIX ACLs are fine-grained access rights for files and directories. An Access Control Lists (ACL) consists of entries specifying access permissions on an associated object. POSIX ACLs provides more granular control over file and directory permissions than the traditional POSIX permission model. The traditional POSIX permission model uses a set of file bits to define permissions for the owner, group, and other users. In contrast, POSIX ACLs provide a more flexible and fine-grained access control mechanism by allowing multiple entries in an access control list, each of which specifies a different user or group and a different set of permissions. With POSIX ACLs, you can grant or deny specific permissions to individual users or groups for a particular file or directory. For example, you can allow a particular user to read and write a file, but deny them the ability to execute it. You can also grant a group of users read-only access to a directory, but prevent them from modifying or deleting any files in that directory. POSIX ACLs are supported on many Unix-based operating systems, including Linux, BSD, and macOS. They can be managed using command-line utilities such as setfacl and getfacl. Not all filesystems support POSIX ACLs. Their behavior may vary across different implementations. #### Showback What is Showback Reporting? Showback is a method of tracking data center utilization rates of an organization's business units or end users. Similar to IT chargeback, the metrics for showback are for informational purposes only; no one is billed. Some organizations refer to showback reporting as "shameback." The Showback model aims to allocate the costs of IT resources and services to the business units or departments that consume them. It helps organizations to better understand and track their IT expenses and make informed decisions about resource allocation and utilization. Showback involves collecting data on IT usage and presenting it in a way that is transparent and easily understandable for business stakeholders. This information can be used to make informed decisions about future investments in IT infrastructure, as well as to negotiate service level agreements and establish chargeback policies. In the white paper Getting Departments to Care About Storage Savings, the Showback model is explained. With Komprise analytics-driven unstructured data management, authorized departmental users can monitor and understand their data usage (examples: how many and what type of files, where stored and biggest consumers) in an interactive dashboard. This is an essential part of a showback model. Read the blog post: Komprise brings data storage insights to business teams and departments. What Does a Showback Report from Komprise Looks Like? Read the blog post and learn more about Komprise Analysis. Read the blog post: New Reports on Unstructured Data and Storage Costs from Komprise. Why is showback reporting important to manage data storage costs? Showback reporting is important for managing data storage costs because it creates visibility, accountability, and better decision-making across the enterprise. Here’s why it matters: Transparency of Usage and Costs: Most storage teams know total spend, but not who is consuming it. Showback reporting breaks down storage usage by department, project, or user, making costs visible to the people generating the data. Accountability Without Blame: Unlike chargeback (where IT bills back costs), showback simply shows usage and cost allocations. This avoids friction while still encouraging responsible data behavior. Informed Decision-Making: Departments can see how much of their budget is tied up in storage and make trade-offs. For example: Archive old data, tier to cheaper storage, dlete unnecessary copies see ROT data). Supports Cost Optimization & Forecasting: Showback highlights patterns of growth and waste, helping IT teams predict future capacity needs. It’s easier to make the right data storage investments or cloud migration strategies when you can show usage by business function. (See Capacity Planning) Enables Data Governance for AI & Analytics: As organizations prepare data for AI, showback helps identify where large, unused, or stale datasets are sitting. This ensures only the right data is kept hot and accessible, optimizing both costs and AI readiness. Showback reporting shines a light on hidden storage consumption, empowers departments to take ownership, and helps IT organizations manage growth sustainably, with fewer political challenges that are common with the direct chargeback model. How is Showback Reporting different than Chargeback? Showback reporting tracks and displays storage usage and costs by department, project, or business unit but does not directly bill them. Its purpose is transparency and accountability. Chargeback, on the other hand, allocates and bills the actual costs back to the business units consuming storage, creating stricter budget enforcement but often with more resistance. Showback is easier to adopt since no money changes hands, while chargeback enforces cost control through direct financial impact. Komprise makes both approaches more effective by providing detailed usage analytics across storage silos, delivering showback dashboards that highlight who owns what data and how much it costs, and automating the movement of cold or redundant data to lower-cost storage tiers. This visibility and actionability enable organizations to cut storage costs by 50–70% while ensuring the right data remains easily accessible and AI-ready. Learn more about Komprise and Data Storage as a Service (STaaS). #### Primary Storage Primary Storage is the main area where data is stored for quick access. It’s faster and more expensive as compared to secondary storage, so it shouldn’t hold cold data. Primary storage is typically the main data storage system in an enterprise that supports critical business operations, applications, and workloads. It is designed for high performance, low latency, and immediate access to data, making it essential for databases, virtual machines, and real-time analytics. Key Features of Primary Storage in the Enterprise High Performance – Optimized for fast read/write speeds, ensuring smooth application and system performance. Low Latency – Reduces data retrieval time, which is crucial for transactional systems and real-time processing. Reliability & Availability – Often includes redundancy features like RAID, snapshots, and replication to ensure data integrity and uptime. Scalability – Allows businesses to expand storage as data grows without affecting performance. Integration with Cloud & Secondary Storage – Often works with secondary storage (for backup and archiving) and cloud solutions to optimize cost and efficiency. Types of Primary Storage in the Enterprises Flash & SSD Storage – Used for high-speed applications like databases and AI/ML workloads. Storage Area Network (SAN) – High-performance block storage used for mission-critical applications. Network Attached Storage (NAS) – File-based storage ideal for shared access across multiple users. Hybrid Storage – A mix of SSDs and HDDs, balancing performance and cost. Cloud-Based Primary Storage – Primary data storage solutions hosted on cloud platforms like AWS, Azure, or Google Cloud. Primary Storage Strategy for Enterprises Data Tiering – Storing frequently accessed data on SSDs while moving less-used data to lower-cost storage. Backup & Disaster Recovery Integration – Ensuring primary storage works with secondary solutions for failover. Data Deduplication & Compression – Optimizing storage efficiency and reducing redundancy. Security & Compliance – Implementing encryption, access controls, and compliance measures for data protection. Automation & AI-Driven Management – Using intelligent storage solutions to optimize performance and detect potential failures. Primary Storage Magic Quadrant Image source: Blocks & Files In the 2024 Gartner Magic Quadrant for Primary Storage Platforms, the Leaders were: Pure Storage: Recognized for the fifth consecutive year as the highest and furthest positioned leader. Hewlett Packard Enterprise (HPE): Achieved leadership status for the 15th consecutive time. NetApp: Maintained its position as a leader for the 12th consecutive year. Other vendors in the Leaders quadrant include IBM and Dell. A Blocks & Files overview noted that in 2024, Gartner redefined the criteria for primary storage platforms, emphasizing the need for platform-native services and hybrid cloud capabilities. This shift led to some vendors, such as Hitachi Vantara, Huawei, and Infinidat, moving from the Leaders quadrant to the Challengers quadrant. These changes highlight the evolving landscape of primary storage solutions, with a focus on integrated services and cloud adaptability. Gartner's Magic Quadrant evaluates vendors based on their Ability to Execute and Completeness of Vision, providing insights into each vendor's performance and strategic direction. Download the report here (Gartner subscription required.) Storage Newsletter described the changes to the Gartner Magic Quadrant Bizarre: Gartner Magic Quadrant 2024 for Primary Storage Platforms Bizarre.The analyst notes: Year after year, Gartner MQ lost their ability to present a real snapshot of the market and continues to provide a wrong product and technology definition. Again a primary storage is not (only) a block storage but very often a block storage is used as primary. IT is just a storage used to support the business then having the primary role and this is independent of the media technology (HDD, SSD…), connectivity (FC, IB, Ethernet), access methods (block, file, S3, HDFS…), residence (on-premises, cloud, etc), subscription model and data nature (structured or non structured data). So Gartner should rename this MQ primary block storage. #### Storage Pool What is a storage pool? Storage pools are collections of storage volumes exported to a shared storage environment. Traditionally, storage pools were limited to storage volumes from a single vendor – for instance, you may have Flash and Disk storage volumes in a storage pool. Storage pools may be homogeneous – that is, all the storage volumes are SSD/Flash, or all the storage volumes are disk, etc.  or they may be heterogeneous – the storage volumes are different classes of storage e.g. Flash, Disk, etc. Storage data tiering is an integral solution to handling heterogeneous storage pools. What is storage tiering within a storage pool and why is it needed? Storage tiering is a technique whereby the file metadata and the frequently-accessed blocks are stored in the highest tier and less-accessed blocks are downgraded to lower, cheaper tiers within a storage pool. This automated storage tiering approach allows the vendor to reduce costs by using smaller, faster tiers while still providing good performance. Storage tiering is often touted as a storage efficiency technique for customers to save on storage costs.  But a key thing to remember is that the bulk of the cost of data is not in the storage but in the active management and backups of the data.  Storage efficiency impacts the storage cost but not the active data management costs. What is cloud tiering and how does it relate to storage pools? Storage array vendors are now using their tiering technologies to tier data to the cloud. This is not what the technology was originally designed for, since the storage pool is no longer under a single vendor’s control and no longer local to a network. Storage array vendors like NetApp and Dell EMC have created “Pool” solutions to externally tier data to less expensive storage such as in the cloud. Storage pools can reduce the cost of fast, expensive flash-based storage by migrating non-critical data sets to lower-cost storage for archiving and compliance and for "cold” data which hasn’t been accessed for a designated period of time to the cloud. See NetApp FabricPool See Dell EMC Isilon Cloud Pools Read the blog post: What you need to know before jumping into the cloud tiering pool What are the challenges and considerations for cloud storage pools? While these solutions work well for tiering secondary data such as snapshot copies to the cloud, they result in unnecessary costs and lock-in when tiering and archiving files. As well, the pool approach tiers data in proprietary blocks versus files that all applications can understand. This presents the following challenges: Policies to specify the blocks to be tiered are limited, resulting in much higher access rate to the cloud, and higher egress costs. Block tiering to the cloud can reduce the performance of the storage array. Given the vast quantities of data most enterprises are dealing with today, block tiering is not suited for general data tiering to a public cloud across high latency channels. Block tiering locks you into your storage vendor. Since the cold data is tiered to the cloud in a proprietary format, when it is time to decommission your storage array and replace it with a new one you must stay with the same vendor. Proprietary lock-in. You cannot directly use native cloud services to access your data in the cloud. It has to be through the proprietary storage filesystem itself. This creates unnecessary licensing costs that customers must pay forever to access their data., resulting in much higher access rate to the cloud, and higher egress costs. Download the white paper: Cloud Tiering: Storage-Based vs Gateways vs File-Based: Which is Better and Why? #### Stubs What are Stubs? Stubs are placeholders of the original data after it has been migrated to the secondary storage. Stubs replace the archived files in the location selected by the user during the archive. Because stubs are proprietary and static, if the stub file is corrupted or deleted, the moved data gets orphaned. Komprise does not use stubs, which eliminates this risk of disruption to users, applications, or data protection workflows. Challenges with Stubs Stubs are brittle. When stubbed data is moved from its storage (file, object, cloud, or tape) to another location, the stubs can break. The storage management system no longer knows where the data has been moved to and it becomes orphaned, preventing data access. Most storage management solutions on the market use client-server architecture and do not scale to support data at massive scale. Proprietary interface like stubs can be used to make tiered data appear to reside on primary storage, but the transparency ends there. To access data, the storage management system intercepts access requests, retrieves the data from where it resides, and then rehydrates it back to primary storage. This process adds latency and increases the risk of data loss and corruption. Standards-Based Transparent Data Tiering A true transparent data tiering solution creates no disruption, and that’s only achievable with a standards-based approach. Komprise Intelligent Data Management is the only standards-based transparent data tiering solution that uses Transparent Move Technology™ (TMT), which uses Dynamic Links that are based on industry-standard symbolic links instead of proprietary stubs. Learn more about the differences between stubs, symbolic links and Dynamic Links from Komprise. Read the Komprise Architecture Overview white paper to learn more. #### Symbolic Link What is a Symbolic Link? What is a symlink? Symbolic Links, also known as symlinks and symbolic linking, are file-system objects that point toward another file or folder. These links act as shortcuts with advanced properties that allow access to files from locations other than their original place in the folder hierarchy by providing operating systems with instructions on where the “target” file can be found. For the operating system, the symlink is transparent for many operations and functions in the same manner as the target file or folder would even though it’s only a link that points to the original. For example, if a program needs to be in folder A to run, but you want to store it in folder B instead, the entire A folder could be moved into the B folder with a symbolic link created in folder A which points to folder B. When the program is launched, the operating system would refer to folder A, find the symbolic link to folder B, and run the program from folder B as if it was still in its original place in folder A. This method is widely used in the storage industry in programs such as OneDrive, Google Drive, and Dropbox to sync files and folders across different platforms of storage or in the cloud. These types of links began to appear in operating systems in the late 70’s such as RDOS. In modern computing, symbolic links are present in most Unix-like operating systems which are supported by the POSIX standard such as Linux, macOS, and Tru64. This feature was also added to Microsoft Windows starting with Windows Vista. Symbolic Links vs Hard Links Both types of symbolic links (also known as symbolic linking) allow seamless and mostly transparent targeting of a file, but they do so in different ways. Soft links, also referred to as symbolic links by Microsoft, work similarly to a normal shortcut in the sense that they point directly to file or folder itself. These types of links also use less memory overall. On the other hand, hard links point to the storage space designated to hold the contents of the file or folder. In this sense, if the location or the name of the file changes, then a soft link would no longer work since it was pointing to the original file itself, but with a hard link, any changes made to the original file or the hard link contents are mirrored by the other because both are pointing to the same location on the storage. Hard links act as a secondary entrance to the same file or folder which they are linked to, but they can only be used to connect two entities within the same file system, whereas soft links can bridge the gap between different storage devices and file systems. Hard symbolic links also have more restrictive requirements than soft links: Hard links may not be able to link to directories. The target file or folder for a hard link must exist. Hard links cannot point to targets that are located on different partitions, volumes, or file systems. Junctions A Junction is a lesser-used, third type of symbolic link that combines aspects from both hard and soft links. The target file must exist for the junction to be created, but if the target file or folder is erased afterward, the link will still be there but will no longer be functional. How are Soft and Hard Symbolic Links Commonly Used? Hard links are used to create “backups” on filesystems without using any additional storage space. This is a benefit as it is often easier to manage a single directory with multiple references pointing to it rather than managing multiple instances of the same directory. If the file or folder is no longer accessible from its original location, then the hard link can be used as a backup to regain access to those files. The Time Machine feature on macOS uses hard symbolic links to create images to be used for backup. Soft links are used more heavily to enable access for files and folders on different devices or filesystems. These types of symbolic links are also used in situations where multiple names are being used to link to the same location. Types of Businesses that Make Use of Symbolic Links Symbolic links are leveraged in nearly every industry that uses computers, but some industries make use of these links more than others. Below are industries where symbolic links are most commonly used: Firms that offer big data management or analytics services Engineering & Semiconductor companies Financial service firms Genomics & healthcare companies Higher education institutions Media & entertainment organizations Oil & gas producers Agencies in the public sector Creating Symbolic Links The process used to create symbolic links is different on each type of operating system. Below are brief instructions on how a soft or hard link can be set up in Linux and Windows. How to Create a Soft Link in Linux To create a soft symbolic link in Linux, the ln command-line utility can be used as such: ln -s [OPTIONS] FILE LINK The FILE argument represents the origin of the link. The LINK argument represents the target destination for the soft link. When the command is successful, there is no output and the command-line will return zero. How to Create a Hard Link in Linux For creating hard links in Linux, a similar version of the ln command is used but without the -s: ln [OPTIONS] FILE LINK The FILE argument is still the origin location and the LINK argument is still the destination file or directory. Creating a Windows Soft Link The mklink command can be used to create soft links in Windows Vista & later through a command prompt or powershell with elevated permissions. By default, this command with no options will produce a soft link. mklink command: mklink Link Target The Link argument is the origin file/directory location and the Target argument represents the intended destination file. For creating a soft link pointing to a directory, this command is used instead: mklink /D Link Target Creating a Windows Hard Link Similarly to creating a soft link in Windows, the mklink can also be used to create hard links when /H is included as an option as such: mklink /H Link Target For creating a junction, the /J option is used instead of /H: mklink /J Link Target Komprise Transparent Move Technology (TMT) and Symlinks The patented Komprise Transparent Move Technology™ (TMT) goes beyond storage-based data tiering to analyze, migrate, tier and replicate data across multi-vendor storage and clouds while enabling native use of the data at each layer. This storage-agnostic data management is possible without disrupting users and without locking data in a proprietary format one vendor's storage silo. Komprise Dynamic Links Komprise TMT uses the standard, built-in feature of Windows, Linux, and Mac symbolic links, which replace a file with a tiny pointer to another location. By using Dynamic Links inside the standard symbolic link, Komprise extends the file system to call these files from the cloud or other storage systems. Dynamic Links dynamically bind a request to the actual data so it can move a file from NFS or SMB to a native cloud object and still provide transparent access from the source. Read the white paper: Leveraging the Full Power of the Cloud with Komprise Transparent Move Technology.   What is a symbolic link? A symbolic link, also known as a symlink or soft link, is a file that serves as a reference or pointer to another file or directory in a file system. Unlike a hard link, which points directly to the index node (aka inode – a data structure on a file system on Unix-like operating systems that stores information about a file or a directory), a symbolic link contains a reference to the file’s pathname. What is a Dynamic Link? Komprise uses a patented mechanism called Dynamic Links that use standard protocol constructs, eliminate proprietary agents and do not get in the hot data path. Komprise Transparent Move Technology (TMT) uses the standard, built-in feature of Windows, Linux, and Mac called symbolic links which replace a file with a tiny pointer to another location. By using the Komprise Dynamic Link inside the standard symbolic link, the file system is extended to call these files from the cloud or other storage systems. Dynamic Links dynamically bind a request to the actual data so it can move a file from NFS or SMB to a native cloud object and still provide transparent access from the source. As a user if you want to see how this works, click a file that Komprise tiered to the cloud and you get it back instantly. Simply right click the file and you will see that the path points to the Komprise Dynamic Link instead of a file on your computer. Komprise TMT is a scalable, storage-agnostic data movement solution that maintains file and object duality to give you the best of both worlds – transparent data access from the source and native data access outside the data path. In this blog post, Komprise cofounder and CEO noted: With our patented Dynamic Links, Komprise stays outside the hot data path to deliver a standards-based open data management solution that is resilient and avoids the pitfalls of static stubs or symlinks. How is symbolic linking different than stubs? Symbolic linking is a file system feature that allows the creation of references to files or directories, providing a form of aliasing. On the other hand, stubs are placeholders or temporary implementations used in software development, often related to the linking and compilation process, and are replaced with the actual code or functionality at a later stage. The key difference lies in their purposes and the context in which they are used. Symbolic linking and stubs serve different purposes and operate at different levels within a system. Symbolic Linking: Symbolic linking, also known as symlink or soft link, is a mechanism in file systems that allows the creation of a special type of file that serves as a symbolic reference or pointer to another file or directory. Purpose: Symbolic links, or symlinks, provide a way to create references or pointers to files or directories. They are used to create aliases or shortcuts to other files or directories within the file system. Mechanism: A symbolic link is a separate file that contains a path reference to the target file or directory. When the symlink is accessed, the system follows the path reference to the target location. Independence: The symlink and the target file or directory have different inodes (index nodes), and they can be located on different file systems. Deleting the symlink does not affect the target, but deleting the target may leave a “dangling” symlink. Example: If you have a file named file.txt and create a symlink named link.txt pointing to it, accessing link.txt will effectively access the contents of file.txt. Note that Komprise uses Dynamic Links. Learn more here. Stubs: In software development, a stub is a piece of code or a placeholder that stands in for a more complete or complex implementation. Stubs are used in various stages of the software development life cycle and serve different purposes, primarily related to testing, development, and dependency management. Purpose: Stubs are pieces of code or placeholders used in software development and deployment. They are typically temporary implementations or references that are later replaced with the actual code or functionality. Mechanism: Stubs can be placeholders or simplified versions of functions or modules. During development, they may serve as stand-ins for more complex or complete implementations until those are available. Dependency Resolution: Stubs are often used in the context of linking and compilation. They allow code to be compiled and linked even when some dependencies are not fully implemented. Example: In software development, a stub may be used to simulate the behavior of a network interface or a hardware device before the actual device or interface is available. Once the real device is in place, the stub is replaced with the complete implementation. Traditionally, unstructured data management solutions that move data have relied on one of two approaches: They either move the data entirely out of the primary storage, which is undesirable in most scenarios because it creates user friction as users think their files have disappeared. If they try to move data transparently, they leave behind a proprietary “stub” file that points to the moved file. Stubs are problematic for two reasons: A stub is proprietary and you need an agent installed on the file storage to detect when the stub is opened. This puts the data movement tool in the hot data path, which is undesirable, as it impacts performance and can become a bottleneck. Because a stub statically points to the new location of the file, if a stub is accidentally deleted, then data can get orphaned. Also, a stub can only go to another similar file system, so you cannot bridge file and object for example. The bottom line is stubs are hard to manage. They need to be backed up, they’re in the data path, they’re limiting, and they are risky because they are a single point of failure. Komprise eliminates these issues by using a patented mechanism called Dynamic Links that uses standard protocol constructs and eliminate proprietary agents without getting in the hot data path. Watch the TechTalk with Komprise cofounder and CTO. #### Adaptive Data Management What is adaptive unstructured data management? As data footprint continues to grow, businesses are struggling to manage petabytes of data, often consisting of billions and billions of files. To manage at this scale, intelligent automation that learns and adapts to your environment is needed. Data management needs to happen continuously in the background and not interfere with active usage of storage or the network by users and applications. This is because unstructured data management is an ongoing function, much like a housekeeper of data. Just as you would not want your housekeeper to be clearing dishes as your family is eating at the dinner table, data management needs to run non-intrusively in the background. To do this, an adaptive data management solution is needed – one that knows when your file system and network are in active use and throttles itself back, and then speeds back up when resources are available. An adaptive data management system learns from your usage patterns and adapts to the environment. In The 10 Principles of Komprise Intelligent Data Management, adaptive data management is summarized this way: Komprise throttles back as needed when your data storage or network are in active use, so you never have to monitor or schedule when Komprise runs. #### Analytics-driven Data Management Analytics-driven data management is a core principle of the standard-based platform of Komprise Intelligent Data Management that’s based on data insight and automation to strategically and efficiently manage and move unstructured data at massive scale. With Komprise, you can know first, move smart, and take control of massive unstructured data growth while cutting 70% of your enterprise data storage costs, including backup and cloud costs. Know First: Get insight into your data before you invest. See across your data storage silos, vendors, and clouds to make informed storage and backup decisions. Analyze any NAS, S3 Plan and project storage cost savings Search, tag, build virtual data lakes with a global file index Move Smart: Ensure the right data is in the right place at the right time. Establish analytics-driven policies to manage data based on its need, usage, and value. Transparently data tier and data archive cold data without users noticing any difference Migrate NFS file data 27 times faster and SMB file data workloads 25 times faster Replicate file data to the cloud for 50% less Take Control: Get back to the business at hand while reducing your storage, backup, and cloud costs and get the fastest, easiest path to the cloud for your file and object data. Ensure you have data mobility and avoid storage-vendor lock-in Open, standards-based platform Native cloud access Read the Komprise Architecture Overview white paper. #### Amazon Glacier (AWS Glacier) What is Amazon S3 Glacier (AWS Glacier)? Amazon S3 Glacier, also known as AWS Glacier, is a class of cloud storage available through Amazon Web Services (AWS).  Amazon S3 Glacier is a lower-cost storage tier designed for use with data archiving and long-term backup services on the public cloud infrastructure. Amazon S3 Glacier was created to house data that doesn’t need to be accessed frequently or quickly. This makes it ideal for use as a cold storage service, hence the inspiration for its name. Amazon S3 Glacier retrieval times range from a few minutes to a few hours with three different speed options available: Expedited (1-5 minutes), Standard (3-5 hours), and Bulk (5-12 hours). Amazon S3 Glacier Deep Archive offers 12-48-hour retrieval times. The faster retrieval options are significantly more expensive, so having your data organized into the correct tier within AWS cloud storage is an important aspect of keeping storage costs down. Other Glacier features: The ability to store an unlimited number of objects and data Data stored in S3 Glacier is dispersed across multiple geographically separated Availability Zones within the AWS region An average annual durability of 99.999999999% Checksum uploads to validate data authenticity REST-based web service Vault, Archive, and Job data models Limit of 1,000 vaults per AWS account Main Applications for Amazon S3 Glacier Storage There are several scenarios where Glacier is an ideal solution for companies needing a large volume of cloud storage. Huge data sets. Many companies that perform trend or scientific analysis need a huge amount of storage to be able to house their training, input, and output data for future use. Replacing legacy storage infrastructure. With the many advantages that cloud-based storage environments have over traditional storage infrastructure, many corporations are opting to use AWS storage to get more out of their data storage systems. AWS Glacier is often used as a replacement for long term tape archives. Healthcare facilities’ patient data. Patient data needs to be kept for regulatory or compliance requirements. Glacier and Glacier Deep Archive are ideal archiving platforms to keep data that will hardly need to be accessed. Cold data with long retention times. Finance, Research, Genomics, and Electronic Design Automation and Media, Entertainment are some examples of industries where cold data and inactive projects may need to be retained for long periods of time even though they are not actively used.  AWS Glacier storage classes are a good fit for these types of data.  The project data will need to be recalled before it is actively used to minimize retrieval delays and costs. Amazon S3 Glacier vs S3 Standard Amazon’s S3 Standard storage and S3 Glacier are different classes of storage designed to handle workloads on the AWS cloud storage platform. S3 Glacier is best for cold data that’s rarely or never accessed Amazon S3 Standard storage is intended for hot and warm data that needs to be accessed daily and quickly The speed and accessibility of S3 Standard storage comes at a much higher cost compared to S3 Glacier and the even more economical S3 Glacier Deep Archive storage tiers. Having the right data management solution is critical to help you identify and organize your hot and cold data into the correct storage tiers, saving a substantial amount on storage costs. Benefits of a Data Management System to Optimize Amazon S3 Glacier A comprehensive suite of unstructured data management and unstructured data migration capabilities allow organizations to reduce their data storage footprint and substantially cut their storage costs. These are a few of the benefits of integrating an analytics-driven data management solution like Komprise Intelligent Data Management with your AWS storage: Get full visibility of your AWS and other storage data Across AWS and other cloud platforms to understand how much NAS data is being accrued and whether it’s hot or cold so you make better data storage investment and data mobility decisions. Intelligent tiering and life cycle management for AWS storage Optimize and improve how you manage files and objects across EFS, FSX, S3 Standard and S3 Glacier storage classes based on access patterns. Intelligent AWS data retrievals Don’t get hit with unexpected data retrieval fees on S3 Glacier – Komprise enables intelligent recalls based on access patterns so if an object on Glacier becomes active again, Komprise will move it up to an S3 storage class. Bulk retrievals for improved AWS user performance Improve performance across entire projects from S3 Glacier storage classes – if an archived project is going to become active, you can prefetch and retrieve the entire project from S3 Glacier using Komprise so users don’t have to face long latencies to get access to the data they need. Minimize AWS storage costs With analytics-driven cloud data management that monitors retrieval costs, egress costs and other costs to minimize them by promoting data up and recalling it intelligently to more active storage classes. Access AWS data natively Access data that has been moved across AWS as objects from Amazon S3 storage classes or as files from File and NAS storage classes without the need for additional stubs or agents. Reduce AWS cloud storage complexity Reduce the complexity of your cloud storage and NAS environment and manage your data more easily through an intuitive dashboard. Optimize the AWS storage savings Komprise Intelligent Data Management allows you to better manage all the complex data storage, retrieval, egress and other costs. Know first. Move smart. Take control. Easy, on-demand scalability Komprise provides you with the capacity to add and manage petabytes without limits or the need for dedicated infrastructure. Integrate data lifecycle management Integrate easily with an AWS Advanced Tier partner such as Komprise for lifecycle management or other use cases. Move data transparently to any tier within AWS Your users won't experience any difference in terms of data access. You'll notice a huge difference in cost savings and unstructured data value with Komprise. Create automated data management policies and data workflows Continuously manage the lifecycle of the moved data for maximum savings. Build Smart Data Workflows to deliver the right data to the right teams, applications, cloud services, AI/ML engines, etc. at the right time. Streamline Amazon S3 Glacier Operations with Komprise Intelligent Data Management Komprise’s Intelligent Data Management allows you to seamlessly analyze and manage data across all of your AWS cloud storage classes so you can move data across file, S3 Standard and S3 Glacier storage classes at the right time for the best price/performance. Because it’s vendor agnostic, its standards-driven analytics and data management work with  the largest storage providers in the industry and have helped companies save up to 50% on their cloud storage costs. If you’re looking to get more out of your AWS storage, contact a data management expert at Komprise today and see how much you could save on data storage costs. Read the white paper: Smart Data Migration for AWS. #### Amazon S3 (AWS S3) Amazon Simple Storage Service, known as Amazon S3 or AWS S3, is an object storage service that offers industry-leading scalability, data availability, security, and performance. See S3 in our glossary for further information. Learn more about Komprise Intelligent Data Management for AWS data storage. #### AWS Snowball What is AWS Snowball Edge? AWS Snowball Edge is a hardware appliance used to migrate petabyte-scale data into and out of Amazon S3, mitigating issues with large-scale data transfers including high network costs, limited connectivity such as in remote locations, long transfer times, and security concerns. Beyond data transfer and cloud data migration use cases, the Snowball Edge device features on-board storage and compute power to enable local processing and analytics at the edge. Once transferred into AWS S3, an organization can move the data into other storage classes as needed. Snowball appliances are shipped to the customer and deployed on the customer’s network. Data is copied to the Snowball appliance and then return shipped to AWS where the data is copied to the appropriate AWS storage tier and made available for access. According to Hackernoon, Snowball Edge has been used in oil rigs, with the U.S. Department of Defense, and in an emergency situation for the U.S. Geological Survey needing to quickly export data from its data center during a volcanic eruption. Considerations for AWS Snowball Edge Enterprises have two options for AWS Snowball: AWS Snowball Edge Storage Optimized devices provide both block storage and Amazon S3-compatible object storage, and 40 vCPUs. They are well suited for local storage and large scale-data transfer. It’s possible to combine up to 12 devices together and create a single S3-compatible bucket that can store nearly 1 petabyte of data. Snowball Edge Compute Optimized devices provide 52 vCPUs, block and object storage, and an optional GPU for use cases including machine learning and full motion video analysis. Snowball supports specific Amazon EC2 instance types and AWS Lambda functions, so you can develop and test in the AWS Cloud, then deploy applications on devices in remote locations to collect, pre-process, and ship the data to AWS. Snowball can transport multiple terabytes of data and multiple devices can be used in parallel or clustered together to transfer petabytes of data into or out of AWS. Cloud Tiering to AWS By using Komprise for cloud tiering to AWS, you can save not only on your on-premises storage but also on your cloud costs. Users get transparent access to the files moved by Komprise from the original location, and with Komprise moving data in native format, you can give users direct, cloud-native access to data in AWS while eliminating egress fees and rehydration hassles. Learn more about the benefits of moving data in cloud native format. Smart Data Migration for AWS A smart data migration strategy for enterprise file data means an analytics-first approach ensuring you know which data can migrate, to which class and tier, and which data should stay on-premises in your hybrid cloud storage infrastructure. This paper introduces the benefits of a smart data migration strategy for file workloads to AWS cloud storage services. Komprise and AWS enable your organization to: Understand your NAS & object data usage and growth. Estimate the ROI of AWS storage in your environment. Migrate smarter to Amazon FSx for NetApp ONTAP. Access moved data as files without stubs or agents. Reduce complexity and scale on-demand. Deliver native data access in the cloud without lock-in. Read the white paper: Smart Unstructured Data Migration for AWS Learn more about your Cloud Tiering choices. Learn more about Komprise for AWS. #### AWS Storage What is AWS Cloud Storage? The AWS cloud service has a full range of options for individuals and enterprises to store, access and analyze data. AWS offers options across all three types of cloud data storage object storage, file storage and block storage. Here are the Amazon Storage /  AWS Storage choices: Amazon Simple Storage Service (S3): S3 is a popular AWS service that provides scalable and highly durable object storage in the cloud. AWS Glacier: Glacier provides low-cost highly durable archive storage in the cloud. It’s best for cold data as access times can be slow. Amazon Elastic File System (Amazon EFS): EFS provides scalable network file storage for Amazon EC2 instances. Amazon Elastic Block Store (Amazon EBS): This service provides low-latency block storage volumes for Amazon EC2 instances. Amazon EC2 Instance Storage. An instance store is ideal for temporary storage of information that changes frequently, such as buffers, caches and scratch data, and consists of one or more instance store volumes exposed as block devices. AWS Storage Gateway. This is a hybrid storage option that integrates on-premises storage with cloud storage. It can be hosted on a physical or virtual server. AWS Snowball. This data migration service transports large amounts of data to and from the cloud and includes an appliance that’s installed in the on-premises data center. Each of these Amazon storage classes has several tiers at different price points – so it is important to put the right data in the right storage class at the right time to optimize price and performance. Komprise Intelligent Data Management for AWS Storage Komprise helps organizations get more value from their AWS storage investments while protecting data assets for future use through analysis and intelligent data migration and cloud data tiering. Learn more at Komprise for AWS. #### Azure Data Box What is Azure Data Box? Microsoft Azure Data Box is a hardware appliance designed to allow customers to import or export large amounts of data—more than 40TB— into and out of Azure offline. It is especially helpful when there is zero or limited network connectivity. Microsoft ships customers a proprietary Data Box storage device with a rugged casing to protect and secure data during the transit. A customer may choose Data Box for a one-time or the occasional cloud migration or an initial bulk data transfer followed by periodic transfers. Microsoft also promotes the Data Box as a solution for exporting data from Azure back on-premises for disaster recovery or other needs or to move to another cloud service provider.   There are three different types of physical Data Box solutions based on data size: Data Box: This device has 100TB capacity and uses standard NAS protocols and common copy tools. It features AES 256-bit encryption for safer transit. Data Box Heavy: This larger device is designed to lift 1PB of data to the cloud. Data Box Discs: Discs have capacity of 8TB SSD with a USB/SATA interface featuring 128-bit encryption. Customers can buy in packs of up to five for a total of 40TB. Considerations for Cloud Migrations Using Azure Data Box  Azure Data Box is a good solution to consider if online data transfer is not possible either because the network bandwidth is limited or because it can take too long. But offline transfers can be very tedious and error prone if done manually. Choosing what data to migrate, moving the data into Azure Data Box, and then ensuring the data lands in the cloud can be time consuming to manage. Managing access control and security of file data, and ensuring transfer of all metadata and permissions of files can be very tedious. Often, enterprises want to move some file data to the cloud and keep the rest on-premises. In such situations, using Azure Data Box manually without any automation becomes even more tricky because it can disrupt users and applications. Azure Data Box Gateway for Inline Data Transfers Azure also offers a virtual appliance called Azure Data Box Gateway that resides on-premises and enables customers to write data to it using NFS and SMB protocols. The device then transfers the data to Azure block, Blob, or Azure File. But Azure Data Box gateway has several limitations and can be used only for very small amounts of data in limited circumstances. See full set of limitations here. Komprise allows you to migrate large amounts of data reliably and effortlessly to Azure using its patented Elastic Data Migration, which is 27 times faster than alternatives. You can also use Komprise to transparently tier data to Azure. Tiering cold data is a great way to offload 80% of your data to the cloud without any disruption to users and applications.  By using Komprise for cloud tiering to Azure, you can save not only on your on-premises storage but also on your cloud costs since you do not have to tier to Azure Files, you can tier directly to Azure Blob. Users get transparent access to the files moved by Komprise from the original location, and with Komprise moving data in native format, you can give users direct, cloud-native access to data in Azure while eliminating egress fees and rehydration hassles.  Learn more about your Cloud Tiering choices  Learn more about Komprise for Microsoft Azure. #### Azure Storage What is Azure Storage? Microsoft Azure hosts a complete array of cloud data storage options to meet the diverse data needs of enterprises today, including backup, tiering, data lakes, structured and unstructured data management. Azure Storage Services include: Azure Blob: This is a scalable object store best suited for storing and accessing unstructured data and to support analytics and data lake projects. Azure Files: File shares for cloud or on-premises deployments that you can access through the Server Message Block (SMB) protocol. Azure Queues: Allows for asynchronous message between application components. Azure Tables: A NoSQL solution for schema-less storage of structured data. Azure Disks: Allows data to be persistently stored in blocks and accessed from an attached virtual hard disk. Azure Data Lake Storage: A storage platform for ingestion, processing, and visualization that supports common analytics frameworks and provides automatic geo-replication. Greater Azure Storage Savings and Value with Komprise Komprise helps organizations get the most value from their Azure Blob and Azure File storage investments while protecting data assets for future use through analysis and intelligent data migration and cloud data tiering. Komprise Intelligent Tiering for Azure is the only Microsoft Azure Marketplace solution that gives customers access to file analysis and data tiering to and within Azure. Customers whose use case is primarily tiering and cost-efficiency can now acquire this without purchasing the full Komprise Intelligent Data Management platform. Learn more here. How to save 70% on File Data Costs Additional resources:  Komprise for Azure File and Azure Blob data management and migration. ## Events and Conferences > Komprise participates in leading enterprise IT, storage, cloud, and AI industry events. Key appearances include customer summits (Komprise AI Days), partner events (Pure Accelerate, NetApp Insight, AWS re:Invent), and regional IT conferences. ### Innotech OKC Join Komprise at InnoTech Oklahoma City (October 22nd) The premier business and technology conference for IT professionals, featuring sessions on innovation, cloud solutions, security, and data management, plus opportunities to network with industry leaders. ### Interface Boise Join Komprise at INTERFACE Boise (August 30th) with our partner Qumulo. The regional IT conferences bringing together technology decision-makers for a day of education, networking, and collaboration. Topics include cybersecurity, infrastructure, cloud strategies, and data management. ### Interface Bozeman Join Komprise at INTERFACE Bozeman (July 30) with our partner Qumulo. The regional IT conferences bringing together technology decision-makers for a day of education, networking, and collaboration. Topics include cybersecurity, infrastructure, cloud strategies, and data management. Stop by booth 206 to explore how Komprise helps IT teams manage unstructured data more intelligently, reducing costs and accelerating cloud initiatives. ### Intelligent Data Management for Pure Storage We'll review the highlights of Pure Accelerate Las Vegas 2024 and introduce Komprise Intelligent Data Management, including powerful tiering, data migration and Smart Data Workflows for Pure Storage. Presenters Randy Hopkins, Darren Cunningham ### Komprise Intelligent Data Management for Pure Storage Hear the highlights and headlines from Pure Accelerate 2024 in Las Vegas and get an overview and demonstration of Komprise Intelligent Data Management for Pure Storage. Presenters Randy Hopkins, Darren Cunningham ### What's New in the Komprise Intelligent Data Management Platform Find out what's new in our latest release, talk to the experts and see a demonstration. Presenters Paul Chen, Krishna Subramanian, Darren Cunningham ### How to Save 70% on File Storage Costs with Komprise and Azure Speakers: Tim Kresler, Microsoft Azure Product Management Steve Moore, Komprise Information Architect Darren Cunningham, Komprise Marketing ### Migrate More for Less: Intelligent Data Management for Azure Speakers: Paul Chen, Darren Cunningham, Karl Rautenstrauch ### Smart Data Migration for File and Object Data Speakers: Darren Cunningham, Steve Pruchniewski File data’s time for the cloud has come, but the wrong moves can cost you millions. A “smart data migration” strategy for enterprise file data means an analytics-first approach ensuring you know which data can migrate, to which class and tier, and which data should stay on-premises in your hybrid cloud storage infrastructure. Komprise Elastic Data Migration makes cloud data migrations simple, fast and reliable with continuous data visibility and optimization. Join us to learn more about what it means to take a more intelligent approach to file and object data migration. In this session you'll see a live demonstration of Komprise Elastic Migration and ask the expects questions about your unique requirements. ### Preparing for a File and Object Data Migration: Know Before You Go Speakers: Darren Cunningham, Ben Henry File and object data are migrating to the cloud....fast. Are you ready? Do you have a plan? What about your network? Will you get the best performance possible? In this interactive webinar we'll review the checklist for to prepare for a file and object data migration. Hear from the experts and learn best practices as you invest in a more Intelligent Data Migration, Management and Mobility strategy with Komprise. ### Cloud Native Access – What is it and Why Does it Matter? Speakers: Darren Cunningham, Krishna Subramanian With Komprise Intelligent Data Management you can access your data wherever it’s stored, whenever you want, without rehydration. Because your moved data is always intact, with Komprise you can extract data value with both file and cloud native access. No penalty. No lock-in. In this interactive webinar we'll discuss the growing importance of cloud native data access and dive into a demonstration of Komprise Transparent Move Technology and how we ensure you're getting maximum value from your file and object data in the cloud. ### Building a Modern Data Strategy for the Automotive Industry with Komprise and AWS When: Tuesday, June 28th, 9AM Pacific, 12PM Eastern Storage is evolving and data is more valuable to automotive manufacturers than ever. Learn how Komprise and AWS are helping customers in the auto industry transform their unstructured data with data services in the cloud using an analytics-first approach. 1) Gain analytics-driven visibility of NFS, SMB and S3 data across your enterprise 2) Drive intelligent movement of target data sets to Amazon S3 for access in native format 3) Mobilize AI-ready data across Amazon S3 to leverage services like Macie, Redshift, Comprehend and more Automotive leaders are transforming their use of unstructured data for business insights while optimizing costs and planning for the future of their data centers. Join us to learn more. ### TAGITM 2022 Annual Conference The TAGITM Annual Education Conference will be held April 26-29, 2022 at the Moody Gardens Hotel, Galveston, Texas. The TAGITM Conference provides technology education specific to county and city IT Managers and staff. If you are in charge of the technical strategic direction for your county or city or are involved in making technology decisions and recommendations for your municipality, you will want to attend! The TAGITM Conference Committee delivers an outstanding program. Over 40+ years of leadership, insight and innovation go into the planning of this conference. The conference provides excellent education sessions, opportunities to explore the exhibit hall to learn about new cutting edge technologies and services from some of the industry’s leading providers, and networking with business partners from across the state. Visit Komprise at Booth #65. ### NAB 2022 Qumulo @ NAB 2022 We're incredibly excited to join the leaders from Media & Entertainment at NAB again this year. It’ll be a busy show and we’re planning exciting events so make sure to stay up to date on all the latest happenings: Qumulo Booth | N1721 We're showcasing our next-generation file data platform and powerful solutions, so make sure to stop by our booth to get a demo or to talk about things like: Studio Q - A remote editing studio solution Qumulo’s involvement with Amazon Nimble Studio And how we help you with things like Post-Production, Animation and so much more! Qumulo Booth Partners We’re joining forces with some key partners to showcase the power of Qumulo. Stop by to see Qumulo in action with... Komprise ### Accelerate Cloud Data Migrations to Azure with Komprise without the cost! Through the Microsoft Azure File Migration Program, Komprise Elastic Data Migration and Analytics is now being offered to customer at zero cost, fully sponsored by Microsoft. Learn about Komprise’s powerful migration engine and analytics tools. The company’s Intelligent Data Management and Mobility platform is focused on 3 primary customer needs: 1) Analytics-first assessments of file data 2) Intelligent data migration 3) Delivering AI-ready data In this session, we’ll focus on the Komprise analytics and migration engine to Microsoft Azure’s file and object data platforms, Azure Files, Azure Netapp Files, and Azure Blob. There will be a LIVE DEMO of the Komprise solution to Azure, showcasing Komprise’s analytics-first user interface. ### New Feature: Multisite Deep Dive In 2021, Komprise launched multisite management allowing our customers to set up multiple sites, each with their own storage and with separate data management policies and activities. In this 15-minute TechKrunch session you’ll see how multiple sites are managed from a single Director and dashboard, enabling centralized management for cost and performance optimization, providing a consolidated view across all sites in your deployment. ### Florida Technology Summit Interested in Attending? Use Code FTS2021Komprise to waive your event registration fee. ### TechKrunch: Deep Analytics Actions with One Global File Index In this 15-minute interactive discussion and demo, Komprise engineers will share how Deep Analytics Actions enables precise data management at enterprise scale. Learn how to search the Komprise Global File Index spanning petabytes of unstructured data to find specific data sets and then create a data management plan to systematically take action on your data set. ### Move to the Cloud with Qumulo and Komprise If your current NAS is holding you back from moving to the cloud, it’s time to discover how Qumulo and Komprise can help. Migration from legacy NAS can be a challenge if you don’t have the right tools and the experts to put the plan in motion. Qumulo and Komprise have the shared mission to help customers unlock the power of their data in the cloud and have the experience to make migration from any NAS to Qumulo fast, reliable, and efficient. ### Enhance Ransomware Defense - Cloud Tiering and Immutable Storage Join us for a coffee break where Komprise engineers will present a short 10-minute demo on how to use Komprise for moving and tiering files to a WORM-Compliant object store target as additional protection against ransomware. ### What is the State of Unstructured Data Management in the Enterprise? The recently published 'Komprise State of Unstructured Data Management Report' found a prevailing interest in analytics, followed closely by data lakes, to foster better ROI from data management. Join the Komprise team to review the highlights of the report. We’ll discuss the implications for storage leaders in the enterprise and share key insights and recommendations from the report. ### TechKrunch: Komprise TMT (Transparent Move Technology) - Dynamic Links Deep Dive In this interactive 15-minute technical webinar, solution engineers Randy Hopkins and Eric Platt will discuss the Komprise approach to file-based data tiering, which moves all of your data and leaves a symbolic link behind so users don’t experience any change. The session includes a brief demo. ### How Pfizer Used Analytics to Accelerate Cloud Data Migration Webinar Presented by AWS and Komprise Attend this webinar to learn how Komprise helped Pfizer stop 20 years of increasing storage costs and leverage the data tiered to AWS for research, all without changing how users and applications access their files. ### Fast, Reliable, Intelligent Data Migrations from Any NAS to Qumulo Join our team of experts to see a live demo and learn ways to find and migrate the right data from any file platform into Qumulo with speed, efficiency, and reliability. Qumulo gives you the option of running on pre-configured hardware platforms, including HPE Apollo Gen 10 or HPE ProLiant DL325 Gen10 Plus servers. Hear about proven solutions for all industries including healthcare, genomics, media and entertainment, financial, higher education and public sector. ### Migrating NFS & SMB Data with Komprise Randy Hopkins, VP of Systems Engineering and Chris Dearden, Sr. Systems Engineer at Komprise, will deliver a short demo of selecting and running data migrations. They will review the Komprise Multi-Level Parallelism & Protocol Optimization to show how you can achieve 7-25x performance with your migrations. Learn a lot in just 15 minutes! ### Cloud Data Migration and Cloud Data Tiering: Know Your Choices Unstructured data is everywhere. From genomics and medical imaging to streaming video, electric cars, and IoT products, all sectors generate unstructured file data. While file data growth is exploding, IT budgets are not. That’s why enterprises need to migrate file workloads to the cloud. But not all data migration and cloud data tiering solutions are the same. Join industry experts from Komprise for an interactive discussion and live demo of cloud migration and transparent cloud tiering. ### Netherlands Partner Webinar: Analyze & Manage Unstructured Data met Komprise More than 60% of the unstructured data that your organization has stored has not been used in the past year, our experience shows. This takes up space from your valuable production storage and takes you time for backups and recovery. ### TechKrunch: Komprise Intelligent Data Management for Nutanix Komprise makes it easy to migrate file data to Nutanix and cut ongoing costs by analyzing and transparently archiving/tiering cold data to the cloud without any disruption. Komprise scales elastically to grow as your data grows, and it moves data without any stubs, agents or changes to your hot data paths. ### TechKrunch: Async Replication for Pure FlashArray Files Komprise and Pure Storage recently expanded our partnership, with the announcement of Komprise Async Replication for Pure FlashArray files customers. In this TechKrunch, you’ll see what’s new and hear about what’s next. You’ll see how easy it is to start with data replication for Pure and expand to the complete Komprise Intelligent Data Management solution. ### TechKrunch: Cloud-to-Cloud Data Replication According to IDC, 60% of the storage budget is not really spent on storage. It’s spent on secondary copies of data for data protection – backups, backup software licenses, replication, and disaster recovery. Replication does not always have to be synchronous and like-to-like. Did you know that you can cut 60%+ of replication costs by replicating to the cloud and replicating from one cloud to another? This not only lowers costs but it also improves data resiliency. In this TechKrunch session, our experts will discuss and demonstrate why and when cloud-to-cloud data migration is a good idea and how it is supported with Komprise cloud data management. ### TechKrunch: Data Migration or Data Tiering/Archiving What Makes Sense and When? At Komprise we like to say Know First and Move Smart. But when it comes to NFS and SMB, what is the difference between copy, migration (replication), and migration (cutover)? Which one would you us and why? What are the different use cases? In this session we’ll focus on: The Copy function and the NAS use case Data Migration with no cutover and the difference between Data Replication Data Migration with cutover and the NAS use case Presenters: Eric Platt, Sr Sales Engineer, Komprise Randy Hopkins, VP Global Systems Engineering and Enablement, Komprise ### Faster Recovery: File Replication for Pure FlashArray with Komprise If your disaster recovery plan for unstructured data doesn’t offer fast recovery, your phone could be buzzing with complaints for hours. Recovering data from backups can be much slower than your users can tolerate. That’s why data replication, not backup, is the strategy you need for business continuity. Attend this live demo webinar with Pure Storage and Komprise to learn how you can: Increase availability and minimize data loss by having a consistent replication copy always available Replicate entire shares or specific managed directories and schedule them to fit your organization’s needs Manage for all your Pure FlashArray replications from a single intuitive console, and automate enterprise-scale replications using APIs Date: Wednesday, March 10, 2021 Time: 9am PST / 12pm EST/ 5pm GMT Presenters: Paul Chen, Director Product Management, Komprise Alan Driscoll, Sr. Product Manager, Pure Storage Dominque Garcia, Director of Alliances, Komprise ### TechKrunch: Transparent Tiering for Microsoft Azure Files and Azure BLOB Learn how and watch a demonstration of how Komprise uses Transparent Move Technology (TMT) to tier cold data to Microsoft Azure Files and Microsoft Azure BLOB. In this interactive session you’ll see a demo of: Komprise archiving cold data to Azure Files Archive archiving data to Azure Blob ### Cut Public Cloud Costs by 40% with Komprise Intelligent Data Management 85% of enterprises are now using public clouds, but as cloud usage grows, it becomes more critical to understand cloud data usage, to optimize costs, and to manage public cloud data. Join this webinar to learn: How you can understand data usage and costs across all your cloud accounts and buckets How you can manage cloud data lifecycle efficiently to cut costs The difference between a cloud data management solution and general cloud management tools Live demo of Komprise on AWS How other companies save 40%+ of monthly cloud storage costs ### Take Control of Your Data and Your Budget Move the right data to the cloud and replace additional, expensive NAS investments. Join this webinar to learn how ComSource can provide you with ORock’s high-performing, government-grade cloud object storage combined with an analytics-driven cloud data management solution by Komprise. ORock and Komprise make it easy to analyze storage usage across your NAS and clouds and transparently archive, migrate, and replicate to ORock for 80%+ savings! ### Komprise and NetApp: More Cloud. Less Cost. Join the team of experts from Komprise for an interactive overview and demonstration of our analytics-first data management solution for NetApp. Get to the cloud faster and smarter with Komprise. That’s where Komprise comes in. We work with NetApp customers to help them seamlessly migrate, archive, replicate and shrink backup costs. With Komprise you’ll quickly understand the ROI of NetApp. • Get a single view of how NAS data is growing, being used, and what data is hot or cold. • Access data from NetApp StorageGRID or NAS with zero lock-in. • Migrate to Cloud Volumes ONTAP (CVO) and Azure NetApp Files 27x faster. ### TechKrunch: Komprise “Confine Function” – What the heck is it? Learn how to automatically go out and clean those cold and dormant files that aren’t needed. This technical session(demonstration) will show how to build an automated plan for storage management leveraging Komprise “confine”, and why you might want to do it. ### TechKrunch: How to find hot data for migrations Join us for an interactive TechKrunch session on the topic of migrating hot data. Wondering how to know what data to migrate to an all-flash environment? And once you do, how can it be done in a way that’s not so time-consumingly painful? But that’s just for starters, because this session is really about opening it up to your questions. Our systems engineers are looking forward to fielding your Qs with some in-depth As—and a side-story or two—so be ready to fire away. ### TechKrunch: How to access archived data in the cloud This interactive TechKrunch session revolves around the topic of archiving data in the cloud. It’s a complicated affair and we’re going to talk about a few things you should know, like how to have native S3 access to archived file data and the different data access options. But we expect you’ll have plenty of questions on this topic, so our systems engineers are ready and looking forward to giving detailed As to your Qs, so come curious. ### How to Cut Spiraling NAS Costs: IBM & Komprise - Data Management for the new era In these unprecedented times, one thing is certain: the continued growth of NAS data and the rising costs to manage it. The key to cutting costs is understanding your data across your storage silos to make the right decisions. With an analytics-driven approach to data management, you can move your cold data to less expensive storage, which significantly lowers the cost of both storage, back-up, and DR replication. See how customers are saving over 70% with IBM and Komprise! During a live demo, we’ll show you a new way to manage your data and save. Analyze: Understand data across your storage silos before making decisions Optimize: Transparently archive cold data seamlessly to IBM Cos Data Migration: migrate data quickly and reliably Bridge Big Data projects: build data lakes across all storage Cyber resiliency: create low cost, air-gap copy and protect it on IBM Cos Access Data Anywhere: Access archived data directly—no middleman or rehydration Know your data to make the right moves. Free Data Assessment. ### TechKrunch: Using Data Analytics & Modeling Join us for an interactive TechKrunch session on the topics of data analytics and data modeling. We’ll talk about how to find how much cold data you have and talk about how peers are finding different types of cold data. It’s the first step in offloading your expensive NAS, and it’s easier than you think. But that’s just a quick kick off, because this is really about opening it up to your questions. Our systems engineers are ready to give detailed As to your Qs, so be ready to fire away. ### How to Cut Spiraling NAS Costs: IBM & Komprise - Data Management for the new era In these unprecedented times, one thing is certain: the continued growth of NAS data and the rising costs to manage it. The key to cutting costs is understanding your data across your storage silos to make the right decisions. With an analytics-driven approach to data management, you can move your cold data to less expensive storage, which significantly lowers the cost of both storage, back-up, and DR replication. See how customers are saving over 70% with IBM and Komprise! During a live demo, we’ll show you a new way to manage your data and save. Analyze: Understand data across your storage silos before making decisions Optimize: Transparently archive cold data seamlessly to IBM Cos Data Migration: migrate data quickly and reliably Bridge Big Data projects: build data lakes across all storage Cyber resiliency: create low cost, air-gap copy and protect it on IBM Cos Access Data Anywhere: Access archived data directly—no middleman or rehydration Know your data to make the right moves. Free Data Assessment. ### 3 Ways to Control Data Costs with Analytics-driven Data Management Data growth is skyrocketing. Storage capacity is running out, backups are taking longer, and budgets can’t keep up with the unstructured data deluge. The answer isn’t so much a storage issue as it is how the data in your storage is managed. Because treating all your data the same is a costly error. This webinar looks at three key elements that a data management solution should have to address these issues—and without complications of data access hassles and vendor lock-in issues. Learn what to look for so you know your data up front, and take control to make smarter storage decisions, without creating access disruption and without proprietary layers that lock you in. Join the webinar to learn how you can: - Analyze data usage and costs across silos to inform decisions - Efficiently manage your data lifecycle to cut costs - Transparently archive cold data without affecting user access or needing rehydration - Save 40%+ of monthly storage and backup costs - Live demo of Komprise on AWS ### 3 Ways to Control Data Costs with Analytics-driven Data Management Data growth is skyrocketing. Storage capacity is running out, backups are taking longer, and budgets can’t keep up with the unstructured data deluge. The answer isn’t so much a storage issue as it is how the data in your storage is managed. Because treating all your data the same is a costly error. This webinar looks at three key elements that a data management solution should have to address these issues—and without complications of data access hassles and vendor lock-in issues. Learn what to look for so you know your data up front, and take control to make smarter storage decisions, without creating access disruption and without proprietary layers that lock you in. Join the webinar to learn how you can: - Analyze data usage and costs across silos to inform decisions - Efficiently manage your data lifecycle to cut costs - Transparently archive cold data without affecting user access or needing rehydration - Save 40%+ of monthly storage and backup costs - Live demo of Komprise on AWS ### How to Cut NAS costs without Users Noticing Any Difference One constant you can continue to count on? The growth of unstructured data and the costs to manage it. Discover a cost-effective strategy to quickly lower NAS and cloud storage costs with an analytics-driven approach to data management. This webinar explains that by knowing your data first, you can easily move your cold data to less expensive storage, significantly lowering the cost of both storage, back-up, and DR replication. But the real trick is doing it in a way that creates no disruption between users and workflows and their data—whether on prem or in the cloud. Join the webinar to learn how you can: Analyze data usage and costs across silos and cloud accounts to inform decisions How you can manage cloud data lifecycle efficiently to cut costs Transparently archive cold data without affecting user access or needing rehydration Save 40%+ of monthly storage and backup costs Live demo of Komprise on AWS ### How to Cut NAS costs without Users Noticing Any Difference One constant you can continue to count on? The growth of unstructured data and the costs to manage it. Discover a cost-effective strategy to quickly lower NAS and cloud storage costs with an analytics-driven approach to data management. This webinar explains that by knowing your data first, you can easily move your cold data to less expensive storage, significantly lowering the cost of both storage, back-up, and DR replication. But the real trick is doing it in a way that creates no disruption between users and workflows and their data—whether on prem or in the cloud. Join the webinar to learn how you can: Analyze data usage and costs across silos and cloud accounts to inform decisions How you can manage cloud data lifecycle efficiently to cut costs Transparently archive cold data without affecting user access or needing rehydration Save 40%+ of monthly storage and backup costs Live demo of Komprise on AWS ### Cut Public Cloud Costs by 40% with Komprise Intelligent Data Management 85% of enterprises are now using public clouds, but as cloud usage grows, it becomes more critical to understand cloud data usage, to optimize costs, and to manage public cloud data. Join this webinar to learn: How you can understand data usage and costs across all your cloud accounts and buckets How you can manage cloud data lifecycle efficiently to cut costs The difference between a cloud data management solution and general cloud management tools Live demo of Komprise on AWS How other companies save 40%+ of monthly cloud storage costs ### Your Data’s Doubling, But Your Budget Isn’t. IBM & Komprise: Data Management for the new era In these unprecedented times, one thing is certain: the continued growth of unstructured data and the rising costs to manage it. The key to cutting costs is understanding your data across your storage silos to make the right decisions. With an analytics-driven approach to data management, you can move your cold data to less expensive storage, which significantly lowers the cost of both storage, back-up, and DR replication. See how customers are saving over 70% with IBM and Komprise! During a live demo, we’ll show you a new way to manage your data and save. Analyze: Understand data across your storage silos before making decisions Optimize: Transparently archive cold data seamlessly to IBM Cos Data Migration: migrate data quickly and reliably Bridge Big Data projects: build data lakes across all storage Cyber resiliency: create low cost, air-gap copy and protect it on IBM Cos Access Data Anywhere: Access archived data directly—no middleman or rehydration Know your data to make the right moves. Free Data Assessment. ### Your NAS is full of cold data. Identify, archive, and save with Komprise Cost savings has become a white-hot focus for organizations. A prime target for uncovering significant savings can be found in the way unstructured data growth is managed. Most aren’t aware that over 70% of their NAS is filled with cold data—a needless waste of budget. This webinar looks at a cost-effective data management strategy to quickly lower storage costs with an analytics-driven approach. By knowing your data first, you can transparently archive your cold data to less expensive storage, which significantly lowers the cost of storage, back-up and DR replication costs. Join the webinar to learn how you can: Analyze data across silos before making any decisions Transparently archive cold data without affecting user or app access Access archived data directly—no middleman or rehydration Slash your storage and backup costs fast ### Session 2: Komprise & Cloudian Got the Memo, But Data Didn’t: Contain Rising Storage Costs In these unprecedented times, one thing is a constant: the continued growth of unstructured data and the costs to manage it. This webinar looks at a cost-effective strategy to quickly lower storage costs with an analytics-driven approach to data management. By knowing your data first, you can move your cold data to less expensive storage, which significantly lowers the cost of both storage, back-up and DR replication. Join the webinar to learn how you can: Analyze data across silos before making any decisions Move cold data seamlessly, with the same access for users and apps Access archived data directly—no middleman or rehydration Slash your storage and backup costs fast ### Session 1: Komprise & Cloudian Got the Memo, But Data Didn’t: Contain Rising Storage Costs In these unprecedented times, one thing is a constant: the continued growth of unstructured data and the costs to manage it. This webinar looks at a cost-effective strategy to quickly lower storage costs with an analytics-driven approach to data management. By knowing your data first, you can move your cold data to less expensive storage, which significantly lowers the cost of both storage, back-up and DR replication. Join the webinar to learn how you can: Analyze data across silos before making any decisions Move cold data seamlessly, with the same access for users and apps Access archived data directly—no middleman or rehydration Slash your storage and backup costs fast ### Know Your Data to Slow Your Storage Costs The costs of unstructured data growth continue to cripple IT budgets—wherever you’re working from these days. Your next storage refresh is a chance to stop the cycle and make smarter data decisions to save significant costs. In this webinar you’ll learn how Komprise and Veristor can help you know your data first to place it in the right storage to save massive costs. We’ll discuss how you can: Analyze data across silos before making any decisions Move cold data seamlessly, with the same access for users and apps Access archived data directly—no middleman or rehydration Slash your storage costs fast ### Get 2020 Vision Into Your Cold Data: Know Before You Act By 2025, 175 ZB of data will be created, up from just 33 ZB in 2018, including structured, semi-structured and unstructured data. The result? Costs to store and transmit data , while always a concern, has taken on increased importance in this era of explosive data growth and pressure to become data-driven. How can you keep up with today’s growth while identifying the “who, when and what” of your unstructured data? It starts with knowing your data, improving your organization’s competencies to manage and capitalize data, helping you to better-understand data types, access patterns, and place them strategically in the right infrastructure Join our upcoming webinar to understand the value of Intelligent Data Management as a critical first step to become a data-driven enterprise. We’ll cover how in three easy steps: Analyze data prior to making any decision Move data with zero interference to apps, users or hot data Access your data whenever, wherever ### Get 2020 Vision Into Your Cold Data: Know Before You Act By 2025, 175 ZB of data will be created, up from just 33 ZB in 2018, including structured, semi-structured and unstructured data. The result? Costs to store and transmit data , while always a concern, has taken on increased importance in this era of explosive data growth and pressure to become data-driven. How can you keep up with today’s growth while identifying the “who, when and what” of your unstructured data? It starts with knowing your data, improving your organization’s competencies to manage and capitalize data, helping you to better-understand data types, access patterns, and place them strategically in the right infrastructure Join our upcoming webinar to understand the value of Intelligent Data Management as a critical first step to become a data-driven enterprise. We’ll cover how in three easy steps: Analyze data prior to making any decision Move data with zero interference to apps, users or hot data Access your data whenever, wherever ### Quit your addiction to storage Do you own your data, or does your data own you? You were listening when they told you that data was the new oil, so you spend your working lives buying, tending and managing storage. But we don’t spend enough time talking about your data. What is it? How much is anyone actually using? And is there a more efficient way of managing it than traditional storage tiering? Komprise argues that the answer to the last question is a big yes. It has helped customers to quit their addiction to storage using intelligent deep analysis of their data, which it claims leads to radically better cost and capacity management. Join us live as Krishna Subramanian, COO at Komprise, explains to The Reg’s Tim Phillips how you can slash storage costs and improve performance. And especially for skeptics: a live demo of how Komprise’s tech works, plus a Q&A. ### Storage Field Day 19 Field Day events bring together innovative IT product vendors and independent thought leaders to share information and opinions in a presentation and discussion format. Independent bloggers, speakers, freelance writers, and podcasters have a public presence that has immense influence on the ways that products and companies are perceived by IT practitioners. About Field Day Events: Field Day events bring together innovative IT product vendors and independent thought leaders to share information and opinions in a presentation and discussion format. Independent bloggers, speakers, freelance writers, and podcasters have a public presence that has immense influence on the ways that products and companies are perceived by IT practitioners. The world of media has changed, with social media and blogging gaining special importance. ### Know Before You Act: Analyze Data, Not Storage By 2025, 175 ZB of data will be created, up from just 33 ZB in 2018, including structured, semi-structured and unstructured data. The result? Costs to store and transmit data , while always a concern, has taken on increased importance in this era of explosive data growth and pressure to become data-driven. How can you keep up with today’s growth while identifying the “who, when and what” of your unstructured data? It starts with knowing your data, improving your organization’s competencies to manage and capitalize data, helping you to better-understand data types, access patterns, and place them strategically in the right infrastructure Join our upcoming webinar to understand the value of Intelligent Data Management as a critical first step to become a data-driven enterprise. We’ll cover how in three easy steps: Analyze data prior to making any decision Move data with zero interference to apps, users or hot data Access your data whenever, wherever ### Block-Level vs. File-Level Tiering – What’s the Difference? As data grows exponentially, your storage costs continue to escalate. While it’s easy to think the solution is more efficient storage, the real cause is poor data management. Over 70% of data is cold and has not been accessed in months, yet it sits on expensive storage and consumes the same backup resources as hot data. As a result, storage costs are rising, backups are slow, recovery is unreliable, and the sheer bulk of this data makes it difficult to leverage new options like Flash and Cloud. Join our upcoming webinar on Block-Level versus File-Level tiering to learn how smarter management of cold data could save you millions. Topics: Storage strategies of the past Differences between block vs file-level tiering Evolving your storage strategy ### Stop Paying the 400% Data-Tax: How to Control Your Data Destiny Rising storage costs, long backup windows, unreliable recovery…Sound familiar? In the midst of exponential data growth, costs are staggering. While many perceive storage as the villain, the more costly problem is the 400% Data-Tax you pay on each file and keep paying, forever. Compounded with the statistic that Enterprises have over 80% of cold data…you end up being unnecessarily taxed 4x on data that has not been used in over a year. ### Stop Paying the 400% Data-Tax: How to Control Your Data Destiny Rising storage costs, long backup windows, unreliable recovery…Sound familiar? In the midst of exponential data growth, costs are staggering. While many perceive storage as the villain, the more costly problem is the 400% Data-Tax you pay on each file and keep paying, forever. Compounded with the statistic that Enterprises have over 80% of cold data…you end up being unnecessarily taxed 4x on data that has not been used in over a year. ### Stop Paying the 400% Data-Tax: How to Control Your Data Destiny Rising storage costs, long backup windows, unreliable recovery…Sound familiar? In the midst of exponential data growth, costs are staggering. While many perceive storage as the villain, the more costly problem is the 400% Data-Tax you pay on each file and keep paying, forever. Compounded with the statistic that Enterprises have over 80% of cold data…you end up being unnecessarily taxed 4x on data that has not been used in over a year. ### Manage Research, Not Storage with Komprise Learn how an Ivy-League University Enabled Departments to Keep More Data with Flat Budgets. With universities and education institutions on the forefront of research and technology advances, the pace of innovation is accelerating. This means that we are generating far more data than ever before. The challenge? The majority of Education IT budgets are staying flat or in some cases, even shrinking! Meanwhile, the data deluge still needs to be stored, managed, protected, and harnessed. How can you do more with less? With storage already consuming anywhere from 25% to 50% of an average Educational Institution’s IT budget, major universities are asking that exact question. Join Steve DeGroat, Manager Enterprise Storage for a premier research institution, to hear how they are intelligently managing data and leveraging the cloud, ultimately to cut costs and improve data access — with Komprise. ### Komprise Deep Analytics: Discover the Value in Data Finding just the right data across billions of files can be as challenging as finding a needle in a haystack, until now. Komprise Deep Analytics uses powerful data search and indexing technology to automate the process of finding unstructured data based on specific criteria and across disparate storage platforms. Use the search results as a dynamic data lake to both plan your data management and enable new uses like Big Data Analytics. Now, sorting through petabytes of data is like finding a needle in a haystack, in minutes! Join our upcoming webinar to learn how Komprise Deep Analytics can unlock the full value within your data. ### Transforming Your Data Management Strategy Unstructured data growth is exploding. IDC recently predicted that the sum of the world’s data will grow to 163 zettabytes by 2025. Obviously, existing data management approaches were never built to handle this growth. Tune into this audio webcast featuring Komprise COO Krishna Subramanian and IDC Research Director Phil Goodwin as they discuss why the time to start planning for an effective data management solution is now. ### Stop Paying the 400% Data-Tax: How to Control Your Data Destiny Rising storage costs, long backup windows, unreliable recovery…Sound familiar? In the midst of exponential data growth, costs are staggering. While many perceive storage as the villain, the more costly problem is the 400% Data-Tax you pay on each file and keep paying, forever. Compounded with the statistic that Enterprises have over 80% of cold data…you end up being unnecessarily taxed 4x on data that has not been used in over a year. ### Understanding Komprise Transparent Move Technology™ (TMT) With data footprints growing fast, managing all of it with ease has become crucial. Komprise data management software analyzes and manages stored data across any storage. It does not use any storage agents or use any proprietary HW, SW or static pointers. Komprise identifies and copies or moves cold data by policy, using a patented Transparent Move Technology™ (TMT). ### Take Control of Your Cold Data, Save Big on Storage Costs For most firms, 60% of their data is cold data. By getting a handle on your cold data, you can save big on storage costs. Data is growing rapidly, more than doubling every two years, and legacy approaches to addressing storage are increasingly bringing challenges. IDC recently predicted that the sum of the world’s data will grow to 163 zettabytes by 2025. This is exactly why Vicom and Komprise have partnered: to help companies gain visibility into their data growth and take control. The time for smarter data management is now. Join Vicom and Komprise for an engaging webinar on July 25th at 11am EST that will discuss how to manage and get a handle on your cold data and more. ### Unleash the Full Potential of Your Data Hewlett Packard Enterprise and Komprise are partnering to transform data management. Komprise analyzes data across all storage, provides visibility into data growth, and transparently moves infrequently accessed data to cost-efficient HPE Scalable Storage. Join HPE and Komprise to learn how to modernize your infrastructure and unleash the full potential of your data. Some of the topics to be covered: Data growth industry trends Analyze and move cold data to HPE Scalable Storage Modernize Your Data Environment Komprise and HPE joint value proposition ### Department of Energy: The Smarter Way To Handle Data Growth We will arm you with the tools and knowledge to learn how to easily identify, classify, analyze and cost-efficiently archive and manage hot and cold data, then transparently and securely move it to economic storage tiers, without disruption to users or applications in this webinar. We will discuss data management strategies that your organization can implement today to enable your organization to scale with exponential data growth.  Some of the topics covered:  The growth of data 5 Tips for Smarter Data Management Architectural considerations across storage environments ### Wait! There’s A Smarter Way... Know your Data, Move your Data with Komprise and Cloudian ### Komprise & Western Digital: New Approach to Managing Unstructured Data Today's data management encompasses features that help organizations better understand their data, its purpose, its location, and more. As data management becomes more sophisticated, additional capabilities are being introduced. Advances in storage systems, data protection, secondary storage, and database management platforms give IT organizations with data management challenges a plethora of solutions to choose from. Join Komprise and the Western Digital ActiveScale team to learn about the data management solutions available to your organization and discover how these solutions can solve your data management challenges. Here’s why you should attend Learn more about the exploding data landscape and analyst projections Learn the latest data management solutions and how they can help your IT organization Discover how we can help you wrangle your data and help your company save time and money while eliminating your data management headaches! See a live demo of the solution in action! ### Mo Knows: Transforming Data Management Without Disruption In this session of Mo Knows, he will cover the fact that data growth is exploding, and you are being asked to address growing capacity needs within tight budgets that are remaining flat. Mo will show you how you can achieve modern data management that can keep up with the explosion of data growth, while your budgets remain flat.  Most importantly, achieving this without disruption to the users. ### Ready for a New Approach to Managing Unstructured Data? The term "Data Management" is taking on a much broader definition as visionary vendors look for ways to help their customers operate more effectively and efficiently. Today's data management encompasses features that help organizations better understand their data, its purpose, its location, and more. As data management becomes more sophisticated, additional capabilities are being introduced. Advances in storage systems, data protection, secondary storage, database management platforms, and security solutions give IT organizations with data management challenges a plethora of solutions to choose from. ### New Year, New NAS: Komprise for NAS Migration Migrating NAS file data can be a nightmare. Join VP of Engineering, Mohit Dhawan, as he walks through how Komprise eliminates the errors and the guesswork by automating the migration with a reliable solution that is resilient and handles network and storage glitches. ### Stoppen Sie das Wachstum des NAS-Speichers mit IBM & Komprise Erfahren Sie gemeinsam mit Komprise und IBM, wie moderne Unternehmen die Art und Weise, wie sie ihr unstrukturiertes Datenmanagement mithilfe von Komprise- und IBM-Speicher verwalten, verändern. ### Stop NAS Storage Growth with IBM & Komprise Join Komprise and IBM to learn how modern enterprises are transforming the way they manage their unstructured data management utilizing Komprise and IBM storage. ### Additional Events Smarter Unstructured Data Migration from Isilon with Komprise and IBM, Komprise AI Days Los Angeles: Unstructured Ignition, Komprise AI Days Raleigh: Unstructured Ignition, Pure NYC Roadshow, IPDS Tech Summit, NetApp Insight 2025, 3 Customer Success Webinars, Do More with Less: How to cut costs, shrink ransomware risk and leverage AI for your file data with Azure and Komprise, Gartner Infrastructure, Operations & Cloud Strategies, SuperCompute 2025, Webinar: Introducing Storage Insights - What's New in Komprise 5.0, Webinar: Komprise Elastic Data Migration 5.0, NetApp + Komprise: Right Data. Right Place. Right Time., Nth Generation 2022 IT & Cybersecurity Symposium in San Diego, Nth Generation 2022 IT & Cybersecurity Symposium Orange and LA County, Storage Field Day 22 ## Open Positions > Komprise is a Campbell, CA-based enterprise SaaS company with engineering in Bangalore, India. Open roles reflect investment in AI data pipeline product development, demand generation, partner alliances, and technical support. ### Senior Knowledge and Content Specialist Location: Bengaluru, India Company Overview: Komprise is an enterprise SaaS specializing in analytics-driven unstructured data management. Komprise founders started the company to address a gap in innovation for managing massively growing volumes of unstructured data which was creating complexity and stretching IT budgets from the endless need to buy more storage. Komprise Intelligent Data Management is one platform to analyse, move and manage unstructured data. We work with large enterprises across many sectors so they can optimize storage and backup costs, automate AI data workflows, reduce compliance and security risks and gain greater value from file and object data. We are looking for someone who is bright, passionate, wants to work on something new and disruptive and who enjoys a dynamic environment where they can make an impact.   Job Summary: We are seeking a results-driven Senior Knowledge and Content Specialist to join our team in our Bengaluru, India headquarters. You will have the opportunity to drive knowledge management team to produce and publish technical documentation and develop training programs and curriculum to meet our employees’ and customers’ education needs. You will also manage the development of the next generation of Komprise online technical documentation, including site organization, AI-enhanced search capabilities, AI-based self-help and a modern look-and-feel. As an ideal candidate, you are a strategic leader who is passionate about both producing engaging and effective technical documentation in various media and designing and implementing effective education programs and curriculum. You are experienced in talent development, organizational learning, and managing knowledge and training teams. Komprise invites qualified individuals with strong leadership skills and a passion for employee development and customer education to apply for this important role.   Responsibilities include: Documentation: Manage knowledge management professionals, produce technical content including release notes, operational procedures and manuals, troubleshooting and best practices guides, tutorial videos on features and use cases, and both document- and video-based training content. Planning: Develop knowledge management plans, procedures, and processes. Process driven: Redesign, reorganize, and maintain online internal and external knowledge portals. Technical Acumen: Leverage AI technology to improve searchability of technical content and provide a ChatGPT interface to our knowledge base. Content Management: Develop and maintain a consistent, clear style of content. Technical Content Maintenance: Manage updates and revisions to all technical content. Learning Strategy: Develop and implement the learning strategy in alignment with organizational goals and objectives. Team Leader: Lead a team of instructional designers, trainers and coordinators to deliver effective, high-quality training programs and curricula for technical development of both employee and customer audiences. Identify Learning Opportunities: Identify education needs and priorities through assessments, performance evaluations and stakeholder feedback. Training: Design, develop, and deliver education programs using various instructional techniques and formats. KPI Driven: Evaluate training effectiveness through metrics and KPIs based on assessments, surveys and feedback to measure education outcomes and make continuous improvements.   Requirements Education – B.S. degree in a technical discipline. Work experience: 3+ years’ experience as an education manager or similar role, with demonstrable track record of designing and implementing education programs. Experience: Prior experience developing modern documentation and self-help AI-based interfaces. Design Principles: Knowledge of instructional design principes and adult learning theory. Project Management: Project management skills for managing education initiatives and timelines. Requirements Evaluation and execution: Ability to assess training needs and develop curricula appropriate for relevant audiences. Cross-functional and Coaching - Strong leadership and team management abilities, to inspire and motivate others. Communication - Excellent communication and interpersonal skills, with the ability to build relationships and influence stakeholders at all levels. Technical: Proficiency in learning management systems and other training technology platforms Strong technical background needed to understand our solution. Detail Oriented: Strong attention to details. Tools: 1) Expert in handling Knowledge Management Platforms and helpdesk or customer tools with knowledge management ability (FD, Zendesk, Dev Rev, Doc 360, etc.,) 2) Collaborative and Documentation tools 3) MS Office Suite 4) Media editing (any).   How to Apply: Submit your resume highlighting your relevant experience to: india_careers@komprise.com. ### Alliances Operations Manager The Alliances Operations Manager is responsible for the day-to-day operational management of our strategic partnerships with Microsoft Azure, AWS, IBM, NetApp, Pure Storage, Rubrik, and other key technology partners. This role ensures partner program compliance, accurate pipeline tracking, process efficiency, and seamless integration between partner systems and internal tools such as Salesforce (SFDC). You will to keep partner engagements running smoothly - managing program submissions, reporting requirements, deal registrations, and data accuracy - while supporting alliance managers, sales, and marketing teams in executing partner-driven initiatives. Key Responsibilities Partner Program Administration Maintain partner profiles, certifications, and program statuses across all major alliance portals (Microsoft Partner Center, AWS ACE, IBM, NetApp, Pure, Rubrik, etc.). Track partner competency renewals, program deadlines, and required deliverables. Manage co-sell opportunity submission, approval, and tracking with partners. Process MDF requests, approvals, and claims with complete documentation. Data & Reporting Ensure all partner-related opportunities are accurately entered and maintained in Salesforce (SFDC). Build and distribute weekly/monthly partner pipeline and performance reports. Reconcile internal records with partner deal registration systems to ensure data integrity. Track ROI of partner-funded activities and campaigns. Process Coordination Act as the operational liaison between alliance managers, sales, marketing, and finance. Document and maintain standard operating procedures (SOPs) for alliance operations. Coordinate partner portal logins, permissions, and account access for internal teams. Systems & Tools Administer partner portal accounts, deal registration tools, and integration workflows with SFDC. Manage data imports, exports, and partner reporting automation. Support adoption of collaboration tools such as SharePoint for alliance tracking. Qualifications Required Operational role supporting alliances, channels, or partnerships. Experience working with Microsoft, AWS, IBM, NetApp, Pure, Rubrik, or similar partner programs. Strong Salesforce (SFDC) skills: data entry, reporting, dashboards, and workflows. Highly organized, process-driven, and detail-oriented. Proficient in Microsoft Office Workspace, and familiar with online apps for collaboration Preferred Familiarity with partner funding programs and co-sell processes. Experience reconciling data between internal CRM and external partner systems. Exposure to cloud consumption tracking and subscription billing models. ### Demand Generation Manager Komprise is seeking a strategic and hands-on Demand Generation Manager to lead pipeline creation programs targeting data-heavy enterprise IT organizations, reporting to the VP of Marketing. If you thrive on creating measurable demand and love working at the intersection of marketing, sales, and revenue operations, this role is for you. You’ll own the development and execution of integrated inbound, outbound and partner campaigns that fuel sales pipeline. This role will work closely with BDRs, channel and alliance partners, and revenue operations to align on priorities, deliver compelling messaging, and optimize programs based on data-driven insights. In this role you will collaborate with the marketing team on our content strategy, bring experience with measurement tools, use AI to enhance results, and demonstrate an ability to bring new fresh ideas to help accelerate company growth and customer success. Key Responsibilities Campaign Strategy & Execution Design and execute multi-channel demand generation programs to drive qualified pipeline from enterprise IT decision-makers (infrastructure, data leaders) Own target account marketing (TAM) strategy and execution across outbound, partner, and digital channels Build content-rich nurture, outreach, and campaign flows aligned to buying stages Run a metrics driven operation that strives to improve demand generation outcomes and provide executive dashboards and reports tracking historical progress Collaboration with Sales & BDRs Partner with the BDR team to launch outbound campaigns via Outreach, develop talk tracks, and iterate based on performance Partner with the Customer Success team to nurture existing customers and drive expansions, and broader use of our solutions Provide enablement and campaign kits to support outbound engagement Analyze outbound data and feedback to continuously improve lead quality and conversion Partner & Channel Campaigns Build and co-run joint campaigns with channel and alliance partners (e.g., cloud providers, storage vendors, resellers) Manage co-marketing plans including webinars, content syndication, and field events Track and report partner-sourced and influenced pipeline RevOps & Campaign Analytics Work closely with Revenue Operations to define campaign goals, track KPIs, and ensure accurate attribution Measure and report performance across systems (Salesforce, Marketo, Outreach), including MQL → SQL conversion, influence, and ROI Help manage and optimize the marketing tech stack, including supporting a potential migration from Marketo to HubSpot What You Bring 5+ years of experience in demand generation or campaign marketing, with a focus on enterprise B2B Strong background marketing to enterprise IT buyers Proven ability to create campaigns that generate measurable pipeline Expertise in Salesforce, Outreach, and Marketo or HubSpot Familiarity with target account programs, segmentation, and lifecycle metrics Excellent collaboration skills with sales, RevOps, and partner marketing teams Data-driven mindset with the ability to analyze and optimize at every stage Bonus Points Experience with ABM platforms (e.g., Demandbase, 6sense) Prior experience migrating from Marketo to HubSpot Familiarity with data infrastructure, unstructured data, cloud storage, or AI-related technology Why Komprise? The volume of unstructured data is exploding – and enterprise IT teams are under pressure to reduce storage costs, govern risk, and prepare for AI. Komprise gives them the visibility, control, and automation to succeed. As our Demand Generation Manager, you’ll be a key player in helping them discover our platform – and you’ll be at the heart of a high-growth, high-impact go-to-market team. Ready to drive demand that turns into real pipeline? Submit your resume highlighting your relevant experience to: us_careers@komprise.com. ### Product Manager, AI Data Pipelines & Developer Community Company Overview: Data is growing exponentially as the world becomes more digitized. IDC states that there will be 175 zettabytes by 2025. Over 90% of this data is unstructured, consisting of video files, audio files, MRIs, log files, and more. AI requires unstructured data to properly train LLMs and unstructured data requires AI to extract value and add structure. Komprise is at the crossroads of these two huge tailwinds: unstructured data and AI. Komprise powers the connection between unstructured data management and AI. Komprise Intelligent Data Management delivers a single platform to easily analyze, migrate, transparently tier and manage the lifecycle of petabytes of file and object data across hybrid environments. With Komprise, enterprise IT gains full visibility across silos to optimize storage, backup, ransomware and cloud costs. Komprise Smart Data Workflows and the Komprise Global File Index unlock unstructured data insights and access for AI. Komprise is recognized as the leader in unstructured data management by analysts such as Gartner and GigaOm, and has been selected by Inc 5000 and Deloitte 500 for its high rate of growth. Komprise customers include the who’s-who of enterprises across financial services, media and entertainment, healthcare, genomics, manufacturing, and public sectors. Komprise has a notably strong partner ecosystem: AWS, Azure, IBM, Pure Storage, and NetApp are all resellers of Komprise. Job Summary: As a Product Manager for AI Data Pipelines & Developer Community, you will be instrumental in bridging the gap between cutting-edge AI data infrastructure and a thriving developer ecosystem. You will lead the strategy, roadmap, and execution of scalable, efficient AI data pipelines that power our AI-driven products and empower our internal and external developer community to build innovative solutions on our platform.  We are looking for a go-getter who is self-motivated and thrives in an entrepreneurial environment, who takes initiative and appreciates the unique opportunity to define a product from the ground-up. Key Responsibilities: Define and Drive Product Strategy: Develop and communicate a clear vision and roadmap for the AI data pipeline platform and associated developer tools, ensuring alignment with business goals and market needs. Build and Optimize AI Data Pipelines: Work closely with our product team and IT, end-users, data engineers and data scientists from our customers to build, optimize, and maintain scalable data pipelines that allow them to enrich, classify, curate data for AI agents for augmentation and inferencing. Translate User Needs into Product Requirements: Gather and prioritize user requirements, translating them into detailed product specifications and coordinating development efforts with engineering and data science teams. Champion Developer Experience: Understand the needs of the developer community, both internal and external, and develop tools, resources, and programs that enhance their experience building with our AI data platform. Investigate participation in partner developer communities and open-source communities. Grow and Engage the Developer Community: Lead efforts to foster a vibrant developer community, potentially through events, forums, technical content, and strategic outreach. Measure and Analyze Product Performance: Define and track key metrics to measure the success and impact of both the AI data platform and community engagement efforts, using data-driven insights to iterate and improve. Cross-Functional Collaboration: Partner effectively with cross-functional teams, including engineering, marketing, sales, and customer success, to ensure product vision alignment and drive successful execution. Stay Ahead of the Curve: Monitor industry trends, emerging technologies, and competitive offerings related to AI, data infrastructure, and developer ecosystems. Qualifications: Product Management Experience: Proven experience in product management, particularly with a focus on data and AI-driven products. Technical Acumen: Strong understanding of technical data architectures, machine learning models, and AI applications in a business context. Developer Relations/Community Building: Demonstrated success in growing technical product adoption and developer communities. Data Expertise: Experience working with large datasets, data pipelines, and data platforms. Communication Skills: Excellent communication and interpersonal skills, with the ability to convey complex ideas to both technical and non-technical audiences. Problem-Solving: Proven ability to think strategically and execute methodically, translating complex problems into actionable solutions. Educational Background: A Bachelor's degree in Computer Science, Data Science, Business, or a related field is preferred. Preferred Qualifications: Experience with specific AI or ML platforms and frameworks. Experience with modern data platforms like Databricks and Snowflake. Experience with cloud platforms (e.g., AWS, Azure, GCP). Familiarity with open-source community dynamics. Location: Campbell, CA How to Apply: Submit your resume highlighting your relevant experience to: us_careers@komprise.com. ### Senior Software QA/Escalation Engineer Company Overview: Komprise is an enterprise SaaS specializing in analytics-driven unstructured data management. Komprise founders started the company to address a gap in innovation for managing massively growing volumes of unstructured data which was creating complexity and stretching IT budgets from the endless need to buy more storage. Komprise Intelligent Data Management is one platform to analyse, move and manage unstructured data. We work with large enterprises across many sectors so they can optimize storage and backup costs, automate AI data workflows, reduce compliance and security risks and gain greater value from file and object data. Job Summary: We are looking for an experienced QA Engineer for our Bangalore location who is keen on developing the next generation cloud-based data management SaaS platform. The ideal candidate will bring  passion to work on something new, thinks disruptively and who enjoys a dynamic environment where they can make an impact. This is an opportunity to start on the ground floor and grow with a company that has huge potential. Key Responsibilities: • Quality Assurance experience to help engineering team in understanding complex customer issues and can collaborate and communicate with the engineering team effectively. • Work with a highly agile, engaged, and motivated engineering and support team. • Identify customer issues and improve the product quality. • Software Development Methodology: Work on agile, customer focused and fast paced team with direct customer interaction. • Analysis: Responsible for reproducing customer issues in the lab, troubleshoot, analyse quickly and provide on-the-spot workaround/scripts/solutions to customers, collaborating with the Support team. Provide resolutions in a timely manner. • Quality Design: Should be able to design and implement highly performant, scalable distributed systems. Qualifications: • Problem Solving: Ability to solve difficult problems with a simple elegant solution. • Application Development: Experience in developing management applications and performance management applications is ideal. • File Systems: Experience with NAS and object-based file systems and REST interfaces is a plus (e.g. Amazon S3, Azure, Google Cloud Service). • Education: Should have a BE or higher in CS, EE, Math or related engineering or science field. • Experience: At least 5+ years of experience in software deployment and 2+ years of experience in dealing with customer/field issues. • Tech Stack: CIFS, NFS, Object Storage, Linux OS, Jenkins, Jira, Hypervisors, Github, Python(desirable). How to Apply: Submit your resume highlighting your relevant experience to: india_careers@komprise.com. ### Technical Support Engineer Job Summary: We’re seeking a passionate, experienced Technical Support Engineer to deliver exceptional enterprise-level support. This role goes beyond traditional support. It's an opportunity to shape product direction, collaborate with industry-leading partners and work with cutting-edge technology in data management. If you thrive in a dynamic environment, take pride in helping customers succeed, and want to make a measurable impact, we want to hear from you. Key Responsibilities: Serve as a trusted advisor to Komprise customers by delivering expert-level support with professionalism and urgency. Troubleshoot and resolve complex technical issues across Linux, networking, NFS/SMB protocols, and Windows Active Directory. Reproduce customer environments to isolate issues, create bug reports, and collaborate with engineering teams to drive resolutions. Contribute to support documentation and participate in forums and online communities to assist users in real time. Stay up-to-date on Komprise features and architecture, and provide feedback to shape future releases. Mentor junior team members and foster a collaborative team environment. Engage with Product Management and Engineering to influence roadmap based on customer insights. Qualifications: At least 5+ years in a Technical Support role, supporting enterprise software or SaaS products. Strong problem-solving skills and technical expertise in Linux, networking, NFS/SMB protocols and Active Directory. Excellent verbal and written communication skills, with the ability to simplify complex topics. Comfortable working in a fast-paced, agile environment with frequent product updates. Scripting experience in Shell or Python (a strong plus). Bachelor’s degree in Computer Science, Engineering, or a related field (or equivalent experience). Outgoing personality with a passion for helping customers and collaborating with partners. Bonus: Experience with VMware, cloud platforms (AWS, Azure, GCP, IBM Cloud), or hybrid storage environments. Why Join Us? Make an impact in a fast-growing tech company where your voice matters. Work on innovative data management solutions with a passionate, tight-knit team. Enjoy a culture that values curiosity, continuous learning, and customer success. Interested? We’d love to hear from you! Email india_careers@komprise.com and tell us why Komprise is the right place for your next career step. ### Engineering Manager Job Summary: We are looking for an experienced Team lead/Engineering Manager for our Bangalore location who is keen on developing the next generation cloud-based data management SaaS platform. We are looking for someone who is motivated to work in a dynamic environment with the latest technologies. Someone who is excited at the prospect of joining a core team that is working on a disruptive SaaS data management solution. This is an opportunity to start on the ground floor and grow with a company that has huge potential. Key Responsibilities: Design: Responsible for designing and developing features that power Komprise data management platform managing billions of files and petabytes of data. Technical Expertise: Responsible for designing and owning major components and systems of our product architecture, ensuring that Komprise data management platform is highly available and scalable Agile: Work in agile, customer focused and fast paced team with direct interaction with the customers. Cross Functional: Working with Product, Testing and Customer teams to understand customer requirements and use cases, ensure proper testing of functional areas and deliver a high quality product, and take feedback from customers to improve the product. Problem Management: Responsible for analysing customer escalated issues and provide resolutions in a timely manner. Release Management: Ensuring timely delivery of features and projects with high quality. People Management: Project and people management of team(s) within the BE organisation. Team Management: Performance evaluation and assessment of subordinates. Qualifications: Experience: 8+ years of software development experience with at least 3 years as a team lead or 1 year as an engineering manager. Data Structures: Solid grasp of computer science fundamentals and especially data structures, algorithms, multi-threading Problem Solving: Ability to solve difficult problems with a simple elegant solution Programming: Should have solid object-oriented programming background in  Java with impeccable design skills Cloud Systems: Experience with object-based cloud systems is a plus (e.g. Amazon S3, Azure, Google Cloud Platform) Distributed Systems: Experience with distributed systems, clustering and scaling is a plus Project Execution: Ability to manage and execution of projects by leading a team of engineers Coaching: Understanding and inculcating the cultural values of our organization Tech Stack: Java, Maven Virtualisation, SaaS, Github, Jira, Slack, Cloud Solutions and Hypervisors How to Apply: Submit your resume highlighting your relevant experience to: india_careers@komprise.com. ### Software Engineer 2 / Senior Software Engineer Job Summary: We are looking for an experienced Software Engineers for our Bangalore location who are keen on developing the next generation cloud-based data management SaaS platform. We are looking for someone who is motivated to work in a dynamic environment with the latest technologies. Someone who is excited at the prospect of joining a core team that is working on a disruptive SaaS data management solution. This is an opportunity to start on the ground floor and grow with a company that has huge potential. Key Responsibilities: Development: Responsible for designing and developing features that powers Komprise data management platform to manage billions of files and petabytes of data. Writing and Maintaining: Responsible for designing of major components and systems of our product architecture, ensuring that Komprise data management platform is highly available and scalable Coding: Responsible for writing performance code, evaluate feasibility, develop for quality and optimize for maintainability. Software Development Methodology: Work in agile, customer focused and fast paced team with direct interaction with the customers. Analysis: Responsible for analysing customer escalated issues and provide resolutions in a timely manner. Quality Design: Should be able to design and implement highly performant, scalable distributed systems. Qualifications: Data Structures: Solid grasp of computer science fundamentals and especially data structures, algorithms, multi-threading Problem Solving: Ability to solve difficult problems with a simple elegant solution Programming: Should have solid object-oriented programming background with impeccable design skills Application Development: Experience in developing management applications and performance management applications is ideal File Systems: Experience with object-based file systems and REST interfaces is a plus (e.g. Amazon S3, Azure, Google Cloud Service) Education: Should have a BE or higher in CS, EE, Math or related engineering or science field Experience: At least 5+ years of experience in software deployment Tech Stack: Java, Maven Virtualisation, SaaS, Github, Jira, Slack, Cloud Solutions and Hypervisors How to Apply: Submit your resume highlighting your relevant experience to: india_careers@komprise.com. ### Software Development Engineer in Test Job Summary: As a Software Development Engineer in Test, you will be responsible for designing, developing and executing on quality, automation strategies; including leveraging tools, technologies for test automation as applicable. Creating, reviewing the automation, triage automation reports, Quality & Engineering artifacts. Evaluate and propose automation techniques and processes. Work with a highly agile, engaged and motivated engineering and support team. 2-4 years of in-depth experience in automating software product(s) Key Responsibilities: Automation Design and Development: Use your strong scripting, programming experience Design, Develop and Execute on Quality, Automation Strategies, including leveraging tools, technology for Test Automation as applicable Create and Review the Automation, Triage automation reports, Quality & Engineering artifacts Process and Collaboration: Inspire, Mentor other junior members in the Automation Engineering team Evaluate and Propose Automation techniques and processes Conduct Interviews to identify best matching talent for the Komprise team Integrating the Automation with Jenkins and Git Review the peer’s code thoroughly ensuring production level quality of automation code Running, triaging and maintaining automated regression suites Ability to evaluate & recommend appropriate automation tools and framework Experience in manual testing of the product aligning to business priorities Qualifications: Experience: 2-4 years of in-depth experience in automating software product as well as writing framework and tools to test the product for functional and non-functional aspects. BE/MS Computer Science or equivalent Skills: Expertise in coming up Automation Architecture, Strategy, Frameworks 2+ years of hand-on experience in Python, Object Oriented Programming and creating custom libraries, production-level test suites 2+ years of experience working/integrating automation with Robot Framework Integrating the Automation with Jenkins and Git Collaboration: Excellent interpersonal and communication skills, with a demonstrated ability to work cross-functionally with dev, support and escalation teams Results-Driven: Proven background as an individual contributor testing, automating highly complex, scalable and reliable software. Adaptability: Comfortable working in a fast-paced, Experience with Agile software development methodologies and principles Benefits: Competitive salary Flexible vacation policy and remote work options Opportunity to grow with a rapidly scaling company How to Apply: Submit your resume and a cover letter highlighting your relevant experience and why you’d be a great fit for this role to: india_careers@komprise.com. ### Technical Project Manager Job Summary: We are looking for a full-time Technical Project Manager to help manage product development and delivery effort for our Data Management platform and releases. The ideal candidate is someone with great energy, passion, and prior experience as a Technical Project Manager at software companies both small and large, and preferably also at enterprise software development companies. This position requires strong leadership capabilities as it will encompass working with cross-functional teams, including Engineering, Product Management, Support teams in the U.S. and in India. Key Responsibilities: Planning: Collaborating with stakeholders to define project outcomes and deliverables, creating engineering plan to drive timelines, evaluating risks, and help with identifying the project team. Resource Allocation & Negotiation: Tracking assigned tasks to team members, leaders, and managers, while creating schedules, negotiating priorities, and driving deadlines. Collaboration: Working with department heads, engineering leadership, customer success managers, product managers, software engineers, and other team leaders to ensure effective and timely deliverable of requirements and enhancements. Monitoring: Overseeing the entire life cycle of projects, including execution of tasks and phases, and ensuring that everyone is meeting deadlines, and identifying technical standards. Budget management: Tracking the project budget and adjusting as needed. Communication: As one of the key liaisons between product management, development, quality engineering, this candidate will drive alignment on priorities, resources, and strategies. They will keep stakeholders informed about project progress and issues, and translating technical details into general terms as well as generating reports for different stakeholders. Drive: Self-managing, flexibility, interpreting vagueness, and articulating problems (e.g., resource conflict, customer satisfaction, customer issues, etc.) are all qualities expected for the candidate in this position. Qualifications Experience: A minimum of 4-6 years’ experience in Technical Program Management with experience in software product engineering, deployment, and integration. Experience at both large and small companies is ideal. Education: Bachelor’s degree in computer science, Software Engineering, or similar technical field, or a Degree in Business Administration or similar field of study. Methodologies: In depth knowledge on scrum methodology and agile project management skills. Project Management: Advanced knowledge and experience in product development planning, leadership, and of the full life cycle of product development. Prior launches of multiple successful products and features. Time Management: Ability to multi-task, manage multiple deadlines, complete tasks on time with high quality. Communication: Solid written and verbal communication skills. Customer focused: Have a deep understanding and empathy for customers. Stakeholder Management: Excellent people skills, collaborative, a positive “can-do” attitude even under pressure. Adaptability: A strong work ethic and resourcefulness with the ability to learn on the job. Benefits: Competitive salary Flexible vacation policy and remote work options Opportunity to grow with a rapidly scaling company How to Apply: Submit your resume and a cover letter highlighting your relevant experience and why you’d be a great fit for this role to: india_careers@komprise.com.