Glossary of Terms

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

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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.

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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.

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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?
• 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 sorry

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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.

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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.

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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:

1. Gain Accurate Visibility Across Cloud Accounts into Actual Usage
2. Forecast Savings and Plan Data Management Strategies for Cloud Cost Optimization
3. 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.
4. 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 Services

Cloud Data Management platform

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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. Komprise Elastic Data Migration makes cloud data migrations simple, fast and reliable with continuous data visibility and optimization.

The Komprise Smart Data Migration Strategy

Read the blog post: Smart Data Migration for File and Object Data Workloads

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 – 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 and planning
• 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.

• To learn more about the seven reasons why cloud data migrations are dreaded, watch the webinar.
• Learn more about why Komprise is the fast, no lock-in approach to unstructured cloud data migrations: Path to the cloud.

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:

1. Define Sources and Targets
2. Know the Rules & Regulations
3. Proper Data Discovery
4. Define Your Path
5. Test, Test, Test
6. Free Tools vs. Enterprise
7. Establish a Communication Plan

Watch the webinar: Preparing for a Cloud File Data Migration

What is a Smart Data Migration?

Know your cloud data migration choices for file and object data migration.

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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.

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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.

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. 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.

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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.

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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.

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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

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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

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?

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.
• Access and Permission Analysis: Review and audit access permissions to files and folders to ensure that only authorized users have access. This analysis helps enhance security and compliance with data privacy regulations.
• 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.

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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.

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.

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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.

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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.

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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.

In 2023 Komprise published an eBook: 8 Ways to Save on File Storage and Backup Costs.

1. Consolidate storage and data management solutions.
2. Adopt a data services mindset:
3. Adopt new data management metrics.
4. Introduce an analytics approach for departments and users:
5. Become a cloud cost optimization expert.
6. Develop best practices for data lifecycle management.
7. Develop a ransomware strategy that also cuts costs.
8. 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.

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