A Smarter Cloud NAS Migration Strategy

Enterprise NAS devices are typically refreshed at three- to five-year intervals. And while IT admins may be aware that a large proportion of data on this expensive storage is cold, migrating it can be complex and risky. Archived data that’s needed by users and apps often causes operational disruption. Permission to archive cold data storage is rarely given, and when it is, identifying and migrating the correct unstructured data to the cloud is an extensive, cumbersome manual process involving spreadsheets, reporting tools, and various software applications. As your data footprint grows, so too do these challenges. Now there’s a smarter solution.

Komprise and Google Cloud Storage: Accelerate Cloud File Migration

Komprise coupled with Google Cloud automatically identifies and moves cold data by policy from any NAS to Google Cloud Storage. This is accomplished without disruption because with patented Transparent Move Technology, the moved data still looks like it’s stored on the primary NAS. When a user or an application accesses this data, Komprise automatically recalls it with a transparent bridge of object data to files. IT can manage their storage farms efficiently, automatically and seamlessly—without having to ask for user permission.

Another IT issue is the rate unstructured data is being generated. Because backing it all up is simply too costly, in most cases, such data is never backed up. With Komprise, you can replicate unstructured data to durable Cloud Storage, providing an automated, simple way to help protect your data and facilitate DR. Know First. Move Smart.

Analyze Data Usage and Growth Across Storage

Komprise provides dynamic data analysis across storage silos to identify how much data is hot and how much is cold and to help answer the following:

• What types of files are they?
• What’s the distribution of file sizes?
• Who is accessing which files?
• How fast is file storage growing?
• How much data is inactive?

Charts provide a quick visual representation of the data profile.

For more granular decision-making, Komprise also provides access and aging information that’s based on file type, size, owner, group, and directory.

Komprise allows you to run “what if” scenarios and get subsequent capacity needs and cost savings in seconds. Want to know what would happen if you moved all data untouched in over a year to Cloud Storage? Get an instant analysis based on your data, historical data growth patterns and which Cloud Storage class you want to use: Regional, Multi-Regional, Nearline, or Coldline.

Control File Data Moves with Policies

You can easily set policies to automatically move and copy your data based on your organization’s needs using Transparent Move Technology™ (TMT).

• The move policy continuously moves inactive and cold data to Cloud Storage as the data ages. Identifying and moving cold data eliminates the ongoing need to increase the capacity of on-premises NAS storage.
• The copy policy allows you to select different conditions for copying data. E.g., only replicate to the cloud data that’s been modified in the last year.
• Specific with a policy that obsolete data be removed (moved to a NAS trash folder) rather than moved or copied to a new storage platform.
• If certain data should not be moved or copied, create specific exclusions using file types, size, and folders.
• Create custom policies for data that has unique needs; Komprise dynamically calculates the estimated capacity that will be freed up and your projected cost savings.

Information Lifecycle Management for File and Object Data

Komprise uses tiered Cloud Storage to further reduce costs. Through policies that you set in Komprise, you can tier data from Nearline storage to the less expensive Coldline storage based on the age of and lack of access to the data after you have moved it to Nearline storage. Both provide similar access times, so you can reduce costs further by using Coldline storage without affecting your ability to access the data when you need it.

Komprise runs as a hybrid cloud service with a grid of one or more Komprise virtual appliances, called Observers and Proxies, deployed on premises. The grid has a highly parallelized, scaleout architecture. Observers analyze data across on-premises NAS storage, move and replicate data by policy, and provide transparent file access to data that is stored in the cloud. Komprise Proxies encapsulate the extended Server Message Block (SMB) or Common Internet File System (CIFS) metadata and permission structure for compatibility with modern object architectures and accelerate file transfer to Cloud Storage. Finally, a Komprise Director virtual machine (VM) runs in the cloud and provides the management console.

Komprise Intelligent Data Management

SCALE OUT: Komprise does not require any dedicated hardware and runs as a scale-out grid of VMs that are managed as one logical unit. There are no centralized databases, which allows Komprise to grow on-demand to handle data at massive scale. The grid is highly available and so long as at least one Observer is healthy, access to all moved data remains intact. Komprise does not store data, and simply moves data through SSL to Cloud Storage, which is HIPAA compliant.

NON-DISRUPTIVE: A typical challenge with traditional storage services is that they can disrupt end-user access. Komprise preserves the directory structure as well as file attributes on the target, unlike cloud migration tools that strip data off file attributes and move blocks to the cloud that can only be accessed and understood using the application going forward. With Komprise, end users can continue accessing files just as they always did, because the location of data is transparent to them.

HIGH PERFORMANCE: Many migration solution providers significantly reduce the performance of storage during data moves. Komprise, however, is invisible to the hot data path and does not get inline. It adaptively throttles back when the storage systems are actively in use so that Komprise analytics runs non-disruptively in the background. This means that the performance of the active data is unchanged and may even improve as the primary storage becomes less overloaded.

NO STATIC STUBS: A stub, which contains the location to which a file has been moved, can be easily deleted or corrupted, orphaning the files that were moved to the target storage. As file systems grow to hyperscale, multi-petabyte size, managing these stubs becomes increasingly challenging, requiring large and complex database management to protect them to maintain data accessibility. Komprise delivers transparent access by using standard protocol constructs when moving data. When you move a file, a symbolic link containing all the properties of the original file is left behind as a pointer. Users and apps continue to see and can open the file from the original location keeping all the permissions and access control intact. No invasive agents or stubs means no disruption to users, applications, or the data protection workflows.

Komprise ensures that data is protected and encrypted by default providing the following security options for moving data to GCP:

Encryption in Transit and at Rest

In this default mode, data is transmitted between Komprise observer and GCP using SSL, and Google encrypts the data using AES 256-bit symmetric key encryption using Google keys before storing the data. The keys are managed by Google, and Komprise never receives the encryption keys. During access, Google decrypts the data and sends it securely over HTTPS using SSL to the Komprise observers. Data is then transferred to end users accessing the data.

End-to-End Encryption

In this mode, data is encrypted on Komprise Observers using AES 256-bit symmetric key encryption before transferring to GCP. During access, the Komprise Observer retrieves encrypted data from Google that is transmitted in encrypted format. The Komprise Observer then decrypts the data using the Data Encryption Key and then sends it to the user. This is a heightened security mode where data is only available through the Komprise grid and not directly in Google.

Google Cloud Storage and Komprise Analytics-First Data Management

With the Google Cloud Platform service Cloud Storage and Komprise, you can realize the benefits of actively tiering, archiving and replicating data to the Google Cloud without disrupting users and applications.

FAQs

Why has extending NAS to Google Cloud Storage become more urgent — and why does the approach matter as much as the decision to move?

Classic NAS has become an expensive tier of storage for seldom-accessed data; enterprise NAS devices are typically refreshed at three to five year intervals; and while IT admins may be aware that a large proportion of data on this expensive storage is cold, migrating it can be complex and risky; archived data needed by users and apps often causes operational disruption; permission to archive cold data is rarely given, and when it is, identifying and migrating the correct unstructured data to the cloud is an extensive, cumbersome manual process involving spreadsheets, reporting tools, and various software applications. Every dimension of that observation has intensified since the white paper was written:

• Hardware refresh costs have risen structurally — TrendForce projects NAND Flash contract prices to rise sharply through the current pricing cycle, with meaningful supply expansion unlikely for at least two to three years; the cost of refreshing NAS hardware at the end of a three to five year cycle has increased significantly; organizations that extended NAS capacity to Google Cloud Storage to defer refreshes made a sound financial decision that is now more financially compelling than ever
• The cold data backlog grows faster than refresh cycles — 60 to 80% of enterprise file and object data has not been accessed in over a year; a NAS device refreshed today will accumulate the same proportion of cold data within months; extending NAS to Google Cloud Storage is not a one-time relief valve — it is a continuous intelligent tiering operation that prevents cold data from consuming the new hardware as fast as it consumed the old
• The manual curation problem is now solvable — the white paper identified manual curation as the reason organizations rarely captured the full cold data savings opportunity; the Komprise Global Metadatabase, available in Komprise Intelligent Data Management, continuously indexes all data across NAS and Google Cloud Storage simultaneously, enabling Deep Analytics queries that identify cold data candidates instantly without spreadsheets, manual reporting, or per-file review
• Google Cloud Storage tiering is automatic once policies are set — through policies set in Komprise, data can be tiered from Nearline storage to less expensive Coldline storage based on age and lack of access after it has been moved to Nearline storage; both provide similar access times so costs can be reduced further by using Coldline storage without affecting the ability to access the data; the policy runs continuously without IT intervention, capturing new cold data as it accumulates rather than requiring a periodic manual archiving project
• Komprise is the metadata and orchestration layer for enterprise unstructured AI data — extending NAS to Google Cloud Storage is not just a cost exercise; data tiered to Google Cloud Storage by Komprise is stored in native object format, immediately accessible to Google Vertex AI, BigQuery, and Google Cloud ML services without conversion; the storage cost optimization of today is the AI data access of tomorrow

How does Komprise maintain transparent access when files are tiered from NAS to Google Cloud Storage, and why is transparency the prerequisite for organizational buy-in?

The technical challenge of extending NAS to Google Cloud Storage is straightforward to describe but genuinely difficult to execute without disrupting the users and applications that depend on NAS data. When Komprise moves data to Google Cloud Storage, the moved data is still accessed as files from the NAS or as files or objects from GCS; users and applications access moved data just like before without any disruption; this transparency is the organizational prerequisite for systematic tiering at scale:

• The access experience is identical before and after tiering — Komprise replaces tiered files on the NAS with Dynamic Links built on industry-standard operating system symbolic link constructs; users opening files, applications reading data, backup software scanning volumes, and scripts referencing paths all behave identically after tiering because the Dynamic Link resolves transparently to the file at its Google Cloud Storage destination; nothing changes from any application or user perspective
• No agents on NAS or client machines required — Komprise drops into any environment in 15 minutes — no agents, stubs, complexity, or infrastructure; scale as needed to handle petabytes and beyond by simply adding more virtual machines with no scaling limits or dedicated infrastructure; the absence of agents means there is nothing to install on NAS systems, nothing to maintain across software upgrades, and no compatibility risk with NetApp, Dell, IBM, VAST Data, or any other NAS vendor’s latest firmware release
• Dual access from NAS and natively from Google Cloud Storage — data tiered by Komprise is simultaneously accessible as a file from its original NAS path via the Dynamic Link and as a native object from Google Cloud Storage directly; Google Vertex AI, BigQuery, and any other GCP service can read tiered files as native GCS objects without routing through the NAS; this file and object duality is what makes the tiering investment serve both cost optimization and AI access goals simultaneously
• Transparency is what converts organizational skepticism into tiering participation — the white paper identified organizational resistance — specifically the difficulty of getting permission to archive data — as the primary obstacle to systematic tiering; transparency eliminates the objection that users will not be able to find their data; once IT teams can demonstrate that tiered files remain fully accessible, departmental owners are far more likely to approve tiering policies voluntarily rather than requiring IT to fight for each approval
• Showback reporting closes the organizational loop — Komprise pre-built showback reports show each department’s cold data percentage, current storage cost, and savings from tiering already completed; when a department head can see that 70% of their team’s data is cold, what it costs monthly on NAS, and how much has already been saved by tiering to Google Cloud Storage, the conversation shifts from “why are you moving our data” to “how do we tier more”

What are the Google Cloud Storage tiers that Komprise manages and how does it determine which data belongs on which tier?

Google Cloud Storage offers multiple storage classes designed for different access frequency profiles — Standard, Nearline, Coldline, and Archive. The cost difference between Standard and Archive is an order of magnitude. The question is not whether to use lower-cost tiers but which data belongs on each tier and how to keep it there accurately as access patterns change over time:

• Analytics before movement determines the right tier for each dataset — Komprise Analysis profiles the full NAS estate before any data moves to Google Cloud Storage, identifying access frequency, file age, file type, and department ownership for every file; this profile is what makes tier selection precise rather than approximate; data accessed weekly belongs on Standard or Nearline; data not accessed in a year belongs on Coldline or Archive; data tiered to the wrong class either generates unnecessary retrieval fees or sits on a more expensive tier than necessary
• Nearline for moderately cold data; Coldline for genuinely inactive data — Komprise uses tiered Cloud Storage to further reduce costs; through policies set in Komprise, data can be tiered from Nearline storage to less expensive Coldline storage based on age and lack of access; this two-stage tiering captures the maximum savings opportunity across data that is moderately cold versus data that is genuinely inactive, without requiring IT to manage the transition between stages manually
• Access time is more accurate than modification time for tier decisions — Komprise uses actual last access time as the primary signal for tiering decisions, not modification time or creation time; a file modified once two years ago but accessed monthly is not a tiering candidate; a file created last month but never opened after its initial upload is; this precision prevents the retrieval fee surprises that come from tiering data that is actually still in use
• Policies run continuously as new cold data accumulates — tiering to Google Cloud Storage is not a one-time project with a defined endpoint; Komprise policies run continuously, identifying new cold data as it accumulates on NAS and tiering it to the appropriate GCS tier automatically; the cold data opportunity on any NAS estate is a moving target, and continuous policy enforcement is the only approach that captures the full savings over time
• The Flash Stretch Assessment quantifies the GCS tiering opportunity before commitment — for qualified enterprises managing 500TB or more on NAS with Google Cloud Storage as a potential destination, the Flash Stretch Assessment models the cold data volume, current cost on primary NAS, and projected savings from tiering to the appropriate GCS tiers; this assessment makes the financial case for Google Cloud tiering specific to the actual environment rather than based on industry averages

How does data tiered from NAS to Google Cloud Storage become part of the AI data foundation rather than just a cold storage destination?

The Google Cloud Storage white paper was written when tiering to GCS was evaluated purely as a cost optimization decision. The addition of the Komprise Global Metadatabase, Deep Analytics, Smart Data Workflows, and KAPPA data services has changed that calculus entirely. Data tiered from NAS to Google Cloud Storage by Komprise does not disappear into a cost-optimized archive — it becomes part of a continuously indexed, queryable, AI-accessible data estate:

• Native GCS objects are immediately accessible to Google Cloud AI services — data tiered by Komprise to Google Cloud Storage is written in native object format, directly readable by Google Vertex AI, BigQuery, Google Cloud ML, and any GCP-native analytics service without conversion, ETL, or secondary migration; the cost optimization decision of tiering to GCS simultaneously creates the AI data access that GCP workloads require
• The Global Metadatabase indexes every tiered file across NAS and GCS simultaneously — as each file is tiered from NAS to Google Cloud Storage, the Komprise Global Metadatabase records its new location, access history, file type, sensitivity status, and any enriched metadata attributes; the cold data archive in GCS is not a dead end — it is a continuously updated, cross-silo metadata layer that Deep Analytics can query for AI dataset curation without moving data again
• Deep Analytics finds specific datasets across NAS and GCS in a single query — an AI team needing to find specific research files across a hybrid estate that spans on-premises NAS and Google Cloud Storage can run a single Deep Analytics query that returns results from both simultaneously; the query reduces a petabyte-scale multi-silo estate to exactly the right cohort for a given AI use case in seconds, regardless of whether the data is on NAS or has been tiered to GCS
• Smart Data Workflows automate AI delivery from GCS-tiered data — Smart Data Workflows, available in Komprise Intelligent Data Management, can identify specific subsets of GCS-tiered data for AI ingestion, enrich them with KAPPA data services metadata extraction, exclude sensitive content, and deliver curated datasets to Google Vertex AI or any other AI service directly from the GCS location; tiered data becomes an active AI asset rather than a passive archive
• KAPPA data services enrich GCS-resident data without rehydrating it — KAPPA data services run serverless processing functions directly against files at their GCS destination, extracting custom metadata attributes from proprietary file formats and writing them back to the Global Metadatabase; genomics files, medical images, and domain-specific research data tiered to Google Cloud Storage become precisely queryable for AI use cases without ever needing to be moved back to NAS

What has changed about the value proposition for extending NAS to Google Cloud Storage since the white paper was written, and what can enterprises do today that was not possible then?

The white paper described a straightforward proposition: cold data on expensive NAS should be transparently tiered to lower-cost Google Cloud Storage, with users experiencing no disruption. That proposition remains correct. What has been added since is a layer of intelligence, automation, and AI connectivity that transforms the GCS tiering investment from a cost management action into a strategic data foundation:

• The Global Metadatabase replaces manual cold data identification — the white paper acknowledged that identifying cold data for GCS tiering was “an extensive, cumbersome manual process involving spreadsheets, reporting tools, and various software applications”; the Global Metadatabase eliminates this entirely; continuous automatic indexing of every file across all NAS sources and GCS destinations means cold data candidates are identified instantly without any manual effort, and the process runs continuously rather than requiring periodic manual review cycles
• Multi-vendor NAS tiering to GCS from a single platform — organizations in the current enterprise storage landscape manage data across NetApp, Dell, IBM, VAST Data, Nasuni, and Everpure simultaneously; Komprise tiers cold data from every one of these platforms to Google Cloud Storage using the same policy engine, the same analytics foundation, and the same management interface; this cross-vendor tiering to GCS was not practically achievable with the tooling available when the white paper was written
• AI data governance was not a consideration then; it is a requirement now — the white paper did not address PHI, PII, or IP as considerations for GCS tiering because enterprise AI tools that could inadvertently surface sensitive tiered data did not exist as operational concerns; Komprise Sensitive Data Management, available in Komprise Intelligent Data Management, scans NAS data before it is tiered to GCS and classifies sensitive content with enforcement policies that prevent it from reaching unauthorized AI pipelines or cloud analytics platforms
• Komprise is the metadata and orchestration layer for enterprise unstructured AI data across every cloud including Google Cloud; the data management capabilities added since the white paper was written — the Global Metadatabase, Deep Analytics, Smart Data Workflows, KAPPA data services, Intelligent AI Ingest, and Sensitive Data Management — all apply to Google Cloud Storage environments with the same depth as AWS and Azure environments; the GCS tiering decision made for cost reasons becomes the AI data foundation decision simultaneously
• The upgrade path from tiering to full AI data management is a single platform — organizations that begin with transparent NAS-to-GCS tiering using Komprise can upgrade to Komprise Intelligent Data Management at any time to unlock the full Global Metadatabase, Deep Analytics, Smart Data Workflows, KAPPA data services, and Intelligent AI Ingest; every file tiered to GCS by Komprise is already indexed and ready for these capabilities the moment the upgrade is applied; there is no re-analysis, no secondary migration, and no additional implementation work

Learn more about how Komprise and Google can help you cut costs, free up primary storage capacity, and strengthen data protection. Schedule a demonstration today.