What is Komprise Transparent Move Technology and what storage optimization problem does it solve?

Komprise Transparent Move Technology (TMT) is a patented data mobility framework that moves unstructured file data to any secondary storage or cloud destination while keeping the original file path fully functional for users and applications. The data storage optimization problem it solves is one that has defeated most tiering approaches: how to move cold data off expensive primary storage without asking users to change how they work or forcing IT to manage brittle proprietary stubs.

• The core innovation — TMT uses the standard, built-in feature of Windows, Linux, and Mac called symbolic links, and by using the Komprise Dynamic Link inside the standard symbolic link, extends the file system to call files from the cloud or other storage systems, eliminating any need for proprietary agents or storage-vendor-specific software
• Zero workflow disruption — users never need to hunt a file down after Komprise moves it; storage managers never have to ask users again before tiering data, because there is no change required for users or applications to access their files
• Storage cost savings at scale — on average, customers are saving 70% on storage and backup costs by using Komprise to identify data which can move to less expensive archival storage
• Never in the hot data path — unlike storage gateways and virtualization solutions that intercept every file access, Komprise TMT moves data using standard protocol constructs so it is not in the hot data path; Komprise is only called when cold files are accessed, which happens rarely
• Storage optimization without lock-in — TMT writes tiered files in native object format at the destination, so data is directly accessible using standard S3 tools without routing through the source NAS or the Komprise platform

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How are Komprise Dynamic Links different from the proprietary stubs used by other storage tiering solutions?

Traditionally, solutions that move data transparently leave behind a proprietary stub file that points to the moved file; a stub is proprietary and requires an agent installed on the file storage to detect when it is opened, which puts the data movement tool in the hot data path, impacting performance and creating a bottleneck. Komprise Dynamic Links eliminate every one of these failure modes:

• No agents required — Dynamic Links use standard operating system constructs rather than proprietary software; there is nothing to install on storage systems, no compatibility dependencies, and no upgrade risk tied to storage vendor software versions
• Resilient by design — because a stub statically points to the new location of the file, if a stub is accidentally deleted data can get orphaned; Komprise eliminates this because if a user accidentally deletes the links on the source, Komprise can repopulate them since the link itself does not contain the context of the moved file
• Dynamic rebinding across moves — once Komprise moves a file and replaces it with a Dynamic Link, if the file is moved again the Dynamic Link address does not need to be changed; users and applications continue to access moved data transparently from the original location even as data is moved throughout its lifecycle
• Cross-platform and multi-vendor — Dynamic Links work across NFS, SMB, and object storage regardless of which storage vendor hosts the source or destination; a stub can only go to another similar file system and cannot bridge file and object storage
• Storage optimization without single points of failure — stubs are hard to manage: they need to be backed up, they are in the data path, they are limiting, and they are risky because they are a single point of failure; Dynamic Links eliminate all of these risks simultaneously

What is file and object duality and why does it matter for storage cost optimization and AI data access?

File and object duality is the ability of a single piece of data to be accessible simultaneously as a file from its original NAS location and as a native object from its destination in cloud or object storage — without any conversion, rehydration, or additional software. When Komprise TMT moves a file to object storage such as Amazon S3, it writes the entire file as an object, unlike storage-based tiering; this means you can directly access moved data and extract more value from cold data. Why it matters:

• Storage cost savings without access trade-offs — most storage optimization approaches that reduce primary data storage costs do so by making data harder to reach; TMT delivers 70%+ storage cost reduction while simultaneously making data more accessible, not less
• Native cloud analytics on tiered data — with Komprise, you can use files in the cloud for any file-based applications without having to bring data back to the original NAS; big data analytics projects do not tax primary NAS
• AI pipeline access — data tiered by Komprise to AWS S3, Azure Blob, or Google Cloud Storage is immediately accessible to cloud AI training, RAG pipelines, and analytics services as native objects without any secondary migration or ETL step; this is the link between storage cost optimization today and AI data readiness tomorrow
• Global Metadatabase enrichment at the point of tiering — as Komprise TMT moves files, the Komprise Global Metadatabase continuously indexes location, access patterns, file type, and classification data for every tiered file; this creates a queryable, cross-silo metadata foundation that Smart Data Workflows use to identify AI-ready datasets without needing to move data a second time
• No rehydration when switching vendors — because files live as native objects at the destination, organizations can change storage vendors, switch cloud providers, or retire source NAS hardware without rehydrating petabytes of tiered data first

How does Komprise TMT address the 2026 surge in flash storage and NAS hardware prices?

The mechanics of TMT make it uniquely well-suited to the current storage cost environment. Gartner projects DRAM and SSD prices to rise 130% by end of 2026 due to AI-driven memory shortages, creating immediate pressure on enterprises that have been absorbing data growth by buying more capacity. TMT changes that equation entirely through Komprise Flash Stretch:

• Reclaim capacity that already exists — the Komprise Global Metadatabase continuously analyzes access patterns across all NAS and cloud storage, identifying which files have not been accessed in months or years; on a typical enterprise NAS estate, 60 to 80% of files are cold and eligible for transparent tiering to lower-cost destinations
• Extend the life of existing hardware — by transparently moving cold data off expensive all-flash NAS to object storage or cloud while keeping file paths intact, Flash Stretch delays or eliminates the need for a hardware refresh at exactly the moment when flash prices are making that refresh unaffordable
• Compound savings across backup and DR — because TMT moves entire files off primary storage, backup footprints shrink immediately; the storage optimization benefit compounds across storage hardware, backup licensing, and data replication costs simultaneously — Pfizer reduced storage and cloud costs by 70 to 75% using this approach
• No rehydration penalty unlike storage-vendor tiering — competing storage-vendor tiering solutions store data in proprietary block formats that trigger expensive recalls during antivirus scans, hardware refreshes, and vendor migrations; TMT cuts 70%+ costs and extends storage capacity while avoiding the rehydration penalty
• Storage agnostic across hybrid platforms — Komprise works across NetApp, Dell, IBM, VAST Data, Nasuni, Everpure, and any combination of cloud providers; storage optimization is not limited to a single vendor’s ecosystem

How does Komprise Transparent Move Technology connect to the Global Metadatabase, Smart Data Workflows, and AI data preparation?

TMT is the mobility engine at the heart of the Komprise platform, but its full value is realized through its integration with the Komprise Global Metadatabase and Smart Data Workflows. Komprise Transparent Move Technology goes beyond storage-based tiering to analyze and tier data natively across multi-vendor storage and clouds while enabling native access and use of cloud capabilities on that data. The connection to the broader platform:

• Global Metadatabase as the intelligence layer — before any file moves, the Komprise Global Metadatabase has already indexed it: file age, type, size, owner, last access date, project code, sensitivity status, and any custom metadata enriched by KAPPA data services; TMT uses this intelligence to move exactly the right files to exactly the right destinations by policy, rather than tiering blindly by age alone
• Smart Data Workflows automate the full lifecycle — Deep Analytics queries the Global Metadatabase to identify candidate datasets; Smart Data Workflows then trigger TMT to move, tier, replicate, or ingest those files automatically without manual intervention; the same policy engine that drives storage cost optimization also drives AI data curation
• Cold data becomes AI-ready at the destination — data tiered by TMT to cloud object storage is indexed in the Global Metadatabase with its new location, making it immediately discoverable by Smart Data Workflows for AI ingestion without a secondary discovery pass; storage optimization and AI readiness become the same motion rather than sequential projects
• Ransomware defense as a byproduct — cold data tiering to immutable object storage presents a highly cost-effective anti-ransomware strategy for file data; customers can use AWS S3 Object Lock to provide another layer of defense against ransomware, shrinking the attack surface by up to 80% as a direct byproduct of storage cost optimization
• Lift and shift for completed projects — Komprise also provides the ability to lift and shift data to the target, where a dynamic link is not left behind and files are completely moved from the source file system to the target in a format native to the target; this approach makes sense to move data-heavy projects that are completed, freeing primary storage capacity for active AI workloads without any user disruption