Data Management Glossary
Transparent File Tables
What are Komprise Transparent File Tables?
Transparent File Tables are a capability of the Komprise Intelligent Data Management platform that exposes unstructured enterprise file and object data as a structured, queryable table directly inside data lakehouses and analytics platforms. Using the open Apache Iceberg table format, Komprise Transparent File Tables make petabytes of file data visible and queryable by data engineers, data scientists, and analysts in their familiar environment, without requiring bulk data movement or custom ingestion pipelines.
The table presents Komprise-enriched metadata alongside a pointer to each file. Data loads dynamically only when a query or AI pipeline actually needs it. The files themselves stay where they live across on-premises NAS, cloud object stores, and hybrid storage environments until they are required.
Transparent File Tables are part of a broader shift in how enterprises connect their unstructured data estate to AI. Rather than forcing a choice between expensive data migrations and leaving file data dark to analytics, Transparent File Tables give data and AI teams structured access to the full unstructured data estate from within the tools they already use.
Why unstructured data has been dark to AI
Over 80% of enterprise data is unstructured, yet less than 1% of it has ever reached AI or a data lakehouse. The data exists: decades of research files, medical images, instrument data, documents, contracts, and digital assets sit across enterprise file systems and object stores. The problem is access.
Unstructured data has three properties that make conventional AI ingestion approaches fail at enterprise scale.
- It lacks consistent schema. A directory of DICOM files, a folder of research PDFs, and a set of video assets have no inherent structure that a lakehouse can query.
- Quality is inconsistent. Without classification and enrichment, raw file stores contain ROT data (redundant, obsolete, and trivial content) alongside high-value material, with nothing to distinguish them. And the volume is prohibitive.
- Complexity and cost. Copying petabytes of files into a lakehouse before a data team can run a single query is too slow and too expensive to be practical for most organizations.
Current ingestion mechanisms compound the problem. Tools designed to move structured data copy everything first, then query it. For unstructured data at petabyte scale, that model is backward. Data and AI teams need to query the metadata first, identify precisely what they need, and then retrieve only those files.
According to Kumar Goswami, CEO and co-founder of Komprise, the reason 99% of enterprise unstructured data has been dark to AI and analytics is that discovering it, generating its schema, and moving it is inherently complex and costly. Transparent File Tables address all three barriers.
TFT: The Technical Architecture
Transparent File Tables leverage the Komprise distributed, scale-out architecture that continuously classifies and indexes unstructured data across hybrid storage environments into the Komprise Global Metadatabase. That index captures file system metadata, sensitive data labels applied by Smart Data Workflows, and custom attributes extracted by KAPPA data services for industry-specific file types.
When IT exports a Transparent File Table, Komprise presents a tabular schema of that enriched metadata as an Apache Iceberg table. Apache Iceberg is the open table format adopted across the major data lakehouse platforms. Data teams can query Transparent File Tables using their preferred BI and analytics tools without any knowledge of or direct access to Komprise.
The table includes a pointer to each file using Transparent Move Technology rather than the file itself. This is the same transparency model Komprise uses for data tiering: data appears available and accessible in the query environment, but it resides on its original storage tier until accessed. When a query or AI pipeline requires the full file content, Komprise dynamically loads only the files that are needed. If full files must be delivered to an AI pipeline, Komprise Intelligent AI Ingest moves only the required files at 2X the speed of standard data transfer tools.
IT teams manage the full pipeline from a single interface. Data teams query and use the results in their lakehouse environment. The two workflows connect without requiring a shared infrastructure build.
How Transparent File Tables fit into the AI data pipeline
Transparent File Tables are the query and delivery layer in a broader AI data pipeline that Komprise manages end to end. The pipeline starts with visibility: Deep Analytics indexes all file and object data into the Global Metadatabase and makes it queryable by file system metadata, custom tags, and enriched attributes without opening file content. Smart Data Workflows apply PII and PHI detection across file stores so sensitive data is identified and governed before reaching any AI system. KAPPA data services enrich files with industry-specific metadata, such as DICOM imaging attributes, ELN project codes, or instrument metadata, at petabyte scale using custom Python functions.
Transparent File Tables take that enriched, governed metadata layer and expose it where data and AI teams already work. A data analyst at a pharmaceutical, life sciences, or genomics organization can query a Transparent File Table for all project files generated by a specific instrument across every storage system in the environment, join that result with structured operational data, and build a dashboard or AI training dataset, all within their existing lakehouse tools. The files they need are delivered on demand. The files they do not need never move.
This is what AI-ready data looks like for unstructured content at enterprise scale: not a one-time migration, but a continuously updated, governed, queryable layer that connects dormant file stores to the AI programs that need them.
Transparent File Tables FAQs
FAQs
Why can’t data lakehouses access unstructured data today?
Unstructured data makes up 80% of the enterprise data footprint, but less than 1% of it has ever been analyzed by a data lakehouse or AI system. The barriers are structural: unstructured data lacks consistent schema, accumulates quality and ROT problems over time, and exists at volumes that make bulk movement impractical. Data lakehouses were built for structured and semi-structured data with defined schemas. File data from NAS systems, research repositories, imaging archives, and object stores has never had a native path into those environments. Transparent File Tables are that path.
How does Komprise Transparent File Tables use Apache Iceberg?
Apache Iceberg is the open table format adopted across major lakehouse platforms as the standard for large analytic tables. Komprise exports the Global Metadatabase schema as an Apache Iceberg table, making enriched unstructured file metadata queryable through any Iceberg-compatible analytics tool without requiring direct access to Komprise. Data teams use the SQL interfaces, BI tools, and AI frameworks they already work in. Iceberg’s open format also means Transparent File Tables are not tied to a single lakehouse vendor or query engine.
How does the Global Metadatabase make Transparent File Tables possible?
Komprise indexes all file and object data across hybrid storage environments into the Global Metadatabase, capturing file system metadata alongside custom attributes enriched by KAPPA data services, such as DICOM imaging fields, ELN project codes, and instrument metadata, and sensitive data labels applied by Smart Data Workflows. That schema gives unstructured data the structure and context it needs to be queryable as a table. Transparent File Tables expose this enriched schema directly in the lakehouse, so data teams are not just seeing raw file listings. They are querying a governed, contextually rich metadata layer they can filter, join, and act on.
What can data and AI teams do once Transparent File Tables are in their lakehouse?
Data and AI teams can query unstructured file data alongside any other table in their lakehouse. They can filter by metadata attributes to identify the exact files relevant to a specific AI or analytics use case, join file records with structured business data, and trigger targeted ingestion of only the files they need. If the full file content is required for AI, Komprise Intelligent AI Ingest delivers only those files at 2X the speed of standard data transfer tools. IT and data teams work from the same view of the organization’s full data estate without requiring a migration project.
Which industries benefit most from Transparent File Tables?
Transparent File Tables has seen strong interest and adoption in pharmaceutical, life sciences, and genomics organizations that need to join research file data with structured clinical and operational datasets for AI model development. Healthcare organizations managing large DICOM imaging archives can make imaging metadata queryable in their analytics environment without migrating petabytes of files. Media and entertainment companies can expose production file metadata for analytics alongside structured asset management data. Any enterprise with large unstructured file stores that data and AI teams cannot currently reach from their lakehouse is a candidate.