DATA LAKES AND LAKEHOUSES

Send the lakehouse only the files that belong there.

Your lakehouse queries rows, while most of your data lives in files. Diskover indexes the lake and every other storage system in place, adds business context, and delivers a curated set of file metadata as queryable rows to the Dell Data Lakehouse, Snowflake, or the platform you run. The files stay on the storage you already own.

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WORKS WITH YOUR LAKEHOUSE • DELL DATA LAKEHOUSE • SNOWFLAKE

Unstructured data in the lakehouse: files stay on storage while Diskover delivers one row per file, with owner, project, age, and tags

THE PROBLEM

Most of your data never reaches the lakehouse.

Gartner puts unstructured data at 70% to 90% of enterprise data. It sits in files on network storage, object storage, and clouds, outside the tables your analysts and AI tools query. Copying all of it into the lake brings duplicates, stale versions, and restricted files along with the useful ones.

Diskover describes every file first, then sends the lakehouse a curated set of rows that it can query, join, and trust.

70–90%

of enterprise data is unstructured

Gartner, 2026

BENEFITS

Why it matters.

Your lakehouse already holds your business data. Diskover adds your files: a clean table of the ones that matter, ready to join with sales, project, or research data for analytics and AI.

Files become queryable.

File metadata arrives as rows, so analysts use the SQL and BI tools they already know.

Lower storage and compute costs.

Files are never copied into the lakehouse, and only the curated set flows, so you pay for the rows that matter.

Traceable AI datasets.

Each row keeps its path and tags, so teams can always see which files fed a model, report or project.

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WHAT GETS DELIVERED

What the lakehouse receives.

Each file becomes one row, with the context your queries need. The file content stays on your storage until a workflow or a query calls for it. Analysts join file metadata with the tables they already have, such as projects, cost centers, or sales, in the tools they already use.

Core fields.

name, path, type, size, owner, group, and created, modified, and accessed dates, and more.

Business context.

project, client, department, workflow stage, and the details plugins read from file headers.

Decisions.

tags such as approved, archive candidate, or retention class, applied manually or automatically by rules.
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DELIVERY

Three ways to deliver.

Three ways Diskover delivers unstructured data to the lakehouse: the Snowflake app, Parquet files, and the REST API and Python SDK

Snowflake app.

Install the Diskover app from the Snowflake Marketplace, connect your storage, and define the rules for what flows. Curated metadata arrives as rows in your Snowflake account, and you can pay with Snowflake credits.
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Parquet files.

Diskover writes file metadata as Parquet files to a folder or an S3 bucket, ready for the Dell Data Lakehouse and any tool that reads Parquet. You choose which fields to include, and scheduled reports can also write a curated set as Parquet after each scan. The Dell Data Lakehouse can also query the Diskover catalog directly, with no export.
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REST API and Python SDK.

Query the catalog from your own scripts and pipelines, then send the results to BI tools, AI pipelines, or any system that needs them.
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THE DATA LAKE

Keep the lake itself in shape.

A data lake is storage too, and it fills with the same duplicates, abandoned runs, and stale extracts as any file share. Diskover indexes the lake in place with your other storage, so the same reports, tags, and policies clean it up.

Find duplicates and stale data

in the lake and across every other system.
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Tier what is cold

to lower-cost object storage, and keep it searchable.
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Retire what is expired by policy

with a dry run first.

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BETTER TOGETHER

Partners.

Dell Technologies, a technological partner of Diskover Data

Dell Data Lakehouse

Diskover indexes PowerScale and the lakehouse in place, and organizes file metadata before it reaches the lakehouse, so analytics and AI pipelines work from curated datasets.

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Snowflake logo, a technology partner of Diskover Data

Snowflake

Diskover delivers curated file metadata as queryable rows, ready for analytics, dashboards, and Snowflake Cortex.

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FAQ

Questions about unstructured data in the lakehouse.

Diskover sends metadata about your files as rows, not the files. The file content stays on your storage until a workflow or a query calls for it.
The Dell Data Lakehouse and Snowflake through partner integrations, and any platform that reads Parquet files or calls a REST API, including cloud analytics services.
Define the set with a catalog query on any metadata field, including tags, for example, approved files from one project in the last year. The Snowflake app and scheduled reports deliver only that set, and the delivery follows the rules as files change. A full Parquet export sends every scanned file, with the fields you choose.
Delivery runs after each scan or on the schedule you set, so the rows follow your storage as it changes.
A data lake stores raw files. A data lakehouse adds tables and queries on top of that storage. Diskover helps with both: it keeps the lake clean and sends the lakehouse curated rows.
Files like images, videos, and documents don’t fit in tables, so you bring their metadata instead. Diskover scans your storage, adds business context and tags, and delivers the files you choose as rows with their metadata, such as path, owner, project, and tags. The files stay where they are. Your lakehouse gets a clean table it can query and join with other business data. Learn the difference between a data lake and a lakehouse.

See it on your own data.

We’ll index a sample of your environment and show you
what’s recoverable against your current storage spend.

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