Use cases

The same platform, described by the job

Teams rarely arrive looking for “a data platform”. They arrive with a problem. These are the four we hear most often.

Four common starting points

Find what is wrong before it reaches a report.

Let the people with the questions answer them.

Get data into a shape a model can be trusted with.

Move from an old system to a new one without losing the meaning.

If you already know which part of the lifecycle is hurting.

Land rows from files, databases, APIs, CRMs, lakes and document stores.

Fix nulls, duplicates, types and names — with the AI proposing the fixes.

Reverse-engineer a source model, design a target, and map between them.

Build the flow on a canvas, run it, version it, promote it.

Charts, pivots and scenario simulation over the data you just prepared.

Publish governed data products and access-controlled shares.

Profiling, quality rules, lineage, PII detection and AI cost observability.

DataLens is in private beta. Bring a file, a database or an API and work through the whole lifecycle in one sitting.