Data pipelines from 75+ sources, set up in minutes.
Point Databasin at a source, choose how the data should land, and the pipeline builds itself: schema mapped, schedule set, changes tracked, arriving as open Apache Iceberg tables.
The people who feel it most never see this page. They just stop opening a dashboard that’s three days stale. And when something does break, you find out before the person who opens the dashboard does.
ETL or ELT? Both.
Data lands in the lakehouse first and is shaped there, which is ELT in the usual sense. Heavy transformation runs on Databasin Sail, our Spark-compatible engine, against the same tables, and SQL, dbt, PySpark and notebooks all run as scheduled tasks.
Each transform runs wherever it’s cheapest and simplest to run. We set it up this way so your team can start landing tables without settling an architecture debate first.
Query it before you commit to it.
Supported sources can be queried the moment they connect, straight against the source, with no sync and no copy. It’s usually the fastest way to find out whether the data is worth keeping before you put it on a schedule.
Five ways data can land.
Set per table rather than per pipeline, so one pipeline can refresh a small reference table in full while loading a large fact table incrementally.
- SnapshotA full refresh every run. Simple, and right for small or reference tables.
- DeltaAn incremental upsert against a watermark, so only what changed moves.
- HistoricalAppend only. Nothing is overwritten, so the history stays queryable.
- CDCChange data capture, for sources that can tell you what changed.
- Stored procedureThe source defines its own extract and Databasin runs it.
The parts that usually eat a sprint, already done.
75+ connectors, about 30 of them native. For the native ones you don’t configure routes or write schemas. When a vendor changes their API we change the connector on our side, and your pipeline keeps running.
- PaginationIncluding the awkward dependent kind, where one call decides the next.
- WatermarksIncremental loads, so a re-run doesn’t re-pull the year.
- Schema driftWhen a vendor adds a field without telling anyone.
- Delete detectionRows removed at the source stop being reported as current.
When a pipeline breaks at 3am.
Run history, row counts and failure alerts to Slack, Teams or email. A failed run can explain itself, so the on-call person reads a sentence instead of a stack trace.
Connect one source and watch it land.
Chris Lundeberg, one of our co-founders, runs the technical demos himself. Bring your hardest data problem; he’d rather see that than a slide.
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