Nine ways in, one catalogue
Every connector produces the same thing: a profiled dataset in the catalogue that behaves identically from that point on. Which source it came from stops mattering the moment the rows land.
All connectors
CSV & Excel upload
Drag a file in and start working. Profiled on arrival, no schema declared up front.
Database
Read from your existing relational database and pull tables in as datasets.
REST API
Point at an endpoint, map the response shape, and land it as rows.
FTP & SFTP
Collect the drops that still arrive as files on a server.
CDC sync
Change data capture, so the dataset follows the source instead of ageing.
CRM
Bring customer records across without exporting them to a spreadsheet first.
Data lake
Read from — and write back to — object storage you already own.
Documents
PDFs and documents stored whole, for retrieval and generation rather than rows.
AI model providers
Your own model credentials, held as a connection like any other source.
What every connector has in common
A connection holds its own credentials, its environment and its status, and is tested before it is saved. Credentials are held by the platform rather than pasted into a pipeline definition, so rotating one does not mean editing every flow that uses it.
Once connected, the source lands rows, those rows are profiled, and the dataset joins the catalogue with its lineage already recorded. Nothing downstream — transforms, models, flows, products — needs to know which connector produced it.
See it on your own data
DataLens is in private beta. Bring a file, a database or an API and work through the whole lifecycle in one sitting.
Request beta access