Welcome to Databasin
A 5-minute tour of what's inside.
Databasin is a single workspace for everything that used to take five different tools: connecting to your data, moving it where it needs to go, running SQL against it at scale, asking questions of it in plain English, and publishing the answers.
This page is a short tour. When you're done, you'll know roughly where each feature lives and which guide to read next.
The four things Databasin does
| Thing | Where it lives | Who uses it |
|---|---|---|
| Connect to sources and destinations | Integrations → Connectors | Everyone who touches data |
| Move & transform data | Integrations → Pipelines & Automations | Data engineers, analysts |
| Query data with SQL at scale | Developer → SQL Editor | Analysts, engineers |
| Ask questions in plain English | Databasin One | Everyone else |
A couple of things worth knowing up front: connectors, pipelines, and automations all live together under the Integrations hub. You write SQL in the SQL Editor (under Developer), while Lakehouse is where you manage the compute clusters and file shares behind it.
Most days, you'll bounce between two or three of these. That's the point — it all runs on the same underlying data, so there's no exporting, syncing, or CSV hand-offs between tools.
How a typical first week looks
Here's a shape most teams end up following. Follow it as a checklist or skip to whichever bit matters most.
- Day 1. Connect to one source — your prod database, a SaaS API, or a bucket. Build your first connector
- Day 2. Pipe that data into your Lakehouse with a scheduled sync. Build your first pipeline
- Day 3. Open Databasin One and ask a question about it. Meet Databasin One
- Day 4. Turn one of the charts into a tile, then publish a dashboard to your team. Use the Canvas
- Day 5. Invite a teammate, set permissions, and watch usage against credits. Set up users
If you're brand new, resist the urge to jump ahead to Databasin One. It's the most fun part, but it works best once there's real data to talk about.
The two things that catch people off guard
Everything is project-scoped.
Connectors, pipelines, automations, dashboards — they all belong to a project. A project is a workspace with its own data and its own members. Billing and clusters live one level up at the organization, so multiple projects in the same org share compute and credits.
Compute costs money.
Lakehouse clusters and AI calls both use your credits. Clusters sleep when idle and wake on demand. Databasin One queries are budgeted per conversation. You don't have to manage any of this actively — but if you see a "Credits exhausted" screen, that's what it means. More on this in Credits and billing.
Where to go next
The category menu on the left is always one click away. Press / to search.