dbt Core runs inside Automations

If your transformations live in dbt Core, they now run as a first-class task inside Databasin Automations. No separate runner, no second scheduler.

How it works

• Point at your repo and the task clones your dbt project from git
• Profiles, generated: connection profiles are built for you against your lakehouse engines, with no credentials pasted into YAML
• Run and report — dbt run executes, and per-model status flows back into the automation monitor, so a failed model reads like any other failed task

Why it matters

Your dbt models join the same stages as everything else: refresh pipelines in stage one, run dbt in stage two, let an AI agent summarize what changed in stage three, deliver to Slack in stage four. One canvas, one schedule, one run history. Retries and versioned snapshots cover the whole automation.

Keep your dbt project exactly as it is. Just stop babysitting the thing that runs it.