June 24, 2026 Automations

dbt Core runs inside Automations

Point a task at your dbt Core repo and Databasin does the rest: clone, generate profiles, run, and report per-model status back into the automation monitor — your existing dbt project, scheduled next to SQL, notebooks, and AI agents with no separate runner.

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 — the task clones your dbt project from git
Profiles, generated — connection profiles are built for you against your lakehouse engines; 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 — with retries and versioned snapshots of the whole automation.

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