Metabase + Bruin

Source

Ingest Metabase data into your warehouse with incremental loading, quality checks, and full lineage. Defined in YAML, version-controlled in Git.

For business teams

What you get

Dashboards powered by clean data
Feed Metabase from Bruin pipelines with built-in quality checks. No more stale or incorrect dashboards.
End-to-end lineage to ${pn}
Trace data from raw sources through every transformation to Metabase. Find the root cause of wrong numbers in seconds.
Scheduled data refresh
Bruin pipelines refresh Metabase data on a cron. Dependencies resolve automatically, Metabase only sees complete data.
Multi-source, one destination
Combine 100+ sources into clean models that power Metabase. Bruin handles the pipeline, Metabase handles the visuals.

For data & engineering teams

How it works

Dependency-aware scheduling
Bruin ensures upstream transforms complete before Metabase gets new data. No more stale or partial dashboards.
YAML-defined, Git-versioned
Every pipeline feeding Metabase is a YAML file. Review in PRs, deploy with CI/CD, roll back with git revert.
Quality gates before visualization
Quality checks run before data reaches Metabase. If checks fail, Metabase keeps showing the last known good data.
End-to-end lineage
Trace data from raw sources through transforms to Metabase dashboards. Find root causes in seconds, not hours.

Before you start

Metabase instance (Cloud or self-hosted)

Admin account credentials

API access enabled

Step 1

Add your Metabase connection

Connect using Metabase API credentials. Add this to your Bruin environment file, credentials are stored securely and referenced by name in your pipeline YAML.

Parameters

usernameMetabase admin email
passwordMetabase admin password
hostMetabase instance URL

connections:
  metabase:
    type: metabase
    uri: "metabase://username:[email protected]"

Step 2

Create your pipeline

Define a YAML asset that tells Bruin what to pull from Metabase and where to land it. This file lives in your Git repo, reviewable, version-controlled, and deployable with CI/CD.

Available tables

questionsdashboardscollectionsdatabasescardsactivity

name: raw.metabase_questions
type: ingestr

parameters:
  source_connection: metabase
  source_table: 'questions'
  destination: bigquery

Step 3

Add quality checks

Add column-level and custom SQL checks to your Metabase data. If a check fails, the pipeline stops, bad data never reaches downstream models or dashboards.

Validate data freshness before it reaches dashboards

Ensure IDs are unique across syncs

Block stale data from appearing in visualizations

columns:
  - name: id
    checks:
      - name: not_null
      - name: unique

custom_checks:
  - name: data is fresh
    query: |
      SELECT MAX(updated_at) >
        TIMESTAMP_SUB(CURRENT_TIMESTAMP(), INTERVAL 24 HOUR)
      FROM raw.metabase_questions

Step 4

Run it

One command. Bruin connects to Metabase, pulls data incrementally, runs your quality checks, and lands clean data in your warehouse. If a check fails, the pipeline stops, bad data never reaches downstream.

Backfill historical data with --start-date

Schedule with cron or trigger from CI/CD

Full lineage from Metabase to your dashboards

$ bruin run .

Running pipeline...

  metabase_questions
    ✓ Fetched 2,847 new records
    ✓ Quality: campaign_id not_null     PASSED
    ✓ Quality: spend not_null           PASSED
    ✓ Quality: no negative ad spend     PASSED
    ✓ Loaded into bigquery

  Completed in 12s

Other Data Analytics Platform integrations

Looker

Power BI

Tableau

Ready to connect Metabase?

Start for free, or book a demo to see how Bruin handles ingestion, quality, lineage, and scheduling for your entire data stack.

Book a demo Read the docs