Looker + Bruin

Source

Ingest Looker 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 Looker 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 Looker. Find the root cause of wrong numbers in seconds.
Scheduled data refresh
Bruin pipelines refresh Looker data on a cron. Dependencies resolve automatically — Looker only sees complete data.
Multi-source, one destination
Combine 100+ sources into clean models that power Looker. Bruin handles the pipeline, Looker handles the visuals.

For data & engineering teams

How it works

Dependency-aware scheduling
Bruin ensures upstream transforms complete before Looker gets new data. No more stale or partial dashboards.
YAML-defined, Git-versioned
Every pipeline feeding Looker 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 Looker. If checks fail, Looker keeps showing the last known good data.
End-to-end lineage
Trace data from raw sources through transforms to Looker dashboards. Find root causes in seconds, not hours.

Before you start

Looker instance with API access

API3 credentials from Admin panel

Viewer or Admin role

Step 1

Add your Looker connection

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

Parameters

client_idLooker API3 client ID
client_secretLooker API3 client secret
instanceLooker instance hostname

connections:
  looker:
    type: looker
    uri: "looker://client_id:[email protected]"

Step 2

Create your pipeline

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

Available tables

looksdashboardsexploresmodelsusersqueries

name: raw.looker_looks
type: ingestr

parameters:
  source_connection: looker
  source_table: 'looks'
  destination: bigquery

Step 3

Add quality checks

Add column-level and custom SQL checks to your Looker 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.looker_looks

Step 4

Run it

One command. Bruin connects to Looker, 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 Looker to your dashboards

$ bruin run .

Running pipeline...

  looker_looks
    ✓ 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

Metabase

Power BI

Tableau

Ready to connect Looker?

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