Google Ads + Bruin

Source

Ingest Google Ads 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

Cross-channel ad reporting
See Google Ads spend alongside Google Ads, Facebook, and every other channel, in one place, updated automatically.
True ROAS, not estimated
Join Google Ads spend with actual revenue from Stripe or your CRM. Know your real return on ad spend, not what the ad platform tells you.
No more manual exports
Stop downloading CSVs from Google Ads. Stakeholders get fresh data every morning without asking anyone.
Catch budget anomalies early
Quality checks flag unexpected spend spikes or zero-impression campaigns before they burn budget.

For data & engineering teams

How it works

Incremental sync with lookback
Bruin handles Google Ads's attribution windows automatically. Set lookback days in the connection URI, no custom logic needed.
YAML-defined, Git-versioned
Your Google Ads pipeline is a YAML file. Review in PRs, deploy with CI/CD, roll back with git revert.
Column-level quality checks
Validate spend, impressions, and clicks with not_null, unique, and custom SQL checks. Pipeline stops on failure.
Multi-destination support
Land Google Ads data in BigQuery, Snowflake, Redshift, or DuckDB. Switch destinations by changing one line.

Before you start

Google Ads API access

Developer token

OAuth2 setup

Step 1

Add your Google Ads connection

Connect using Google Ads API credentials with OAuth2. Add this to your Bruin environment file, credentials are stored securely and referenced by name in your pipeline YAML.

Parameters

client_idGoogle Ads API client ID
client_secretGoogle Ads API client secret
refresh_tokenOAuth2 refresh token
customer_idGoogle Ads customer ID (without dashes)
developer_tokenGoogle Ads API developer token

connections:
  googleads:
    type: googleads
    uri: "google-ads://?client_id=<client_id>&client_secret=<client_secret>&refresh_token=<refresh_token>&customer_id=<customer_id>&developer_token=<developer_token>"

Step 2

Create your pipeline

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

Available tables

campaignsad_groupsadskeywordsperformance_reports

name: raw.googleads_campaigns
type: ingestr

parameters:
  source_connection: googleads
  source_table: 'campaigns'
  destination: bigquery

Step 3

Add quality checks

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

Catch negative ad spend before it reaches reports

Validate impressions >= clicks on every sync

Flag campaigns with missing IDs or null spend

columns:
  - name: campaign_id
    checks:
      - name: not_null
  - name: spend
    checks:
      - name: not_null
  - name: impressions
    checks:
      - name: not_null

custom_checks:
  - name: no negative ad spend
    query: |
      SELECT COUNT(*) = 0
      FROM raw.googleads_campaigns
      WHERE spend < 0

Step 4

Run it

One command. Bruin connects to Google Ads, 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 Google Ads to your dashboards

$ bruin run .

Running pipeline...

  googleads_campaigns
    ✓ 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 Ad Platform integrations

Adjust

AdMob

AdRoll

Amazon Ads

AppLovin

AppLovin Max

Ready to connect Google Ads?

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

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