Facebook Ads + Bruin
Ingest Facebook 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 Facebook Ads spend alongside Google Ads, Facebook, and every other channel — in one place, updated automatically.
True ROAS, not estimated
Join Facebook 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 Facebook 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 Facebook Ads's attribution windows automatically. Set lookback days in the connection URI — no custom logic needed.
YAML-defined, Git-versioned
Your Facebook 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 Facebook Ads data in BigQuery, Snowflake, Redshift, or DuckDB. Switch destinations by changing one line.
Before you start
Step 1
Add your Facebook Ads connection
Connect using Facebook Marketing API access token. Add this to your Bruin environment file — credentials are stored securely and referenced by name in your pipeline YAML.
Parameters
access_tokenFacebook Marketing API access tokenaccount_idsComma-separated list of Facebook Ad Account IDs
connections:
facebookads:
type: facebookads
uri: "facebook-ads://?access_token=<access_token>&account_ids=<account_ids>"Step 2
Create your pipeline
Define a YAML asset that tells Bruin what to pull from Facebook Ads and where to land it. This file lives in your Git repo — reviewable, version-controlled, and deployable with CI/CD.
Available tables
name: raw.facebookads_campaigns
type: ingestr
parameters:
source_connection: facebookads
source_table: 'campaigns'
destination: bigqueryStep 3
Add quality checks
Add column-level and custom SQL checks to your Facebook Ads data. If a check fails, the pipeline stops — bad data never reaches downstream models or dashboards.
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.facebookads_campaigns
WHERE spend < 0Step 4
Run it
One command. Bruin connects to Facebook 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.
--start-date$ bruin run .Running pipeline...
facebookads_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 12sOther Ad Platform integrations
Ready to connect Facebook Ads?
Start for free, or book a demo to see how Bruin handles ingestion, quality, lineage, and scheduling for your entire data stack.

