Outbrain + Bruin

Source

Ingest Outbrain 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 Outbrain spend alongside Google Ads, Facebook, and every other channel — in one place, updated automatically.
True ROAS, not estimated
Join Outbrain 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 Outbrain. 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 Outbrain's attribution windows automatically. Set lookback days in the connection URI — no custom logic needed.
YAML-defined, Git-versioned
Your Outbrain 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 Outbrain data in BigQuery, Snowflake, Redshift, or DuckDB. Switch destinations by changing one line.

Before you start

Outbrain Amplify API access token from account manager

Step 1

Add your Outbrain connection

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

Parameters

tokenOutbrain Amplify API authentication token
account_idYour Outbrain marketer account ID

connections:
  outbrain:
    type: outbrain
    uri: "outbrain://?token=<your-api-token>&account_id=<your-account-id>"

Step 2

Create your pipeline

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

Available tables

campaignssectionspromoted_linksreportsbudgets

name: raw.outbrain_campaigns
type: ingestr

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

Step 3

Add quality checks

Add column-level and custom SQL checks to your Outbrain 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.outbrain_campaigns
      WHERE spend < 0

Step 4

Run it

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

$ bruin run .

Running pipeline...

  outbrain_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

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Ready to connect Outbrain?

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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