AppLovin Max + Bruin

Source

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

Before you start

Report key from AppLovin Max dashboard

Step 1

Add your AppLovin Max connection

Connect using report key with application IDs. Add this to your Bruin environment file, credentials are stored securely and referenced by name in your pipeline YAML.

Parameters

api_keyReport key for authenticating the request

connections:
  applovin_max:
    type: applovin_max
    uri: "applovinmax://?api_key=<your_api_key>"

Step 2

Create your pipeline

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

Available tables

user_ad_revenue:<application_ids>

name: raw.applovin_max_user_ad_revenue:<application_ids>
type: ingestr

parameters:
  source_connection: applovin_max
  source_table: 'user_ad_revenue:<application_ids>'
  destination: bigquery

# Syncs campaign spend, impressions, clicks,
# and conversions incrementally.
# Backfill: bruin run --start-date 2024-01-01

Step 3

Add quality checks

Validate AppLovin Max data on every sync. Catch negative spend, impossible click-to-impression ratios, and missing campaign IDs before they reach your reports.

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.applovin_max_user_ad_revenue:<application_ids>
      WHERE spend < 0
  - name: impressions >= clicks
    query: |
      SELECT COUNT(*) = 0
      FROM raw.applovin_max_user_ad_revenue:<application_ids>
      WHERE clicks > impressions

Step 4

Run it

One command. Bruin connects to AppLovin Max, 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 AppLovin Max to your dashboards

$ bruin run .

Running pipeline...

  applovin_max_user_ad_revenue:<application_ids>
    ✓ 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

AppsFlyer

Ready to connect AppLovin Max?

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