AppLovin + Bruin
Ingest AppLovin 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 spend alongside Google Ads, Facebook, and every other channel — in one place, updated automatically.
True ROAS, not estimated
Join AppLovin 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. 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's attribution windows automatically. Set lookback days in the connection URI — no custom logic needed.
YAML-defined, Git-versioned
Your AppLovin 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 data in BigQuery, Snowflake, Redshift, or DuckDB. Switch destinations by changing one line.
Before you start
Step 1
Add your AppLovin connection
Connect using report key from AppLovin account. Add this to your Bruin environment file — credentials are stored securely and referenced by name in your pipeline YAML.
Parameters
api_keyReport key generated from your AppLovin account
connections:
applovin:
type: applovin
uri: "applovin://?api_key=<your_api_key>"Step 2
Create your pipeline
Define a YAML asset that tells Bruin what to pull from AppLovin and where to land it. This file lives in your Git repo — reviewable, version-controlled, and deployable with CI/CD.
Available tables
name: raw.applovin_publisher-report
type: ingestr
parameters:
source_connection: applovin
source_table: 'publisher-report'
destination: bigquery
# Syncs campaign spend, impressions, clicks,
# and conversions incrementally.
# Backfill: bruin run --start-date 2024-01-01Step 3
Add quality checks
Validate AppLovin data on every sync. Catch negative spend, impossible click-to-impression ratios, and missing campaign IDs before they reach your reports.
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_publisher-report
WHERE spend < 0
- name: impressions >= clicks
query: |
SELECT COUNT(*) = 0
FROM raw.applovin_publisher-report
WHERE clicks > impressionsStep 4
Run it
One command. Bruin connects to AppLovin, 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...
applovin_publisher-report
✓ 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 AppLovin?
Start for free, or book a demo to see how Bruin handles ingestion, quality, lineage, and scheduling for your entire data stack.

