Revenue · Source connector
AppLovin MAX ELT Connector
Extract brings AppLovin MAX revenue data into data warehouses and databases for analysis with the rest of your business data. Teams use it to analyze campaign, creative, placement, spend, and revenue performance at a granular level. Flexible scheduling keeps reporting current for routine analysis and time-sensitive decisions.
View setup guide
Use cases
What teams use this connector for
Granular campaign optimization
Analyze AppLovin MAX campaign, creative, placement, geo, device, spend, and revenue detail to find opportunities to improve performance.
Direct-network reporting
Compare granular AppLovin MAX data with standard MMP and cost-aggregation reporting to work with dimensions those tools do not make available.
Spend and revenue reconciliation
Reconcile AppLovin MAX spend and revenue with finance, attribution, and internal performance data to investigate discrepancies.
Flexible performance monitoring
Refresh AppLovin MAX pacing, anomaly, and performance views every 15 minutes, hourly, daily, or on a custom schedule.
Warehouse and database analysis
Use Extract to make AppLovin MAX revenue data available in data warehouses and databases such as Snowflake, BigQuery, Redshift, and PostgreSQL for governed analysis.
Audience and campaign activation
Combine AppLovin MAX performance data with approved customer models, then use Extract Reverse ETL to activate audience or campaign updates downstream.
Connector data
Available streams
| Stream | Description | Sync mode |
|---|---|---|
max_report | Fields include ad_format, application, attempts, country. | Incremental |
profit_analysis_report | Fields include account, ad_format, application, average_cpc. | Incremental |
Schema
Data model at a glance
max_report
ad_formatstringapplicationstringattemptsintegercountrystringdaystringdevice_typestringestimated_revenuenumberimpressionsinteger
profit_analysis_report
accountstringad_formatstringapplicationstringaverage_cpcnumberclicksintegercountrystringdaystringecpmnumber
No verified relationships are available in this schema preview.
Connector pairings
Popular destinations
Commonly paired with AppLovin MAX in Extract workflows.
Why Extract
Why Extract for AppLovin MAX
- Rust-native performanceUse Rust-native data movement to refresh AppLovin MAX reporting every 15 minutes, hourly, daily, or on a custom schedule as volume grows.
- Full run transparencyInspect AppLovin MAX API activity, execution steps, and timing in Extract's run-level logs when data needs review.
- ELT + Reverse ETL in oneCombine AppLovin MAX data with customer and finance models, then send approved outputs through a separately configured Reverse ETL destination.
FAQ
Frequently asked questions
How does Extract handle incremental and full AppLovin MAX syncs?
An incremental sync processes only new or changed data since a stored checkpoint or new date partition, while a full sync processes the complete selected dataset or date range again. By avoiding repeated processing and writes for the full dataset, incremental syncs can reduce destination database updates and warehouse compute costs. Stream schemas define partition-based extraction. Behavior varies by stream and configuration. Extract records run details so teams can verify each sync.
Can I backfill historical AppLovin MAX data with Extract?
Historical coverage for AppLovin MAX: Backfill (Days) (backfill): The backfill setting revisits and updates data from the past X days, ensuring the most current and accurate information replaces any previously pulled data. Use the setup guide and run details to confirm the available source records and selected settings.
How can teams monitor AppLovin MAX data quality and freshness?
Compare expected reporting periods and record totals in your models, then use Extract's run-level logs to inspect AppLovin MAX API activity, execution steps, and timing. This helps teams distinguish a source-data delay from a failed or incomplete run.
How often can Extract sync data from AppLovin MAX?
Extract supports schedules every 15 minutes, hourly, daily, or on a custom cadence. Teams can choose a frequency that matches routine reporting, time-sensitive monitoring, or operational activation needs.
What does the AppLovin MAX data model look like in the destination?
The Available streams table describes the datasets Extract can read from AppLovin MAX. The inline schema previews entities and fields. Only relationships verified from the exported schema are shown. Use the full schema link for more details.