Marketing · Source connector
AppLovin ELT Connector
Extract brings AppLovin marketing 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 campaign, creative, placement, geo, device, spend, and revenue detail to find opportunities to improve performance.
Direct-network reporting
Compare granular AppLovin data with standard MMP and cost-aggregation reporting to work with dimensions those tools do not make available.
Spend and revenue reconciliation
Reconcile AppLovin spend and revenue with finance, attribution, and internal performance data to investigate discrepancies.
Flexible performance monitoring
Refresh AppLovin pacing, anomaly, and performance views every 15 minutes, hourly, daily, or on a custom schedule.
Warehouse and database analysis
Use Extract to make AppLovin marketing data available in data warehouses and databases such as Snowflake, BigQuery, Redshift, and PostgreSQL for governed analysis.
Reporting-to-management workflow
Use AppLovin reporting data to identify campaign changes, then continue campaign-management work in the separate AppLovin Management connector.
Explore AppLovin ManagementConnector data
Available streams
| Stream | Description | Sync mode |
|---|---|---|
advertiser_report | Fields include ad, ad_id, ad_type, app_id_external. | Incremental |
asset_report | Fields include asset_id, asset_name, asset_url, campaign. | Incremental |
playable_metrics | Fields include asset_id, average_duration, black_view_error, black_view_error_rate. | Incremental |
Schema
Data model at a glance
advertiser_report
adstringad_idintegerad_typestringapp_id_externalstringcampaignstringcampaign_ad_typestringcampaign_id_externalstringcampaign_package_namestring
asset_report
asset_idintegerasset_namestringasset_urlstringcampaignstringcampaign_idstringcampaign_package_namestringclicksintegercostnumber
playable_metrics
asset_idintegeraverage_durationnumberblack_view_errorintegerblack_view_error_ratenumberchallenge_failedintegerchallenge_failed_ratenumberchallenge_pass_25integerchallenge_pass_25_ratenumber
No verified relationships are available in this schema preview.
Connector pairings
Popular destinations
Commonly paired with AppLovin in Extract workflows.
Why Extract
Why Extract for AppLovin
- Rust-native performanceUse Rust-native data movement to refresh AppLovin reporting every 15 minutes, hourly, daily, or on a custom schedule as volume grows.
- Full run transparencyInspect AppLovin API activity, execution steps, and timing in Extract's run-level logs when data needs review.
- ELT + Reverse ETL in oneCombine AppLovin 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 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 data with Extract?
Historical coverage for AppLovin: 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 data quality and freshness?
Compare expected reporting periods and record totals in your models, then use Extract's run-level logs to inspect AppLovin 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?
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 data model look like in the destination?
The Available streams table describes the datasets Extract can read from AppLovin. The inline schema previews entities and fields. Only relationships verified from the exported schema are shown. Use the full schema link for more details.