Marketing · Source connector
AppLovin Management ELT Connector
Extract brings AppLovin Management 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 Management campaign, creative, placement, geo, device, spend, and revenue detail to find opportunities to improve performance.
Direct-network reporting
Compare granular AppLovin Management data with standard MMP and cost-aggregation reporting to work with dimensions those tools do not make available.
Spend and revenue reconciliation
Reconcile AppLovin Management spend and revenue with finance, attribution, and internal performance data to investigate discrepancies.
Flexible performance monitoring
Refresh AppLovin Management pacing, anomaly, and performance views every 15 minutes, hourly, daily, or on a custom schedule.
Warehouse and database analysis
Use Extract to make AppLovin Management marketing data available in data warehouses and databases such as Snowflake, BigQuery, Redshift, and PostgreSQL for governed analysis.
Audience and campaign activation
Combine AppLovin Management 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 |
|---|---|---|
campaign | Fields include id, bidding_strategy, budget, created_at. | Full refresh |
assets | Fields include asset_type, id, name, resource_type. | Full refresh |
campaign_target | Fields include campaign_id, country_code, region_codes. | Full refresh |
creative_set | Fields include assets, campaign_id, countries, hashed_id. | Full refresh |
Schema
Data model at a glance
campaign
idstringbidding_strategystringbudgetjsoncreated_atstringend_datestringgoaljsonhashed_idstringis_composite_banner_enabledboolean
assets
asset_typestringidstringnamestringresource_typestringstatusstringupload_timestringurlstring
campaign_target
campaign_idstringcountry_codestringregion_codesarray
creative_set
assetsarraycampaign_idstringcountriesarrayhashed_idstringidstringlanguagesarraynamestringproduct_pagestring
No verified relationships are available in this schema preview.
Connector pairings
Popular destinations
Commonly paired with AppLovin Management in Extract workflows.
Why Extract
Why Extract for AppLovin Management
- Rust-native performanceUse Rust-native data movement to refresh AppLovin Management reporting every 15 minutes, hourly, daily, or on a custom schedule as volume grows.
- Full run transparencyInspect AppLovin Management API activity, execution steps, and timing in Extract's run-level logs when data needs review.
- ELT + Reverse ETL in oneCombine AppLovin Management 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 Management 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 full refresh. Behavior varies by stream and configuration. Extract records run details so teams can verify each sync.
Can I backfill historical AppLovin Management data with Extract?
Historical coverage for AppLovin Management: No connector-specific backfill parameter is exposed. Initial historical coverage depends on the stream and provider. Use the setup guide and run details to confirm the available source records and selected settings.
How can teams monitor AppLovin Management data quality and freshness?
Compare expected reporting periods and record totals in your models, then use Extract's run-level logs to inspect AppLovin Management 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 Management?
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 Management data model look like in the destination?
The Available streams table describes the datasets Extract can read from AppLovin Management. The inline schema previews entities and fields. Only relationships verified from the exported schema are shown. Use the full schema link for more details.