Revenue · Source connector
AppTweak ELT Connector
Extract brings AppTweak revenue data into data warehouses and databases for analysis with the rest of your business data. Teams use it to reconcile revenue, monitor yield, and investigate performance changes. Flexible scheduling keeps reporting current for routine analysis and time-sensitive decisions.
View setup guide
Use cases
What teams use this connector for
Revenue reconciliation
Compare AppTweak revenue with finance and internal performance records to investigate discrepancies.
Yield and inventory analysis
Use AppTweak data to monitor yield, inventory, and monetization performance over time.
Performance anomaly detection
Refresh AppTweak performance views on a flexible schedule to identify unexpected revenue changes.
Flexible reporting cadence
Refresh AppTweak reporting every 15 minutes, hourly, daily, or on a custom schedule as business needs change.
Warehouse and database analysis
Use Extract to make AppTweak revenue data available in data warehouses and databases such as Snowflake, BigQuery, Redshift, and PostgreSQL for governed analysis.
Connector data
Available streams
| Stream | Description | Sync mode |
|---|---|---|
category_ranking | Fields include _singular_extract_timestamp, app, category, category_name. | Incremental |
featured_content | Fields include _singular_extract_timestamp, app, apps, artwork_name. | Incremental |
free_apps_conversion_rate_benchmarks | Fields include _singular_extract_timestamp, category, category_name, country. | Incremental |
keywords_ranking_current | Fields include _singular_extract_timestamp, app, country, device. | Full refresh |
keywords_ranking_history | Fields include _singular_extract_timestamp, app, country, date. | Incremental |
paid_apps_conversion_rate_benchmarks | Fields include _singular_extract_timestamp, category, category_name, country. | Incremental |
reviews_stats | Fields include _singular_extract_timestamp, app, average, count.1. | Incremental |
Schema
Data model at a glance
category_ranking
_singular_extract_timestampstringappstringcategorystringcategory_namestringchart_typestringcountrystringdatestringdevicestring
featured_content
_singular_extract_timestampstringappstringappsarrayartwork_namestringcountrystringdatestringdevicestringheaderstring
free_apps_conversion_rate_benchmarks
_singular_extract_timestampstringcategorystringcategory_namestringcountrystringdevicestringend_datestringimpressions_to_installsnumberimpressions_to_page_viewsnumber
keywords_ranking_current
_singular_extract_timestampstringappstringcountrystringdevicestringinstalls.datestringinstalls.effective_valuenumberinstalls.valuenumberkeywordstring
keywords_ranking_history
_singular_extract_timestampstringappstringcountrystringdatestringdevicestringinstalls.datestringinstalls.effective_valuenumberinstalls.valuenumber
paid_apps_conversion_rate_benchmarks
_singular_extract_timestampstringcategorystringcategory_namestringcountrystringdevicestringend_datestringimpressions_to_installsnumberimpressions_to_page_viewsnumber
No verified relationships are available in this schema preview.
Connector pairings
Popular destinations
Commonly paired with AppTweak in Extract workflows.
Why Extract
Why Extract for AppTweak
- Rust-native performanceUse Rust-native data movement to refresh AppTweak reporting every 15 minutes, hourly, daily, or on a custom schedule as volume grows.
- Full run transparencyInspect AppTweak API activity, execution steps, and timing in Extract's run-level logs when data needs review.
- ELT + Reverse ETL in oneCombine AppTweak 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 AppTweak 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, partition-based extraction. Behavior varies by stream and configuration. Extract records run details so teams can verify each sync.
Can I backfill historical AppTweak data with Extract?
Historical coverage for AppTweak: Backfill (Days) (backfill): The backfill setting revisits and updates data from the past X days, The max backfill for AppTweak is 14. Use the setup guide and run details to confirm the available source records and selected settings.
How can teams monitor AppTweak data quality and freshness?
Compare expected reporting periods and record totals in your models, then use Extract's run-level logs to inspect AppTweak 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 AppTweak?
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 AppTweak data model look like in the destination?
The Available streams table describes the datasets Extract can read from AppTweak. The inline schema previews entities and fields. Only relationships verified from the exported schema are shown. Use the full schema link for more details.