Revenue · Source connector
A4G ELT Connector
Extract brings A4G 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 A4G revenue with finance and internal performance records to investigate discrepancies.
Yield and inventory analysis
Use A4G data to monitor yield, inventory, and monetization performance over time.
Performance anomaly detection
Refresh A4G performance views on a flexible schedule to identify unexpected revenue changes.
Flexible reporting cadence
Refresh A4G reporting every 15 minutes, hourly, daily, or on a custom schedule as business needs change.
Warehouse and database analysis
Use Extract to make A4G 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 |
|---|---|---|
publisher_report | Fields include ad_slot_id, adx_impressions, adx_responses_served, affiliate_id. | Incremental |
Schema
Data model at a glance
publisher_report
ad_slot_idstringadx_impressionsintegeradx_responses_servedintegeraffiliate_idnumberbundleidstringclicksintegercountrystringdatestring
No verified relationships are available in this schema preview.
Connector pairings
Popular destinations
Commonly paired with A4G in Extract workflows.
Why Extract
Why Extract for A4G
- Rust-native performanceUse Rust-native data movement to refresh A4G reporting every 15 minutes, hourly, daily, or on a custom schedule as volume grows.
- Full run transparencyInspect A4G API activity, execution steps, and timing in Extract's run-level logs when data needs review.
- ELT + Reverse ETL in oneCombine A4G 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 A4G 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 A4G data with Extract?
Historical coverage for A4G: 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 A4G data quality and freshness?
Compare expected reporting periods and record totals in your models, then use Extract's run-level logs to inspect A4G 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 A4G?
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 A4G data model look like in the destination?
The Available streams table describes the datasets Extract can read from A4G. The inline schema previews entities and fields. Only relationships verified from the exported schema are shown. Use the full schema link for more details.