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