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