Marketing · Source connector
ironSource ELT Connector
Extract brings ironSource 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 ironSource campaign, creative, placement, geo, device, spend, and revenue detail to find opportunities to improve performance.
Direct-network reporting
Compare granular ironSource data with standard MMP and cost-aggregation reporting to work with dimensions those tools do not make available.
Spend and revenue reconciliation
Reconcile ironSource spend and revenue with finance, attribution, and internal performance data to investigate discrepancies.
Flexible performance monitoring
Refresh ironSource pacing, anomaly, and performance views every 15 minutes, hourly, daily, or on a custom schedule.
Warehouse and database analysis
Use Extract to make ironSource marketing data available in data warehouses and databases such as Snowflake, BigQuery, Redshift, and PostgreSQL for governed analysis.
Audience and campaign activation
Combine ironSource 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 |
|---|---|---|
reports | Fields include ad_unit_name, application_id, application_name, campaign_id. | Incremental |
titles | Fields include id, bundle_id, name, os. | Full refresh |
creative_assets | Fields include creative_id, id, title_id, usage_type. | Incremental |
assets | Fields include id, duration, name, orientation. | Full refresh |
creatives | Fields include id, language, name, orientation. | Full refresh |
Schema
Data model at a glance
reports
ad_unit_namestringapplication_idintegerapplication_namestringcampaign_idintegercampaign_namestringclicksintegercompletionsintegercountrystring
titles
idintegerbundle_idstringnamestringosstring
creative_assets
creative_idintegeridintegertitle_idintegerusage_typestring
assets
idintegerdurationintegernamestringorientationstringsourcestringstatusstringtitle_idintegertypestring
creatives
idintegerlanguagestringnamestringorientationstringtitle_idintegertypestring
No verified relationships are available in this schema preview.
Connector pairings
Popular destinations
Commonly paired with ironSource in Extract workflows.
Why Extract
Why Extract for ironSource
- Rust-native performanceUse Rust-native data movement to refresh ironSource reporting every 15 minutes, hourly, daily, or on a custom schedule as volume grows.
- Full run transparencyInspect ironSource API activity, execution steps, and timing in Extract's run-level logs when data needs review.
- ELT + Reverse ETL in oneCombine ironSource 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 ironSource 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 ironSource data with Extract?
Historical coverage for ironSource: 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 ironSource data quality and freshness?
Compare expected reporting periods and record totals in your models, then use Extract's run-level logs to inspect ironSource 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 ironSource?
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 ironSource data model look like in the destination?
The Available streams table describes the datasets Extract can read from ironSource. The inline schema previews entities and fields. Only relationships verified from the exported schema are shown. Use the full schema link for more details.