Marketing · Source connector
StackAdapt ELT Connector
Extract brings StackAdapt 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 StackAdapt campaign, creative, placement, geo, device, spend, and revenue detail to find opportunities to improve performance.
Direct-network reporting
Compare granular StackAdapt data with standard MMP and cost-aggregation reporting to work with dimensions those tools do not make available.
Spend and revenue reconciliation
Reconcile StackAdapt spend and revenue with finance, attribution, and internal performance data to investigate discrepancies.
Flexible performance monitoring
Refresh StackAdapt pacing, anomaly, and performance views every 15 minutes, hourly, daily, or on a custom schedule.
Warehouse and database analysis
Use Extract to make StackAdapt marketing data available in data warehouses and databases such as Snowflake, BigQuery, Redshift, and PostgreSQL for governed analysis.
Audience and campaign activation
Combine StackAdapt 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 |
|---|---|---|
campaign_insights_by_categories | Fields include campaign_id, line_item, _singular_extract_timestamp, acomp_0. | Incremental |
native_ads | Fields include id, _singular_extract_timestamp, api_frameworks, audit_status. | Full refresh |
campaign_group_buyer_account_stats | Fields include id, _singular_extract_timestamp, atos, click. | Incremental |
campaign_insights_by_creative_tactics | Fields include campaign_id, line_item, _singular_extract_timestamp, acomp_0. | Incremental |
campaign_group_insights_by_demographics | Fields include campaign_group_id, line_item, _singular_extract_timestamp, acomp_0. | Incremental |
campaign_insights_by_devices | Fields include campaign_id, line_item, _singular_extract_timestamp, acomp_0. | Incremental |
advertisers | Fields include id, _singular_extract_timestamp, all_line_item_ids, description. | Full refresh |
campaign_group_insights_by_creative_tactics | Fields include campaign_group_id, line_item, _singular_extract_timestamp, acomp_0. | Incremental |
campaign_groups | Fields include id, advertiser_id, _singular_extract_timestamp, all_campaign_ids. | Full refresh |
advertiser_buyer_account_stats | Fields include id, _singular_extract_timestamp, atos, click. | Incremental |
advertiser_delivery_group_by_campaign | Fields include campaign_id, id, _singular_extract_timestamp, app_name. | Incremental |
native_ads_buyer_account_stats | Fields include id, _singular_extract_timestamp, atos, click. | Incremental |
campaign_buyer_account_stats | Fields include id, _singular_extract_timestamp, atos, click. | Incremental |
campaign_group_insights_by_devices | Fields include campaign_group_id, line_item, _singular_extract_timestamp, acomp_0. | Incremental |
campaign_group_delivery_group_by_native_ad | Fields include campaign_id, id, native_ad_id, _singular_extract_timestamp. | Incremental |
campaign_group_delivery_group_by_campaign | Fields include campaign_id, id, _singular_extract_timestamp, app_name. | Incremental |
campaigns | Fields include id, advertiser_id, line_item_id, _singular_extract_timestamp. | Full refresh |
campaign_insights_by_demographics | Fields include campaign_id, line_item, _singular_extract_timestamp, acomp_0. | Incremental |
campaign_group_insights_by_categories | Fields include campaign_group_id, _singular_extract_timestamp, acomp_0, acomp_25. | Incremental |
Schema
Data model at a glance
campaign_insights_by_categories
campaign_idintegerline_itemstring_singular_extract_timestampstringacomp_0integeracomp_25integeracomp_50integeracomp_75integeracomp_95integer
native_ads
idinteger_singular_extract_timestampstringapi_frameworksarrayaudit_statusstringbrandnamestringchannelstringclick_urlstringcreated_atstring
campaign_group_buyer_account_stats
idinteger_singular_extract_timestampstringatosnumberclickintegerconvintegerconv_imp_derivedintegerconv_revnumbercostnumber
campaign_insights_by_creative_tactics
campaign_idintegerline_itemstring_singular_extract_timestampstringacomp_0integeracomp_25integeracomp_50integeracomp_75integeracomp_95integer
campaign_group_insights_by_demographics
campaign_group_idintegerline_itemstring_singular_extract_timestampstringacomp_0integeracomp_25integeracomp_50integeracomp_75integeracomp_95integer
campaign_insights_by_devices
campaign_idintegerline_itemstring_singular_extract_timestampstringacomp_0integeracomp_25integeracomp_50integeracomp_75integeracomp_95integer
No verified relationships are available in this schema preview.
Connector pairings
Popular destinations
Commonly paired with StackAdapt in Extract workflows.
Why Extract
Why Extract for StackAdapt
- Rust-native performanceUse Rust-native data movement to refresh StackAdapt reporting every 15 minutes, hourly, daily, or on a custom schedule as volume grows.
- Full run transparencyInspect StackAdapt API activity, execution steps, and timing in Extract's run-level logs when data needs review.
- ELT + Reverse ETL in oneCombine StackAdapt 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 StackAdapt 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 StackAdapt data with Extract?
Historical coverage for StackAdapt: 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 StackAdapt data quality and freshness?
Compare expected reporting periods and record totals in your models, then use Extract's run-level logs to inspect StackAdapt 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 StackAdapt?
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 StackAdapt data model look like in the destination?
The Available streams table describes the datasets Extract can read from StackAdapt. The inline schema previews entities and fields. Only relationships verified from the exported schema are shown. Use the full schema link for more details.