Marketing · Source connector
Apple Search Ads ELT Connector
Extract brings Apple Search Ads 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 Apple Search Ads campaign, creative, placement, geo, device, spend, and revenue detail to find opportunities to improve performance.
Direct-network reporting
Compare granular Apple Search Ads data with standard MMP and cost-aggregation reporting to work with dimensions those tools do not make available.
Spend and revenue reconciliation
Reconcile Apple Search Ads spend and revenue with finance, attribution, and internal performance data to investigate discrepancies.
Flexible performance monitoring
Refresh Apple Search Ads pacing, anomaly, and performance views every 15 minutes, hourly, daily, or on a custom schedule.
Warehouse and database analysis
Use Extract to make Apple Search Ads marketing data available in data warehouses and databases such as Snowflake, BigQuery, Redshift, and PostgreSQL for governed analysis.
Audience and campaign activation
Combine Apple Search Ads 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 |
|---|---|---|
ad | Fields include id, campaign_id, creative_id, ad_group_id. | Incremental |
ad_level_report | Fields include product_page_id, ad_display_status, ad_group_id, ad_id. | Incremental |
adgroup | Fields include id, campaign_id, automated_keywords_opt_in, cpa_goal.amount. | Incremental |
adgroup_level_report | Fields include ad_group_display_status, ad_group_id, ad_group_name, ad_group_serving_state_reasons. | Incremental |
app | Fields include adam_id, org_id, app_name, country_or_region_codes. | Full refresh |
campaign | Fields include id, ad_channel_type, adam_id, billing_event. | Incremental |
campaign_level_report | Fields include ad_channel_type, app.adam_id, app.app_name, avg_cpm.amount. | Incremental |
creative | Fields include id, product_page_id, adam_id, creation_time. | Incremental |
impression_share_report | Fields include adam_id, app_name, country_or_region, date. | Incremental |
keyword | Fields include id, campaign_id, ad_group_id, bid_amount.amount. | Incremental |
keyword_level_report | Fields include keyword, ad_group_deleted, ad_group_id, ad_group_name. | Incremental |
negative_keyword | Fields include id, campaign_id, ad_group_id, creation_time. | Incremental |
organization | Fields include org_id, currency, display_name, org_name. | Full refresh |
product_page | Fields include adam_id, id, org_id, creation_time. | Full refresh |
searchterm_level_report | Fields include keyword, ad_group_deleted, ad_group_id, ad_group_name. | Incremental |
Schema
Data model at a glance
ad
idintegercampaign_idintegercreative_idintegerad_group_idintegercreation_timestringcreative_typestringdeletedbooleanmodification_timestring
ad_level_report
product_page_idstringad_display_statusstringad_group_idnumberad_idnumberad_serving_state_reasonsarrayavg_cpm.amountnumberavg_cpm.currencystringavg_cpt.amountnumber
adgroup
idintegercampaign_idintegerautomated_keywords_opt_inbooleancpa_goal.amountnumbercpa_goal.currencystringcreation_timestringdefault_bid_amount.amountnumberdefault_bid_amount.currencystring
adgroup_level_report
ad_group_display_statusstringad_group_idnumberad_group_namestringad_group_serving_state_reasonsarrayad_group_serving_statusstringad_group_statusstringavg_cpm.amountnumberavg_cpm.currencystring
app
adam_idintegerorg_idintegerapp_namestringcountry_or_region_codesarraydeveloper_namestring
campaign
idintegerad_channel_typestringadam_idintegerbilling_eventstringbudget_amount.amountnumberbudget_amount.currencystringbudget_ordersarraycountries_or_regionsarray
No verified relationships are available in this schema preview.
Connector pairings
Popular destinations
Commonly paired with Apple Search Ads in Extract workflows.
Why Extract
Why Extract for Apple Search Ads
- Rust-native performanceUse Rust-native data movement to refresh Apple Search Ads reporting every 15 minutes, hourly, daily, or on a custom schedule as volume grows.
- Full run transparencyInspect Apple Search Ads API activity, execution steps, and timing in Extract's run-level logs when data needs review.
- ELT + Reverse ETL in oneCombine Apple Search Ads 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 Apple Search Ads 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, incremental changes, partition-based extraction. Behavior varies by stream and configuration. Extract records run details so teams can verify each sync.
Can I backfill historical Apple Search Ads data with Extract?
Historical coverage for Apple Search Ads: 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 Apple Search Ads data quality and freshness?
Compare expected reporting periods and record totals in your models, then use Extract's run-level logs to inspect Apple Search Ads 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 Apple Search Ads?
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 Apple Search Ads data model look like in the destination?
The Available streams table describes the datasets Extract can read from Apple Search Ads. The inline schema previews entities and fields. Only relationships verified from the exported schema are shown. Use the full schema link for more details.