Quickstart · Source connector
Apple App Store ELT Connector
Extract brings Apple App Store quickstart data into data warehouses and databases for analysis with the rest of your business data. Teams use it to build recurring reporting and compare business activity across systems. Flexible scheduling keeps reporting current for routine analysis and time-sensitive decisions.
View setup guide
Use cases
What teams use this connector for
Business performance reporting
Use Apple App Store data to monitor recurring activity and investigate meaningful changes over time.
Cross-system analysis
Combine Apple App Store data with customer, finance, and operational metrics for consistent reporting.
Data quality monitoring
Compare Apple App Store record totals and reporting periods to identify incomplete or unexpected data.
Flexible reporting cadence
Refresh Apple App Store reporting every 15 minutes, hourly, daily, or on a custom schedule as business needs change.
Warehouse and database analysis
Use Extract to make Apple App Store quickstart 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 |
|---|---|---|
app_clip_usage | Fields include app_version, app_apple_identifier, app_clip_event_type, app_name. | Incremental |
app_clip_usage_detailed | Fields include app_version, app_apple_identifier, app_clip_event_type, app_name. | Incremental |
app_sessions | Fields include app_version, app_apple_identifier, app_download_date, app_name. | Incremental |
app_sessions_detailed | Fields include app_version, app_apple_identifier, app_download_date, app_name. | Incremental |
app_versions | Fields include app_id, app_store_state, app_version_state, copyright. | Full refresh |
apps | Fields include bundle_id, id, primary_locale, sku. | Full refresh |
crashes | Fields include app_version, app_apple_identifier, app_name, crashes. | Incremental |
discovery_and_engagement | Fields include app_apple_identifier, app_name, counts, date. | Incremental |
discovery_and_engagement_detailed | Fields include app_apple_identifier, app_name, campaign, counts. | Incremental |
downloads | Fields include app_version, app_apple_identifier, app_name, counts. | Incremental |
downloads_detailed | Fields include app_version, app_apple_identifier, app_name, campaign. | Incremental |
finance | Fields include apple_fiscal_period, apple_identifier, artist_show_developer_author, country_of_sale. | Incremental |
finance_detail | Fields include apple_fiscal_period, apple_identifier, artist_show_developer_author, country_of_sale. | Incremental |
installations_and_deletions | Fields include app_version, app_apple_identifier, app_download_date, app_name. | Incremental |
installations_and_deletions_detailed | Fields include app_version, app_apple_identifier, app_download_date, app_name. | Incremental |
pre_orders | Fields include app_apple_identifier, app_name, date, device. | Incremental |
pre_orders_detailed | Fields include app_apple_identifier, app_name, campaign, date. | Incremental |
purchases | Fields include app_apple_identifier, app_download_date, app_name, content_apple_identifier. | Incremental |
purchases_detailed | Fields include app_apple_identifier, app_download_date, app_name, campaign. | Incremental |
review_responses | Fields include id, app_id, review_id, created_date. | Incremental |
reviews | Fields include id, app_id, body, created_date. | Incremental |
sales_summary_daily | Fields include apple_identifier, begin_date, category, client. | Incremental |
subscriber_detailed_daily | Fields include app_apple_id, app_name, client, country. | Incremental |
subscription_event_summary_daily | Fields include app_apple_id, app_name, cancellation_reason, client. | Incremental |
subscription_summary_daily | Fields include active_free_trial_introductory_offer_subscriptions, active_pay_as_you_go_introductory_offer_subscriptions, active_pay_up_front_introductory_offer_subscriptions, active_standard_price_subscriptions. | Incremental |
Schema
Data model at a glance
app_clip_usage
app_versionstringapp_apple_identifierintegerapp_clip_event_typestringapp_namestringcountsnumberdatestringdevicestringplatform_versionstring
app_clip_usage_detailed
app_versionstringapp_apple_identifierintegerapp_clip_event_typestringapp_namestringcampaignstringcountsnumberdatestringdevicestring
app_sessions
app_versionstringapp_apple_identifierintegerapp_download_datestringapp_namestringdatestringdevicestringpage_typestringplatform_versionstring
app_sessions_detailed
app_versionstringapp_apple_identifierintegerapp_download_datestringapp_namestringcampaignstringdatestringdevicestringpage_titlestring
app_versions
app_idintegerapp_store_statestringapp_version_statestringcopyrightstringcreated_datestringdownloadablebooleanearliest_release_datestringidstring
apps
bundle_idstringidintegerprimary_localestringskustring
No verified relationships are available in this schema preview.
Connector pairings
Popular destinations
Commonly paired with Apple App Store in Extract workflows.
Why Extract
Why Extract for Apple App Store
- Rust-native performanceUse Rust-native data movement to refresh Apple App Store reporting every 15 minutes, hourly, daily, or on a custom schedule as volume grows.
- Full run transparencyInspect Apple App Store API activity, execution steps, and timing in Extract's run-level logs when data needs review.
- ELT + Reverse ETL in oneCombine Apple App Store 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 App Store 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 App Store data with Extract?
Historical coverage for Apple App Store: 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 App Store data quality and freshness?
Compare expected reporting periods and record totals in your models, then use Extract's run-level logs to inspect Apple App Store 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 App Store?
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 App Store data model look like in the destination?
The Available streams table describes the datasets Extract can read from Apple App Store. The inline schema previews entities and fields. Only relationships verified from the exported schema are shown. Use the full schema link for more details.