Marketing · Source connector
Snapchat Ads ELT Connector
Extract brings Snapchat 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 Snapchat Ads campaign, creative, placement, geo, device, spend, and revenue detail to find opportunities to improve performance.
Direct-network reporting
Compare granular Snapchat Ads data with standard MMP and cost-aggregation reporting to work with dimensions those tools do not make available.
Spend and revenue reconciliation
Reconcile Snapchat Ads spend and revenue with finance, attribution, and internal performance data to investigate discrepancies.
Flexible performance monitoring
Refresh Snapchat Ads pacing, anomaly, and performance views every 15 minutes, hourly, daily, or on a custom schedule.
Warehouse and database analysis
Use Extract to make Snapchat Ads marketing data available in data warehouses and databases such as Snowflake, BigQuery, Redshift, and PostgreSQL for governed analysis.
Audience and campaign activation
Combine Snapchat 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, ad_squad_id, created_at, creative_id. | Incremental |
ad_squad_targeting_geos | Fields include ad_squad_id, country_code, metro_id, operation. | Incremental |
ad_squad_targeting_locations | Fields include ad_squad_id, circles.latitude, circles.longitude, circles.name. | Incremental |
ad_squad_targeting_app_install_states | Fields include ad_squad_id, snap_app_ids, state. | Incremental |
creative | Fields include id, ad_account_id, ad_product, app_install_properties.android_app_url. | Incremental |
funding_source | Fields include id, available_credit_micro, billing_address.country_code, budget_spent_micro. | Incremental |
ad_squad_frequency_cap_config | Fields include ad_squad_id, frequency_cap_count, frequency_cap_interval, frequency_cap_type. | Incremental |
campaign | Fields include id, ad_account_id, buy_model, cell_ids. | Incremental |
insights_delivery_and_conversion | Fields include account_id, attachment_quartile_1, attachment_quartile_2, attachment_quartile_3. | Incremental |
billing_center | Fields include id, address_line_1, administrative_district_level_1, country. | Incremental |
invoice | Fields include invoice_id, ad_account_id, amount_cent, billing_period. | Incremental |
stats_daily | Fields include account_id, android_installs_app, android_installs_offline, android_installs_web. | Incremental |
ad_account | Fields include id, advertiser_organization_id, agency_representing_client, billing_center_id. | Incremental |
media | Fields include id, ad_account_id, ad_product, app_install_properties.android_app_url. | Incremental |
ad_squad_targeting_devices | Fields include ad_squad_id, connection_type, marketing_name, operation. | Incremental |
organization | Fields include id, accepted_term_version, address_line_1, administrative_district_level_1. | Incremental |
ad_squad_targeting_segments | Fields include ab_segment_id, ad_squad_id, operation, segment_id. | Incremental |
stats_hourly | Fields include account_id, android_installs_app, android_installs_offline, android_installs_web. | Incremental |
ad_squad_targeting_interests | Fields include ad_squad_id, category_id, operation, segment_id. | Incremental |
ad_squad | Fields include id, auto_bid, bid_micro, bid_strategy. | Incremental |
ad_squad_targeting_demographics | Fields include ad_squad_id, age_groups, gender, languages. | Incremental |
insights_delivery | Fields include account_id, attachment_quartile_1, attachment_quartile_2, attachment_quartile_3. | Incremental |
Schema
Data model at a glance
ad
idstringad_squad_idstringcreated_atstringcreative_idstringdelivery_statusarraynamestringrender_typestringreview_statusstring
ad_squad_targeting_geos
ad_squad_idstringcountry_codestringmetro_idarrayoperationstringregion_idarray
ad_squad_targeting_locations
ad_squad_idstringcircles.latitudenumbercircles.longitudenumbercircles.namestringcircles.radiusnumbercircles.unitstringoperationstringproximity_unitstring
ad_squad_targeting_app_install_states
ad_squad_idstringsnap_app_idsarraystatestring
creative
idstringad_account_idstringad_productstringapp_install_properties.android_app_urlstringapp_install_properties.app_namestringapp_install_properties.icon_media_idstringapp_install_properties.ios_app_idstringapp_install_properties.product_page_idstring
funding_source
idstringavailable_credit_microintegerbilling_address.country_codestringbudget_spent_microintegercard_typestringcreated_atstringcredit_account_typestringcurrencystring
No verified relationships are available in this schema preview.
Connector pairings
Popular destinations
Commonly paired with Snapchat Ads in Extract workflows.
Why Extract
Why Extract for Snapchat Ads
- Rust-native performanceUse Rust-native data movement to refresh Snapchat Ads reporting every 15 minutes, hourly, daily, or on a custom schedule as volume grows.
- Full run transparencyInspect Snapchat Ads API activity, execution steps, and timing in Extract's run-level logs when data needs review.
- ELT + Reverse ETL in oneCombine Snapchat 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 Snapchat 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 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 Snapchat Ads data with Extract?
Historical coverage for Snapchat 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 Snapchat Ads data quality and freshness?
Compare expected reporting periods and record totals in your models, then use Extract's run-level logs to inspect Snapchat 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 Snapchat 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 Snapchat Ads data model look like in the destination?
The Available streams table describes the datasets Extract can read from Snapchat 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.