Attribution · Source connector
Singular User Level ELT Connector
Extract brings Singular User Level attribution data into data warehouses and databases for analysis with the rest of your business data. Teams use it to compare attribution results, partner performance, and conversion trends. Flexible scheduling keeps reporting current for routine analysis and time-sensitive decisions.
View setup guide
Use cases
What teams use this connector for
Attribution analysis
Compare Singular User Level attribution results across partners, campaigns, and conversion paths to understand performance.
Partner reconciliation
Reconcile Singular User Level attribution records with internal conversion and finance data to investigate discrepancies.
Performance monitoring
Refresh Singular User Level attribution views on a flexible schedule to spot material changes and delayed reporting.
Flexible reporting cadence
Refresh Singular User Level reporting every 15 minutes, hourly, daily, or on a custom schedule as business needs change.
Warehouse and database analysis
Use Extract to make Singular User Level attribution 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_skan_postbacks | Fields include ad_interaction_type, app_longname, city, coarse_conversion_value. | Incremental |
assists | Fields include affiliate_id, affiliate_name, android_id, android_id_md5. | Incremental |
attributions | Fields include affiliate_id, affiliate_name, android_id, android_id_md5. | Incremental |
clicks | Fields include affiliate_id, affiliate_name, android_id, android_id_md5. | Incremental |
events | Fields include adjusted_timestamp, adjusted_timestamp_unix_utc, affiliate_id, affiliate_name. | Incremental |
fps | Fields include app_longname, app_version, creative, device_id. | Incremental |
impressions | Fields include account, adwords_timestamp_unix_utc, affiliate_id, affiliate_name. | Incremental |
postbacks | Fields include affiliate_id, affiliate_name, app_longname, attribution_country. | Incremental |
skan_postbacks | Fields include ad_interaction_type, app_longname, city, coarse_conversion_value. | Incremental |
user_attributions | Fields include affiliate_id, affiliate_name, android_id, android_id_md5. | Incremental |
user_events | Fields include adjusted_timestamp, adjusted_timestamp_unix_utc, app_longname, app_version. | Incremental |
Schema
Data model at a glance
app_skan_postbacks
ad_interaction_typestringapp_longnamestringcitystringcoarse_conversion_valuestringconversion_model_measurement_periodnumberconversion_model_namestringconversion_model_typesstringconversion_model_versionnumber
assists
affiliate_idstringaffiliate_namestringandroid_idstringandroid_id_md5stringandroid_sha1stringapp_longnamestringapp_versionstringassist_touchpoints_countnumber
attributions
affiliate_idstringaffiliate_namestringandroid_idstringandroid_id_md5stringandroid_sha1stringapp_longnamestringapp_versionstringbanner_namestring
clicks
affiliate_idstringaffiliate_namestringandroid_idstringandroid_id_md5stringandroid_id_sha1stringapp_longnamestringbanner_namestringbid_typestring
events
adjusted_timestampstringadjusted_timestamp_unix_utcnumberaffiliate_idstringaffiliate_namestringandroid_idstringandroid_id_md5stringandroid_id_sha_1stringapp_longnamestring
fps
app_longnamestringapp_versionstringcreativestringdevice_idstringdevice_makestringdevice_modelstringetl_record_processing_hour_utcstringfraud_external_reasonstring
No verified relationships are available in this schema preview.
Connector pairings
Popular destinations
Commonly paired with Singular User Level in Extract workflows.
Why Extract
Why Extract for Singular User Level
- Rust-native performanceUse Rust-native data movement to refresh Singular User Level reporting every 15 minutes, hourly, daily, or on a custom schedule as volume grows.
- Full run transparencyInspect Singular User Level API activity, execution steps, and timing in Extract's run-level logs when data needs review.
- ELT + Reverse ETL in oneCombine Singular User Level 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 Singular User Level 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 partition-based extraction. Behavior varies by stream and configuration. Extract records run details so teams can verify each sync.
Can I backfill historical Singular User Level data with Extract?
Historical coverage for Singular User Level: No connector-specific backfill parameter is exposed. Initial historical coverage depends on the stream and provider. Use the setup guide and run details to confirm the available source records and selected settings.
How can teams monitor Singular User Level data quality and freshness?
Compare expected reporting periods and record totals in your models, then use Extract's run-level logs to inspect Singular User Level 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 Singular User Level?
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 Singular User Level data model look like in the destination?
The Available streams table describes the datasets Extract can read from Singular User Level. The inline schema previews entities and fields. Only relationships verified from the exported schema are shown. Use the full schema link for more details.