Marketing · Source connector
Replai ELT Connector
Extract brings Replai marketing data into data warehouses and databases for analysis with the rest of your business data. Teams use it to compare channel performance, audience engagement, and campaign trends. Flexible scheduling keeps reporting current for routine analysis and time-sensitive decisions.
View setup guide
Use cases
What teams use this connector for
Marketing performance analysis
Compare Replai channel, campaign, and engagement trends to identify opportunities for improvement.
Audience and content analysis
Use Replai data to understand which audiences, content, and activities contribute to engagement.
Cross-channel reporting
Combine Replai metrics with other marketing and business data for consistent performance reporting.
Flexible reporting cadence
Refresh Replai reporting every 15 minutes, hourly, daily, or on a custom schedule as business needs change.
Warehouse and database analysis
Use Extract to make Replai marketing 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 |
|---|---|---|
apps | Fields include id, connected_networks, name, replai_platform_url. | Full refresh |
videos | Fields include id, countries, download_url, name. | Full refresh |
video_placements | Fields include network, network_creative_id, network_creative_name, video_id. | Incremental |
video_tags | Fields include category_name, end, name, start. | Incremental |
Schema
Data model at a glance
apps
idstringconnected_networksarraynamestringreplai_platform_urlstring
videos
idstringcountriesarraydownload_urlstringnamestringnetworksarrayreplai_platform_urlstring
video_placements
networkstringnetwork_creative_idstringnetwork_creative_namestringvideo_idstring
video_tags
category_namestringendnumbernamestringstartnumbervideo_idstring
No verified relationships are available in this schema preview.
Connector pairings
Popular destinations
Commonly paired with Replai in Extract workflows.
Why Extract
Why Extract for Replai
- Rust-native performanceUse Rust-native data movement to refresh Replai reporting every 15 minutes, hourly, daily, or on a custom schedule as volume grows.
- Full run transparencyInspect Replai API activity, execution steps, and timing in Extract's run-level logs when data needs review.
- ELT + Reverse ETL in oneCombine Replai 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 Replai 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 Replai data with Extract?
Historical coverage for Replai: 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 Replai data quality and freshness?
Compare expected reporting periods and record totals in your models, then use Extract's run-level logs to inspect Replai 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 Replai?
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 Replai data model look like in the destination?
The Available streams table describes the datasets Extract can read from Replai. The inline schema previews entities and fields. Only relationships verified from the exported schema are shown. Use the full schema link for more details.