Marketing · Source connector
CloudX ELT Connector
Extract brings CloudX 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 CloudX channel, campaign, and engagement trends to identify opportunities for improvement.
Audience and content analysis
Use CloudX data to understand which audiences, content, and activities contribute to engagement.
Cross-channel reporting
Combine CloudX metrics with other marketing and business data for consistent performance reporting.
Flexible reporting cadence
Refresh CloudX reporting every 15 minutes, hourly, daily, or on a custom schedule as business needs change.
Warehouse and database analysis
Use Extract to make CloudX marketing data available in data warehouses and databases such as Snowflake, BigQuery, Redshift, and PostgreSQL for governed analysis.
Connector data
Configured streams
Configure the API resources or reports to replicate when creating the Extract connection.
Schema
Data model at a glance
Connector pairings
Popular destinations
Commonly paired with CloudX in Extract workflows.
Why Extract
Why Extract for CloudX
- Rust-native performanceUse Rust-native data movement to refresh CloudX reporting every 15 minutes, hourly, daily, or on a custom schedule as volume grows.
- Full run transparencyInspect CloudX API activity, execution steps, and timing in Extract's run-level logs when data needs review.
- ELT + Reverse ETL in oneCombine CloudX 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 CloudX 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 CloudX data with Extract?
Historical coverage for CloudX: Backfill (Days) (backfill): The number of recent days to re-pull so finalized reporting data is refreshed. Use the setup guide and run details to confirm the available source records and selected settings.
How can teams monitor CloudX data quality and freshness?
Compare expected reporting periods and record totals in your models, then use Extract's run-level logs to inspect CloudX 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 CloudX?
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 CloudX data model look like in the destination?
The Available streams table describes the datasets Extract can read from CloudX. Use the connector documentation link for the available schema details and configuration requirements.