Please follow this link to check how to join different streams in ADF
and this one to see how to create a data flow
and finally this one for supported data sources
Azure Data Factory's mapping data flows do support operations on heterogeneous data sources. However, there are certain factors that may influence the successful execution of such operations.
Here are a few things you might consider:
- Data Integration Runtime: Ensure that the Azure Integration Runtime instance is correctly configured. The Integration Runtime is responsible for the movement of data between different data stores and for dispatching and monitoring of data flow activities.
- Data Store Connectivity: Validate the connection and access permissions to both SQL Server and Snowflake.
- Schema Compatibility: Check the compatibility of the schemas of the SQL Server and Snowflake tables. They need to be compatible in terms of data types and structure for the join operation to work correctly.
- Query Optimization: Heterogeneous operations are generally more resource-intensive and slower than operations on homogeneous data sources. Review your data flow design and queries to ensure they are optimized for performance.
- Debug Mode vs. Triggered Run: In debug mode, data flows run on a warm, always-up cluster which makes them execute faster compared to the triggered runs where clusters need to be started up, causing some delay. However, this shouldn't result in the data flow being stuck in progress forever.