You can configure SQL ELT optimization from a mapping task to enhance the mapping performance.
After you create a mapping or a mapping in advanced mode, add the mapping to a mapping task, and then configure SQL ELT optimization in the mapping task. You can select how Data Integration handles SQL ELT optimization in the SQL ELT Optimization Fallback Option menu on the Runtime Options tab.
The task converts the transformation logic to Snowflake queries, sends the queries to Snowflake, and the mapping logic is processed in the Snowflake database.
If your mapping contains multiple pipelines, you can define the flow run order to load the targets from the pipelines in a particular order.
You can configure SQL ELT optimization for a mapping or a mapping in advanced mode in the following scenarios:
Snowflake to Snowflake
Read from and write to Snowflake using a Snowflake Data Cloud connection.
Amazon S3 to Snowflake
Read from Amazon S3 using an Amazon S3 V2 connection in the Source transformation and write to Snowflake using a Snowflake Data Cloud connection in the Target transformation.
Microsoft Azure Data Lake Storage Gen2 to Snowflake
Read from Microsoft Azure Data Lake Storage Gen2 using a Microsoft Azure Data Lake Storage Gen2 connection in the Source transformation and write to Snowflake using a Snowflake Data Cloud connection in the Target transformation.
Google Cloud Storage to Snowflake
Read from Google Cloud Storage using a Google Cloud Storage connection in the Source transformation and write to Snowflake using a Snowflake Data Cloud connection in the Target transformation.