When you run a mapping in SQL ELT mode, the transformation logic is translated into ecosystem-specific commands and SQL statements that run in the underlying cloud infrastructure. Because data isn't moved out of the cloud infrastructure to transform it, the overall processing speed increases.

Mappings in SQL ELT mode can read data from a cloud data warehouse and write it to the same cloud data warehouse. They can also read data from a data lake in your cloud ecosystem and write it to a cloud data warehouse in the same ecosystem.

Mappings in SQL ELT mode can load data to the following targets and extract data from the following sources based on the target type:

To create a mapping in SQL ELT mode, you create a mapping and select

Mapping - SQL ELT

as the mapping type. You're then prompted to choose a target connection. If your organization doesn't have any connections, you're prompted to create one.

After you choose the target connection, the Mapping Designer opens.

The following image shows the Mapping Designer for a mapping in SQL ELT mode:

The mapping is automatically configured with the target connection you chose. You can add additional sources, targets, and transformations to the mapping. The transformations available in the transformation palette are transformations that the target cloud data warehouse can process. For example, Snowflake can't process the logic in an SQL transformation, so the transformation palette for mappings in SQL ELT mode doesn't include the SQL transformation.

To run a mapping in SQL ELT mode, you create a mapping task.