AWS Glue now supports native Snowflake connectivity with new ETL capabilities (preview)
AWS Glue for Spark now supports the Snowflake connector for Spark out-of-the-box with new ETL capabilities enabling users to preview Snowflake data, use Snowflake SQL queries as a source, and write to new or existing Snowflake tables. AWS Glue Studio offers a visual extract-transform-and-load (ETL) interface that helps ETL developers to author, run, and monitor AWS Glue ETL jobs quickly. With this new feature, ETL developers can read and write data into Snowflake more effectively using AWS Glue.
AWS Glue for Spark now supports the Snowflake connector for Spark out-of-the-box with new ETL capabilities enabling users to preview Snowflake data, use Snowflake SQL queries as a source, and write to new or existing Snowflake tables. AWS Glue Studio offers a visual extract-transform-and-load (ETL) interface that helps ETL developers to author, run, and monitor AWS Glue ETL jobs quickly. With this new feature, ETL developers can read and write data into Snowflake more effectively using AWS Glue.
When authoring visual ETL, users can now choose Snowflake as a source or target directly. Alternatively, select source data by pasting your SQL query from Snowflake directly into AWS Glue Studio. These capabilities enable a ETL developer to work with Snowflake and AWS Glue across a variety of data situations within a single interface.
To try this feature, create a Visual ETL job and add a Snowflake source or target to your job. This feature is currently in preview and available in the following AWS regions: US East (Ohio), US East (N. Virginia) and US West (Oregon).
To learn more, visit the AWS Glue documentation.