Snowflake connectivity for AWS Glue for Apache Spark is now generally available

AWS Glue for Apache Spark now supports native connectivity to Snowflake, which enables users to read and write data from Snowflake without the need to install or manage Snowflake connector libraries. In addition, AWS Glue Studio has new visual ETL capabilities available for Snowflake source and targets to save time when authoring data pipelines for Snowflake. AWS Glue Studio enables ETL (Extract, Transform and Load) developers to visually transform data with a no-code, drag-and-drop interface. With this new connector and visual capabilities, ETL developers can read and write data into Snowflake more effectively using AWS Glue.

When authoring visual ETL jobs using AWS Glue Studio, developers can now choose a Snowflake table as a direct source or use Snowflake SQL to define a custom source. When writing to Snowflake, users can define target operations using common Snowflake commands including drop, truncate, upsert, create and merge. These capabilities enable ETL developers to work with Snowflake and AWS Glue across a variety of data situations within a single interface.

To get started, create a new Snowflake connection within the Glue Data Catalog and add a Snowflake source or target to your job. This feature is available in all commercial AWS Regions where AWS Glue is available.

To learn more, visit the AWS Glue documentation.