Amazon SageMaker Studio’s JupyterLab notebooks now come with a built-in SQL extension with which data scientists can seamlessly discover, explore, and transform data from multiple data sources using SQL and Python right from the notebooks.

Data scientists working on Studio notebooks can now seamlessly connect to popular data services including Amazon Athena, Amazon Redshift, and Snowflake through AWS Glue connections. Administrators can securely manage these connections, allowing data scientists to access authorized data without the need to manage credentials manually. Once connected to a data source, data scientists can easily browse and search for databases, schemas, tables, and views, and can preview data within the notebook interface. They can then mix SQL and Python code in the same notebook for efficient exploration and transformation of data for use in machine learning projects. Additional features such as SQL command completion, code formatting assistance and syntax highlighting help accelerate code development and improve overall developer productivity. By integrating popular data services, SQL/Python data exploration, and end-to-end machine learning into a unified user interface, SageMaker Studio reduces the need for data scientists to switch between tools while working on analytics and machine learning tasks, resulting in significant time savings and increased productivity.

This feature is available in all commercial AWS regions where SageMaker Studio is available.

To learn more, see this blog and the SageMaker Developer Guide.