Explore data with ease: Use SQL and Text-to-SQL in Amazon SageMaker Studio JupyterLab notebooks

Amazon SageMaker Studio provides a fully managed solution for data scientists to interactively build, train, and deploy machine learning (ML) models. In the process of working on their ML tasks, data scientists typically start their workflow by discovering relevant data sources and connecting to them. They then use SQL to explore, analyze, visualize, and integrate data from various sources before using it in their ML training and inference. Previously, data scientists often found themselves juggling multiple tools to support SQL in their workflow, which hindered productivity.

We’re excited to announce that JupyterLab notebooks in SageMaker Studio now come with built-in support for SQL. Data scientists can now:

• Connect to popular data services including Amazon Athena, Amazon Redshift, Amazon DataZone, and Snowflake directly within the notebooks
• Browse and search for databases, schemas, tables, and views, and preview data within the notebook interface
• Mix SQL and Python code in the same ...