Data preparation using AWS Glue interactive sessions

AWS Glue interactive sessions is a serverless service that you can enlist to collect, transform, clean, and prepare data for storage in your data lakes and data pipelines. AWS Glue interactive sessions provides an on-demand, serverless Apache Spark runtime environment that you can initialize in seconds on a dedicated Data Processing Unit (DPU) without having to provision and manage complex compute cluster infrastructure. After initialization, you can browse the AWS Glue data catalog, run large queries, access data governed by AWS Lake Formation, and interactively analyze and prepare data using Spark, right in your Studio or Studio Classic notebooks. You can then use the prepared data to train, tune, and deploy models using the purpose-built ML tools within SageMaker Studio or Studio Classic. You should consider AWS Glue Interactive Sessions for your data preparation workloads when you want a serverless Spark service with moderate control of configurability and flexibility.

You can initiate an AWS Glue interactive session by starting a JupyterLab notebook in Studio or Studio Classic. When starting your notebook, choose the built-in Glue PySpark and Ray or Glue Spark kernel. This automatically starts an interactive, serverless Spark session. You do not need to provision or manage any compute cluster or infrastructure. After initialization, you can explore and interact with your data from within your Studio or Studio Classic notebooks.

Before starting your AWS Glue interactive session in Studio or Studio Classic, you need to set the appropriate roles and policies. Additionally, you may need to provide access to additional resources, such as a storage Amazon S3 bucket. For more information about required IAM policies, see Permissions for AWS Glue interactive sessions in Studio or Studio Classic.

Studio and Studio Classic provide a default configuration for your AWS Glue interactive session, however, you can use AWS Glue’s full catalog of Jupyter magic commands to further customize your environment. For information about the default and additional Jupyter magics that you can use in your AWS Glue interactive session, see Configure your AWS Glue interactive session in Studio or Studio Classic.