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Access Amazon Redshift Managed Storage tables through Apache Spark on AWS Glue and Amazon EMR using Amazon SageMaker Lakehouse

Big Data Blog

This article discusses how to access Amazon Redshift Managed Storage (RMS) tables through Apache Spark using Amazon SageMaker Lakehouse, AWS Glue, and Amazon EMR.

  • SageMaker Lakehouse unifies data across S3 data lakes and Redshift data warehouses
  • Enables accessing RMS tables through Apache Iceberg APIs and AWS Glue Data Catalog
  • Supports integration with SageMaker Unified Studio, Amazon EMR 7.5.0+, and AWS Glue 5.0
  • Requires specific Spark configurations to access RMS tables
  • Demonstrates creating and querying RMS tables using Spark SQL in a JupyterLab notebook

The article provides a step-by-step guide to creating a Lakehouse catalog, configuring Spark sessions, and querying RMS tables using different compute options like AWS Glue and Amazon EMR Serverless.

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