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Configure cross-account access of Amazon SageMaker Lakehouse multi-catalog tables using AWS Glue 5.0 Spark
- Amazon SageMaker Lakehouse allows organizations to unify data analytics and AI/ML workflows securely without data replication.
- It organizes data using logical containers called catalogs, enabling seamless querying and analysis across ecosystems.
- AWS Glue 5.0 supports SageMaker Lakehouse, unifying data across S3 data lakes and Redshift data warehouses.
- The article demonstrates sharing Redshift and Amazon S3-based Iceberg tables across AWS accounts using Spark in AWS Glue 5.0.
- The setup involves prerequisites like two AWS accounts with Lake Formation sharing and IAM roles for permissions.
- Steps include creating catalogs, databases, granting permissions, and running PySpark jobs in AWS Glue.
- Checks and verifications are done using tools like Athena to ensure proper setup and access.
- Resource cleanup steps are provided to avoid unnecessary costs on AWS accounts.
- In conclusion, the article highlights the process of sharing and querying data across AWS accounts using SageMaker Lakehouse and AWS Glue 5.0.
- The appendices include detailed steps for creating tables in S3 and Redshift for demonstration.
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