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Catalog and govern Amazon Athena federated queries with Amazon SageMaker Lakehouse
- Amazon SageMaker Unified Studio is a newly-announced data and artificial intelligence (AI) integration platform for Amazon S3 data lakes and third-party sources including Snowflake.
- Amazon SageMaker Lakehouse breaks down data silos, ensuring governance, security and compliance upon data expansion.
- Data analysts can securely query external data sources, including Amazon Redshift data warehouses and Amazon DynamoDB databases, through a single, unified experience.
- Administrators can apply access controls at different levels of granularity to ensure sensitive data remains protected while expanding data access.
- This allows organizations to accelerate data initiatives while maintaining security and compliance, leading to faster, data-driven decision-making.
- Amazon SageMaker Lakehouse streamlines connecting to, cataloging, and managing permissions on data from multiple sources, allowing analysts to run SQL queries on federated data catalogs.
- Set-up is further facilitated thanks to the easy-to-use SageMaker Unified Studio, which integrates with SageMaker Lakehouse to provide flexibility to end-users working with their preferred tools.
- This blog post demonstrates how to connect to, govern, and run federated queries on data, covering Redshift, DynamoDB (Preview), and Snowflake (Preview).
- The blog presents a solution where a company is using multiple data sources containing customer data. Regulations require personally identifiable information (PII) data to be secured; an administrator sets up fine-grained access controls using Lake Formation.
- We encourage readers to try fine-grained access controls on federated queries today in SageMaker Unified Studio, and to share feedback. For more on federated queries in Athena and the data sources that support fine-grained access controls, see Register your connection as a Glue Data Catalog in the Athena User Guide.
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