Optimize Amazon S3 Tables queries with Amazon Redshift

This article explains how to optimize Amazon S3 Tables queries using Amazon Redshift through three key optimization techniques.

• Create external schemas to simplify query syntax from three-part to two-part notation
• Build materialized views to store pre-computed results locally in Redshift
• Configure S3 Tables compaction strategies (sort, z-order, binpack) matching query patterns
• Use IAM federation with SESSION credentials for new applications and interactive users
• Materialized views support incremental refresh for INSERT, DELETE, UPDATE operations
• Sort compaction optimizes single-column filters; z-order handles multi-column filters
• Coordinate snapshot retention with materialized view refresh intervals to avoid full recomputes
• Monitor compaction operations via AWS CloudTrail management events

These three approaches work together to make S3 Tables queries faster, simpler, and more cost-efficient for both recurring dashboards and ad hoc analysis at scale.