Question

A data engineering team manages a 40-node Amazon Redshift cluster supporting a growing analytics platform. Query patterns are evolving as new dashboards are added, and the team lacks the capacity to continuously monitor and hand-tune distribution and sort keys on hundreds of tables. They want Redshift to observe workloads and adjust sort and distribution keys over time without dropping and recreating tables manually. Which approach best meets this requirement with the least ongoing operational effort?

Accepted Answer

Enable Automatic Table Optimization (ATO) so Redshift uses machine learning to recommend and apply sort and distribution key changes based on observed query patterns.

Answer

Schedule a nightly job that runs VACUUM and ANALYZE on all tables to keep statistics current and reclaim space.

Answer

Set all tables to DISTSTYLE ALL and remove sort keys so the optimizer no longer depends on key configuration.

Answer

Enable Concurrency Scaling to add transient clusters that automatically optimize key layouts during peak load.