Question

A data engineering team runs a business intelligence dashboard backed by Amazon Redshift. The dashboard executes the same complex aggregation query (multiple joins across a large fact table and several dimension tables, plus GROUP BY rollups) every few minutes for hundreds of users. The underlying fact table receives incremental inserts throughout the day. Query latency has become unacceptable, and the team wants to reduce repeated computation while keeping results reasonably fresh with minimal maintenance overhead. Which approach best addresses this?

Accepted Answer

Create a materialized view on the aggregation query and enable automatic refresh so Redshift incrementally maintains precomputed results

Answer

Create a standard view over the same query so the aggregation logic is reused across dashboard sessions

Answer

Increase the cluster node count and rely on the result cache to serve repeated dashboard queries

Answer

Unload the aggregated results to Amazon S3 nightly and query them with Redshift Spectrum external tables