How would you optimize a slow, expensive data warehouse?
Tests your diagnostic approach to performance issues. A good answer first analyzes query patterns, then applies partitioning by date, clustering by high-cardinality keys, and materialized views for aggregations.
This question tests your ability to diagnose performance issues from symptoms and apply cost-effective data modeling solutions. A strong answer outlines a process: first, analyze query patterns to find bottlenecks; second, apply partitioning by date to prune data; third, add clustering on high-cardinality filter columns; and finally, use materialized views for common aggregations. A major red flag is jumping to a solution without first proposing a diagnostic phase to understand the query workload.
Read the original → docs.cloud.google.com
- #data warehousing
- #sql
- #bigquery
- #performance
- #system design
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