How would you optimize a slow, expensive data warehouse?

What's really being asked

This tests your ability to diagnose performance issues from symptoms (slow queries, high cost) and apply the correct, cost-effective data modeling solutions. It's not just about listing features like partitioning or clustering. It's about demonstrating a methodical, data-driven approach: analyze first, then implement. The interviewer wants to see that you understand the trade-offs and can choose the right tool for the specific query patterns at hand.

The full answer

An excellent answer follows a clear, ordered plan. First, Diagnosis: start by proposing an analysis of query history (e.g., BigQuery's INFORMATION_SCHEMA.JOBS) to identify the most frequent and expensive queries, paying close attention to their WHERE clauses and JOIN keys. Second, Partitioning: based on the diagnosis, recommend partitioning the event tables by a time-unit column, like a DATE or TIMESTAMP. This is the single most effective strategy for time-series data. Third, Clustering: after partitioning, suggest clustering on up to four high-cardinality columns that are frequently used together in filters or joins (e.g., customer_id, project_id). Explain that this sorts data within each partition. Fourth, Materialized Views: for dashboards with common, expensive aggregations, propose creating materialized views to pre-compute results, acknowledging the cost/latency trade-offs.

The mistakes people make

A major red flag is jumping to a solution without a diagnostic step, e.g., "I'd partition the table." This shows a lack of analytical rigor. Another common mistake is confusing partitioning and clustering, for example, suggesting partitioning on a high-cardinality key like user_id. This would create millions of tiny, inefficient partitions and drastically increase table management overhead. A junior answer simply lists the features ("You can use partitioning, clustering, and materialized views") without explaining the why, when, and how for each in the context of the problem.

What usually comes next

Be prepared for follow-ups like: "How would you decide between clustering and a materialized view for a specific dashboard?" or "What are the cost implications of enabling materialized views?" or "How would you handle backfilling partitions and clusters on a multi-billion row table without incurring massive downtime or cost?"

A concrete example

For a table with 3 billion rows per month (100M/day), a query for the last 7 days without partitioning would scan all 3 billion rows. By partitioning on event_date, the same query scans only the 7 relevant partitions, or ~700M rows—a 75%+ reduction in cost and query time. If those queries also filter on user_id, the query still scans all 700M rows. By adding clustering on user_id, the query engine can use the cluster metadata to read only the specific blocks within those 7 partitions that contain the relevant user's data, potentially reducing the data scan from hundreds of gigabytes to just megabytes.