Optimizing a Slow, Expensive Data Warehouse for BI Dashboards

What's really being asked

This question tests your ability to think systemically about data performance and cost at scale. The interviewer is looking for a multi-layered strategy that goes beyond simple query tuning. They want to see if you understand the core architectural features of modern columnar data warehouses (like BigQuery or Snowflake) and can apply them to a real-world business problem involving both latency (slow dashboards) and budget (expensive queries).

The full answer

A great answer proposes solutions in three distinct layers, from the physical storage up to the query execution. First, physical data layout optimization. The most critical step is partitioning the raw event table, almost always by a date or timestamp column. This is the single most effective way to prune data for time-series analysis. Then, add clustering on columns frequently used in WHERE clauses, such as user_id or region, to further reduce the amount of data scanned within each partition. Second, pre-computation. Propose creating materialized views or summary tables that aggregate the raw data to the specific grain needed by the dashboards (e.g., daily active users per country). This shifts the heavy computation from query time to ingestion time, making dashboards fast and cheap to run. Third, cost and query governance. Mention using the query plan analyzer to identify expensive full-table scans. Discuss implementing platform-specific cost controls, like setting a maximum bytes billed per query or per user in BigQuery, and evaluating the pricing model (e.g., moving from on-demand to flat-rate pricing for predictable BI workloads).

The mistakes people make

The most common red flag is a junior-level answer that focuses only on rewriting the SQL queries. While important, it's insufficient for billions of rows. Another weak answer is to simply suggest 'throwing more hardware at it' or 'increasing the warehouse size', which shows a misunderstanding of both cost control and the auto-scaling nature of modern data warehouses. Using incorrect terminology, like 'adding an index' instead of the correct terms 'partitioning' and 'clustering' for columnar stores, is also a negative signal. Finally, an answer that ignores the 'expensive' part of the prompt and only solves for 'slow' is incomplete.

What usually comes next

Expect follow-ups like: 'How would you choose the clustering columns?' (Answer: Analyze dashboard filter usage; pick 1-4 high-cardinality columns that are most frequently used together in WHERE clauses). 'When would you use a materialized view versus a manually managed summary table?' (Answer: MV for simplicity, automatic refresh, and query rewriting support. Manual summary table for complex, multi-stage transformations or when you need fine-grained control over the refresh schedule via an orchestrator like Airflow).

A concrete example

For a table with 2 billion events per month (~2TB of new data), partitioning by day is essential. A query for a single day's activity would then scan only ~67 million rows (~65GB) instead of 2TB, a 97% reduction in data scanned. If dashboards frequently filter by user_id, clustering on that column would then co-locate a specific user's data within that day's partition. This could reduce a query for one user's activity from scanning 65GB to just a few hundred megabytes. An aggregate materialized view of 'daily active users' would be a tiny table, making the dashboard load instantly and cost fractions of a cent per query.