Reviewed by Aditya Kumar · Last reviewed 2026-03-24
Architectural Logic: Indexing—speeds reads, slows writes, costs storage; partitioning—prunes at scan, requires partition design; materialized views—precompute, cost refresh. Why each: Index for point/range lookup on filter columns; partition for time/entity-based pruning; MV for...
This hard-level SQL question appears frequently in data engineering interviews at companies like Bristol Myers Squibb. While less common, it tests deeper understanding that distinguishes strong candidates. Mastering the underlying concepts (optimization, partition, window) will help you answer variations of this question confidently.
This is a senior-level question that tests architectural thinking. Lead with the high-level design, then drill into specifics. Discuss trade-offs explicitly - there is rarely one correct answer. Show awareness of scale, fault tolerance, and operational complexity.
Architectural Logic: Indexing—speeds reads, slows writes, costs storage; partitioning—prunes at scan, requires partition design; materialized views—precompute, cost refresh. Why each: Index for point/range lookup on filter columns; partition for time/entity-based pruning; MV for repeated aggregations. Scalability: Indexes on wide tables → write amplification; over-partitioning → small-file problem; MVs → refresh time and storage. Cost: Many indexes increase storage and backup; partitions increase path depth; MVs double storage. Quantifying impact: Measure before/after latency, resource usage, and user satisfaction. Best practice: Always EXPLAIN/ANALYZE; test with production-like data; document optimization rationale; monitor after deployment. Example: Add partition filter, covering index, rewrite correlated subquery to window function.
Red Flag: Adding indexes without measuring—can slow writes more than it helps reads. Pro-Move: Profile execution plan first; prefer partitioning for time-series, indexes for low-cardinality filters; benchmark with representative data volume.
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According to DataEngPrep.tech, this is one of the most frequently asked SQL interview questions, reported at 1 company. DataEngPrep.tech maintains an editor-reviewed database of 1,863 data engineering interview questions across 7 categories.