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....
The complete answer continues with detailed implementation patterns, architectural trade-offs, and production-grade considerations. It covers performance optimization strategies, common pitfalls to avoid, and real-world examples from companies like Bristol Myers Squibb. The answer also includes follow-up discussion points that interviewers commonly explore.
Continue Reading the Full Answer
Unlock the complete expert answer with code examples, trade-offs, and pro tips - plus 1,863+ more.
Or upgrade to Platform Pro - $39
Engineers who used these answers got offers at
AmazonDatabricksSnowflakeGoogleMeta
According to DataEngPrep.tech, this is one of the most frequently asked SQL interview questions, reported at 1 company. DataEngPrep.tech maintains a curated database of 1,863+ real data engineering interview questions across 7 categories, verified by industry professionals.