**ROW_NUMBER()**: Unique sequential numbers (1, 2, 3...); no ties—deterministic only with ORDER BY uniqueness. **RANK()**: Same rank for ties; skips (1, 2, 2, 4). **DENSE_RANK()**: Same rank for ties; no gaps (1, 2, 2, 3). **Project examples**: ROW_NUMBER() to deduplicate events...
Red Flag: Using ROW_NUMBER for ranking when ties should exist. Pro-Move: Explain that ROW_NUMBER for dedup requires a deterministic ORDER BY; otherwise results can vary across runs.
This medium-level SQL question appears frequently in data engineering interviews at companies like Aarete, Accenture, Fossil Group, and 1 others. While less common, it tests deeper understanding that distinguishes strong candidates. Mastering the underlying concepts (window) will help you answer variations of this question confidently.
Break this problem into components. Identify the core trade-offs involved, then walk the interviewer through your reasoning step by step. Demonstrate awareness of edge cases and production considerations - this is what separates good answers from great ones.
ROW_NUMBER(): Unique sequential numbers (1, 2, 3...); no ties—deterministic only with ORDER BY uniqueness. RANK(): Same rank for ties; skips (1, 2, 2, 4). DENSE_RANK(): Same rank for ties; no gaps (1, 2, 2, 3). Project examples: ROW_NUMBER() to deduplicate events by (user_id, event_time) keeping first—critical when upstream sends duplicates. DENSE_RANK() for 'top 10 products per category' reports—avoids gaps when filtering. RANK() for leaderboards where ties share position (e.g., two people at 2nd). Why it matters architecturally: Dedup with ROW_NUMBER affects downstream freshness and storage; wrong choice can inflate fact tables. Scalability: Window functions are expensive on huge datasets; push filters before the window when possible.
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