**Architectural Logic**: Window functions compute over a "frame" of rows related to the current row without collapsing rows. Syntax: func() OVER (PARTITION BY ... ORDER BY ... [frame]). Categories: Ranking (ROW_NUMBER, RANK, DENSE_RANK), Aggregate (SUM, AVG over partitions),...
Red Flag: Using unbounded frames (e.g., ROWS UNBOUNDED PRECEDING) without understanding memory impact. Pro-Move: 'For running totals I use ROWS BETWEEN 90 PRECEDING AND CURRENT ROW to bound memory.'
This medium-level SQL question appears frequently in data engineering interviews at companies like Aarete, Dunnhumby, Incedo. While less common, it tests deeper understanding that distinguishes strong candidates. Mastering the underlying concepts (join, partition, sql) 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.
Architectural Logic: Window functions compute over a "frame" of rows related to the current row without collapsing rows. Syntax: func() OVER (PARTITION BY ... ORDER BY ... [frame]). Categories: Ranking (ROW_NUMBER, RANK, DENSE_RANK), Aggregate (SUM, AVG over partitions), Value (LAG, LEAD, FIRST_VALUE). Why: Enable row-level analytics (running totals, moving averages, prior/next comparisons) without self-joins. Self-joins duplicate data and are slower. Scalability: Requires sort within partition; PARTITION BY narrows sort scope. Unbounded frames (e.g., SUM OVER (ORDER BY ...)) can cause memory pressure; use ROWS BETWEEN when possible. Cost: Window functions often cheaper than correlated subqueries or self-joins. Example: AVG(salary) OVER (PARTITION BY dept) adds department average to each row.
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Analyze My Answer — FreeAccording to DataEngPrep.tech, this is one of the most frequently asked SQL interview questions, reported at 3 companies. DataEngPrep.tech maintains a curated database of 1,863+ real data engineering interview questions across 7 categories, verified by industry professionals.