**Architectural Logic:** Window functions compute values over a set of rows related to the current row (defined by OVER) without collapsing rows like GROUP BY. **Why windows over self-joins:** Single scan, no explosion; optimal for rankings, running totals, period-over-period....
This medium-level SQL question appears frequently in data engineering interviews at companies like Citi, Freecharge. While less common, it tests deeper understanding that distinguishes strong candidates. Mastering the underlying concepts (join, partition, 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.
Architectural Logic: Window functions compute values over a set of rows related to the current row (defined by OVER) without collapsing rows like GROUP BY. Why windows over self-joins: Single scan, no explosion; optimal for rankings, running totals, period-over-period. Scalability: PARTITION BY and ORDER BY affect sort/spill; large windows with RANGE BETWEEN UNBOUNDED PRECEDING can spill to disk. Cost: ROWS vs. RANGE—RANGE requires sorting by frame boundaries; ROWS is typically more efficient. Example: SELECT employee_id, department, salary, AVG(salary) OVER (PARTITION BY department) AS dept_avg, RANK() OVER (PARTITION BY department ORDER BY salary DESC) AS salary_rank FROM employees. Use LAG/LEAD for YoY; explicit frame (ROWS/RANGE) when behavior matters.
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Analyze My Answer — FreeAccording to DataEngPrep.tech, this is one of the most frequently asked SQL interview questions, reported at 2 companies. DataEngPrep.tech maintains a curated database of 1,863+ real data engineering interview questions across 7 categories, verified by industry professionals.