**SQL**: SELECT user_id, date, clicks, AVG(clicks) OVER (PARTITION BY user_id ORDER BY date ROWS BETWEEN 6 PRECEDING AND CURRENT ROW) AS ma_7d FROM clicks. **Spark**: Window.partitionBy('user_id').orderBy('date').rowsBetween(-6, 0). **Why ROWS**: ROWS 6 PRECEDING = 7 rows...
Pro-Move: 'For sparse data we use RANGE INTERVAL—handles missing days; ROWS fails when dates skip.' Red Flag: ROWS 7 PRECEDING—that is 8 rows; 7-day = 6 PRECEDING.
This medium-level General/Other question appears frequently in data engineering interviews at companies like Matrix. While less common, it tests deeper understanding that distinguishes strong candidates. Mastering the underlying concepts (partition, spark, 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.
SQL: SELECT user_id, date, clicks, AVG(clicks) OVER (PARTITION BY user_id ORDER BY date ROWS BETWEEN 6 PRECEDING AND CURRENT ROW) AS ma_7d FROM clicks. Spark: Window.partitionBy('user_id').orderBy('date').rowsBetween(-6, 0). Why ROWS: ROWS 6 PRECEDING = 7 rows (current + 6). Use RANGE for gaps. Best practice: Handle NULLs; ensure date continuity.
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Analyze My Answer — FreeAccording to DataEngPrep.tech, this is one of the most frequently asked General/Other 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.