**SQL**: SELECT city_id, order_date, order_count, AVG(order_count) OVER (PARTITION BY city_id ORDER BY order_date ROWS BETWEEN 6 PRECEDING AND CURRENT ROW) AS ma_7d FROM daily_orders. **Spark**: Window.partitionBy('city_id').orderBy('order_date').rowsBetween(-6, 0)....
Pro-Move: 'We partition by city_id and use ROWS—city-level aggregation is dense; RANGE adds overhead.' Red Flag: Forgetting PARTITION BY city_id—mixing cities in one average.
This medium-level General/Other question appears frequently in data engineering interviews at companies like Swiggy. 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 city_id, order_date, order_count, AVG(order_count) OVER (PARTITION BY city_id ORDER BY order_date ROWS BETWEEN 6 PRECEDING AND CURRENT ROW) AS ma_7d FROM daily_orders. Spark: Window.partitionBy('city_id').orderBy('order_date').rowsBetween(-6, 0). Best practice: Same as 472; partition by city for per-city series.
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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.