**SQL**: SELECT product_id, store_id, sale_date, amount, SUM(amount) OVER (PARTITION BY product_id, store_id ORDER BY sale_date ROWS UNBOUNDED PRECEDING) AS cumulative_sales FROM sales. **Spark**:...
Pro-Move: 'We had duplicate sale_dates—used ROWS UNBOUNDED PRECEDING for deterministic order; RANGE was non-deterministic.' Red Flag: Using RANGE with duplicates—undefined order; use ROWS.
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 product_id, store_id, sale_date, amount, SUM(amount) OVER (PARTITION BY product_id, store_id ORDER BY sale_date ROWS UNBOUNDED PRECEDING) AS cumulative_sales FROM sales. Spark: Window.partitionBy('product_id','store_id').orderBy('sale_date').rowsBetween(Window.unboundedPreceding, Window.currentRow). Why: Running total for YTD, progress. Best practice: Ensure sale_date uniqueness per partition or use ROWS for determinism.
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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.