**Architectural Logic**: Window function with unbounded preceding for running total. **Implementation**: `SELECT date, amount, SUM(amount) OVER (ORDER BY date) AS cumulative_sum FROM sales`. Per group: `SUM(amount) OVER (PARTITION BY product_id ORDER BY date)`. **Frame**:...
This medium-level SQL question appears frequently in data engineering interviews at companies like Hexaware. 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.
Architectural Logic: Window function with unbounded preceding for running total. Implementation: SELECT date, amount, SUM(amount) OVER (ORDER BY date) AS cumulative_sum FROM sales. Per group: SUM(amount) OVER (PARTITION BY product_id ORDER BY date). Frame: Default ROWS UNBOUNDED PRECEDING. Use: Running balance; YTD. Scalability: Single pass; efficient. Cost: No extra scan. Best Practice: Deterministic ORDER BY (tie-breaker if needed); understand RANGE vs ROWS. In Spark: same syntax.
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Analyze My Answer — FreeAccording to DataEngPrep.tech, this is one of the most frequently asked SQL 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.