Reviewed by Aditya Kumar · Last reviewed 2026-03-24
To get first and last order per customer, use window functions MIN/MAX over order dates, or FIRST_VALUE/LAST_VALUE. Example: SELECT customer_id, order_id, order_date, FIRST_VALUE(order_id) OVER (PARTITION BY customer_id ORDER BY order_date) AS first_order_id,...
This medium-level SQL question appears frequently in data engineering interviews at companies like Wipro. While less common, it tests deeper understanding that distinguishes strong candidates. Mastering the underlying concepts (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.
To get first and last order per customer, use window functions MIN/MAX over order dates, or FIRST_VALUE/LAST_VALUE. Example: SELECT customer_id, order_id, order_date, FIRST_VALUE(order_id) OVER (PARTITION BY customer_id ORDER BY order_date) AS first_order_id, LAST_VALUE(order_id) OVER (PARTITION BY customer_id ORDER BY order_date ROWS BETWEEN UNBOUNDED PRECEDING AND UNBOUNDED FOLLOWING) AS last_order_id FROM sales. Simpler: SELECT customer_id, MIN(order_date) AS first_order, MAX(order_date) AS last_order FROM sales GROUP BY customer_id. To get full rows: WITH ranked AS (SELECT , ROW_NUMBER() OVER (PARTITION BY customer_id ORDER BY order_date) rn_first, ROW_NUMBER() OVER (PARTITION BY customer_id ORDER BY order_date DESC) rn_last FROM sales) SELECT FROM ranked WHERE rn_first=1 OR rn_last=1; Why it matters: Design choices compound at scale—wrong approach can cause 100× overhead. Scalability trade-offs: Profile before optimizing; validate on sample then full. Cost implications: Suboptimal choices multiply at billion-row scale.
Red Flag: Generic textbook answers. Pro-Move: 'At scale we measured X, implemented Y, achieved Z%—validated and iterated.'
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According to DataEngPrep.tech, this is one of the most frequently asked SQL interview questions, reported at 1 company. DataEngPrep.tech maintains an editor-reviewed database of 1,863 data engineering interview questions across 7 categories.