Window example—rank per group: SELECT *, ROW_NUMBER() OVER (PARTITION BY dept ORDER BY salary DESC) rn FROM employees. PySpark: w = Window.partitionBy('dept').orderBy(col('salary').desc()); df.withColumn('rn', row_number().over(w)). Use for: top-N, dedup, running metrics....
This medium-level SQL question appears frequently in data engineering interviews at companies like Capgemini. 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.
Window example—rank per group: SELECT *, ROW_NUMBER() OVER (PARTITION BY dept ORDER BY salary DESC) rn FROM employees. PySpark: w = Window.partitionBy('dept').orderBy(col('salary').desc()); df.withColumn('rn', row_number().over(w)). Use for: top-N, dedup, running metrics. Combine with filter: WHERE rn <= 3. 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.
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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.