**SQL**: SELECT product_id, SUM(units_sold) AS total_units FROM transactions GROUP BY product_id. **With product name**: JOIN products. **Spark**: df.groupBy('product_id').agg(F.sum('units_sold').alias('total_units'))....
Pro-Move: 'We use COALESCE(units_sold,0) and filter date range—analysts often forget; we bake into view.' Red Flag: SUM on nullable column without handling—NULL propagates.
This medium-level General/Other question appears frequently in data engineering interviews at companies like Wayfair. While less common, it tests deeper understanding that distinguishes strong candidates. Mastering the underlying concepts (join, 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, SUM(units_sold) AS total_units FROM transactions GROUP BY product_id. With product name: JOIN products. Spark: df.groupBy('product_id').agg(F.sum('units_sold').alias('total_units')). Best practice: Add date filter for period; handle NULL units_sold.
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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.