**Architectural Logic**: Pivoting (rows→columns) is a reshape operation with cardinality and performance implications. **SQL**: Aggregate + CASE: `SELECT product_id, MAX(CASE WHEN month='Jan' THEN sales END) AS jan_sales, ... FROM sales GROUP BY product_id`. Or PIVOT (SQL...
This easy-level SQL question appears frequently in data engineering interviews at companies like Comcast. While less common, it tests deeper understanding that distinguishes strong candidates. Mastering the underlying concepts (spark, sql) will help you answer variations of this question confidently.
Start by clearly defining the core concept being asked about. Interviewers want to see that you understand the fundamentals before diving into implementation details. Structure your answer with a definition, then explain the practical application with a concise example.
Architectural Logic: Pivoting (rows→columns) is a reshape operation with cardinality and performance implications. SQL: Aggregate + CASE: SELECT product_id, MAX(CASE WHEN month='Jan' THEN sales END) AS jan_sales, ... FROM sales GROUP BY product_id. Or PIVOT (SQL Server). Spark: df.groupBy("product_id").pivot("month").agg(sum("sales")). Why Cardinality Matters: High cardinality in pivot column (e.g., pivot by user_id) causes column explosion—hundreds of columns, schema bloat. Scalability: Dynamic pivots require dynamic SQL or Spark; static pivots are efficient. Cost: Wide tables increase storage and scan cost; consider whether long format is preferable for downstream analytics. Trade-off: Pivot for human-readable reports; long format for ML and flexible querying.
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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.