Convert row-level records to column records.

SQLeasy0.5 min readPremium

Frequency

Low

Asked at 1 company

Category

487

questions in SQL

Difficulty Split

130E|271M|86H

in this category

Total Bank

1,863

across 7 categories

Asked at these companies

Comcast

Key Concepts Tested

spark sql

Expert AnswerPremium

99 wordsInterview-ready

**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....

The complete answer continues with detailed implementation patterns, architectural trade-offs, and production-grade considerations. It covers performance optimization strategies, common pitfalls to avoid, and real-world examples from companies like Comcast. The answer also includes follow-up discussion points that interviewers commonly explore.