Pivot Sales Data — Solution & ExplanationProblems / Pivot Sales Data / EditorialApproachThis is the classic pivot pattern — converting row values into column headers.Since SQL has no universal PIVOT keyword, we use conditional aggregation:

GROUP BY product_name to get one row per product.

For each quarter, use SUM(CASE WHEN quarter = 'Q1' THEN revenue END) to extract that quarter's revenue into its own column.

When no rows match the CASE condition (no ELSE clause), the result is NULL — which handles products missing a quarter.

Key InsightThe number of output columns must be known at query-write time. This technique generalizes to any row-to-column transformation where the set of pivot values is fixed.Complexity

Time: O(n) — single pass over the data

Space: O(distinct products) for the grouped output

Hints

Use conditional aggregation: SUM(CASE WHEN quarter = 'Q1' THEN revenue END) to extract each quarter into its own column.

In PySpark, F.when() inside F.sum() achieves the same conditional aggregation pattern.

GROUP BY product_name collapses all quarters for each product into one row. Missing quarters naturally become NULL.

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