Validate Data Quality — Solution & ExplanationProblems / Validate Data Quality / EditorialApproachData validation flags are useful for auditing data quality before further processing.SQLCombine all validity rules into a single CASE WHEN with AND:```sqlCASEWHEN price > 0AND quantity > 0AND transaction_date <= '2024-12-31'AND product_name IS NOT NULLAND TRIM(product_name) != ''THEN TRUEELSE FALSEEND AS is_valid```Checking IS NOT NULL before TRIM is important because TRIM(NULL) returns NULL, and NULL != '' is NULL (not TRUE), which would fall through to the ELSE branch correctly. However, being explicit improves readability.Hints

Use a CASE expression that checks ALL validity conditions with AND — if all pass, the row is valid.

Remember to handle the NULL product_name case separately with IS NOT NULL before checking TRIM.

In PySpark, F.when() with chained & conditions and .otherwise() maps directly to SQL's CASE WHEN.

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