Handle Missing Data — Solution & ExplanationProblems / Handle Missing Data / EditorialApproachReal-world data cleaning often requires different NULL-handling strategies per column:

Fill with default: COALESCE(department, 'Unassigned') — when a reasonable default exists.

Fill with zero: COALESCE(salary, 0) — common for numeric fields where NULL means 'no data'.

Exclude rows: WHERE hire_date IS NOT NULL — when the missing value makes the row unusable.

Fill with neutral value: COALESCE(performance_rating, 3) — when a middle/neutral default makes sense.

The key is choosing the right strategy for each column based on business context.Hints

Different columns may need different NULL-handling strategies: fill with a default, filter out, or leave as-is.

COALESCE fills NULLs. WHERE ... IS NOT NULL excludes NULLs. These are complementary tools.

In PySpark, .filter(F.col('x').isNotNull()) removes NULL rows, while F.coalesce() fills them.

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