Reviewed by Aditya Kumar · Last reviewed 2026-03-24
Handling null values depends on business context. Options: (1) Fill with default—COALESCE(column, 0) for numeric, COALESCE(column, 'Unknown') for strings; (2) Forward/backward fill for time-series—LAG/LEAD or pandas ffill/bfill; (3) Impute (mean, median, mode) for ML pipelines;...
This easy-level SQL question appears frequently in data engineering interviews at companies like Infosys. While less common, it tests deeper understanding that distinguishes strong candidates.
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.
Handling null values depends on business context. Options: (1) Fill with default—COALESCE(column, 0) for numeric, COALESCE(column, 'Unknown') for strings; (2) Forward/backward fill for time-series—LAG/LEAD or pandas ffill/bfill; (3) Impute (mean, median, mode) for ML pipelines; (4) Exclude—WHERE column IS NOT NULL when nulls are invalid; (5) Treat as separate category—CASE WHEN column IS NULL THEN 'Missing' ELSE column END. For single-column analysis, use COUNT(), COUNT(column), and COUNT() - COUNT(column) to gauge null prevalence. Document null semantics (missing vs. unknown vs. inapplicable). In production, enforce NOT NULL where appropriate and use schema validation. Example: SELECT COALESCE(region, 'Unassigned') AS region, SUM(sales) FROM fact_sales GROUP BY 1; Why it matters: Design choices compound at scale—wrong approach can cause 100× overhead. Scalability trade-offs: Profile before optimizing; validate on sample then full. Cost implications: Suboptimal choices multiply at billion-row scale.
Red Flag: Generic textbook answers. Pro-Move: 'At scale we measured X, implemented Y, achieved Z%—validated and iterated.'
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According to DataEngPrep.tech, this is one of the most frequently asked SQL interview questions, reported at 1 company. DataEngPrep.tech maintains an editor-reviewed database of 1,863 data engineering interview questions across 7 categories.