Reviewed by Aditya Kumar · Last reviewed 2026-03-24
**Architectural Logic**: NULL represents unknown/missing; it propagates through expressions (NULL + 1 = NULL). Handling: IS NULL / IS NOT NULL for predicates; COALESCE(val1, val2, ...) for first non-NULL (portable); ISNULL/IFNULL for dialect-specific default; NULLIF(val1, val2)...
Red Flag: Joining on nullable columns without handling—'orphan' semantics. Pro-Move: 'We use COALESCE(join_key, -1) for nullable keys and document -1 as 'unknown' in the data dictionary.'
This medium-level SQL question appears frequently in data engineering interviews at companies like Aarete, Dunnhumby, Incedo. While less common, it tests deeper understanding that distinguishes strong candidates. Mastering the underlying concepts (join, sql) will help you answer variations of this question confidently.
Break this problem into components. Identify the core trade-offs involved, then walk the interviewer through your reasoning step by step. Demonstrate awareness of edge cases and production considerations - this is what separates good answers from great ones.
Architectural Logic: NULL represents unknown/missing; it propagates through expressions (NULL + 1 = NULL). Handling: IS NULL / IS NOT NULL for predicates; COALESCE(val1, val2, ...) for first non-NULL (portable); ISNULL/IFNULL for dialect-specific default; NULLIF(val1, val2) to normalize to NULL. Why: JOIN on NULL yields no match (NULL != NULL). Aggregates ignore NULL except COUNT(*). Explicit handling prevents silent exclusions. Scalability: COALESCE chains are cheap; avoid NULL in join keys—consider sentinel values or pre-join normalization. Cost: NULL in indexes can affect pruning; sparse columns may benefit from nullable design. Best practice: Be explicit; use COALESCE for portability; document NULL semantics in contracts.
Red Flag: Joining on nullable columns without handling—'orphan' semantics. Pro-Move: 'We use COALESCE(join_key, -1) for nullable keys and document -1 as 'unknown' in the data dictionary.'
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According to DataEngPrep.tech, this is one of the most frequently asked SQL interview questions, reported at 3 companies. DataEngPrep.tech maintains an editor-reviewed database of 1,863 data engineering interview questions across 7 categories.