Reviewed by Aditya Kumar · Last reviewed 2026-03-24
**Architectural Logic**: TRUNCATE vs DELETE and UNION vs UNION ALL have semantic and performance implications. **TRUNCATE vs DELETE**: TRUNCATE = metadata-only, fast, no WHERE, no rollback. DELETE = row-level, supports WHERE, rollback, slower. Use TRUNCATE for full refresh;...
This hard-level SQL question appears frequently in data engineering interviews at companies like Presidio. While less common, it tests deeper understanding that distinguishes strong candidates. Mastering the underlying concepts (etl, spark) will help you answer variations of this question confidently.
This is a senior-level question that tests architectural thinking. Lead with the high-level design, then drill into specifics. Discuss trade-offs explicitly - there is rarely one correct answer. Show awareness of scale, fault tolerance, and operational complexity.
Architectural Logic: TRUNCATE vs DELETE and UNION vs UNION ALL have semantic and performance implications. TRUNCATE vs DELETE: TRUNCATE = metadata-only, fast, no WHERE, no rollback. DELETE = row-level, supports WHERE, rollback, slower. Use TRUNCATE for full refresh; DELETE for selective removal. UNION vs UNION ALL: UNION = deduplicates (implicit sort/distinct); slower. UNION ALL = keeps all rows; faster. Use UNION when dedup required; UNION ALL when impossible or acceptable. Scalability: TRUNCATE O(1); DELETE O(n). UNION ALL avoids shuffle for dedup. Cost: TRUNCATE for staging; UNION ALL for ETL appends. Distributed: UNION ALL preferred in Spark to avoid expensive dedup.
Red Flag: Using UNION when UNION ALL is correct—unnecessary cost. Pro-Move: Know when duplicates are impossible (e.g., disjoint date ranges); prefer UNION ALL; apply DISTINCT separately if needed.
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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.