**Fact tables**: Measures (quantity, amount); granular events; append-only; FKs to dimensions; high row count. **Dimension tables**: Descriptors (product name, customer); lower row count; used for grouping/filtering. **Why separate**: Facts grow linearly; dimensions change...
This medium-level SQL question appears frequently in data engineering interviews at companies like Myntra. While less common, it tests deeper understanding that distinguishes strong candidates. Mastering the underlying concepts (join, partition) 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.
Fact tables: Measures (quantity, amount); granular events; append-only; FKs to dimensions; high row count. Dimension tables: Descriptors (product name, customer); lower row count; used for grouping/filtering. Why separate: Facts grow linearly; dimensions change slowly. Star schema optimizes analytics—filter/group by dimension, aggregate facts. Scalability: Facts partitioned by date; dimensions often fit in memory for broadcast. Cost: Denormalized facts = storage bloat; normalized dimensions = join cost.
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Analyze My Answer — FreeAccording to DataEngPrep.tech, this is one of the most frequently asked SQL interview questions, reported at 1 company. DataEngPrep.tech maintains a curated database of 1,863+ real data engineering interview questions across 7 categories, verified by industry professionals.