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Why star schema? Compared with snowflake schema and normalized approaches.

SQLmedium0.5 min read

**Star schema**: One central fact table; dimensions connected directly; denormalized dimensions. **Snowflake**: Normalized dimensions (dim → sub-dim). **Normalized (3NF)**: Many tables, minimal redundancy. **Star benefits**: Simpler queries, fewer JOINs, optimized for analytics,...

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Frequency
Low
Asked at 1 company
Category
487
questions in SQL
Difficulty Split
130E|271M|86H
in this category
Total Bank
1,863
across 7 categories
Asked at these companies
Daniel Wellington
Key Concepts Tested
joinsnowflake

Why This Question Matters

This medium-level SQL question appears frequently in data engineering interviews at companies like Daniel Wellington. While less common, it tests deeper understanding that distinguishes strong candidates. Mastering the underlying concepts (join, snowflake) will help you answer variations of this question confidently.

How to Approach This

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.

Expert Answer
93 words

Star schema: One central fact table; dimensions connected directly; denormalized dimensions. Snowflake: Normalized dimensions (dim → sub-dim). Normalized (3NF): Many tables, minimal redundancy. Star benefits: Simpler queries, fewer JOINs, optimized for analytics, intuitive for BI. Snowflake adds flexibility, reduces redundancy—useful when dimension hierarchies are complex and storage matters. Normalized: Less redundancy; complex for analytics. Why star for analytics: Query simplicity and performance; BI tools expect star. Scalability trade-offs: Star can duplicate dimension attributes; snowflake reduces that at cost of joins. Cost: Star = more storage, faster reads; normalized = less storage, slower reads.

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