**Architectural Logic**: Fact table + dimensions = star schema; optimizes analytical query patterns. **Fact Table**: Measures (amount, quantity); FKs to dimensions; large; grain defined by dimensions. Additive or semi-additive. **Star Schema**: Fact surrounded by dimension...
This medium-level SQL question appears frequently in data engineering interviews at companies like HCL. 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.
Architectural Logic: Fact table + dimensions = star schema; optimizes analytical query patterns. Fact Table: Measures (amount, quantity); FKs to dimensions; large; grain defined by dimensions. Additive or semi-additive. Star Schema: Fact surrounded by dimension tables; denormalized dimensions; simple structure. Example: fact_sales (sale_id, date_key, product_key, store_key, amount, qty); dim_date, dim_product, dim_store. Why: Aligns with business thinking (measures by dimensions); fewer joins; BI-friendly. Scalability: Partition fact by date; surrogate keys for dimensions. Best Practice: Define grain explicitly; design for incremental loads.
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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.