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Dimensional Modeling and Slowly Changing Dimensions

A star schema splits data into facts (the events, one row per measurement) and dimensions (the descriptive context). The hard part is not the shape, it is grain: stating what one fact row represents before you write anything, because getting that wrong is what makes every number downstream quietly incorrect.

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RELATED CONCEPTS

Lakehouse, Delta and the Medallion Architecture ETL vs ELT The Ontology (Semantic Layer)Data Quality and Validation Query Optimization and Execution Plans

LESSONS THAT TEACH THIS

Foundations of Forward Deployed EngineeringDeclare the grain and prove that the totals survive joins30 min→The FDE EngagementKeep link evidence, uncertainty and history in the query30 min→

PRACTICE THIS IN REAL QUESTIONS

SQL & Data EngineeringExplain Snowflake micro-partitions and clustering, and when a bigger warehouse will NOT make a query faster.→SQL & Data EngineeringHow do you size Snowflake warehouses, when do you scale up vs scale out, and how do you keep the bill sane?→System Design & Production EngineeringDecompose: design a system to detect credit-card fraud.→System Design & Production EngineeringDecompose: design a system to coordinate disaster response for a city.→System Design & Production EngineeringDecompose: merge patient records across a network of hospitals.→SQL & Data EngineeringDesign a star schema for a ride-sharing company's analytics.→