A dimensional data modeling pattern for analytics warehouses — central fact tables (numeric measurements) surrounded by denormalized dimension tables (descriptive context) like a star.
Star Schema is the classic Kimball dimensional model for analytics. Fact tables hold numerical measurements (sales amount, units, durations) keyed by dimension foreign keys. Dimension tables hold descriptive attributes (customer name, product category, date attributes) and are deliberately denormalized — JOIN performance and analyst usability beat storage efficiency in analytics. Snowflake Schema is the more-normalized variant. Star Schema dominates analytics warehouses (Snowflake, BigQuery, Redshift) because it's optimized for the read-heavy aggregation queries analysts run.
Modeling e-commerce orders as a Star Schema: fact_orders with measurements + foreign keys, surrounded by dim_customer, dim_product, dim_date, dim_channel.
Star Schema is the durable default for analytics modeling — a 30-year-old pattern that survives because it matches how analytical queries actually work.
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