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  When designing a data warehouse, the structural foundation you choose impacts everything from query performance to maintenance overhead. While both dimensional models aim to organize data for efficient reporting, the decision usually comes down to a specific trade-off: storage efficiency versus query speed. This debate centers on the star vs snowflake schema architecture. Understanding the nuances between these two models is critical for data architects and engineers who need to optimize their Business Intelligence (BI) environment. The Core Difference Between Star and Snowflake Schema At a high level, the primary difference between star and snowflake schema lies in normalization. Normalization is the process of organizing data in a database to reduce redundancy. Star Schema: Uses a denormalized structure. The dimension tables are not normalized, meaning they may contain redundant data (like repeating "Country" for every "City"). It resembles a star shape with ...