Advantages of star and snowflake schemas:

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A data warehouse is a centralized repository of integrated data from multiple sources, designed to support business analysis and decision-making. The structure of a data warehouse typically follows a star schema or snowflake schema. Star Schema Fact table: Contains measurements or metrics (e.g., sales, revenue, quantity) associated with business events. Dimension tables: Contain attributes that describe the dimensions of the fact table (e.g., time, product, customer). Relationships.
Dimension tables are related to Whatsapp Numberthe fact table through foreign keys. Example: Fact table: Sales Sales ID Product ID Customer ID Time ID Quantity Revenue Dimension tables: Product Product ID Product name Category Customer Customer ID Customer name City Country Time Time ID Date Day of week Month Year Snowflake Schema Fact table: Similar to the star schema. Dimension tables: Can have additional levels of granularity, creating a hierarchical structure. Relationships: Dimension tables are related to the fact table through foreign keys, and to other dimension tables through foreign keys. Example: Fact table: Sales Sales ID Product ID Customer ID Time ID Quantity Revenue Dimension tables.





Product Product ID Product name Category Customer Customer ID Customer name City Country Time Time ID Date Day of week Month Year Location Location ID City Country Region Advantages of star and snowflake schemas: Performance: Optimized for analytical queries. Simplicity: Easy to understand and maintain. Flexibility: Can accommodate different levels of granularity. Choosing between star and snowflake schemas depends on the specific requirements of the data warehouse. For example, if the data warehouse needs to support multiple levels of granularity for a dimension, a snowflake schema might be more suitable. Would you like to know more about data warehouse design, implementation, or best practices? Gemini may display inaccurate info, including about people, so double-check it.