Data warehouse model design is the core link of data warehouse construction

Its purpose is to transform the business data in the enterprise into a data structure that can be used for analysis and decision-making. Through reasonable design, data query efficiency can be improved, complex analysis requirements can be supported, and valuable insights can be provided to the enterprise. 1. Dimensional modeling: Dimension: Describes the attributes of business entities, such as time, location, product, etc.






Fact: Describes the measurement value of business events, such as sales, quantity, etc. Star schema: A fact table is associated with multiple dimension tables to form a star-like structure. Snowflake schema: The dimension table is further decomposed into multiple levels to form [url=https://www.fbusers.club/phone-number/[/url]
 a snowflake-like structure. Opens in a new window www.dataapplab.com Star schema and snowflake schema 2. ER model: Entity: Objects in the real world, such as customers, products, orders, etc. Attribute: Characteristics of an entity, such as customer name, product price, etc. Relationship: The connection between entities.



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Logical model: Based on the conceptual model, select a suitable database model (such as a relational database) for design. Physical model: Determine the physical implementation details such as table structure, index, partition, etc. Design process Requirements analysis: Determine business requirements and clarify the indicators and dimensions that need to be analyzed. Conceptual model design: According to business requirements, establish a conceptual model to describe business entities and relationships. Logical model design: Convert the conceptual model into a logical model and select a suitable database model.
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