Main types of data warehouse model design

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Data warehouse model design: comprehensive analysis and practical guide
What is data warehouse model design?

Data warehouse model design is the core link of data  warehouse construction. It provides logical organization for data in the data warehouse by establishing abstract structures to represent business entities , attributes and relationships in the real world. A good data warehouse model design can effectively support complex analytical queries and help enterprises make more informed decisions.
Why do we need data warehouse model design?
  
Organize data: Integrate data scattered in various systems into a unified model for easy management and query.
Improve query efficiency: Accelerate the execution speed of complex Email List analytical queries by optimizing model design.
Support multidimensional analysis: Provide flexible dimension slicing and drilling functions to meet the diverse analysis needs of different users.
Ensure data consistency: Ensure the consistency of data in the data warehouse with the source system data.
Star model
The simplest and most commonly used model.
It consists of a central fact table and multiple dimension tables.
The fact table stores numerical data, and the dimension table stores descriptive data .
Easy to understand and implement.
Snowflake model

An extension of the star model.
Dimension tables can be further decomposed into multiple levels to form a snowflake structure.
Provides more fine-grained dimension information.
The design is relatively complex.
Constellation model Multiple fact tables share dimension tables. Suitable for data integration of multiple business processes. Flexible design, but relatively complex implementation. Data warehouse model design steps Requirements analysis : Clarify business requirements and determine the business processes and indicators to be analyzed. Conceptual model design: Establish a conceptual model of business entities, attributes and relationships to reflect the essence of the business. Logical model design: Convert the conceptual model into a logical model , define tables, fields and keys. Physical model design: Map the logical model to a specific database system, considering optimization such as storage structure and index. Data warehouse model design principles Subject-oriented: The data warehouse is oriented to a specific subject area and organized around business themes. Integration: Integrate data from multiple source systems into a unified model. Time-varying: The data in the data warehouse changes over time, and historical data needs to be recorded.

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