Main types of data warehouse model design

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Main types of data warehouse model design Data warehouse model design is the core part of data warehouse construction. Different model types are suitable for different business scenarios and needs. Below we will introduce several common data warehouse model types in detail. 1. Dimensional Model The dimensional model is the most commonly used model in data warehouses. It takes the fact table as the core and builds multiple dimension tables around the fact table. It is subject-oriented, business-oriented, and easy to understand and use. There are mainly the following types of dimensional models: Star Schema: The simplest and most commonly used dimensional model. A fact table is directly connected to multiple dimension tables and looks like a star. Advantages: Simple structure and high query efficiency. Disadvantages: There may be data redundancy in dimension tables. Snowflake Schema: There are hierarchical relationships between dimension tables, forming a snowflake-like structure


. Advantages: Reduce data redundancy and conform to Whatsapp Number database paradigm. Disadvantages: The query path is long, which may affect query performance. Constellation Schema : Multiple fact tables share dimensions, suitable for complex business scenarios. Advantages: High flexibility and able to adapt to complex business needs. Disadvantages: Model design is complex. 2. Conceptual Model The conceptual model is the starting point for data warehouse design. It describes the overall structure of the enterprise's business and is the basis for dimensional model design. Conceptual models are often represented using entity-relationship diagrams (ER diagrams) . 3. Logical Model The logical model converts the conceptual model into a table structure in the database and is an abstraction of the physical model. The logical model defines tables, fields, primary keys, foreign keys, et






Physical Model The physical model is the most detailed model in the data warehouse. It defines the physical storage structures such as tables, fields, indexes, and partitions in the database. The optimization of the physical model directly affects the performance of the data warehouse. 5. Other models Data Vault model: A model that emphasizes data history and traceability, and is suitable for scenarios that have high requirements for data traceability. Factless Fact model: A fact table without measure values, often used for event recording. Principles for selecting models Business needs: Choose the most suitable model based on business needs. Data characteristics: Consider the complexity and volume of data. Query mode: Analyze users' common query modes and optimize model design. Performance requirements.