Once data is loaded into the data warehouse

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A data warehouse (DW) is a subject-oriented, integrated, time-varying, non-volatile data collection used to support managers in decision analysis. The data warehouse design plan is the core of data warehouse construction, which determines the structure and organization of the data warehouse and how to efficiently support business analysis. 2. Data warehouse design process Demand analysis: Determine business goals: clarify which business problems the data warehouse will solve and which decisions it will support. Identify data sources: clarify which systems or data sources need to be included in the data warehouse.


Define dimensions and measures determine Special Data the dimensions (such as time, products, customers) and measures (such as sales, costs) for analysis. Determine user needs: clarify the access rights and analysis needs of different users to data. Conceptual model design: Establish the relationship between business entities and attributes to form a conceptual model. ER diagram is a commonly used conceptual model representation method. Logical model design: Convert the conceptual model into a logical model and define the table structure and fields. Determine the fact table and dimension table. Design the dimension hierarchy.



Physical model design Convert the logical model into a physical model and create database tables and indexes. Optimize the table structure and indexes to improve query performance. Data loading: Design ETL (Extract, Transform, Load) process. Extract data from the source system, clean and transform it, and load it into the data warehouse. Metadata management: Establish a metadata management system to record the structure, business rules, data quality and other information of the data warehouse. Test verification: Perform unit testing, integration testing and user acceptance testing. 3. Data warehouse model Dimensional modeling: Star model: A central fact table, and multiple dimension tables around the fact table.