Logical model design: Convert the conceptua

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l model into a logical model, determine the table structure, field type, and primary key and foreign key relationships. Physical model design: Convert the logical model into a physical model, considering database performance optimization, index design, etc. Data warehouse model design principles Subject-oriented: Data warehouses are oriented to specific business subjects, such as sales, finance, customers, etc.






Integration: Integrate data from multiple data sources Email List into a unified view. Time-varying: Data warehouses store historical data and reflect the data change process. Non-volatility: Once data enters the data warehouse, it is generally not modified or deleted. Data warehouse model design tools Power BI Desktop: Microsoft's business intelligence tool that supports data modeling and visualization. Tableau: A powerful data visualization tool that also supports data modeling. SQL Server Integration Services (SSIS): A tool for data extraction, transformation, and loading (ETL). Erwin Data Modeler: A professional database modeling tool.










Data warehouse model design case Retail industry: Analyze sales trends, customer behavior, etc. based on product, sales, time, store, etc. Financial industry: Analyze customer value, risk management, etc. with dimensions such as customers, accounts, transactions, and products. Telecommunications industry: Analyze customer churn, service quality, etc. with dimensions such as customers, services, and equipment. Factors affecting the quality of data warehouse model design Business understanding: In-depth understanding of business needs is the basis for designing high-quality models. Data quality: The quality of data directly affects the accuracy of the model.