Data Warehouse Models | Data Warehousing Architecture

Overview of Data Warehouse Models

Data warehouse models define the structure and organization of data within a data warehouse. Different models are used based on the analytical needs and data characteristics.

Key Points:

Common models include star schema, snowflake schema, and fact constellation.
Each model has its advantages and is suited for different types of data analysis.
The choice of model impacts query performance and data storage efficiency.

Main Types of Data Warehouse Models

Star Schema

The star schema is a widely used model in data warehousing. It consists of a central fact table connected to multiple dimension tables.


// Example: Star schema structure
Fact Table: Sales
Dimension Tables: Product, Time, Customer

Snowflake Schema

The snowflake schema expands on the star schema by normalizing dimension tables into multiple levels of related tables.


// Example: Snowflake schema structure
Fact Table: Sales
Normalized Dimension Tables: Product → Product Category → Product Subcategory

Fact Constellation

The fact constellation (also known as galaxy schema) involves multiple fact tables that share dimension tables.


// Example: Fact constellation structure
Fact Tables: Sales, Inventory
Shared Dimension Tables: Product, Time, Customer

Considerations for Choosing a Data Warehouse Model

When selecting a data warehouse model, consider factors such as query complexity, data relationships, and scalability requirements.

Conclusion

This guide provided an overview of data warehouse models, including star schema, snowflake schema, and fact constellation. Understanding these models helps in designing efficient data warehouses for analytical purposes.

Data Warehousing - Data Warehouse Models