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6. Multidimensional Database Concepts

Data Warehouses view data as a "Cube" rather than a flat table. This allows users to analyze data across multiple dimensions.

6.1 Fact and Dimension Data

Fact Data

• Definition: Facts are the quantitative measurements or metrics of the business. They represent "what we are measuring."
• Characteristics: usually numeric and additive.
• Examples: Sales Revenue ($), Units Sold, Profit Margin, Inventory Level.
• Types of Facts:

1. Additive Facts: Can be summed across all dimensions (e.g., Sales). This is the most flexible type.
2. Semi-Additive Facts: Can be summed across some dimensions but not others (e.g., Bank Account Balance). You can sum balances across customers, but not across time (the balance is a snapshot at a specific moment).
3. Non-Additive Facts: Cannot be summed across any dimension (e.g., Unit Price, Ratios, Percentages).

Dimension Data

• Definition: Dimensions provide the context for the facts. They answer the "Who, What, Where, When, Why" of the data.
• Characteristics: Usually textual, descriptive, and discrete.
• Examples:
• Time Dimension: Date, Month, Quarter, Year.
• Product Dimension: Product Name, Category, Brand.
• Location Dimension: Store Name, City, State, Country.
• Customer Dimension: Name, Age, Gender.