The principles of Dimensional Modeling are based on design techniques that optimize data storage, querying, and reporting, with a primary focus on making the data easy to understand and use for business analysis. These principles ensure that data is organized in a way that makes it intuitive and efficient to query, analyze, and report on. Below are the key principles of dimensional modeling:

1. Focus on Business Process

• Principle: Dimensional modeling is driven by the business process you are trying to analyze, such as sales, inventory, or customer behavior.
• Why: The goal is to model the data in a way that supports reporting on key business metrics and performance indicators. By focusing on the business process, you ensure that the data structure is aligned with how business users think and what they need to analyze.

2. Use of Facts and Dimensions

• Principle: Data is organized into fact tables (for quantitative data) and dimension tables (for descriptive data).
• Why: Facts contain the numerical measurements (e.g., sales amounts, quantities), and dimensions provide context for those facts (e.g., time, product, region). This separation allows easy querying and analysis, as users can aggregate facts based on different dimensions.

3. Grain Definition

• Principle: The "grain" of the fact table must be defined clearly before modeling.
• Why: The grain refers to the level of detail captured in the fact table (e.g., a sales transaction, a daily summary of sales, etc.). Establishing the grain helps ensure that the data is stored at the right level of granularity, avoiding confusion and ensuring consistency.

4. Denormalization (Star Schema)

• Principle: Use denormalization for the dimension tables, favoring a star schema approach, where the fact table is surrounded by dimension tables.
• Why: Denormalization improves query performance by reducing the need for complex joins. In star schema, the fact table and dimensions are related through foreign keys, making it easy for analysts to quickly retrieve and aggregate data.

5. Dimension Attributes

• Principle: Dimension tables should contain attributes that describe the data in meaningful ways.
• Why: Dimension attributes provide rich context to the facts, enabling users to slice and dice the data. For instance, a "Product" dimension table might include attributes like Product_Name, Category, Brand, Color, etc.

6. Conformed Dimensions

• Principle: Dimensions should be conformed across different fact tables if they represent the same entity.
• Why: This ensures consistency when comparing data from different fact tables. For example, the "Time" dimension should have the same structure (with attributes like Year, Quarter, Month) in all fact tables, making it easier to aggregate and compare data across different areas of the business.

7. Slowly Changing Dimensions (SCD)