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Schemas and Data Modelling in Power BI - Core Concepts

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Schemas and Data Modelling in Power BI

Data modelling is one of the most critical steps in building efficient, accurate, and scalable Power BI reports. A well-designed data model improves performance, simplifies DAX calculations and ensures reliable insights.

What is Data Modelling in Power Bi?

Data modelling in Power BI is the process of:

  • Structuring data into logical tables
  • Defining relationships between those tables
  • Optimizing how data is filtered and aggregated

A good model acts as the foundation for all visuals, calculations, and insights.

Fact Tables vs Dimension Tables

Fact Tables

Fact tables contain measurable, quantitative data.

Examples:

  • Sales Amount
  • Quantity Sold
  • Revenue
  • Cost

Characteristics:

  • Large in size
  • Contain foreign keys
  • Central to analysis

Dimension Tables

Dimension tables provide context to facts.

Examples:

  • Customer
  • Product
  • Date
  • Geography

Characteristics:

  • Smaller than fact tables
  • Contain descriptive attributes
  • Used for slicing and filtering data

What Is a Schema?

A schema defines how tables are structured and related in a data model. In Power BI, schemas determine how filters flow and how efficiently the engine processes queries.

Star Schema

The Star Schema is the most recommended schema for Power BI.

Structure

  • One fact table at the center
  • Multiple dimension tables connected directly to the fact table 
Customer   Product   Date
     \        |        /
         Sales Fact
Enter fullscreen mode Exit fullscreen mode

Benefits

  • Better performance
  • Simpler DAX formulas
  • Predictable filter behavior
  • Easy to understand and maintain

Snowflake Schema ❄️

The Snowflake Schema normalizes dimension tables into multiple related tables.

Example

Product → Category → Department → Sales Fact

Drawbacks

  • More joins
  • Slower performance
  • More complex relationships
  • Harder to troubleshoot

Power BI works best with denormalized dimension tables, so snowflake schemas are generally discouraged.

Other Schema Types

Galaxy (Fact Constellation) Schema

  • Multiple fact tables
  • Shared dimension tables

Useful for complex enterprise models but requires careful design to avoid ambiguity.

Relationships in Power BI

Relationships control how tables interact.

Common Relationship Types

  • One-to-Many (1:*) → Most common
  • Many-to-Many (:) → Use cautiously
  • One-to-One (1:1) → Rare

Filter Direction

  • Single Direction (Dimension → Fact)
  • Bi-Directional (only when necessary)

Common Data Modelling Mistakes

  • Using one large flat table
  • Overusing many-to-many relationships
  • Missing a Date table
  • Leaving unused columns in the model
  • Incorrect relationship directions

Why Good Data Modelling Matters

A solid data model:

  • Improves report performance
  • Reduces DAX complexity
  • Ensures accurate calculations
  • Makes reports easier to maintain
  • Enhances user experience

Power BI Data Modelling Best Practices

  • Use a Star Schema
  • Separate facts and dimensions
  • Use one-to-many relationships
  • Prefer single-direction filtering
  • Include a dedicated Date table
  • Remove unused columns

Final Thoughts

Power BI is not just about visuals, it’s about how data is structured behind the scenes. Investing time in proper schemas and data modelling pays off with faster reports, simpler calculations and more trustworthy insights.

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