An Overview of Schemas and Data Modelling in Power BI

Source: Dev.to

Fact and Dimension Tables

Fact Tables

• OrderID, DateKey, ProductKey, and SalesAmount.
• Represent “how much” or “how many”.

Dimension Tables

• Contain “who, what, where, when”.

Types of Schemas in Power BI

Star Schema

• Fact table is surrounded by multiple dimension tables.
• Dimension tables are not connected to each other.

Snowflake Schema

• Product (Fact) table connects to Product (Dim), which then connects to a separate Category (Dim) table.
• Saves a small amount of storage by reducing redundancy but creates complex relationship chains.
• Forces Power BI to work harder to filter data, often leading to slower report performance; generally not ideal for Power BI reporting.

Managing Relationships

• One-to-Many
• Many-to-One
• One-to-One

Why Modeling is Critical

Performance

• A well-designed model reduces memory usage, improves query speed, and makes visuals load faster.

Accurate Reporting

• Good modeling ensures filters propagate correctly and measures calculate as expected.

Simpler DAX Measures

• Clean models lead to shorter DAX formulas, easier debugging, and fewer calculation errors.

Scalability and Maintenance

• A good model is easy to extend with new data and easier for other analysts to understand, supporting long‑term reporting needs.

Best Practices for Power BI Data Modelling

• Use star schema whenever possible.
• Separate facts and dimensions clearly.
• Avoid bi‑directional relationships unless necessary.
• Create a dedicated Date dimension.
• Remove unused columns.
• Keep column names business‑friendly.
• Validate totals against source systems.

Conclusion

Effective Power BI reporting is built on data modeling. Understanding fact tables, dimension tables, relationships, star schemas, and snowflake schemas allows you to construct models that are fast, accurate, and easy to manage. In Power BI, good visualizations start with good models. Performance, dependability, and confidence in your reports are all enhanced by devoting time to accurate data modeling.