Schemas and Data Modelling in Power BI

Data modelling is the backbone of every Power BI report. A well curated model provides fast performance, accurate calculations and easily understood reports
This article explains:

• Power BI data modelling
• Fact and Dimension Tables
• Relationships
• Star schema
• Snowflake schema
• Importance of good modelling

Data modelling in Power BI

Data Modelling is like a foundation. It involves the process of structuring,organizing and connecting data tables so that Power BI can easily analyze them. You;

• Define relationships between tables
• Decide how data flows
• Prepare the model for DAX calculations A good model answers the questions, what happens, when, where and under what conditions? ## Fact Tables These are tables that store measurable and quantitative data. They contain very many rows,numeric values used in calculations and are linked to multiple dimensions.

Examples

this tables links;

• dim Customer-> who made the purchase
• dim Product-> what product was purchased
• dim Territory-> where the purchase was made
• dim Date-> when the events occurred

Dimension tables

These store descriptive attributes that provide context to facts. They are smaller than fact tables,contain text and categorical data and are used for filtering,grouping and slicing data.
Example;

Star Schema

This is a foundational data modelling technique for data warehousing featuring a central fact table surrounded by multiple dimensional tables. It resembles a star.

Benefits

• Has a faster query performance
• simpler DAX formulas
• It's easier to understand and maintain
• Ideal for Power BI

Snowflake Schema

This is a logical data modelling technique where a central fact table connects to normalized dimension tables which are further broken down into sub-dimensions to reduce data redundancy.
It is an extension of star schema and is often used for complex and hierarchical data to enable storage efficiency at the cost of query performance due to many required joints.

Benefits

• Efficient storage utilization
• Cleaner data structures
• Better handling of complex data
• Easier maintenance ### Disadvantages
• Slower performance due to increases query
• More complex relationships
• Harder DAX calculations
• Commonly used in warehouses not ideal for Power BI

Relationships in Power BI

Relationships define how tables are connected enabling data modelling, filtering and analyzing without merging.
Relationship types include:

• One to many (1:*)- commonly used
• many-to-many (:)
• one-to-one (1:1)- rarely used

Importance of data modelling

Performance

• Faster visualization due to fewer tables and joins
• Star schemas reduce query complexity
• Efficient models use less memory

Accuracy

• Proper relationships ensure correct totals
• DAX behaves predictably

Scalability

• Easy to add new measures
• Model supports large datasets
• Reports remain responsive as data grows

Usability

• Dimensions are easy to understand
• Clear fields for slicing and filtering
• Clean and professional report structure

In summary, investing time in proper modelling saves time of debugging and ensures your report tells the right story with confidence.