Data modelling is the foundation of building scalable and
high-performance dashboards in Power BI. While many developers focus
heavily on visuals and DAX calculations, the true performance and
accuracy of a report depend heavily on how data is structured.
🧱 What Is Data Modelling in Power BI?
Data modelling refers to structuring data into logical formats that
support analysis and reporting. Dimensional modelling organizes data
into:
- Facts (measurable metrics)
- Dimensions (descriptive attributes)
A well-designed Power BI model determines:
- How tables relate
- How filters propagate
- How fast reports load
- How accurate calculations are
⭐ Star Schema
✅ What Is Star Schema?
A Star Schema organizes data using:
- One central fact table
- Multiple dimension tables connected directly to the fact table
The structure resembles a star, where dimension tables surround the
central fact table.
📊 Components of Star Schema
Fact Table
Contains measurable and quantitative data such as:
- Sales revenue
- Quantity sold
- Profit
- Discount
Each row represents a business event like a transaction.
Dimension Tables
Provide descriptive context to fact data such as:
- Customer details
- Product attributes
- Date/Time
- Store location
🚀 Benefits of Star Schema
- ✔ High query performance
- ✔ Simple design
- ✔ Easier DAX calculations
- ✔ Optimized for reporting and dashboards
⚠️ Limitations
- Data redundancy
- Higher storage usage
❄️ Snowflake Schema
✅ What Is Snowflake Schema?
A Snowflake Schema extends star schema by normalizing dimension
tables into multiple related tables.
Example:
Customer → City → Country
📌 Features
- Normalized dimension tables
- Supports hierarchical drill-down analysis
- Improves data integrity
- Requires additional joins
⚖️ Advantages
- ✔ Reduced redundancy
- ✔ Better data consistency
- ✔ Supports complex hierarchies
⚠️ Limitations
- More complex design
- Slower performance due to joins
📊 Fact Tables vs Dimension Tables
📊 Fact Tables
Store numeric metrics and foreign keys linking to dimensions.
Usually contain transactional data and large volumes of records.
🧾 Dimension Tables
Store descriptive attributes that provide context to fact tables.
Used for filtering, grouping, and categorizing data.
🔗 Relationships in Power BI
Relationships connect tables and enable filtering across datasets.
📌 Types of Relationships
- One-to-Many -- One dimension record links to many fact records
- Many-to-One -- Reverse of one-to-many
- Many-to-Many -- Multiple matching records on both sides
🚨 Why Good Data Modelling Is Critical
⚡ Performance Optimization
- Improves query speed
- Reduces memory usage
- Enables faster dashboard loading
🎯 Accurate Reporting
- Ensures correct aggregations
- Maintains reliable filter behavior
🧠 Easier DAX Calculations
- Simplifies analytical queries
- Improves calculation accuracy
🔧 Scalability
- Supports future data expansion
- Easier troubleshooting and maintenance
⭐ Star Schema vs Snowflake Schema
Feature Star Schema Snowflake Schema
Structure Denormalized Normalized
Performance Faster queries Slower queries
Complexity Simple Complex
Storage Higher storage Lower storage
Use Case Reporting dashboards Large complex warehouses
🏆 Power BI Data Modelling Best Practices
- Use Star Schema whenever possible
- Separate fact and dimension tables
- Maintain clear relationships
- Optimize data types
- Reduce unnecessary joins
📌 Real-World Analogy
Think of a library:
- Fact tables = Books (contain measurable information)
- Dimension tables = Catalogue system (organizes and locates books)
🎯 Conclusion
Data modelling is one of the most important skills for Power BI
developers. Understanding schema design ensures dashboards are:
- Fast
- Accurate
- Scalable
- Maintainable
Before building visuals or writing DAX formulas, always ask:
Is my data model structured correctly?
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