Is Your Power BI Report Slow Fix This First.

• Is Your Power BI Report Slow? Fix This First.

The Star Schema Goal

• A core goal of good data modeling is speed
• Fast reports mean happy and productive users
• Slow reports lead to frustration and abandoned insights
• The star schema is the gold standard for performance
• It simplifies the data structure for the engine
• A clean model is easier to maintain and understand

Mistake 1: Flat Tables

• Avoid using one giant, wide table for everything
• Flat tables contain redundant, repeated data
• This dramatically increases the data model size
• Larger models require more memory and processing
• Relationships and filters become difficult to manage
• Always split data into facts and dimensions

Fix: Build Facts & Dimensions

• Fact tables hold numerical values you want to analyze
• Examples include sales amounts, quantities, or costs
• Dimension tables hold descriptive attributes for filtering
• Examples include dates, products, customers, or regions
• Connect them with simple, single-column relationships
• This is the foundation of the star schema

Mistake 2: Many-To-Many

• Relationships should ideally be one-to-many
• A many-to-many relationship is often a red flag
• It can cause duplicate aggregation of numbers
• This leads to incorrect results in your reports
• The engine must work harder to resolve the logic
• It is a common side effect of poor table design

Fix: Use Bridge Tables

• Resolve many-to-many relationships with a bridge table
• A bridge table sits between two dimension tables
• It breaks the complex link into two simple ones
• This ensures accurate filtering and correct calculations
• Always validate your numbers after implementing a bridge
• This maintains data integrity and report accuracy

Mistake 3: Wrong Data Types

• Using text data types for numbers or dates is costly
• Text columns are much larger and slower to process
• Operations like sorting numbers stored as text are inefficient
• Always use the most specific data type possible
• Use whole numbers for IDs and decimal numbers for values
• Use date data types for all date fields

Pro Performance Tips

• Hide unnecessary columns from the model view
• This reduces clutter and improves model refresh time
• Avoid creating calculated columns when possible
• Use measures for on-the-fly calculations instead
• Limit the use of bi-directional relationships
• They can cause ambiguity and performance overhead

Review & Next Steps

• Audit your current models for flat table structures
• Identify and fix any many-to-many relationships
• Verify all column data types are set correctly
• Hide any columns not used in reporting
• Test report performance after each change
• A clean star schema is the key to speed