A Beginner-Friendly Guide to Building Fast and Accurate Reports
Introduction
When working with Power BI, many beginners focus heavily on visuals, charts, and dashboards. While visuals are important, the real foundation of a good Power BI report is the data model behind it.
If your data model is poorly designed:
Reports become slow
Numbers don’t add up correctly
Filters behave unexpectedly
Dashboards are hard to maintain
This is where schemas and data modelling come in. Understanding these concepts early will save you a lot of frustration and help you build reliable, high-performance reports.
What Is a Schema?
A schema is the overall structure or blueprint of how data is organized in a database or data model.
Think of a schema as a map that shows:
What tables exist
What columns (fields) each table contains
How the tables are connected to each other
In Power BI, the schema is what you see in the Model view, where tables are connected by relationship lines.
In simple terms:
A schema explains how your data is arranged and how different pieces of data relate to one another.
Data Modelling
Data modelling is the process of designing that structure in a way that makes data easy to analyze, accurate to report on, and fast to query.
Data modelling involves decisions like:
_Which tables should exist
Which tables store numbers vs descriptions
How tables should be connected
Which relationships should be active
How filters should flow across the model_
You can think of data modelling as:
Designing the “engine room” of your Power BI report.
Good visuals sit on top of a good model. Poor visuals often hide a bad model
Key Building Blocks of Data Models
Before looking at schema types, it’s important to understand two fundamental table types used in BI.
Fact Tables
A fact table stores measurable, numerical, transactional data.
Characteristics:
Contains metrics like sales, quantity, revenue, cost
Usually very large
Records events or transactions
Contains foreign keys that link to dimensions
Example: Sales Fact Table
OrderID
CustomerID
ProductID
DateID
Quantity
Revenue
Fact tables answer questions like:
How much?
How many?
How often?
Dimension Tables
A dimension table stores descriptive information that helps explain or categorize facts.
Characteristics:
Smaller than fact tables
Used for filtering, grouping, and slicing data
Contains names, categories, dates, and attributes
Examples:
Customer Dimension → CustomerID, Name, Age, Region
Product Dimension → ProductID, Product Name, Category, Brand
Date Dimension → DateID, Year, Quarter, Month, Day
Dimension tables answer questions like:
Who?
What?
Where?
When?
Relationships in Power BI
Relationships define how tables are connected.
The most common relationship in Power BI is:
One-to-Many (1:*)
Example:
One customer → many sales
One product → many sales
One date → many transactions
In Power BI:
Relationships are created in Model view
Filters usually flow from dimension tables to fact tables
Correct relationships are critical for accurate calculations
Types of Schemas in Business Intelligence
The most common schemas used in Power BI are:
Star Schema
Snowflake Schema
Galaxy Schema (Fact Constellation)
1. Star Schema
The Star Schema is the most recommended and widely used schema in Power BI.
Structure
A central fact table in the middle
Dimension tables directly connected to the fact table
The layout looks like a star
Components of a Star Schema
Fact Table
Contains numeric measures
Contains foreign keys to dimensions
Large in size
Example: Sales Fact
OrderID
CustomerID
ProductID
DateID
Quantity
Revenue
Dimension Tables
Contain descriptive attributes
Smaller in size
Used in slicers and filters
Examples:
Customer → Name, Age, Region
Product → Category, Brand
Date → Year, Month, Day
Why Star Schema Is Recommended
Star schema is considered best practice in Power BI because:
High Performance
Fewer joins mean faster queries and better dashboard responsiveness.
Simplicity
Easy for beginners, analysts, and business users to understand.
Accurate Calculations
DAX measures behave more predictably with clean one-to-many relationships.
Better Filtering
Filters flow cleanly from dimensions to facts.
Microsoft Recommendation
Microsoft explicitly recommends star schemas for Power BI semantic models.
2. Snowflake Schema
A Snowflake Schema is a more complex version of a star schema.
Instead of having one large dimension table, dimensions are split into multiple related tables.
Structure
Fact table remains central
Dimension tables are normalized
Dimensions branch out into sub-dimensions
Example:
Customer → City → Region → Country
Product → Category → Department
Example Snowflake Design
Fact Table: Sales
OrderID
CustomerID
ProductID
DateID
Revenue
Dimension Tables:
Customer (CustomerID, Name, CityID)
City (CityID, CityName, RegionID)
Region (RegionID, RegionName, CountryID)
Country (CountryID, CountryName)
Advantages of Snowflake Schema
Reduces data redundancy
Saves storage space
Maintains better data consistency
Useful for very large datasets with complex hierarchies
Disadvantages in Power BI
More complex to design and maintain
Slower performance due to many joins
Harder for beginners and business users
More complicated DAX calculations
Because of this, snowflake schemas are generally not recommended for Power BI unless absolutely necessary.
Why Good Data Modelling Is Critical in Power BI
Data modelling directly affects everything in Power BI.
1. Performance
Well-designed models:
Reduce the number of joins
Improve query speed
Use memory efficiently
Keep dashboards responsive
2. Accurate Reporting
Clean relationships ensure:
Correct totals and aggregations
Filters work as expected
No duplicated or missing values
3. Scalability
Good models are:
Easier to extend
Easier to debug
Easier to reuse across reports
4. User Experience
Clear schemas:
Make reports easier to understand
Reduce confusion for stakeholders
Improve self-service analytics
5. Alignment with Best Practices
Power BI works best when you use:
Star schemas
One-to-many relationships
Dedicated date tables
Clean, well-named fields
Final Summary
Schemas **and **data modelling are the foundation of effective Power BI reporting.
A schema defines how data is structured and connected
Fact tables store numbers and metrics
Dimension tables store descriptive information
Star schemas are simple, fast, and recommended
Snowflake schemas save space but add complexity
In short:
Good data modelling turns raw data into fast, accurate, and trusted insights.
Thanks for reading , comment below which schema you prefer and why
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