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Barbara Morara
Barbara Morara

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Understanding Schemas and Data Modelling in Power BI

#analytics #data #dataengineering #microsoft

Introduction

Power BI is a powerful business intelligence tool that enables users to convert raw data into useful insights using interactive reports and dashboards. Nevertheless, the quality, performance, and accuracy of Power BI reports greatly rely on the way the data is modelled.
Data modelling is the process of structuring the data into tables, specifying the relationships between the tables, and selecting the correct schema. This article discusses the key concepts of data modelling for Power BI, which include schema, fact tables, dimension tables, relationships, star schema, and snowflake schema.

What Is Data Modelling in Power BI?

Data modelling in Power BI is defined as the process of organizing and structuring data in such a way that it can be easily analyzed and reported on. Data modelling in Power BI comprises:

  • Organizing the data in a logical table structure
  • Establishing relationships between the tables
  • Selecting the most appropriate schema
  • Ensuring the accuracy and efficiency of the data

Data modelling in Power BI helps in creating reports that are not only efficient but also easier to understand.

Fact Tables

A fact table holds data that is measurable and can be analyzed. This data is usually in numbers and is quite large in size.
Examples of data in a fact table:

  • Sales value
  • Number of units sold
  • Profit
  • Discount Fact tables usually have foreign keys that refer to dimension tables.

Dimension Tables
A dimension table holds data that describes the data in the fact table.

Examples of data in a dimension table:

  1. Customer name
  2. Product category
  3. Date
  4. Region or location Dimension tables are smaller and hold text data.

Relationships in Power BI

Relationships describe how tables are related in a data model. Power BI uses these relationships to merge data from different tables during analysis.

Common Types of Relationships

  • One-to-Many (1:*) – Most common (e.g., one customer → many sales)
  • One-to-One (1:1) – Rare
  • Many-to-Many (:) – Used cautiously

Relationship Direction

  • Single direction (recommended for simplicity and performance)
  • Both directions (can cause ambiguity if misused)

Well-defined relationships are essential to ensure correct calculations and avoid incorrect aggregations.

Schemas in Data Modelling

A schema is a method of organizing tables and relationships in a data model. The two most common schemas used in Power BI are Star Schema and Snowflake Schema.

Star Schema

A star schema is a data model that has a central fact table directly linked to multiple dimension tables. The data model looks like a star.

Features of Star Schema

  • There is only one fact table in the schema.
  • The dimension tables are not linked to each other.
  • The schema is simple and easy to understand.

Benefits of Star Schema

  • It has faster query execution times.
  • DAX calculations are simpler.
  • It is easier to maintain.
  • Recommended by Microsoft for Power BI.

Example

  1. FactSales
  2. DimCustomer
  3. DimProduct
  4. DimDate
  5. DimRegion
  6. DimRegion

Each dimension table is directly linked to the fact table.

Snowflake Schema

A snowflake schema is an extension of the star schema where the dimension tables are further normalized into multiple related tables.

Characteristics of Snowflake Schema

  • Dimension tables are divided into sub-dimension tables
  • More complex structure
  • Uses more relationships

Advantages of Snowflake Schema

  • Reduces data redundancy
  • Saves storage space
  • Disadvantages of Snowflake Schema
  • Slower performance compared to star schema
  • More complex relationships
  • Difficult for a beginner to understand

In Power BI, snowflake schemas are less preferred due to performance issues.

Feature Star Schema Snowflake Schema
Complexity Simple Complex
Performance Faster Slower
Number of Tables Fewer More
Ease of Use Very Easy Moderate to Difficult
Recommended in Power BI Yes Rarely

Why Good Data Modelling Is Critical in Power BI

  1. Improves Performance
    Data modeling can improve query speed and minimize relationships, hence improving user experience through quicker report loading.

  2. Ensures Accurate Reporting
    Good data modeling ensures that relationships are correct to avoid:

  3. Double counting

  4. Incorrect totals

  5. Misleading business decisions

  6. Simplifies DAX Calculations
    Star schema modeling simplifies DAX formula writing, reading, and debugging.

  7. Enhances Scalability
    Data modeling can handle large volumes of data and can grow with business requirements.

  8. Improves User Experience
    Data modeling can ensure that users can understand data correctly and slice it correctly for easier decision-making.

Best Practices for Data Modelling in Power BI

  • Use star schema modeling
  • Dimension tables must be descriptive
  • Avoid many-to-many relationships
  • Use meaningful names
  • Validate relationships
  • Minimize bi-directional filter

Data modeling is a critical aspect of successful Power BI reporting. Understanding different schemas, fact tables, dimension tables, and relationships enables analysts to create successful data models for Power BI reporting. Among different schemas, star schema modeling is highly recommended for use in Power BI reporting because of its simplicity and ability to improve query speed. By using good data modeling practices, users can unlock the full potential of Power BI reporting.

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