Galaxy Schema in Power BI: Complete Guide

Galaxy Schema in Power BI: Complete Guide

Thejaswini S.V

 

What is Galaxy Schema?

A galaxy schema (also known as a fact constellation schema) is a data modeling technique where multiple fact tables share common dimension tables.
  • It contains more than one fact table
  • These fact tables are connected to shared dimension tables
  • The structure looks complex, like a galaxy of stars

That’s why it is called Galaxy Schema.




In Simple Terms

Galaxy Schema = Multiple fact tables + shared dimension tables

Example

Fact Tables:
  • Sales
  • Orders
  • Inventory
Shared Dimension Tables:
  • Product
  • Customer
  • Date
  • Region
All fact tables connect to the same dimension tables.



Why it is Used

  • To handle multiple business processes
  • To integrate data from different departments
  • To build enterprise-level data models

Features of Galaxy Schema

1. Multiple Fact Tables

Unlike Star Schema, it contains multiple fact tables representing different processes.

2. Shared Dimension Tables

Dimension tables like Product, Customer, and Date are shared across fact tables.

3. Complex Structure

The model is more complex due to:
  • Multiple relationships
  • Multiple fact tables

4. Scalable Design

You can easily:
  • Add new fact tables
  • Expand the model for new business needs

5. Supports Advanced Analytics

Useful for analyzing data across:
  • Sales
  • Inventory
  • Orders


Merits of Galaxy Schema

1. Handles Multiple Processes

Supports multiple business operations in a single model.

2. Reusable Dimensions

Common dimension tables can be reused, reducing duplication.

3. Scalable and Flexible

Easy to expand by adding new fact tables.

4. Better Enterprise Modeling

Ideal for large organizations with complex data needs.

Demerits of Galaxy Schema

1. High Complexity

  • Difficult to design and maintain
  • Requires strong data modeling knowledge

2. Performance Challenges

  • Multiple joins may slow down queries

3. Hard for Beginners

  • Not easy to understand for non-technical users

4. Requires Careful Relationship Management

Incorrect relationships can lead to wrong analysis.
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