By Naveen Madishetti, HEXstream data analyst

Data modeling is the backbone of every successful Power BI solution. Whether building an executive dashboard or a complex analytics model, the structure of the data determines performance, flexibility and accuracy of the reports.

Two of the most widely used data-modeling techniques in Power BI are the star schema and snowflake schema. While both aim to organize data efficiently, they differ in design, purpose and performance. In this blog, we will explore what star and snowflake schemas are, their advantages and disadvantages, how they impact performance, and when to use each schema.

What is a star schema?

A star schema is a data-modeling technique where a central fact table connects directly to multiple dimension tables. The design is called a “star” because the fact table sits at the center, while the dimension table branches out around it, forming a star-like shape.

In the Power BI star-schema model, the tables are typically connected using a one-to-many relationship where each dimension table is on the “one” side and the fact table is on the  “many” side, ensuring clean, efficient, high-performance data modeling.

                                                     Star schema

The fact table contains numerical data such as sales amounts, quantities sold and revenues. Along with these metrics, the fact table also includes foreign key columns that connect to different dimension tables. The dimension table has descriptive columns such as product details, customer information and dates.

Advantages of star schema

·       Easy to understand in Power BI: The model is simple, which helps developers and users to easily understand how data is connected.

·       Faster report performance: Reports run faster because Power BI has fewer joins to
process and the model is clean.

·       Reduce relationship complexity: One-to-many relationships between dimensions and fact tables make the model stable, clean and easy to maintain.

Disadvantages of star schema

·       Data redundancy: Dimension tables may have repeated information because the data is not highly normalized.

·       Limited for operational reporting: When reports need very granular transaction-level data, the star-schema structure misses some detailed fields.

·       More storage needed: Since the same data appears multiple times, the overall model size can be larger compared to snowflake schemas.

What is a snowflake schema

A snowflake schema is another data-modeling technique where one central fact table connects to multiple dimension tables, but one or more of those dimension tables are further divided into multiple related sub-dimension tables, resulting in a structure that resembles a snowflake. Instead of having all descriptive information in a single dimension table, the data is split into multiple smaller tables based on logical categories.

In the Power BI snowflake-schema model, the central fact table connects to the primary dimension tables using a one-to-many relationship just like a star schema; the subdimensions table is positioned on the “one” side  and the primary dimension table is positioned on the “many” side.

                                                          Snowflake schema

Advantages of snowflake schema

·       Removes data duplication: Data is stored in multiple related tables, so the same information is not repeated.

·       Suitable for detailed reporting: Extra tables allow detailed information while keeping the main dimension clean and simple.

·       Saves storage space: The model is more normalized; it uses less space compared to a star schema.

Disadvantages of snowflake schema

·       Slower performance: Because data is split into many small tables, Power BI needs more joins, which can slow down the report.

·       More complex model: A snowflake schema has multiple linked tables, which makes the structure harder to understand than a simple star schema.

·       Not ideal for large fact tables: When fact tables are large, too many joins from snowflake dimensions can affect performance.

Performance in Power BI: Star vs snowflake
Performance is one of the biggest differences between the two schemas:

·       The star schema performs faster because it has fewer tables and fewer joins. Power BI’s VertiPaq engine compresses star-schema tables more efficiently, which results in faster visual, faster DAX calculations and better memory usage.

·       Snowflake schema is slower because Power BI needs to combine many small tables using relationships. Every additional join adds processing overhead, which can affect query performance.

In most real-world Power BI solutions, the star schema is preferred for performance.

So how do you decide when to use which schema in Power BI? Use a star schema when:

·       You want fast report performance 

·       Data is not highly detailed or transactional

·       You want a simple model that business users can understand

·       You are building dashboards for executives or business teams

·       Performance is more important that storage optimization

Use a snowflake schema when:

·       Your data is highly detailed and needs multiple descriptive columns

·       You want to avoid data duplication

·       Storage space is a concern

·       You have many hierarchical attributes 

Conclusion
Both star and snowflake schemas have their own strengths for data modeling.

The star schema is easy to understand, performs faster, and is usually the best choice for dashboards that need quick results. On the other hand, the snowflake schema is helpful when the data needs to be well organized, detailed and highly structured.

The best schema depends on what your project needs. If your goal is smooth performance and a clean model, the star schema is the right fit. But if your data is complex and requires proper normalization, the snowflake schema can offer better control and clarity.

In the end, a carefully planned data model leads to faster reports, better insights and a more efficient Power BI solution giving users a clear and reliable reporting experience.