Category: Data Modeling | Level: Advanced | Concepts: Star Schema, Snowflake Schema, VertiPaq Engine, Data Modeling Best Practices

One of the most common questions Power BI developers face — both in interviews and in real project decisions — is star schema vs snowflake schema in Power BI. Choosing the wrong data model doesn't just look messy; it directly affects Power BI report performance, DAX complexity, and refresh times. This article breaks down both approaches and explains why one is almost always the better choice inside Power BI specifically.

What Is a Star Schema in Power BI?

A star schema is a data modeling approach with a single central fact table (containing measures like sales, quantity, or revenue) connected directly to multiple dimension tables (like Date, Product, Customer, Region). Every dimension connects straight to the fact table — there are no intermediate tables between them.

What Is a Snowflake Schema?

A snowflake schema takes those same dimension tables and normalizes them further — splitting a single Product dimension, for example, into separate Product, Category, and Sub-Category tables linked in a chain. This reduces data redundancy but adds extra relationship hops between the fact table and some attributes.

Visual Comparison

STAR SCHEMA SNOWFLAKE SCHEMA Dim_Date Dim_Date | | Dim_Customer - Fact_Sales - Dim_Product Dim_Customer - Fact_Sales - Dim_Product | | | Dim_Region Dim_Region Dim_Category | | Dim_City Dim_SubCategory

Star Schema vs Snowflake Schema — Key Differences

Factor	Star Schema	Snowflake Schema
Query performance	Faster — fewer joins	Slower — more joins/relationships
DAX complexity	Simpler, more predictable	Harder — relationships can cross multiple hops
Data redundancy	Higher	Lower (fully normalized)
VertiPaq compression	Optimized for this shape	Less efficient with chained tables
Ease of maintenance	Simple to understand	More tables to manage and document

Why Power BI's VertiPaq Engine Prefers Star Schema

Power BI's in-memory VertiPaq engine is column-store based and heavily optimized for the star schema pattern — single-hop relationships between fact and dimension tables compress better and resolve faster at query time. When you introduce a snowflake schema in Power BI, DAX measures often need to traverse multiple relationships to reach an attribute, which increases the risk of ambiguous filter propagation and slower visuals, especially on large datasets.

When You Might Still See a Snowflake Schema

Snowflake schemas are more common in traditional data warehouses (especially in normalized OLTP-style source systems) or when a dimension is extremely large and shared across many fact tables in a way that benefits from separation. Even then, most experienced Power BI data modelers flatten these into a star schema at the semantic model layer using Power Query, rather than exposing the snowflake structure directly to DAX.

Conclusion

For almost every real-world Power BI data modeling best practice guide, the recommendation is the same: flatten your snowflake schema into a star schema wherever possible before it reaches your Power BI model. It results in simpler DAX, faster report performance, and a model that's far easier for other analysts to understand and extend — which is exactly why "star schema vs snowflake schema" remains one of the most asked Power BI interview questions on data modeling.