Star schema vs Snowflake schema in Power BI

Difference Between Star schema vs. Snowflake schema in Power BI

A Star schema is a widely followed modeling approach across relational data warehouses that organizes data into a central fact table surrounded by dimension tables, while the Snowflake Schema is a database schema in which a central fact table is connected to multiple dimension tables. The Star schema is called a “star” because the structure resembles a star when diagrammed, while the Snowflake schema forms a shape that resembles a snowflake. A star schema has dimension tables that are denormalized, while a snowflake schema has normalized ones. Compared to the snowflake schema, a star schema is easier to construct and implement.

In this article, we will discuss how to implement both schemas. Read ahead to know more.


Some of the critical differences between Star schema and Snowflake schema include:

  • Structure
    • Star schema structure follows a fact table located centrally surrounded by dimension tables. The fact table contains quantitative data (such as sales or revenue), and the dimension tables provide context and descriptive information related to the data in the fact table.
    • Snowflake schema structure contains the normalized dimension tables, meaning that they are further divided into sub-dimensions or levels. It leads to a more complex structure with additional relationships between the tables.
  • Ease of understanding
    • Star schema is simpler and more denormalized compared to the snowflake schema. It is easier to understand and navigate, making it a preferred choice for Power BI and other BI tools.
  • Performance
    • Star schemas can offer better query performance compared to Snowflake schemas because of their denormalized structure. It reduces the number of joins needed to retrieve data during query execution referencing the fact and dimension tables.
    • Snowflake schemas don’t exhibit good query performance, given the data is stored in normalized form,
  • Normalization
    • The snowflake schema is more normalized compared to the star schema. Normalization reduces redundancy in data but can result in more complex queries due to the need for additional joins.
  • Storage and Maintenance
  • Snowflake schemas may require more storage due to the normalized structure, and they can be more challenging to maintain and understand, especially for users who need to become more familiar with the schema.
  • Joins
    • Star schemas generally require fewer joins in queries, making them simpler and potentially more performant for certain types of queries.
    • Snowflake schemas often involve more joins, which can impact query performance but may be necessary to maintain data integrity.
  • Use Cases
    • Star schemas are well-suited for data warehousing and business intelligence where query performance is a priority.
    • Snowflake schema is preferred in scenarios where data integrity and consistency are more critical than query performance.

What is Star schema in Power BI?

The star schema is a type of data warehouse schema where a central fact table is connected to one or more dimension tables through foreign key relationships. The fact table contains quantitative data (such as sales or revenue), and the dimension tables contain descriptive information related to the data in the fact table (such as product, time, or location). Each of the dimension tables is not interconnected with the other and is represented as a single-dimensional table.

A sample star schema looks like the diagram below.

Star schema vs. Snowflake schema in Power BI-Star schema

Here are the key components of a star schema:

Fact Table Located at the center of the schema.
Contains quantitative data (facts) that can be analyzed, such as sales, revenue, quantity sold, etc.
Each row in the fact table corresponds to a specific business event or transaction.
Dimension Tables Surround the fact table.
Contain descriptive attributes that provide context to the data in the fact table.
Examples of dimension tables include time, geography, product, customer, etc.
Each dimension table is connected to the fact table through a foreign key.
Foreign Key Used to establish a relationship between the fact table and dimension tables.
Exists in the fact table and corresponds to the primary key in a dimension table.
This relationship allows for the integration of data from different tables during queries and analysis.


In Power BI, the star schema is often preferred because it simplifies data modeling, improves query performance, and provides a more intuitive structure for reporting and analysis. It provides a clear and organized structure for data, making it easier for users to navigate and understand relationships within the data model.

What is Snowflake schema in Power BI?

The snowflake schema is a multi-dimensional data model that extends the star schema by normalizing dimension tables.

Snowflake schemas are used primarily in Business Intelligence (BI) and, more specifically, in OLAP data warehouses, data marts, and other relational databases.

In a snowflake schema, the relationships between tables form a pattern that resembles a snowflake, with the central fact table connected to multiple dimension tables and each dimension table potentially connected to additional sub-dimension tables. A sample snowflake schema looks like the diagram below.

Star schema vs. Snowflake schema in Power BI - Snowflake Schema

Here are the key components of a snowflake schema:

Fact table The central table contains the primary metrics or measures, often numerical data, that the business is interested in analyzing.
Examples include sales, revenue, or quantity sold.
Dimension tables Tables that store descriptive information related to the data in the fact table.
Each dimension table is connected to the fact table through foreign key relationships. Dimension tables provide context to the measures in the fact table.
Examples of dimension tables include time, geography, product, and customer tables.

While the snowflake schema can save storage space by avoiding data redundancy, it may introduce more complexity to queries and might not be as intuitive for reporting purposes.

In Power BI, it’s generally recommended to use star schema for simplicity and better performance.

Comparative Table

There are several similarities and differences between the Star schema and the Snowflake schema. This section captures the key differences between these versions in a comparative table below:

 CategoryStar SchemaSnowflake Schema
Handling hierarchical dataSupports all levels of a hierarchy in the same dimension tableIt breaks out the hierarchy levels into multiple tables
Ease of maintenanceStar Schema has redundant data stored across multiple tables, and hence maintenance is complex.The maintenance is easy in Snowflake Schema given lower data redundancy.
Query complexityThe query complexity can be very low and easy to interpretIt requires complex queries to be executed and may not be easy to understand
Query processing timeThe query execution is fast due to fewer foreign keysThe processing time can be higher due to more foreign keys
Join TypeData can be extracted with a single join between the fact table and dimension tables.Multiple joins are needed to extract the data
Table dimensionsStar Schema has a one-dimensional tableSnowflake schema has a multi-dimensional table
UsageStar schema is preferred for non-voluminous dimension tableSnowflake schema is useful for huge dimensional table
Normalization vs DenormalizationStar schema stores the data with both the fact table and dimension tables in a denormalized format.Snowflake schema stores fact tables in denormalized form, but dimension tables are normalized.
Data Model approachThe top-down approach is followed in this schemaThe bottom-up approach is followed here
Memory consumptionIt consumes high memoryLow memory is consumed
Troubleshoot optionsStar schema is difficult to troubleshoot due to denormalized dataSnowflake schema is relatively easy to troubleshoot compared to Star schema


Star schema and snowflake schema are both data warehouse modeling techniques used to organize and structure data for efficient querying and reporting.

In Power BI, which is a business analytics service provided by Microsoft, you can implement both star and snowflake schemas depending on your data modeling requirements. Here are some similarities between the two.

  • Dimension and Fact Tables:
    • Both star and snowflake schemas involve the use of dimension tables and fact tables.
    • Dimension tables contain descriptive attributes and are typically connected to a fact table through foreign key relationships.
  • Data Organization:
    • Both schemas are designed to organize and structure data in a way that facilitates efficient querying and reporting.
    • They are commonly used in data warehousing scenarios where large volumes of data need to be analyzed.
  • Scalability:
    • Both schemas are scalable and can accommodate the addition of new dimensions or facts without significant impact on existing structures.
    • This scalability is important for handling evolving business requirements and growing data volumes.
  • Query Performance:
    • Both schemas aim to optimize query performance by reducing the number of joins required to retrieve relevant information.
    • By structuring data into organized tables, queries can be executed more efficiently, leading to faster response times.
  • BI Tool Compatibility:
    • Power BI, being a versatile business intelligence tool, supports both star and snowflake schemas.
    • Users can create relationships between tables in the Power BI data model, whether they follow a star or snowflake schema.
  • Flexibility:
    • Both schemas provide flexibility in terms of adding or modifying dimensions and facts based on changing business requirements.
    • Power BI’s data modeling capabilities allow users to adapt their data models to evolving analytical needs quickly.
  • Normalization:
    • Star schema typically involves less normalization, with dimension tables being denormalized for simplicity and performance.
    • Snowflake schema, on the other hand, can be more normalized, with dimension tables being further divided into sub-dimensions.

While there are similarities, it’s important to note that the main difference between the two lies in the degree of normalization.

Star schema tends to be more denormalized for simplicity and ease of use, while snowflake schema offers a more normalized structure, potentially saving storage space but requiring more complex queries. The choice between the two depends on the specific requirements and preferences of the organization implementing the data model.

In summary, the choice between star schema and snowflake schema in Power BI depends on the specific requirements of your data and the preferences of your users.

This has been a guide to Star schema vs. Snowflake schema in Power BI. Here we learn the advantages, components, versions, with their differences & similarities. You can learn more from the following articles –

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