Individuals and organizations continuously produce data. In most cases, we wish to save generated data so that we may access it later. All aspects of data storage, including speed, cost, dependability, security, etc., should be practical. For this reason, various methods of data storage exist. The most widely used method is a data store.

Data from the most recent operational procedures is stored in data warehouses. Information that can be utilized for purposes like business analytics or analytics is typically stored in data warehouses.

There are various methods and guidelines for how a data warehouse should be designed, what kind of architecture to utilize, etc. The kind of schema is one of the factors the data warehouse designer should take into account.

In this article, we will delve into the star schema and snowflake schema and explore the key differences between the two, including their underlying properties and characteristics, benefits, and use cases. By the end of this article, you should have a good understanding of the pros and cons of each schema with examples and be able to make an informed decision about which one is best suited for your data warehousing needs.

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What Is a Snowflake Schema?

This particular kind of data warehouse schema is shaped like a snowflake. The snowflake schema aims to normalize the star schema's denormalized data. When the star schema's dimensions are intricate, highly structured, and have numerous degrees of connection, and the kid tables have several parent tables, the snowflake structure emerges. Some of the star schema's common issues are resolved by the snowflake schema.

The snowflake schema can be thought of as a "multi-dimensional" structure. A snowflake schema's central component comprises Fact Tables that link the data inside the Dimension Tables, which then radiate outward like the Star Schema. The snowflake schema, on the other hand, divides the Dimension Tables into several tables, resulting in a snowflake pattern. Up until they are fully normalized, the Dimension Tables are split across multiple tables.

Characteristics of Snowflake Schema

The snowflake schema is characterized by a normalized data structure, with data divided into smaller, more specialized tables that are related to each other through foreign keys. 

These are its main characteristics:

  • Small disc space is required by the snowflake schema.
  • The new dimension to the schema is simple to implement.
  • Performance is impacted because there are numerous tables.
  • Two or even more sets of attributes that describe data at various grains make up the dimension table.
  • A single dimension table's sets of characteristics are filled in by various source systems.

Now that we have a basic understanding of the snowflake schema, let's dive into the specifics of the star schema and explore what sets it apart from other data organization techniques.

What Is a Star Schema?

The star schema is the most straightforward method for arranging data in the data warehouse. Any or even more Fact Tables that index a number of Dimension Tables may be present in the star schema's central area. Dimensions Keys, Values, and Attributes are found in Dimension Tables, which are used to define Dimensions.

The star schema's objective is to distinguish between the descriptive or "DIMENSIONAL" data and the numerical "FACT" data that pertains to a business.

The information displayed in a numerical format, such as cost, speed, weight, and quantity, might be considered fact data. Along with numbers, dimensional data can also contain non-numerical elements like colors, places, names of salespeople and employees, etc. 

While the Dimension Data is contained inside the Dimension Tables, the Fact Data is arranged within the Fact Tables. In a star schema, the Fact Tables are the integrating points at the core of a star.

Characteristics of Star Schema

The star schema is characterized by a denormalized data structure, with all data related to a particular subject stored in a single large table and connected to smaller, dimensional tables through a single join. 

These are some of the main characteristics of the star schema:

  • A single one-dimension table can represent each aspect in a star schema.
  • The collection of attributes should be in the dimension table.
  • Using a foreign key, the dimensions table is connected to the fact table.
  • No connections are made between the dimension tables.
  • Key and measure would be in the fact table.
  • The Star schema offers the best possible disc use and is simple to grasp.
  • Tables for the dimensions are not standardized. As an OLTP architecture would have it, the Country ID in the image above does not have a Country lookup table.
  • BI Tools provide extensive support for the schema.

With a foundational understanding of the snowflake and star schema under our belts, it's time to explore the key differences between the two.

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Star Schema vs. Snowflake Schema

It's important to keep in mind the fundamental differences between star and snowflake schemas when comparing them: star schemas provide an effective method for organizing data in a data warehouse, whereas snowflake schemas are a version of star schemas that facilitate faster data processing

With this in mind, let us explore the various feature and characteristics-based differences between these two schemas.

Basis of Distinction

Star Schema

Snowflake Schema

Definition and Meaning

Both fact tables and dimension tables are present in a star schema.

Dimension tables,  sub-dimension tables, and fact tables are all included in a snowflake schema.

Type of Model

The star schema is a top-down type of model.

The snowflake schema is a bottom-up type of model.


Star schema uses more space compared to Snowflake Schema.

Snowflake schema uses less space comparatively.

Joint Relations

In a star schema, relationships between tables are represented by a single join, resulting in a simple data structure for fast query performance and easy data analysis.

The snowflake schema has a complex data structure with multiple levels of relationships between tables, represented by multiple joins. This can make the data structure more difficult to understand and result in slower query performance.

Response Time for Queries

Star schemas have faster query execution times due to a single join of a fact table and its attributes in dimensional tables.

Snowflake schemas require complex joins between tables, which can slow down query processing and impact other OLAP products.


In a star schema, dimension tables are not organized in a normalized form. They are typically denormalized and contain multiple levels of information about a particular subject in a single table.

Dimension tables in snowflake schema are normalized.

Design Complexity

Has a simpler design compared to snowflake schema.

More complex design compared to star schema.

Query Complexity

Star schemas have simpler query design due to the fact the table is joined to only one level of dimensional tables.

Snowflake schemas, on another hand, have a more complex query design due to the need for multiple joins between the fact table and its dimensional tables. This leads to additional overhead in query writing.

Understanding Complexity

It is simpler to understand compared to snowflake schema. 

More complex to understand compared to star schema.

Foreign Keys

Have a lesser number of foreign keys.

Comparatively has more foreign keys.

Data Redundancy

The star schema stores redundant data in the dimension tables.

The snowflake design fully normalizes the dimension tables and prevents data redundancy,


  • Simple and easy-to-understand data structure.
  • Fast query performance due to the single join between the fact table and its dimensional tables.
  • Suitable for large volumes of data
  • Good for ad-hoc querying and data analysis.
  • Normalized data structure reduces redundancy and increases data integrity.
  • Allows for more complex relationships between data.
  • Allows for easier data maintenance and management.
  • Good for more structured predictable querying.


  • The star schema has a limited ability to depict complex relationships between data.
  • Can suffer from data redundancy and decreased data integrity.
  • May not be suitable for smaller volumes of data.
  • The more complex data structure can be harder to understand and work with.
  • Multiple joins between tables can result in slower query performance.
  • Requires more storage and processing resources due to the larger number of tables.

Star Schema vs. Snowflake Schema - Key Differences

To provide a clear summary of the key differences between the star and snowflake schema and outline their respective use cases, here are some key takeaways to consider:

  • The most basic kind of data storage schema is the star schema. As a result of its star-like structure, it is referred to as a star schema.
  • When comparing the snowflake and star schemas, the snowflake schema extends the star schema by adding more dimensions. Because of how much its diagram resembles a snowflake, it is termed a snowflake.
  • Only a single join in a star schema describes the connection between a fact table and any dimension tables.
  • A fact table and dimension tables circle each other in the star schema.
  • Dimension tables surround the snowflake schema, which is in return surrounded by dimension tables.
  • The data must be fetched via several joins in a snowflake schema.
  • When comparing the snowflake and star schemas, the snowflake schema has a very sophisticated DB design while the Star schema has a basic DB design.

Which schema is the best fit for your data warehousing needs?

When it comes to data warehousing and business intelligence, choosing the right schema is essential for organizing and structuring your data in a way that meets your specific needs and requirements. The star schema and snowflake schema are both popular approaches, each with its own unique characteristics and benefits. 

A star schema may be the best choice if you're looking for a quick and easy cloud data warehousing solution. A snowflake schema, however, can be a better option if you require more adaptability to suit shifting data requirements.

To more clearly understand the differences between the snowflake and star schema, let's take a closer look at both through the use of examples.

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Example of Star Schema

In the following example of a star schema, the central fact table contains keys to each dimension table (such as Dealer_ID, Model_ID, Date_ID, Product_ID, Branch_ID) and attributes like units sold and revenue:



Example of Snowflake Schema

In the following example of a snowflake schema, the Country table has been further normalized into its own separate table:



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The star schema and snowflake schema are two popular approaches to organizing and structuring data in data warehousing and business intelligence projects. Each schema has its own unique characteristics and benefits, and the best fit for your specific needs will depend on the requirements of your project. 

The star schema is characterized by a denormalized data structure and is well-suited for fast query performance and simple data analysis, while the snowflake schema has a normalized data structure and is better for maintaining data integrity and reducing redundancy. It's important to carefully consider the trade-offs between simplicity, performance, and data integrity when choosing which schema is the best fit for your needs.

If you want to learn more about data warehousing and business intelligence, consider enrolling in a certification training course like the PGP Data Analytics Course offered by Simplilearn. This comprehensive course covers a wide range of data analytics concepts and techniques and will give you the skills and knowledge you need to succeed in the field. 


1. Which is better: snowflake schema or star schema?

On the one side, star schemas are less complex, query execution is quicker, and setup is simpler. On the other side, snowflake schemas take up less room, are simpler to maintain, and are less susceptible to data integrity problems.

2. What are star schema and snowflake schema?

It is possible to split facts and dimensions into different tables using the star and snowflake schema designs.

3. What is the advantage of a star schema over a snowflake 3NF?

The star schema has a simpler data structure with a single join between the fact table and its dimensional tables, which can lead to faster query performance and easier data analysis compared to the snowflake schema. It is also generally better suited for handling large volumes of data and is simpler to understand and work with.

4. Is star schema the best?

One of the main reasons that the star schema is widely accepted as a best practice for data organization is its simplicity. Compared to the traditional source system models, the strategy is simpler for the business customer to comprehend.

5. Which ETL tool is best for snowflake?

The snowflake schema can be used with a number of ETL tools, including Talend, Informatica, SSIS, and Talend Cloud. Depending on your unique demands and requirements, you can choose the ideal tool.

6. Is the star schema OLAP or OLTP?

All OLAP systems make extensive use of the star schema to effectively create OLAP cubes. A ROLAP mode of operation, which uses a star schema as an input without creating a cube structure, is actually provided by most major OLAP systems.

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