Star and Snowflake Schema: 5 Comprehensive Aspects

on Data Warehouse, Database Management Systems, ETL • February 15th, 2021 • Write for Hevo

Star and Snowflake Schema

Every business desires to grow, and data is a key component for business growth. Due to this, organizations are placing so much value on data. There are different sources of data, including data generated from the organizational departments as well as data obtained from external sources. In most cases, organizations end up gathering huge volumes of data, running up to petabytes in size. So, where do organizations store such data? They use Data Warehouses. A Data Warehouse can store huge volumes of structured and unstructured data from multiple data sources. 

Just like with a regular database, the Schema is very important to a Data Warehouse since it gives the logical description of the data objects. It’s the Schema that describes how different objects in the Data Warehouse are related. Star and Snowflake Schema are the two types of Data Warehouse Schemas. In this article, you will be learning about the differences between the Star and Snowflake Schema. 

Table of Contents

What is Star Schema?

The Star Schema provides the simplest way of organizing data in a Data Warehouse. The centre of the Star Schema can have one or more Fact Tables indexing a series of Dimension Tables. Dimension Tables are used to describe Dimensions; they contain Dimension Keys, Values, and Attributes.

Star Schema - Star and Snowflake Schema
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The goal of the Star Schema is to separate the numerical “FACT” data that relates to a business from the descriptive or “DIMENSIONAL” data. 

Fact data can include information such as price, speed, weight, and quantities, that is, data presented in a numerical format. Dimensional Data can include uncountable things such as colours, geographical locations, salesperson names, employee names, etc., alongside the numerical information. 

The Fact Data is organized inside the Fact Tables, while the Dimensional Data is inside the Dimension Tables. The Fact Tables form the integration points at the centre of a Star in a Star Schema. 

More information regarding Star Schema can be found here.

What is Snowflake Schema?

This type of Data Warehouse Schema takes the shape of a Snowflake. The goal of the Snowflake Schema is to normalize the denormalized data of the Star Schema. The Snowflake structure materialized when the dimensions of a Star Schema are detailed and highly structured, having several levels of relationship, and the child tables have multiple parent tables. The Snowflake Schema solves some of the common problems associated with the Star Schema. 

You can see the Snowflake Schema as a “multi-dimensional” structure. At the core of a Snowflake Schema is Fact Tables that connect the information contained in the Dimension Tables, which in turn radiate outwards like the Star Schema. However, the Dimension Tables in the Snowflake Schema are divided into more than one table, creating a Snowflake pattern. 

Snowflake Schema - Star and Snowflake Schema
Image Source: Self

The Dimension Tables are turned into more than one table until they are completely normalized. 

More information regarding Snowflake Schema can be found here.

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What is Galaxy Schema?

A Galaxy Schema is made up of two Fact Tables that share dimension tables. Fact Constellation Schema is another name for it. The Galaxy Schema is named for the way the schema is regarded as a collection of stars.

Star and Snowflake Schema: Galaxy Schema
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There are two facts tables as you can see in the sample above.

  • Revenue
  • Product

Conformed Dimensions are shared dimensions in the Galaxy schema.

What is Star Cluster Schema?

The hierarchies in the Snowflake Schema are fully enlarged. However, this increases the Schema’s complexity and necessitates more joins. Star schema, on the other hand, comprises totally collapsed hierarchies, which may result in redundancy. As a result, the optimum approach may be a Star Cluster Schema design that strikes a balance between these two schemas.

Star and Snowflake Schema: Star Cluster Schema
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In hierarchies, overlapping dimensions can be found as forks. When an entity operates as a parent in two different dimensional hierarchies, a fork occurs. The classification of fork entities with one-to-many relationships was then identified.

Comparing Star and Snowflake Schema

Star vs Snowflake Schema
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The following chart summarizes the differences between the Star and Snowflake Schemas by comparing the Star and Snowflake Schema across numerous factors:

Star Schema Snowflake Schema
The Star Schema contains the Fact Tables and the Dimension Tables. The Snowflake Schema contains the Fact Tables, Dimension Tables, and the Sub-Dimension Tables. 
It is a top-down model. It is a bottom-up model.
Queries execute faster in the Star Schema. This is achieved using the Star Join Query Optimization technique. Tables can be connected with Multiple Dimensions. Execution of queries takes a longer time in the Snowflake Schema than in the Star Schema. The centralized Fact Table in the Snowflake Schema is less likely connected with Multiple Dimensions. The longer execution time is also caused by the many number of foreign keys. 
There is no normalization in the Star Schema. In the Snowflake Schema, there is both normalization and denormalization. 
It has a very simple design. This Schema has a complex design because of the Sub-Dimension Tables. 
The Star Schema has a low query complexity. The query complexity of the Snowflake Schema is higher than that of the Star Schema. 
It’s easy to understand the Star Schema. It’s difficult to understand the Snowflake Schema. 
It only has a few number of foreign keys. It has a high number of foreign keys. 
The Star Schema has a high data redundancy, which makes it a bit difficult to maintain and change. The Snowflake Schema has a low data redundancy, which makes it easier to maintain and change. 
The hierarchies for the Dimensions are kept in the Dimensional Table.The hierarchies for the Dimensions are divided into separate tables. 
In the Star Schema, only a single join creates the relationship between the Fact Table and the Dimension Tables. In the Snowflake Schema, many joins are needed to fetch the data. 
It has a simple database design. It has a complex database design. 
Faster cube processing. Cube processing may be slow due to the complex join. 
It is suitable for data marts that have simple relationships (1:1 or 1:many).Suitable for use in the Data Warehouse core for simplifying complex relationships (many:many).  
Should be used when the Dimension Table has less number of rows. Should be used when the Dimension Table has a relatively big size because it reduces space. 
Comparison: Star and Snowflake Schema

Now, It is important to understand the limitations of Star and Snowflake Schema which are as follows:

Limitations of Star Schema

Since you have understood the main differences between Star and Snowflake Schema, you can move on to the limitations of these structures. The following are the challenges of using the Star Schema:

  • Decreased Data Integrity: It’s difficult to impose data integrity effectively in the Star Schema due to its denormalized data structure. 
  • Difficulty Handling Diverse and Complex Queries: The Star Schema can only work well with a narrow set of simple queries due to its denormalized data structure. 
  • Doesn’t Support Many-to-Many Relationships: The Star Schema has a simple Dimension Schema, hence, it can’t handle “many-to-many data relationships” effectively. Thus in the discussion of Star and Snowflake Schema, Star Schema will loose out when dealing with any-to-many relationships.

Limitations of the Snowflake Schema

The following are the challenges of using the Snowflake Schema:

  • Complex Data Schemas: The Snowflake Schema generates many levels of complexity as it normalizes the attributes of the Star Schema. This is a major diference between Star and SnowflakeSchema results and it causes complicated source query JOINS. 
  • Slower Cube Data Processing: This is a result of the complex joins, hence, the Star Schema appears as the better option when deciding between Star and Snowflake Schema (as far as cube data processing is concerned). 
  • Lower Data Integrity: Even after normalization, Snowflake Schemas cannot provide the level of data integrity that is provided by the traditional, highly-normalized database structures. 

That’s it! You now have a strong understanding of the differences between Star and Snowflake Schema

Which one to choose?

Star Schemas, on the one hand, are simpler, run queries faster, and are simpler to set up.
Snowflake Schemas, on the other hand, are less prone to Data Integrity problems, are easier to manage, and take up less space.
It depends on which advantage (or disadvantage) best matches your business use cases, based on the tradeoffs listed above.

Conclusion

Thi article introduced you to the Star and Snowflake Schemas. It also discussed the storage structure of Star and Snowflake Schemas in-depth. You’ve learned the differences between the Star and Snowflake Schemas based on multiple criteria. Moreover, you’ve learned about the limitations of opting for either of the Star and Snowflake Schemas.  

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Share your understanding of the comparison between Star and Snowflake Schema in the comments section below!

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