Star Schema vs. Snowflake Schema: What’s the Difference?

Organizations today face the challenge of ingesting large quantities of information from diverse sources that make up their data sets. Capturing this data in an unstructured fashion does not allow you and your company to take advantage of the insights found within your data that apply during the decision-making process around crucial business choices. Deploying an effective data model to store and handle your data sets enables you to leverage the information you capture for your benefit and provides a coherent look into the data stored within your company for all relevant stakeholders.

Star schema and snowflake schema are both effective designs for data models that contribute to optimally organizing and structuring large quantities of data and information. Snowflake schema is an expanded form of star schema, further normalizing your data in additional tables. Normalized data has a cohesive structure, a high level of organization, and less repetitive data. Depending on your specific needs related to selecting a schema design for your data model, star schema and snowflake schema both have various uses and practical applications today. To determine which schema is right for you, learn more about their uses, benefits, drawbacks, and how they compare in a few key performance areas. 

What is a star schema?

The star schema data model efficiently and concisely organizes your data to assist in interpreting, analyzing, and reporting on the information found in a database or relational data model. Star schema was first presented by Ralph Kimball as a novel data model in the 1990s, with applications to databases, data warehouses, and other data tools [1]. 

Star schema features two types of tables—fact tables and dimension tables—that allow you to slice and filter your data however you need. To give you this capability, these two distinct types of tables connect or join with one another. An infinite number of dimension tables can branch out from a central fact table in the middle of the schema through joins. The features of both types of tables include:

• Fact table: The fact table within star schemas contains numerical information and dimension attribute values. It can also contain quantitative or qualitative measurements related to business processes. The variety of information found in fact tables includes sales data, revenue metrics, market share, product quantities, lists of customers, and inventory information. 

• Dimension table: Dimension tables have two different types of columns that hold specific values. Unique identifiers for the data set live in one column, and descriptive details about the data in your database are in the second column. Your star schema can have many dimension tables connecting to the fact table via surrogate keys, such as primary and foreign keys. 

Compared to other schemas, the star schema’s design results in a denormalized data structure, requiring fewer joins between your tables. This structure results from improved query performance, faster querying speeds, and enhanced readability for users. Since star schemas do not rely on normalized data in their dimension tables, they are generally easy for users to comprehend and construct. Fewer tables in the model exist as a result of the star schema’s denormalized data structure. While this grants a few key benefits, it also causes data redundancy and slows down updates to the system. 

What are the practical uses of star schema?

Utilizing the star schema data model for analysis and query execution are common use cases. Since star schemas have fewer joins or linkages between their tables throughout the data model, they simplify your queries. This speeds up the generation of queries and improves their completion time. 

Business intelligence initiatives often use star schema data models to streamline queries and effectively structure their data sets. Star schema is the recommended model for developing data models on Power BI because it simplifies data analysis expressions (DAX) and delivers advanced performance capabilities compared to other types of models. 

The benefits associated with the design and overall structure of star schema commonly apply when limited maintenance and enhanced performance are the main focus of a project or initiative. Relevant use cases for companies and organizations include:

• Marketing analysis

• Sales analysis

• Inventory management

• Financial reporting

Read more: What Is Data Analysis? (With Examples)

Benefits of using a star schema

Star schemas deliver numerous user benefits in many use cases as a data model schema. Common benefits associated with star schema include:

• Simplified queries

• Enhanced query performance and speed

• Easy to understand and interpret

• Simpler and fewer relationships between tables compared to alternatives

• Compatibility with online analytical processing (OLAP) systems

Drawbacks of using a star schema

Although the star schema provides many pros and is the preferred data model choice in many scenarios, some downsides and drawbacks exist. When choosing a data model schema, take into account some potential drawbacks of the star schema data model, which include:

• Limited set of possible queries

• Narrow analytical power

• Data redundancy

• Complex process of updating data in the schema

What is a snowflake schema?

Snowflake schema is another type of design for data models and database storage systems. Like a star schema, the structure of the snowflake schema progresses from general information in the middle of the schema to more specific information as you move away from the center. It features a fact table, dimension tables, and sub-dimension tables, creating many branches that connect the tables through joins. The structure of the snowflake schema resembles a snowflake, thus matching its name to how it looks. 

As an expanded star schema structure, the snowflake schema's sub-dimension tables provide a location where the data in dimension tables becomes normalized. This feature increases the schema's complexity and can result in a multitude of different relationships between your tables and data.

For example, a snowflake schema for a clothing store could have a central fact table for purchases made in the store. Branching out from the purchases table are dimension tables, such as products, customers, and employees. Each of these tables would then feature sub-dimension tables. For example, the product dimension table's sub-dimension tables may include a size, brand, and gender table. 

What are the practical uses of a snowflake schema?

With applications to large data warehouses, snowflake schema excels in handling large quantities of information that present significant challenges with management and maintenance. For any tasks where retaining data integrity and completeness of your data sets is crucial, a snowflake schema is a valuable option. Additionally, performing analysis where you need to view your data sets using a drill-down method fits the characteristics of a snowflake schema because the design of the schema allows you to handle numerous hierarchy levels better. 

Although overall query performance lacks in a snowflake schema, it is useful to leverage when conducting advanced query analysis. Additional applications for snowflake schema include completing business intelligence tasks, reporting in relational databases, and modeling in online analytical processing (OLAP) data warehouses. 

Benefits of using a snowflake schema

Using snowflake schemas in your data models provides various potential benefits and advantages. Some of the key benefits of a snowflake schema include:

• Ability to handle complex hierarchies and dimensions

• Low data repetition and redundancy

• Capable of utilizing denormalized and normalized data

• Requires a small amount of storage space

• Promotes data integrity and data quality

• Rapid data retrieval capabilities

Drawbacks of using a snowflake schema

Along with many benefits as a schema design for data models, snowflake schemas also have potential drawbacks and challenges to know, including:

• Complex data warehouse design

• Large amount of joins between tables, resulting in slow querying speeds

• Difficult to interpret and understand

• Requires significant effort to maintain due to high data granularity

Star schema vs. snowflake schema: How do they compare?

While star schemas and snowflake schemas are two popular data modeling options for data warehousing, each features its own set of benefits, hindrances, and potential use cases. To decide which option is right for you, learn more about how they differ from one another in key attributes. 

Structure

Star schemas have fact tables and dimension tables connected through joins that make up their structure. A snowflake schema's structure is more complex than the simple arrangement featured in a star schema. Similarly, a snowflake schema has a central fact table and joined dimension tables. They additionally branch further to include sub-dimension tables. The structure of a snowflake schema is a further developed star schema that includes sub-dimension tables, resulting in fully normalized data. 

Query performance

Due to its denormalized structure, a star schema has an advantage over a snowflake schema in terms of query performance and speed. This type of structure optimizes query performance but sacrifices data redundancy as a trade-off. However, if you need to run advanced, complex queries on your database or data warehouse, snowflake schemas are better suited to handle these requests.

Storage efficiency

Snowflake schemas benefit from their normalization to have greater storage efficiency compared to star schema. Normalized data requires less storage space because long data strings convert to keys used in joining tables together. These keys take the form of numbers and naturally need less storage space compared to non-numerical information.

Scalability

Snowflake schemas feature a better opportunity to scale because of their normalized structure and data separation. This allows you to avoid hindrances when loading additional data or modifying your data. This process becomes more complex in star schema due to denormalized data and the possibility of highly redundant data. 

Ease of use

Star schemas provide a higher level of ease of use for users. They apply to smaller data warehouses with fewer dimensions, simpler relationships, and fewer joins to keep track of. The sub-dimension tables included in snowflake schemas contribute to their complexity and expanded structure. Star schemas are perfect for data marts or simpler data warehouses, while snowflake schemas are the right choice for large data warehouses with complex structures. 

Getting started on Coursera

To learn more about the star schema or snowflake schema data model, completing a course or receiving a relevant certificate is a great place to start. On Coursera, you can enroll in some of the top courses in the world. 

Check out The Path to Insights: Data Models and Pipelines by Google. Taught at an advanced level, this course is designed to help you learn how to build data models to analyze business situations, apply ETL processes, utilize ETL tools, and construct pipelines to deliver crucial information and data to your organization. 

Another relevant course worth checking out to build upon your knowledge of data models and data warehousing is the Data Warehousing for Business Intelligence Specialization by the University of Colorado. This course has a flexible schedule and lessons to help you build in-demand skills for this industry. You earn a career certificate from the University of Colorado System at the end of the course.