Star Schema vs Snowflake Schema: Differences and Similarities

If you’re dealing with business intelligence, there are countless decisions you should make before your data appears in front of your desired target audience. From data sources to types of visualization, there’s so much a data scientist or a product manager needs to decide on. And to all that, add the choice of data warehouse modeling techniques.

The two most common choices are the star schema and the snowflake schema, and today, we’re going to show you a tutorial on how to make the right choice for your needs.

What is a star schema?

The star schema is the easier of the two to understand. At the center of the star is the fact table, which hosts numerical, quantitative data sets such as sales numbers, i.e. the hard data that is the basis for analysis. It is primarily used in data warehouses and OLAP apps.

Around the central fact table are dimension tables. These are tables that provide additional context and details in relation to the main table. In the case of sales, dimension tables could contain data such as time, product or location.

The relationships between these tables are simple and often many-to-one. In other words, the fact table contains foreign keys that can be linked to the dimension tables’ primary keys. You can also import these schemas into Excel easily.

Dimension tables commonly have a more denormalized data structure, where data redundancy on certain dimension columns can and will happen. The extra data is there to improve the query performance by avoiding complex joins.

The star schema is simple to create and run and they’re best suited for those situations where query performance is the key consideration. Scalability is not a concern either because more data does not slow down integrity, efficiency or accuracy of your operations.

What is a snowflake schema?

The snowflake schema is a more complex, advanced version of the star schema. The main difference between the two is that the data in the dimension tables is normalized, making for a more hierarchical structure with different levels of related tables. When visualized, these tables resemble the shape of a snowflake.

The reason is that the dimension tables have sub-dimensions that break down tables into related tables, resulting in a more normalized data structure. More sub-dimension tables means less data redundancy, and thus less space taken up in your data warehouse or data marts.

The relationships are typically more complex and instead of many-to-one, you might even have to deal with many-to-many relationships due to e.g. multidimensional hierarchies.

As there is an increased number of separate tables, this could affect query execution and performance. Typically, the query complexity is going to result in a longer query duration which is typically not desired for customer-facing analytics.

Last but not least, the schema design and management are more complex and that requires more hands-on work and management. Similarly, consuming such data models in a BI tool typically requires understanding it in depth, and knowing how and where to retrieve your desired insights from. A Snowflake schema thus often not really facilitates less technical users to create their own insights.

Star schema vs snowflake schema: key differences

With all of that out of the way, here are the key differences between the two so you can have an easier time choosing something for your data warehousing needs.

• The structure: a star schema has a centralized structure with fewer joins; the snowflake schema has a normalized data structure with multiple levels.
• The table relationships: typically, one-to-many relationships in the star schema; the snowflake schema has complex relationships with more joins, resulting in more complex queries.
• Ease of use: star schemas are simpler, easier to use, and perform better; snowflake schemas allow for more flexibility but they are also more complex to use, and may require deeper knowledge of data engineering and data analysis.

Which schema design should I use?

That depends on several factors. To make things simpler, here is when you should use a star schema:

1. When simplicity and ease of use is your primary concern
2. When query performance and speed are an utmost priority
3. In analytical use cases when you need to do ad-hoc queries, create dashboards and reports
4. When you don’t want to worry about maintenance overhead and complexity of your schema design

On the other hand, use the snowflake schema:

1. Data redundancy in the star schema is a concern and when your business intelligence use case requires normalized data
2. When having hierarchical data representation is crucial
3. When you want to maintain data integrity at all times
4. When you use complex data models where there are intricate relationships among data attributes 

To sum up, for customer-facing analytics, the star schema makes more sense. It’s easier to understand and consume for users who are less technically literate, and it provides optimized query performance.

Last but not least, there is less maintenance overhead as the schema design is less complex.

While the snowflake schema has the benefit of avoiding data redundancy, this is a small consideration to make. The cost of storing this redundant data is in most cases significantly lower than the cost of running heavy, slow queries.

So, for an embedded analytics use case, the star schema is the clear winner.

StarSchema pricing: Balancing cost and performance

StarSchema pricing is designed to meet the needs of organizations leveraging star schema design for efficient data management. By organizing data into fact tables and dimension tables, the star schema approach enables businesses to handle many dimensions while maintaining a small number of joins, ensuring simpler queries and faster performance.

In a typical data warehouse toolkit, fact tables like the sales fact table store measurable data, while dimension tables provide context, making the schema highly effective for dimensional modeling. Businesses working with data marts often prefer star schemas due to their straightforward design, allowing for improved scalability and cost control.

Although a single table setup might appear simpler initially, leveraging more tables in a well-structured star schema creates a balanced trade-off between usability and efficiency. Pricing aligns with the complexity of handling tables, dimensions, and the overall size of the schema, ensuring flexibility for organizations scaling their data solutions.

Wrapping up

In the end, the choice of your data warehouse schema boils down to your specific needs and requirements. 

If your end goal is to visualize your key metrics in your SaaS app - we have great news. At Luzmo, we specialize in modern data visualization tools for SaaS products like yours and we can advise you on what to choose and how to build it - even with artificial intelligence and machine learning involved.

Book a free demo with our team and we’ll help you with more info!