When you store data in a database, you need to design the database tables that will hold the data. For analytical use, there are two common choices: star schema or snowflake schema. While there are pros and cons to each database schema type, understanding the differences between them can help you choose the right one for your needs. So, which is better: star or snowflake? The answer depends on your specific situation. Let's take a closer look at both schemas to see how they work.
What is a star schema?
A star schema is a database schema used to store data in a star format. This schema consists of a central table, called the "fact table," and a number of directly connected other tables, called "dimension tables." The fact table contains information about metrics or measures, while the dimension tables contain information about descriptive attributes. The star schema is very simple and easy to understand, making it ideal for cloud data warehousing and business intelligence applications.
What is a snowflake schema?
A snowflake schema is a type of database schema that is used to store data in a more complex format than the star schema. The snowflake schema consists of a central table, which is called the "fact table," and a number of other tables, which are called "dimension tables." As with other schemas, the fact table contains information about events or facts, while the dimension tables contain information about the dimensions of those events or facts.
6 key differences between star schema and snowflake schema:
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A star schema has denormalized dimension tables, while a snowflake schema has normalized dimension tables.
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A star schema is easier to design and implement than a snowflake schema.
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A star schema can be more efficient to query than a snowflake schema, because there are fewer JOINs between tables.
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A star schema can require more storage space than a snowflake schema, because of the denormalized data.
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A star schema can be more difficult to update than a snowflake schema, because of the denormalized data.
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A star schema can be more difficult to troubleshoot than a snowflake schema, because of the denormalized data
Which schema is right for you?
The answer depends on your specific needs and requirements. If you’re looking for a simple, efficient cloud data warehouse solution, a star schema might be the best option. But if you need more flexibility to accommodate changing data requirements, a snowflake schema may be a better choice. No matter which schema you choose, ThoughtSpot can help you get the most out of your data. Most BI tools require a specific schema design to be used, ThoughtSpot has no such restrictions.
The power of ThoughtSpot's query engine allows you to easily explore complex galaxies of star or snowflake schemas, automatically handling complexities like chasm and fan traps while ensuring accurate results are returned. Start a free trial today and see how easy it is to create beautiful interactive visualizations with ThoughtSpot.
