Snowflake Schema in the data warehouse is a popular data modeling technique used to represent data in a structured way. Read Inferenz’s detailed overview of Snowflake Schemas, such as uses, types, benefits, etc.
A Snowflake Schema is one of the popular types of data modeling techniques primarily used in data warehousing. It helps businesses to efficiently store data in a well-structured way optimized for querying massive data volumes.
The dimension tables are present in a normalized form in multiple related tables, removing data redundancy. Similarly, users can improve data integrity by using the foreign key relationship.
In this Snowflake Schema in a data warehouse guide, we will focus on the basics, types, uses, and benefits of Snowflake schema in detail.
What is a Snowflake Schema in Data Warehouse?
Simply put, a Snowflake Schema in a data warehouse model is a logical arrangement of tables in a multidimensional database. These are also termed multidimensional schemas, where dimension tables are divided into subdimensions.
But what is the use of Schema in Snowflake? The core aim of databases and schemas is to organize data stored in Snowflake. For example, you can use the Snowflake Schema in the following instances.
- There are fact tables with multiple detail levels.
- You need a slicing feature or hierarchies for cube design or reporting.
- You require a certain set of dimension data for additional information but not to query it.
Snowflake Schema is commonly used for business intelligence and reporting in relational databases, data marts, and data warehouses.
Characteristics of Snowflake Schema
The main characteristics of the Snowflake Schema include:
- The Snowflake schema has multiple tables, thereby reducing its performance.
- It uses smaller disk space.
- It is easy to implement the dimension that is added to Schema.
- The dimension tables in Snowflake consist of two or more sets of attributes defining information at different levels.
Example of Snowflake Schema
In the Snowflake Schema, the fact table is at its center, surrounded by multiple dimension tables. Unlike other data warehousing models, each dimension table is broken into multiple related tables to form a hierarchical structure that resembles Snowflake.
For instance, in a sales data warehouse, the product dimension table is broken down into multiple related tables, including product details, product categories, and product subcategories.
The Employee Dimension table contains attributes like EmployeeID, EmployeeName, DepartmentID, Region, and Territory. The Department Dimension table connects with the Employee table to provide the complete detail about each department.
Here is what a sales data warehouse in Snowflake Schema looks like.
Why Snowflake is Best for Data Warehouse?
Many organizations are migrating data from SQL to Snowflake due to its unmatchable capabilities. The reason to choose Snowflake Schema as your data warehouse is its intuitive and easy to understand nature.
However, one important thing to understand is that Snowflake data lake and data warehouse are two different things. While the data lake stores the raw data in its native form, data warehouses extract, clean, and transform data per business needs before storing.
Advantages of Snowflake Schema
As the Snowflake schema in the data warehouse model stores structured data, it provides a few main advantages.
- It helps to eliminate the issues related to data integrity.
- It uses small disk space.
- It reduces redundancy, making it easier to maintain data.
- It offers greater scalability in the interrelationship between component and dimension levels.
Disadvantages of Snowflake Schema
While Snowflake Schema has multiple benefits in terms of data modeling and query performance, it comes with a few drawbacks.
- It is challenging to understand complex queries.
- As there are a larger number of tables and joins, it takes a longer query execution time.
If you want to choose Snowflake as your data warehouse, make sure you follow the best practices for Snowflake implementation.
Star Schema Vs Snowflake Schema in Data Warehouse
Two common types of data warehouses include a Star and Snowflake Schema used to organize data in a structured manner.
Star Schema in Data Warehouse
The Star Schema is the most elementary form of the Dimensional Model. 
The core purpose of this type of Schema in multidimensional modeling is that it organizes data in the form of facts and dimensions.
- The fact table in the center contains a foreign key column that resembles various dimensional tables in the Schema.
- Multiple dimensions tables have their primary key column. One key factor to note is that these dimension tables are not located near each other. Instead, these tables are related directly to the fact table.
Snowflake Schema in Data Warehouse
The Snowflake Schema is the expansion of the Star Schema in which each dimension contains sub-dimension tables or levels.
Key Differences Between Star Schema and Snowflake Schema
Below we briefly compare and contrast Star Schema and Snowflake Schema in the table.
| Snowflake Schema | Star Schema |
| Contains fact tables, dimension tables, and sub-dimension tables. | Contains only fact tables and dimension tables. |
| Due to more joins, the query performance is lower. | It has better query performance due to less number of joins. |
| It has low data redundancy and hence uses less disk space. | Star Schema has data redundancy and hence uses more space. |
| High complexity due to normalization of dimension attributes. | Less complex due to data denormalization. |
| Multiple subdimensions lead to more foreign keys. | Fewer foreign keys are involved. |
Note: Besides Snowflake and Star Schema, the third type of data warehouse in Snowflake is Galaxy Schema. The Galaxy Data Warehouse Schema, or Fact Constellation Schema, uses several fact tables joined by shared normalized dimension tables.
Organize Data With Snowflake Schema in a Data Warehouse
Snowflake Schema is useful for SMEs and large organizations to organize and query large, complex databases. It is often utilized in data warehouses and business intelligence applications, where the primary focus is analyzing and reporting large amounts of data. Understanding the complete Snowflake architecture and layers is crucial to learn how the cloud data warehouse works.
Once you understand its architecture, you can migrate data to Snowflake with the help of our step-by-step cloud data migration process tutorial. If you are confused about how to migrate data from on-premise to the cloud, feel free to contact Inferenz experts. The cloud experts at Inferenz will help you weigh the pros and cons of choosing the best cloud computing platform in 2023.
