Understanding Star Schema vs. Snowflake Schema

When designing a data warehouse, choosing the right schema design is crucial for efficient data storage, query performance, and data retrieval. Two of the most common schema designs are the Star Schema and Snowflake Schema. This blog will explain what is Star schema vs. Snowflake schema, their key features, advantages, and when to use each.

What is a Schema?

In the context of data warehousing, a schema refers to the logical structure that defines how data is organised, stored, and connected. It helps in arranging data into tables that can be easily queried and analysed.

1. What is a Star Schema?

The Star Schema is a simple and widely-used design in data warehousing. It gets its name because its structure resembles a star shape. In a Star Schema, a central fact table is surrounded by several dimension tables, and these tables are directly connected to the fact table.

Structure of a Star Schema

• Fact Table: The fact table contains quantitative data (metrics) like sales amount, revenue, or profit. It also stores foreign keys that connect to dimension tables.
• Dimension Tables: These tables store descriptive information (attributes) like product names, dates, and locations. Each dimension table is directly linked to the fact table.

Example:

Let’s consider a simple data warehouse for a retail store.

• Fact Table: Sales (Columns: SaleID, ProductID, DateID, StoreID, CustomerID, Revenue)
• Dimension Tables:
  • Product (Columns: ProductID, ProductName, Category, Brand, Price)
  • Date (Columns: DateID, Date, Month, Quarter, Year)
  • Store (Columns: StoreID, StoreName, Location, Manager, OpeningDate)
  • Customer (Columns: CustomerID, CustomerName, Gender, AgeGroup, MembershipType)
  • State (Columns: StateID, StateName, Country, Region, SalesTaxRate)

Advantages of Star Schema

• Simplicity: Easy to understand and query, especially for users who are not experts in SQL.
• Fast Query Performance: Since the data is not deeply normalized, querying is quick, making it suitable for reporting and data analysis.

2. What is a Snowflake Schema?

The Snowflake Schema is a more complex version of the Star Schema. It also has a central fact table, but its dimension tables are normalized into multiple related tables, leading to a structure that resembles a snowflake. This normalization reduces data redundancy and can save storage space, but it may require more complex queries.

Structure of a Snowflake Schema

• Fact Table: Similar to Star Schema, containing metrics and foreign keys.
• Normalized Dimension Tables: The dimension tables are split into multiple related tables, reducing redundancy. For example, instead of having a single Product table, the Snowflake Schema might split it into Product, Category, and Brand tables.

Example:

Continuing with the retail store example, but in a more detailed structure:

• Fact Table: Sales (Columns: SaleID, ProductID, DateID, StoreID, CustomerID, StateID, Revenue)
• Dimension Tables:
  • Product-Related Tables:
    • Product (Columns: ProductID, ProductName, CategoryID, BrandID, Price)
    • Category (Columns: CategoryID, CategoryName, ParentCategoryID)
    • Brand (Columns: BrandID, BrandName, ManufacturerID)
    • Manufacturer (Columns: ManufacturerID, ManufacturerName, Country)
  • Date-Related Tables:
    • Date (Columns: DateID, Date, DayOfWeek, MonthID)
    • Month (Columns: MonthID, MonthName, Quarter, Year)
  • Store-Related Tables:
    • Store (Columns: StoreID, StoreName, LocationID, ManagerID, OpeningDate)
    • Location (Columns: LocationID, City, StateID, PostalCode)
    • Manager (Columns: ManagerID, ManagerName, ContactNumber)
  • Customer-Related Tables:
    • Customer (Columns: CustomerID, CustomerName, Gender, AgeGroup, MembershipTypeID)
    • MembershipType (Columns: MembershipTypeID, MembershipLevel, DiscountRate)
  • State-Related Tables:
    • State (Columns: StateID, StateName, CountryID, Region)
    • Country (Columns: CountryID, CountryName, Continent)

Advantages of Snowflake Schema

• Reduced Data Redundancy: By normalizing data, Snowflake Schema reduces the amount of redundant data.
• More Storage Efficient: Efficient for storing data, especially when there are many attributes with repetitive values.

Star Schema vs. Snowflake Schema: A Quick Comparison

Feature	Star Schema	Snowflake Schema
Design Complexity	Simple and straightforward	More complex with multiple layers of dimension tables
Query Performance	Faster for querying since data is not normalized	Slightly slower due to multiple joins between tables
Data Redundancy	Higher data redundancy	Reduced data redundancy through normalization
Ease of Use	Easier to understand and work with	Requires understanding of normalization and joins
Storage	May require more storage due to redundancy	More storage efficient due to normalization

When to Use Each Schema

Use Star Schema when:

• You need fast query performance for reports and dashboards.
• The data warehouse is small to medium-sized.
• Simplicity and ease of use are more important than storage efficiency.

Use Snowflake Schema when:

• You have a large data warehouse with complex data structures.
• Storage efficiency is a priority, and you want to avoid data redundancy.
• You are dealing with highly normalized data from multiple data sources.

Conclusion

Choosing between Star Schema and Snowflake Schema depends on your data warehousing needs. The Star Schema is ideal for straightforward, fast querying and ease of use, while the Snowflake Schema is better for complex data models that need to optimize storage. Snowflake, the data warehousing platform, can handle both schemas effectively, giving users the flexibility to choose the design that best fits their needs.