What is Snowflake Schema?
A snowflake schema is a type of database schema that is a more complex version of the star schema. It is used in data warehousing and business intelligence to organize and structure data for efficient querying and analysis. The snowflake schema is named for its snowflake-like shape, with dimension tables normalized into multiple related tables.
Key Components of a Snowflake Schema
Fact Table:
- Definition: The central table that stores quantitative data (measures) for analysis.
- Content: Contains facts or metrics, such as sales revenue, quantities sold, or transaction amounts.
- Keys: Includes foreign keys that reference the primary keys of dimension tables and usually a primary key that uniquely identifies each record.
Dimension Tables:
- Definition: Tables that store descriptive attributes (dimensions) related to the facts. In a snowflake schema, these tables are normalized into multiple related tables.
- Content: Contains attributes like product names, dates, customer details, or geographical information.
- Keys: Each dimension table has a primary key that is referenced by the foreign keys in the fact table.
Example of a Snowflake Schema
Consider a retail business that wants to analyze its sales data. The snowflake schema for this scenario might include the following:
- Fact Table: Sales
- Columns: SaleID (primary key), ProductID (foreign key), CustomerID (foreign key), DateID (foreign key), SalesAmount, QuantitySold
- Dimension Tables:
- Product Dimension:
- Main Table: Products
- Columns: ProductID (primary key), ProductName, CategoryID (foreign key), Price
- Related Table: Categories
- Columns: CategoryID (primary key), CategoryName
- Main Table: Products
- Product Dimension:
- Customer Dimension:
- Main Table: Customers
- Columns: CustomerID (primary key), CustomerName, LocationID (foreign key), AgeGroup
- Related Table: Locations
- Columns: LocationID (primary key), City, State, Country
- Main Table: Customers
- Date Dimension:
- Main Table: Dates
- Columns: DateID (primary key), Date, MonthID (foreign key), Quarter, Year
- Related Table: Months
- Columns: MonthID (primary key), MonthName, MonthNumber
- Main Table: Dates
Star Schema vs Snowflake Schema in Data Engineering
In this article, we are going to explore the difference between the Star Schema and the Snowflake Schema in data engineering
In the field of data warehousing and business intelligence, organizing and structuring large volumes of data efficiently is crucial for effective data analysis and decision-making. Two popular approaches to this challenge are the star schema and the snowflake schema, each with its unique design and purpose. These schemas are foundational to understanding how data can be modeled to support complex analytical queries and reporting needs. Here, we delve into the characteristics, components, and differences of these schemas, shedding light on their practical applications in real-world scenarios. This exploration not only highlights the technical specifics but also the strategic implications of choosing one schema over the other in various business contexts.
Table of Content
- What is a Star Schema?
- What is Snowflake Schema?
- Difference Between Star Schema and Snowflake Schema