Table of contents

Introduction

What is a snowflake schema?

2.1.

SQL Code

2.2.

SQL Code

2.3.

SQL Code

2.4.

SQL Code

2.5.

Designing Snowflake Schema

2.6.

SQL Code

Benefits of Snowflake Schema

Drawbacks

Practical Example

5.1.

SQL Code

5.2.

Advanced Considerations

Frequently Asked Questions

6.1.

How does Snowflake Schema compare to Star Schema?

6.2.

Is Snowflake Schema suitable for all data warehousing scenarios?

6.3.

Can Snowflake Schema handle real-time analytics?

6.4.

What is a snowflake schema in dimensional modeling?

Conclusion

Last Updated: Mar 27, 2024

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Snowflake Schema

Author Pallavi singh

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Introduction

In the realm of data warehousing and Business Intelligence, efficiently organizing data is pivotal. The Snowflake Schema is one such architectural model employed to design data warehouses using normalized data, which aids in reducing data redundancy and improving integrity.

This comprehensive guide dives into the intricacies of the Snowflake Schema, elucidating its structure, implementation, and benefits.

What is a snowflake schema?

Unlike the Star Schema where dimension tables are denormalized, the Snowflake Schema normalizes dimension tables into multiple related tables. It’s termed ‘snowflake’ due to its resemblance to a snowflake shape when visualized.

-- Creating dimension tables in a normalized form

SQL Code

SQL Code

CREATE TABLE Customer (

    CustomerID INT PRIMARY KEY,

    CustomerName VARCHAR(255),

    CustomerCityID INT,

    FOREIGN KEY (CustomerCityID) REFERENCES City(CityID)

);

Let’s see the customer table:

SQL Code

SQL Code

CREATE TABLE City (

    CityID INT PRIMARY KEY,

    CityName VARCHAR(255),

    StateID INT,

    FOREIGN KEY (StateID) REFERENCES State(StateID)

);

Let’s see the city table:

SQL Code

SQL Code

CREATE TABLE State (

    StateID INT PRIMARY KEY,

    StateName VARCHAR(255)

);

Let’s see the state table:

SQL Code

SQL Code

CREATE TABLE  Product ( 

productID int primary key, 

productName varchar(25));

Let’s see the product table:

Designing Snowflake Schema

Designing a Snowflake Schema requires a thorough understanding of the data, its relationships, and normalization principles.

Identify Fact Table: Determine the central fact table which stores quantitative data.
Normalizing Dimension Tables: Normalize the dimension tables to eliminate redundancy.
Establish Relationships: Define foreign key relationships between the fact table and dimension tables, and among dimension tables themselves.

-- Creating a fact table

SQL Code

SQL Code

CREATE TABLE Sales (

    SaleID INT PRIMARY KEY,

    SaleDate DATE,

    CustomerID INT,

    ProductID INT,

    SaleAmount DECIMAL,

    FOREIGN KEY (CustomerID) REFERENCES Customer(CustomerID),

    FOREIGN KEY (ProductID) REFERENCES Product(ProductID)

);

Benefits of Snowflake Schema

Let’s explore the benefits of snowflake schema:

Data Integrity: Normalization ensures data integrity and consistency.
Space Efficiency: Reduces space required to store data due to normalization.
Flexible Queries: Ability to create more complex queries compared to star schema.

Drawbacks

Query Performance: Due to multiple joins, query performance may be slower.
Complexity: More complex to design and maintain.

Practical Example

Let’s consider a retail scenario where we have sales data, and we want to analyze sales by region, city, or customer. Utilizing the Snowflake Schema, we can break down the dimensions into normalized tables and create a schema that allows for this analysis.

-- Query to analyze sales by city

SQL Code

SQL Code

SELECT City.CityName, SUM(Sales.SaleAmount)

FROM Sales

JOIN Customer ON Sales.CustomerID = Customer.CustomerID

JOIN City ON Customer.CustomerCityID = City.CityID

GROUP BY City.CityName;

Advanced Considerations

While Snowflake Schema is potent, understanding its impact on performance and ensuring that it aligns with the business requirements is crucial.

Indexing: Implementing indexes to improve query performance.
Denormalization: Sometimes, a degree of denormalization may be necessary to balance performance and normalization.

- Creating an index to improve query performance

CREATE INDEX idx_CustomerCityID ON Customer (CustomerCityID);

Frequently Asked Questions

How does Snowflake Schema compare to Star Schema?

Snowflake Schema employs normalization reducing data redundancy, while Star Schema focuses on denormalization for faster query performance.

Is Snowflake Schema suitable for all data warehousing scenarios?

The suitability of Snowflake Schema depends on specific project requirements, including the complexity of data and the need for query performance.

Can Snowflake Schema handle real-time analytics?

Due to its normalized structure, Snowflake Schema might not be the ideal choice for real-time analytics as query performance could be slower.

What is a snowflake schema in dimensional modeling?

A snowflake schema in dimensional modeling is a complex database design where the central fact table is connected to multiple dimension tables, which are further normalized into related sub-dimension tables. This design reduces data redundancy but can be more complex to manage.

Conclusion

The Snowflake Schema is an invaluable model for data warehouse design, especially when data integrity and normalization are paramount. Though it may present challenges in query performance and complexity, with a balanced approach and understanding of its mechanics, Snowflake Schema can significantly contribute to achieving insightful data analysis and maintaining a robust data warehouse architecture. Through this guide, you now possess a profound understanding of Snowflake Schema, paving the way for informed decisions in your data warehousing endeavors.

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