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Star Schema vs Snowflake Schema: Differences & Use Cases

This guide breaks down star and snowflake schemas — two common ways to organize data in warehouses. You’ll learn how they work, how they’re different, and when to use each to fit your data needs.
Jan 19, 2025  · 20 min read

If you work with data warehouses, you know how important it is to structure data in a way that’s efficient and easy to handle. But have you ever thought about which database schema best suits your needs? There are two major frameworks that you can use for this: the star schema and the snowflake schema.

The star schema is simple and fast — ideal when you need to extract data for analysis quickly. On the other hand, the snowflake schema is more detailed. It prioritizes storage efficiency and managing complex data relationships. 

In this article, I’ll walk you through the structures of these schemas, highlight their differences, and break down their advantages. By the end, you’ll know where each schema suits and how to decide which is best for your data projects.

What is a Star Schema?

A star schema is a way to organize data in a database, especially in data warehouses, to make it easier and faster to analyze. At the center, there's a main table called the fact table, which holds measurable data like sales or revenue. Around it are dimension tables, which add details like product names, customer info, or dates. This layout forms a star-like shape.

Star schema layout.

Star schema layout. Image by Author.

Let’s look at the key features of the star schema:

  • Single-level dimension tables: The dimension tables connect directly to the fact table without extra layers. Each table focuses on one area, like products, regions, or time, making it simple to use.
  • Denormalized design: In a star schema, related data is stored together in one table using a denormalized approach. For example, a product table may include the product ID, name, and category in the same place. While this may mean some data repetition, it processes queries faster.
  • Common in data warehousing: The star schema is used for quick analysis. It can easily filter or calculate totals, so it’s likely a good choice for data warehouses where fast insights are required.

Let’s understand this with a simple star schema diagram. The fact table Sales is in the center. It holds the numeric data you want to analyze, like sales or profits. Connected to it are dimension tables with descriptive details, such as product names, customer location, or dates:

Real world example of star schema.

Sample star schema. Image by Author. 

Here’s a simple SQL example for setting up a star schema with a Sales fact table and dimension tables for Product, Customer, and Date:

-- Fact table
CREATE TABLE Sales (
    Sales_ID INT PRIMARY KEY,
    Product_ID INT,
    Customer_ID INT,
    Date_ID INT,
    Sales_Amount DECIMAL(10, 2),
    FOREIGN KEY (Product_ID) REFERENCES Product(Product_ID),
    FOREIGN KEY (Customer_ID) REFERENCES Customer(Customer_ID),
    FOREIGN KEY (Date_ID) REFERENCES Date(Date_ID)
);

-- Dimension table: Product
CREATE TABLE Product (
    Product_ID INT PRIMARY KEY,
    Product_Name VARCHAR(100),
    Category VARCHAR(50)
);

-- Dimension table: Customer
CREATE TABLE Customer (
    Customer_ID INT PRIMARY KEY,
    Customer_Name VARCHAR(100),
    Location VARCHAR(50)
);

-- Dimension table: Date
CREATE TABLE Date (
    Date_ID INT PRIMARY KEY,
    Date DATE,
    Year INT,
    Month VARCHAR(20)
);

This layout speeds up queries because there are no complex joins. For example, the following query retrieves total sales grouped by customer location, leveraging the simple joins of the star schema:

SELECT c.Location, SUM(s.Sales_Amount) AS TotalSales
FROM Sales s
JOIN Customer c ON s.Customer_ID = c.Customer_ID
GROUP BY c.Location;

However, you would have to accept some data redundancy since the dimension tables may contain repeated information.

Advantages and limitations of a star schema

Now that you know what star schema is, let’s look at why it stands out:

  • Faster query performance: The star schema simplifies data retrieval by making queries fast. For example, if I want to look at sales trends, I will join the fact table with the right dimension tables. And the best part is that I will do all this without dealing with complex relationships. This would make my queries run faster and save me a lot of time.
  • Easy to understand: Its structure is logical and simple to understand, even for non-technical users. New team members can quickly understand which tables contain the data they need, speeding up analysis and simplifying maintenance.

Despite all the benefits, star schema does have a drawback. As I mentioned before, due to denormalization, dimension tables often contain repeated information, which increases storage use. For example, if several products belong to the same category, each product's name might repeat, taking up more storage space.

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What is a Snowflake Schema?

A snowflake schema is another way of organizing data. In this schema, dimension tables are split into smaller sub-dimensions to keep data more organized and detailed — just like snowflakes in a large lake. 

Snowflake schema layout.

Snowflake schema layout. Image by Author.

Let’s look at the key features of the snowflake schema that make it different from other schemas:

  • Multi-level dimension tables: We can break down our dimension tables into smaller, more specific tables. For example, if I want to track store locations, instead of putting all location details in one big table, I can split them into separate tables for countries, states, and cities. This way, each table would contain only the information it needs to reduce redundancy and improve organization.
  • Normalization for storage efficiency: Unlike star schema, the snowflake schema follows a normalized design, which avoids data duplication. For example, rather than repeating a product category like Electronics for every product, I can store the category in a separate table and link it to individual products.
  • Suitability for complex data environments: The snowflake schema works best for complex data environments because it uses multi-level tables to handle intricate relationships and hierarchical data structures.

Let’s understand this with a simple snowflake schema diagram. At the center is the fact table, which contains measurable data. It connects to dimension tables that describe the facts, and these dimension tables further branch out into sub-dimension tables, forming a snowflake-like structure. 

For example, here I split the Product into Manufacturer and Category tables and the Customer table into Transaction and Location tables:

Real world example of snowflake schema.

Snowflake schema example. Image by Author.

Here’s a SQL example illustrating a snowflake schema where the Product table is further normalized into Category and Manufacturer tables:

-- Fact table remains the same
CREATE TABLE Sales (
    Sales_ID INT PRIMARY KEY,
    Product_ID INT,
    Customer_ID INT,
    Date_ID INT,
    Sales_Amount DECIMAL(10, 2),
    FOREIGN KEY (Product_ID) REFERENCES Products(Product_ID),
    FOREIGN KEY (Customer_ID) REFERENCES Customers(Customer_ID),
    FOREIGN KEY (Date_ID) REFERENCES Dates(Date_ID)
);

-- Dimension table: Product
CREATE TABLE Product (
    Product_ID INT PRIMARY KEY,
    Product_Name VARCHAR(100),
    Category_ID INT,
    Manufacturer_ID INT,
    FOREIGN KEY (Category_ID) REFERENCES Category(Category_ID),
    FOREIGN KEY (Manufacturer_ID) REFERENCES Manufacturer(Manufacturer_ID)
);

-- Sub-dimension table: Category
CREATE TABLE Category (
    Category_ID INT PRIMARY KEY,
    Category_Name VARCHAR(50)
);

-- Sub-dimension table: Manufacturer
CREATE TABLE Manufacturer (
    Manufacturer_ID INT PRIMARY KEY,
    Manufacturer_Name VARCHAR(100)
);

The following query calculates total sales by product category. Although it involves more joins than the star schema, it is more storage-efficient:

SELECT cat.Category_Name, SUM(s.Sales_Amount) AS TotalSales
FROM Sales s
JOIN Product p ON s.Product_ID = p.Product_ID
JOIN Category cat ON p.Category_ID = cat.Category_ID
GROUP BY cat.Category_Name;

Advantages and limitations of a snowflake schema

Like star schema, snowflake schema also has its own advantages. Let’s see what they are:

  • Less data redundancy: Normalization ensures that the same data is not stored multiple times, which reduces duplication.
  • Efficient storage for large datasets: This schema saves storage space by avoiding repeated data, making it ideal for managing large datasets.

However, despite its advantages, there are a few limitations too. For example, queries can be slower because there are more joins between tables. Apart from this, the multi-level structure is more challenging to design and maintain than simpler schemas like star schema. So, go for it only if you have an experienced DBA team.

I recommend checking out the Database Design course if you want to learn more about efficiently structuring data for analysis. 

Differences Between Star Schema and Snowflake Schema

Both star and snowflake schemas are widely used in data warehousing, but their unique characteristics make them suitable for different needs. Let’s see how these schemas differ in structure, performance, storage requirements, and use cases.

Structure

All dimension tables connect directly to one central fact table in a star schema. This means all your reference data is one step away from your main data, making it easy to understand and work with.

In comparison, a snowflake schema breaks dimension tables into smaller, more specific sub-dimension tables. For example, you can have separate tables for countries, states, and cities instead of one location table. While this creates a more organized and detailed structure, it also means more connections (or joins) are required to access your data — a primary reason why snowflake schema is more complex than star schema.

Performance

When it comes to speed, star schemas are better. Since all dimension tables connect directly to the fact table, queries require fewer joins, which means faster performance. Let’s say you want to analyze sales by region — in this case, you can use the star schema to retrieve the data with minimal processing.

Conversely, Snowflake schemas are slower because you have to connect through multiple tables to retrieve the data. Each join adds processing time, making snowflake schemas less efficient for tasks that require quick query results.

The course Joining Data in SQL is an excellent primer for learning how to join tables together, apply relational set theory, and work with subqueries.

Storage requirements

Star schemas take up more storage space because they store redundant information in dimension tables. For example, if multiple products belong to the same category, the category name will repeat for each product, increasing storage needs.

However, snowflake schemas normalize data to store all information only once. For example, instead of repeating category names, they are stored in a separate table and linked to the product table using foreign keys. This design saves storage space, making it ideal for large datasets.

Use cases

Star schemas are ideal for online analytical processing (OLAP) systems, reporting, and business intelligence tasks. Their simplicity makes them perfect for scenarios where speed and ease of use are important, such as generating quick dashboards or sales reports.

Snowflake schemas are often used for financial analysis or customer relationship management (CRM) systems. Organizing detailed hierarchies and saving storage space are more important than query speed in such cases.

When to Use a Star Schema

If you primarily want to organize your data simply and quickly, the star schema would be perfect. Here’s when you can use it:

  • If you want to run simple queries like finding total sales by region, use star schema. Since all the dimension tables connect directly to the fact table, it avoids unnecessary complexity and delivers answers faster.
  • You can even use star schema when speed is your priority. It minimizes the number of table joins, so your queries run faster. I used it once to generate several sales reports, saving me so much time compared to other designs.
  • If your dataset is small to medium, the star schema’s redundancy won’t be a problem. Even with repeated data, it’d work fine without overwhelming your storage.

When to Use a Snowflake Schema

Snowflake schema is more suitable for handling frequent updates or organizing detailed hierarchies. Here’s when you can use it:

  • Use the snowflake schema if you work with large datasets and want to save storage space. It normalizes dimension tables to prevent repeated data, which reduces storage requirements.
  • You can even use the snowflake schema if your data changes often, like updating region names. It maintains consistent updates across all related data to minimize errors and maintenance efforts.
  • If your analysis involves multiple levels of data, the snowflake schema can help you organize and represent these relationships in a simple way.

Summary Table: Star Schema vs. Snowflake Schema 

Here's a quick comparison of the star and snowflake schemas to help you decide which best suits your data needs. I’ve highlighted the key differences in this table, focusing on their structure, performance, storage, and use cases:

Feature

Star schema

Snowflake schema

Structure

Central fact table linked to denormalized dimensions

Central fact table linked to normalized dimensions

Complexity

Simple, with fewer joins

Complex, with more joins

Data redundancy

Higher redundancy due to denormalized dimensions

Lower redundancy due to normalized dimensions

Query performance

Faster queries due to simpler structure

Slower queries because of additional joins

Storage

Requires more storage because of redundancy

Requires less storage due to normalization

Ease of maintenance

Easier to design and maintain

More complex to design and maintain

Best suited for

Small to medium-sized datasets

Large and complex datasets

Final Thoughts

In this blog, I’ve covered the differences between the star and snowflake schemas, their strengths, and when to use each one. I hope you have a clear understanding and practical tips for your work! If you want to learn more, check out these resources on DataCamp:

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FAQs

What is the purpose of indexing in these schemas?

Indexing improves query performance in both schemas by making data retrieval faster.

What is meant by dimension tables and fact tables?

Dimension tables store descriptive attributes (like product names or dates) that describe the data in the fact table.

On the other hand, fact tables store quantitative data, such as sales figures or transaction amounts, and connect to dimension tables.

Are these schemas suitable for unstructured data?

No, these schemas are designed for structured data. Unstructured data requires different models, such as NoSQL or data lakes.

How can I design star and snowflake schemas?

To create and visualize these schemas, you can use data modeling tools (ERDPlus), BI tools (Tableau, Power BI, QlikView), or cloud platforms (Databricks).

Are there any alternatives to star and snowflake schemas?

Yes, you can use Galaxy schemas, Data Vault modeling, or more complex dimensional models. These options differ mainly in organizing data and handling relationships between different information.


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Laiba Siddiqui
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I'm a content strategist who loves simplifying complex topics. I’ve helped companies like Splunk, Hackernoon, and Tiiny Host create engaging and informative content for their audiences.

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