In the realm of data warehousing, star and snowflake schemas play crucial roles in organizing vast amounts of data efficiently. Both of these schemas offer unique advantages and cater to distinct requirements in the data processing landscape. Before diving into the details, let’s first provide a snapshot comparison to set the scene: Star schemas are more straightforward, while snowflake schemas are a more normalized version of star schemas.

Here are some key differences between star schema and snowflake schema:

  • Star schema dimension tables are not normalized while snowflake schema's dimension tables are normalized. 
  • Snowflake schemas will use less space than star schemas to store dimension tables but are more complex.
  • Star schemas will only join the fact table with the dimension tables, leading to simpler, faster SQL queries.
  • Snowflake schemas have no redundant data, so they're easier to maintain.
  • Snowflake schemas are good for data warehouses whereas star schemas are better for datamarts with simple relationships.

Essentially, star schemas offer users a more efficient way to organize data and information in a data warehouse. In comparison, snowflake schemas, which are a variation of star schemas, provide users with more efficiency when it comes to processing data. While the two processes are quite similar, they have key differences that users should be aware of too. In this article, we'll first delve into the star schema and then transition to the snowflake schema to shed light on their individual nuances and comparative benefits.

Read on to learn more about the star schema vs snowflake schema matchup and to discover how the two types of data warehouse schemas can help to improve your organization’s efficiency when it comes to moving, storing, handling, and completing complex analysis with company data.

Table of Contents 

Overview of Star Schema vs Snowflake Schema 

When it comes to star schema and snowflake schema, it's essential to remember their basic definitions:

  • Star Schema: A type of database schema where a single fact table references a number of dimension tables, forming a pattern that resembles a star. Star schemas offer an efficient way to organize information in a data warehouse
  • Snowflake Schema: A more complex variation of the star schema, where dimension tables are normalized, leading to multiple related tables forming a pattern similar to a snowflake. Snowflake schemas are a variation of star schemas that allow for more efficient data processing.
  • Normalization: A database design technique that reduces data redundancy and improves data integrity.

With these clarifications, let's delve deeper into the specifics of these schemas.

Here is a comparison table of star and snowflake schemas: 

Snowflake Schema

Star Schema


Consists of a centralized fact table connected to multiple-dimension tables in a hierarchical manner

Consists of a centralized fact table connected to dimension tables in a star-like structure


Highly normalized design

Partially denormalized design

Query Performance

Excellent for complex queries and aggregations

Better for simple queries and aggregations

Storage Efficiency

Highly efficient for storing data

Less efficient due to denormalization


Highly scalable due to the separation of data

Limited scalability due to denormalization

Data Integrity

Ensures high data integrity

Lower data integrity due to denormalization


More complex to design and maintain

Simpler to design and maintain


More flexible for changes in the data model

Less flexible for changes in the data model


Suitable for large, complex data warehouses

Suitable for small to medium-sized data warehouses

Storage Overhead

Requires less storage space

Requires more storage space

Both schemas improve the speed and simplicity of read queries and complex data analysis—especially when dealing with large data sets that pull information from diverse sources.

Despite their similarities, star schemas and snowflake schemas have key differences that every data scientist and data engineer needs to understand. To answer the question of "What's the difference between star schema vs snowflake schema", we'll begin with an in-depth discussion of star schemas. Then, we'll move into snowflake schemas and explore a tutorial on what makes them unique.

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What Is a Star Schema?

Star schemas offer the simplest structure for organizing data into a data warehouse. The center of a star schema consists of one or multiple “fact tables” that index a series of “dimension tables.” To understand star schemas—and for that matter snowflake schemas—it's important to take an in-depth look at the two separate tables of fact tables and dimension tables.

The purpose of a star schema is to cull out numerical "fact" data relating to a business and separate it from the descriptive, or “dimensional" data. Fact data will include information like price, weight, speed, and quantities—i.e., data in a numerical format. Dimensional data will include uncountable things like colors, model names, geographical locations, employee names, salesperson names, etc., along with numerical information.

The fact data gets organized into fact tables and the dimensional data into dimension tables. Fact tables are the points of integration at the center of the star schema in the data warehouse. They allow machine learning tools to analyze the data as a single unit, and they allow other business systems to access the data together. Dimension tables hold and manage the data—numerical and nonnumerical—which converges through fact tables that make up the data warehouse.

From a more technical perspective, fact tables keep track of numerical information related to different events. For example, they might include numeric values along with foreign keys that map to additional (descriptive and nonnumerical) information in the dimension tables. Getting even more technical, fact tables maintain a low level of granularity (or “detail”), which is to say, they record information at a more atomic level. This could lead to the buildup of many records within the fact table over time.

Types of Fact Tables

There are three main kinds of fact tables:

  • Transaction fact tables: These record information related to events, like individual merchandise sales.
  • Snapshot fact tables: These record information that applies to specific moments in time, like year-end account statements.
  • Accumulating snapshot tables: These record information related to a running tally of data, like year-to-date sales figures for specific merchandise or categories of merchandise.

Types of Dimension Tables

Dimension tables normally store fewer records than fact tables; however—in addition to storing numerical data—the records in dimension tables also include descriptive attributes. There are many types of dimension tables depending on the information system. Here are some examples:

  • Time dimension tables: Information to identify the exact time, date, month, and year different events happened.
  • Geography dimension tables: Address/location information.
  • Employee dimension tables: Information about employees and salespeople, such as addresses, phone numbers, names, employee numbers, and email addresses.
  • Merchandise dimension tables: Descriptive information about products, their product numbers, etc.
  • Customer dimension tables: Customer name, numbers, contact information, addresses, etc.
  • Range dimension tables: Information relating to a range of values for time, price, and other quantities.

How Fact Tables and Dimension Tables Work Together

Dimension tables usually list a surrogate primary key (i.e., a data type that consists of a single-column integer) that maps to attributes related to the natural key. Imagine you have a dimension table with information relating to different stores: "Dim_Store" (see Star Schema illustration below). You can assign an ID number to each store and its row of related nonnumerical and other information—like store name, size, location, number of employees, category, etc. As it follows, wherever you list the Store ID number on the fact table ("Fact_Sales"), it will map to that specific row of store data on the "Dim_Store" dimension table.

Of course, the star schema doesn't stop there—because there are additional points (or dimension tables) with information that links to the fact table. As an example, let's say you want to know the following:

  • How many products were purchased?
  • What products were purchased?
  • In what stores were the products purchased?
  • What were the names and addresses of the products purchased?
  • What brand name manufactured the products purchased?
  • What day of the week did customers make each product purchased?

To conduct a query like this, you'll need to access data contained in all of the dimension tables (Dim_Date, Dim_Store, and Dim_Product). These are separate databases; however, through the fact table—which serves as a point of integration—you can query all of the data, akin to it being in a single table. And that's how a star schema data warehouse works!

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Star Schema Diagram

The following diagram illustrates what a simple star schema looks like:

thumbnail image

*Image: SqlPac at English Wikipedia, CC BY-SA 3.0.

Here, the fact table, Fact_Sales, is at the center of the diagram. Its schema includes the following columns for ID numbers: Date_Id, Store_Id, Product_Id, and Units_Sold. As the point of integration, the fact table integrates the diverse information in the dimension tables: Dim_Product, Dim_Store, and Dim_Date.

As you can see, the star schema gets its name from having a central fact table “core,” and dimension table “points.” When a star schema has many dimension tables, data engineers might refer to it as a centipede schema.

Denormalization of Data in Star Schemas

The star schema’s goal is to speed up read queries and analysis for massive amounts of data contained in diverse databases with different source schemas. The star schema achieves this goal through the “denormalization” of the data within the network of dimension tables.

Traditionally, database managers sought the “normalization” of data by eliminating duplicate copies of the same data, which is to say, the normalization of the duplicate information into one copy. This made writing commands faster because only one copy of the data needed updating.

When a data system expands into multiple dimension tables, however, accessing and analyzing data from multiple sources slows down read queries and analysis. To speed things up, the star schema relaxes the traditional rules of database normalization by “denormalizing” the data.

A star schema pulls the fact data (or ID number primary keys) from the dimension tables, duplicates this information, and stores it in the fact table. In that way, the fact table connects all of the information sources together. This makes read queries and analysis infinitely faster. However, it sacrifices the speed of writing commands. The slower write commands happen because the system needs to update all counterpart copies of the “denormalized” data following each update.

Benefits of Star Schemas

Star schemas offer the following benefits:

  • Queries are simpler: Because all of the data connects through the fact table, the multiple dimension tables are treated as one large table of information, making queries simpler and easier to perform.
  • Easier business insights and reporting: Star schemas simplify the process of pulling business reports like as-of-as and period-over-period reports.
  • Better-performing queries: By removing the bottlenecks of a highly normalized schema, query speed increases, and the performance of read-only commands improves.
  • Provides data to OLAP systems: OLAP (Online Analytical Processing) systems can use star schemas to build OLAP cubes.

Challenges of Star Schemas

As mentioned before, improving read queries and analysis in a star schema could involve certain challenges:

  • Decreased data integrity: Because of the denormalized data structure, star schemas do not enforce data integrity very well. Although star schemas use countermeasures to prevent anomalies from developing, a simple insert or update command can still cause data incongruities.
  • Less capable of handling diverse and complex queries: Database designers build and optimize star schemas for specific analytical needs. As denormalized data sets, they work best with a relatively narrow set of simple queries. Comparatively, a normalized schema permits a far wider variety of query complexity.
  • No many-to-many relationships: Because they offer a single-dimension schema, star schemas don’t work well for “many-to-many data relationships.”

Related Reading: 6 Database Schema Designs and How to Use Them 

What Is a Snowflake Schema?

Now that you understand how star schemas work, you’re ready to explore the snowflake schema— which takes the shape of a snowflake. The purpose of a snowflake schema is to normalize the denormalized data in a star schema. This solves the write command slow-downs and other problems typically associated with “star schemas.”

The snowflake schema is a “multi-dimensional” structure. At its core are fact tables that connect the information found in the dimension tables, which radiate outward like in the star schema. The difference is that the dimension tables in the snowflake schema divide themselves into more than one table. That creates the snowflake pattern.

Through this “snowflaking” method, the snowflake schema normalizes the dimension tables it connects with by (1) getting rid of “low cardinality” attributes (that appear multiple times in the parent table); and (2) turning the dimension tables into more than one table, until the dimension tables are completely normalized.

Like snowflake patterns in nature, the snowflake database becomes exceedingly complex. The schema can produce elaborate data relationships, where child tables have more than one parent table.

IBM offers an excellent comparison of snowflake schemas versus star schemas, stating: “Star and snowflake schema designs are mechanisms to separate facts and dimensions into separate tables ... A snowflake schema can have any number of dimensions and each dimension can have any number of levels.”

Snowflake Schema Diagram

Before we go too deep into the snowflaking concept, it's time to look at an illustration of a snowflake schema:

thumbnail image

*Image: SqlPac at English Wikipedia, CC BY-SA 3.0.

Do you see how the above illustration took the star table example, and "snowflaked" each dimensional table outward? Let's examine the Dim_Product dimension table. What has happened is that various columns of the Dim_Product table have snowflaked outward into lookup tables.

In the star schema example, Dim_Product included the nonnumerical names of the brands. Now it just includes the Brand_Id number which points to the Dim_Brand lookup table. By translating the Dim_Product table into a numerical value like this, we increase the speed at which the system can process queries. More importantly, we reduce the amount of space required to store data. That's because the Dim_Product table no longer includes multiple entries of the full names of brands (which are long strings of data compared to the Brand_Id numbers).

Long story short, a number requires a dramatically reduced amount of space and time for processing than a written name or qualitative descriptive value. Therefore, snowflaking the dimension tables out into lookup tables can save a lot on storage costs when dealing with millions of rows and columns of data.

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Benefits of Snowflake Schemas

Snowflake schemas offer the following benefits compared to normal star schemas:

  • Compatible with many OLAP database modeling tools: Certain OLAP database tools, which data scientists use for data analysis and modeling, are specifically designed to work snowflake data schemas.
  • Saves on data storage requirements: Normalizing the data that would typically get denormalized in a star schema can offer a tremendous reduction in disk space requirements. Essentially, this is because you're converting long strings of non-numerical data (the information pertaining to descriptors and names) into numerical keys that are dramatically less taxing from a storage perspective.

Challenges of Snowflake Schemas

There are three potential challenges relating to snowflake schemas:

  • Complex data schemas: As you might imagine, snowflake schemas create many levels of complexity while normalizing the attributes of a star schema. This complexity results in more complicated source query joins. In offering a more efficient way to store data, Snowflake can result in performance declines while browsing these complex joins. Still, processing technology advancements have resulted in improved snowflake schema query performance in recent years, which is one of the reasons why snowflake schemas are rising in popularity.
  • Slower at processing cube data: In a snowflake schema, the complex joins result in slower cube data processing. The star schema is generally better for cube data processing.
  • Lower data integrity levels: While snowflake schemas offer greater normalization and fewer risks of data corruption after performing UPDATE and INSERT commands, they do not provide the level of transnational assurance that comes with a traditional, highly normalized database structure. Therefore, when loading data into a snowflake schema, it's vital to be careful and double-check the quality of information post-loading.

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To sum up, star and snowflake schemas are instrumental in data warehousing, each offering its unique strengths and facing distinct challenges. While star schemas provide simplicity and faster processing for cube data, snowflake schemas bring about storage efficiency at the cost of complexity. Decision-makers need to weigh these pros and cons based on the specific requirements of their data warehousing projects. Furthermore, with the right tools and strategies in place, many of the challenges associated with these schemas can be effectively navigated.

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