One of the many variations of the star schema is the snowflake schema. The centralized fact table is linked to multiple dimensions in this case. Dimensions are present in a normalized form in multiple related tables in the snowflake schema. 

When the dimensions of a star schema are detailed and highly structured, with several levels of relationship, and the child tables have multiple parent tables, the snowflake structure appears. The snowflake effect only affects dimension tables and has no effect on fact tables.

In a snowflake schema, a central fact table is surrounded by several dimensions that are represented by their tables. The dimensions are connected to the central fact table through foreign keys, which allow the fact table to reference the attributes of the dimensions.

In this article, we will be diving deeper into the topic of the snowflake schema along with examples, its comparison with star schema and also its advantages and disadvantages. So, hold tight and enjoy the ride to the world of snowflake schema!

What is a Snowflake Schema?

The star schema is equivalent to the snowflake schema. A snowflake schema is an expansion of a star schema that includes subdivided dimension tables that follows a multi-dimensional data model.

The snowflake schema is a star schema expansion in which each point of the star explodes into more points. It is known as a snowflake schema because the diagram resembles a snowflake. Snowflaking is a technique used to normalize the dimension tables in star schemas. When we completely normalize all of the dimension tables, the resulting structure resembles a snowflake with the fact table in the center.

The central fact table in a snowflake schema contains the primary data that is being analyzed, while the dimension tables contain data that provides context for the data in the fact table. For example, a fact table might contain data on sales transactions, while the dimension tables might contain data on customers, products, and stores.

The snowflake schema is useful for organizing and querying large, complex databases because it allows for more efficient querying and faster performance. It is often used in data warehouses and business intelligence applications, where the focus is on analyzing and reporting large amounts of data.

Since we got an overview of what exactly the snowflake schema is, let us understand the differences between the snowflake and star schema.

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Star Schema vs Snowflake Schema

A star schema and a snowflake schema are two different types of database schemas that are used to organize data in a structured manner. 

Both types of schema involve a central fact table surrounded by dimension tables, but there are some key differences between the two:

Feature

Star Schema

Snowflake Schema

Structure

A central fact table surrounded by a few dimension tables

A central fact table surrounded by multiple dimension tables, which may be further divided into sub-dimension tables

Data Modeling

Simple and easy to understand

More complex and detailed

Query Performance

Faster query performance due to fewer join

Slower query performance due to more joins

Data Redundancy

Low data redundancy due to denormalized structure

Higher data redundancy due to normalized structure

Data Integrity

Lower data integrity due to denormalized structure

Higher data integrity due to normalized structure

Data Storage

Takes up more space due to the denormalized structure

Takes up less space due to the normalized structure

Overall, the choice between a star schema and a snowflake schema will depend on the specific needs of your database and the type of analysis you are conducting. The star schema is generally simpler and faster, but the snowflake schema provides more detailed and normalized data that may be necessary for certain types of analysis.

To understand snowflake schema in a better way, let us look at some real-world examples.

Examples of Snowflake Schema

The snowflake schema is a type of database design that is used in data warehousing. It is called a snowflake schema because the diagram of the schema resembles a snowflake, with the central fact table at the center and the dimension tables branching out from it like the points on a snowflake.

Here are a few of the real-world use cases of the snowflake schema:

  1. A database for a hospital that stores patient medical records: The central fact table might be the "Patient Visits" table, which contains data on the visits that patients have made to the hospital. Dimension tables might include the "Patients" table, which contains data on the patients themselves, the "Doctors" table, which contains data on the doctors who treated the patients, and the "Procedures" table, which contains data on the medical procedures that were performed.
  2. A database for a university that stores student enrollment and grades: The central fact table might be the "Enrollment" table, which contains data on the courses that students are enrolled in. Dimension tables might include the "Students" table, which contains data on the students themselves, the "Courses" table, which contains data on the courses being offered, and the "Teachers" table, which contains data on the teachers who are teaching the courses.
  3. A database for a social media platform that stores user activity data: The central fact table might be the "Activity" table, which contains data on the actions that users take on the platform, such as liking a post or commenting on a photo. Dimension tables might include the "Users" table, which contains data on the users themselves, the "Posts" table, which contains data on the posts that are made on the platform, and the "Groups" table, which contains data on the groups that users belong to on the platform.

In order to get a sense of whether the snowflake schema is the right fit for our use case, we need to get an overview of its various advantages and disadvantages.

Advantages of Snowflake Schema

A snowflake schema is a type of database schema that is designed to improve the organization and efficiency of a database by providing a more structured and normalized data model. 

Some of the main advantages of the snowflake schema are given below:

  • Data integrity: It helps to improve data integrity by reducing redundancy and ensuring that data is stored in a more organized and consistent way. This can make it easier to maintain the database and ensure that the data is accurate and up-to-date.
  • Lesser disk space required: It uses small disk space because the data is highly structured and normalized. This can be especially useful for large databases that need to be stored on disk, as it can help to save space and reduce the overall size of the database.
  • Eases database organization: Useful tool for organizing and managing complex databases, particularly in data warehousing and business intelligence applications where the focus is on analyzing and reporting on large amounts of data.
  • Easier to update and maintain: It is easier to maintain and update compared to other schemas like the star schema. In a star schema, all of the dimension tables are directly connected to the central fact table, which can make it more difficult to add or modify dimensions. In a snowflake schema, the dimensions are organized into separate tables, which can make it easier to modify the schema as the data warehousing requirements change.

We learned about the main advantages of the snowflake schema, and now let us understand its various disadvantages.

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Disadvantages of Snowflake Schema

While the snowflake schema can provide a number of benefits in terms of query performance and data modeling, it also has a number of potential drawbacks. Some of the main disadvantages of using a snowflake schema include the following:

  • Performance issues: While a snowflake schema can improve query performance in certain situations, it can also introduce performance issues in others. For example, if the schema is not well-optimized or if the data is not distributed evenly, queries may be slow to execute or may require a large number of resources.
  • Reduced flexibility: The hierarchical structure of a snowflake schema can make it more difficult to add or modify data, as changes to one dimension table may require corresponding changes to other tables in the hierarchy. This can be particularly problematic if the schema is not well-designed or if the data requirements change over time.
  • Data redundancy: A snowflake schema may result in data redundancy, as the same data may be stored in multiple tables and in multiple locations within each table. This can lead to issues with data consistency and integrity, as well as increased storage requirements.
  • Increased complexity: Because a snowflake schema involves multiple dimension tables and foreign key relationships, it can be more complex to design and maintain than a simpler, flatter schema. This complexity can make it more difficult for users to understand and work with the data, and it can also increase the risk of errors and inconsistencies.
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In summary, a snowflake schema is a database design that organizes data into a hierarchical structure with multiple levels of dimension tables. It can offer improved query performance and efficient data modeling, but it can also be complex and inflexible. It is important to carefully consider the specific needs and goals of an organization before deciding if a snowflake schema is the right choice.

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FAQs

1. What is a snowflake schema?

A snowflake schema is an expansion of a star schema that includes subdivided dimension tables that follows a multi-dimensional data model.

2. What is the difference between star and snowflake schema?

A star schema contains dimension tables as well as fact tables. A snowflake schema includes all three types of tables: dimension tables, fact tables, and sub-dimension tables.

3. Why is it called a snowflake schema?

It is known as a snowflake schema because the diagram resembles a snowflake. Snowflaking is a technique used to normalize the dimension tables in star schemas.

4. Is the snowflake and snowflake schema the same?

No, "snowflake" and "snowflake schema" refer to two different things.

Snowflake is a specific type of data warehouse software that is designed to work with large amounts of structured and semi-structured data

A snowflake schema, on the other hand, is a specific type of database schema that is used to organize data in a star schema.

5. What are the four types of snowflake tables?

In a snowflake schema, there are typically four types of tables:

  • Fact tables
  • Dimension tables
  • Hierarchy tables 
  • Bridge tables

6. Can we join 2 fact tables?

Joining the two fact tables is much easier now that we have this bridge table with the row identifiers for both fact tables.

7. What are the 3 types of schema?

The 3 types of schema are:

  • Logical Schema
  • Physical Schema
  • View Schema

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