Snowflake Schema in Data Warehousing: What You Need to Know

The snowflake and star schema are logical storage designs most commonly used in data marts and data warehouse architecture. Both schemas use the dimensionalities of data to model the storage system.

Recently, there has been a lot of interest in the answer to the question, “What is snowflake schema?” In simple terms, the snowflake schema is a form of the star schema, a variation of sorts. It is a form of data modeling technique used in data warehousing to represent data in an organized, structured manner so that it becomes optimized for querying massive amounts of data efficiently.

In a snowflake schema, the data dimension tables are structured into several tables, forming a hierarchical structure resembling the shape of a snowflake. Scroll down to read everything you should know about the snowflake schema—from its anatomy to the challenges and considerations.

Defining the Snowflake Schema

In essence, a snowflake schema is an extended version of a star schema, which is a multi-dimensional data model commonly used in OLAP data warehouses for business intelligence and reporting purposes.

Engineers further divide dimension tables into logical subdimensions in snowflake schemas. While this increases the complexity of the data model, it can also offer advantages, particularly for specific data types, making it more manageable for analysts to work with.

The dimensional model of the star and snowflake schema consists of two key elements. Firstly, the facts table, also known as a cube, holds the largest volume of data. Secondly, the dimension tables, often referred to as lookup tables, provide derived data structures that answer ad hoc queries and offer dimensions for analysis.

Some of the main characteristics of a snowflake schema include

• Not taking up a lot of storage
• High granularity
• High levels of data integrity

Purpose and Benefits of the Snowflake Schema

Data professionals rely on the snowflake schema in data warehousing due to its advantages in data organization and query performance. By breaking down dimension tables into logical subdimensions, the snowflake schema offers a more granular and organized structure for data storage. This allows for efficient data retrieval and analysis, enabling data professionals to gain deeper insights.

The benefits include:

• The snowflake schema reduces data redundancy
• Optimizes storage space
• Streamlines data organization and query performance
• Allows for extracting valuable information to make best-informed decisions, etc.

Anatomy of a Snowflake Schema

Generally, a snowflake schema has three main components: the facts tables, the dimension tables, and the subdimension tables.

First, the fact tables contain the primary measures or metrics of interest. These tables hold the numerical data and are typically the largest in size.

Second, dimension tables provide additional context and descriptive attributes related to the facts. These tables store information such as time, location, or product details.

Finally, hierarchies define the relationships and levels within the dimension tables. They establish the drill-down paths from higher-level summaries to more detailed data. Together, these components allow for efficient data organization, analysis, and reporting in data warehousing environments.

When to Use the Snowflake Schema

One of the most common uses of snowflake schemas is in business intelligence. In addition, this data warehousing model is also used for reporting in data marts, OLAP data warehouses, and relational databases. Data engineers further split the individual data tables into logical subdimensions in a snowflake schema.

Regarding its use, the snowflake schema might not be the best choice for small dimension tables. On the other hand, if a business draws information from a million data rows in a customer or product dimension tables, then the snowflake schema can greatly help improve performance.

The snowflake schema design finds widespread application in various industries and domains, such as in the business intelligence and analytics industry, retail and e-commerce, logistics and supply chain, healthcare, telecommunications, finance and banking, etc.

Implementing the Snowflake Schema

Implementing a snowflake schema into a data warehousing project takes planning, patience, and a few considerations. Before implementing this particular schema into your data project, consider the following:

• Identify the business requirements: Define the key dimensions and measures to be included in the schema.
• Normalize dimension tables: Break down dimension tables into logical subdimensions to reduce data redundancy and improve data integrity.
• Establish hierarchies: Define hierarchies within dimension tables to enable drill-down capabilities and facilitate data analysis.
• Use appropriate tools and technologies: Select a suitable data modeling tool and database management system that supports the snowflake schema.
• Follow data integration best practices: Implement robust ETL processes to extract data from various sources, transform it to fit the snowflake schema, and load it into the data warehouse.
• Optimize query performance: Implement indexing strategies, partitioning techniques, and appropriate data compression methods. This will enhance the query performance and reduce the need for storage.
• Implement security and access controls: Define user roles and permissions to ensure data privacy and restrict access based on user requirements.
• Perform regular maintenance and monitoring: Conduct periodic data quality checks and ensure proper backup and recovery processes are in place.

Challenges and Considerations

As a robust data storage design, the snowflake schema has a few challenges. For example, one of the major ones is the increase in complexity of the operation and the number of joins needed to query data. This particularity can impact the performance and usability of the OLAP system due to the queries becoming longer and more difficult to write and comprehend.

Additionally, there’s a chance the snowflake schema is not compatible with some OLAP tools or applications. This is due to the input expectations of the tools and apps requiring a star schema instead. In this context, the snowflake schema might need to be additionally transformed or adapted to comply with the tools and applications.

To make the most of the snowflake schema, there are several considerations to keep in mind, such as:

• Using inner joins to filter out missing values;
• Using subqueries or CTEs (Common Table Expressions) to simplify the query logic and reduce the number of joins;
• Using partitioning or indexing techniques to improve the performance of the query and the scalability;
• Using OLAP operators and functions to calculate percentages, rankings, and more.

Conclusion: What is Snowflake Schema and What You Need to Know

Anyone involved in data science, data computing, or handling massive volumes of data can use the specific data storage model known as the snowflake schema. This particular storage model comes in a design that organizes data into a hierarchical structure with many levels of dimension tables.

Bettering the query performance is one of the main characteristics of the snowflake schema. In addition, the model effectuates data modeling; however, it can be a bit complex and rather inflexible. With the snowflake schema, what matters is to consider your business’ specific needs and goals before choosing the snowflake schema in data warehouse processes.