« Back to Glossary

What is a Snowflake Schema?

Used in data warehousing, a snowflake schema is a multi-dimensional data model in which dimension tables are further broken down into subdimensions to represent multiple levels of granularity. The snowflake schema can be seen as a step forward from star schemas. As is known, only the fact table in normalized in a star schema. Now, going a step further to apply deeper normalization in the dimension tables in order to create subdimensions will result in the creation of a snowflake schema. The highly normalized sub-tables form a structured hierarchy and provide granular control over business data. In conceptual diagrams, when subordinate tables are added to dimension tables, the resulting structure closely resembles the intricate design of a snowflake.

The normalization of its dimension tables is the highlight of the snowflake schema.It breaks larger tables into specialized datasets to eliminate redundancy and ensure segregation. The snowflake schema classifies every unique attribute in a separate normalized table. In the interest of granular data classification, such intense normalization is inherently embedded in the design of this schema.

Key Features of Snowflake Schemas

Snowflake schemas offer detailed data analysis through the following features:

Thorough Normalization: Snowflake schemas are designed with an exceptionally high degree of normalization which can almost be considered aggressive. For data integrity and fragmentation purposes, this structure breaks dimension tables into multiple related tables.
Streamlined Hierarchies: Hierarchical structures are an inherent characteristic of the snowflake schema. When a parent table takes ownership of all its child tables, the resulting structure ensures a streamlined flow of data.
Granular Data Representation: Dimension tables are further normalized into attribute-focused tables to create a hierarchical representation of the data. These tables are then connected to each other with foreign keys to create a framework for data analysis.

Benefits of Snowflake Schema

The above-mentioned features of snowflake schemas deliver the following benefits:

Structural integrity: Foreign keys are the foundation of structural integrity in snowflake schemas. These keys are used to connect certain attributes of a dimension table to all the other tables with mutually referential relationships. This approach ensures the accuracy, reliability and sustainability of the dataset.
Efficient Query Performance: In a star schema, dimension tables are directly linked to fact tables. However, foreign keys are used to further normalize the dimension tables in a snowflake schema. As a result, the interconnected design ends up with numerous additional joins in favor of delivering efficient query performance. Intricate table relationships accelerate data retrieval while running queries with multiple variables.
Minimized Duplication: Featuring a well-defined structure of hierarchically arranged dimension tables, snowflake schemas promote efficient data classification. These schemas are designed to reduce redundancy for compact storage requirements and reduced possibilities of data entry anomalies.

Drawbacks of Snowflake Schemas

Along with the many advantages for complex data analysis, experts may see these potential drawbacks in a snowflake schema:

Intensive Supervision: Multiple levels of normalization between tables contribute to increased complexity in data modeling, analysis and maintenance. The management of a snowflake schema necessitates profound understanding of table relationships and the implications of each normalization step.
Resource Drain: Despite their efficiency in storage usage, snowflake schemas may monopolize system resources during the execution of complex queries. The complexity of this schema’s structure can lead to more CPU and memory resources being used to maintain the schema.
Join Requirements: As compared to a star schema, more joins are required to develop hierarchies in a snowflake schema. The requirement for more complex SQL queries that involve multiple table joins can impact performance, especially when dealing with large data sets.

When to use a snowflake schema implementation?

Snowflake schemas are most useful in business scenarios that need a perfect balance of extensive normalization and simplified analysis. Here are a few business cases which call for a snowflake schema implementation:

Data Complexity: Snowflake schemas are exceptional while managing large datasets having highly interconnected relationships. These schemas possess the ability to organize complex data hierarchies and relationships. This capability makes them suitable for application in industries like retail, manufacturing and healthcare.
Dimensional Analysis: Comprehensive data analysis involving multiple dimensions is critical in the realm of business intelligence and decision making and snowflake schemas offer a robust analytical framework for advanced analytical requirements. They amplify the examination and cross-referencing of various dimensions for organizations looking to derive valuable insights.
Regulatory Compliance: Snowflake schemas can become indispensable in industries like finance and pharmaceuticals due to their need for stringent regulatory compliance. Data integrity offered by these schemas bodes well for adherence to compliance requirements. The high degree of normalization aids in maintaining accurate and auditable records for regulatory reporting.

Snowflake Schema vs. Star Schema

When compared with star schemas, snowflake schemas possess superior normalization as they break down data into interconnected tables. Due to this hierarchical approach, these schemas can represent complex data relationships in an organized manner. However, such magnified normalization gives birth to a complicated web of data which is relatively tougher to manage and query.

Star schemas, on the contrary, have a straightforward and intuitive design. Just like stars help with marine navigation, star schemas help with data navigation. These data structures exhibit clearly defined relationships between the core data and all of its contextual information. Snowflake schemas offer greater ease of use and faster query performance due to their willingness to sacrifice a certain degree of normalization.

How to choose between Snowflake Schema & Star Schema

Organizations should diligently evaluate their data requirements and analytical goals before deciding to implement any of these data structures. A comprehensive analysis of the respective offsets between complexity and ease of use should be conducted before selecting the schema that aligns best with business objectives and operational capabilities. The decision-making process between Snowflake and star schemas involves meticulous evaluation of the specific needs and objectives of the data warehousing application:

Data Structure Demands: Snowflake schemas are highly effective for businesses that require highly normalized data. Conversely, star schemas may be more fitting when for the use cases of data retrieval and ease of comprehension.
Performance Considerations: Snowflake schemas suit environment with complex analytical needs, though they may introduce higher query complexity. Performance-oriented environments tend to lean toward star schemas as they deliver quick responses to complex queries with their denormalized structure.
Need for Nuance: For environments valuing robust, highly normalized structures to uphold data integrity and accuracy, snowflake schemas are ideal. Meanwhile, the simplicity of star schemas aligns with straightforward data models that demand lower levels of normalization.

A snowflake schema implementation results in reduced data redundancy and improved data integrity. However, the increased number of joins for complex querying can impact performance and readability, making it more difficult for end users to understand and explore the data. Therefore, the decision to use a snowflake schema should be made after due consideration regarding the trade-offs between simplicity, performance and data integrity, as well as the specific requirements of the project or organization.

Glossary: Data Architecture
Glossary: Data Cube
Glossary: Data Mart
Data Vault Acceleration with Kyvos’ Smart OLAP™
As Data Vaults continue to grow in order to address that sweet spot between the anarchy of a data lake and the rigidity of traditional data warehouses, Kyvos’ Smart OLAP™ enables Data Vault acceleration, overcoming performance challenges on cloud-scale data volumes.
Embracing Change with a Wide Breadth of Generalized Events
Learn about a cube modeling technique that helps incorporate a wide breadth of event types into OLAP cubes in a generalized, flexible, and scalable manner.
NLP for OLAP Semantic Layers – Part 1
How do BI NLP interfaces work with a ChatGPT-centric approach and ChatGPT plugins? Read this first blog from the series to know more about NLP query interfaces working for BI data sources using Azure Cognitive Services.
Raw Data Vault to Star Schema
This blog focuses on the Kimball Data Warehouse methodology, which is the E-T-L into a star/snowflake structured data warehouse. It also sheds some light on the chasm between the raw data vault to the business vault.
SSAS MD to Kyvos Migration – Part 2
This is the second blog in the three-part series that compares primary objects and features of SSAS and Kyvos cubes, demonstrating the ease of migrating to Kyvos.
SSAS MD to Kyvos Migration – Part 1
This three-part blog series demonstrates the ease of migrating SSAS MD to a modern platform using Kyvos by presenting a detailed technical feature comparison between the two platforms.

« Back to Glossary