Data Forest logo
Home page  /  Glossary / 
Snowflake Schema

Snowflake Schema

Snowflake Schema is a type of database schema used in data warehousing that organizes data into a structured, multi-level, normalized layout. It is an extension of the star schema, where each dimension table is normalized into multiple related tables, creating a hierarchy that resembles a snowflake shape when visualized. The snowflake schema is characterized by its more complex structure, which allows for better organization and reduced data redundancy by dividing dimension tables into additional tables based on data attributes.

Core Characteristics of a Snowflake Schema

  1. Normalized Dimension Tables: In a snowflake schema, dimension tables are normalized by splitting data into additional tables that represent specific attributes of the dimension. For example, in a sales database, the “Geography” dimension may be split into related tables for “Country,” “Region,” and “City.” This normalization reduces redundancy but requires additional joins when querying data.
  2. Hierarchical Structure: The snowflake schema organizes data in a hierarchical structure, where each level of hierarchy corresponds to a specific attribute or level of granularity within the dimension. This structure allows for more precise data categorization and reduces data duplication, enhancing data integrity and consistency.
  3. Fact Table Centralization: Similar to a star schema, the snowflake schema uses a central fact table that contains quantitative data, such as sales amounts, quantities, or revenue. This fact table has foreign keys that link to primary keys in the dimension tables, forming relationships with the various levels in the dimension hierarchy.
  4. Reduced Redundancy: By normalizing dimension tables, the snowflake schema reduces data redundancy. Data that would otherwise be repeated in a flat structure is organized into smaller, related tables, conserving storage space and maintaining a clean, organized data structure.
  5. Increased Join Complexity: While reducing redundancy, the snowflake schema requires additional joins between tables in complex queries, as data points are spread across multiple tables. This can lead to more complex SQL queries and potentially slower performance for large datasets compared to a star schema, as each level of normalization adds another join.

The snowflake schema is commonly used in data warehousing and business intelligence (BI) environments where data consistency, storage efficiency, and organized data hierarchies are prioritized. It is particularly useful in cases where data needs to be queried at multiple levels of granularity and where storage efficiency is essential. Tools like Snowflake, Amazon Redshift, and Google BigQuery often support snowflake schemas, leveraging their hierarchical data structure to maintain high data quality and consistency across complex data warehouse models. Through its normalization, the snowflake schema provides a balance between storage efficiency and structured data organization, making it an effective model for analytics-focused databases in large-scale, complex data environments.

Data Engineering
Thank you! Your submission has been received!
Oops! Something went wrong while submitting the form.

Latest publications

All publications
Article image preview
October 31, 2024
19 min

Data Science Tools: A Business Decision Depends on The Choice

How to Choose a DevOps Provider?
October 29, 2024
15 min

DevOps Service Provider: Building Software Faster, Better, Cheaper

Article image preview
October 29, 2024
18 min

Multimodal AI: Training Neural Networks for a Unified Understanding

All publications
top arrow icon