How many Snowflake designs are there?

When referring to Snowflake designs in the context of data warehousing, it generally pertains to two primary schema designs used for organizing data: the Star Schema and the Snowflake Schema. These are the most common design patterns in data warehouses for structuring relational databases. Here's a breakdown:

1. Star Schema

• Design Overview: In a star schema, the central fact table (which stores transactional data) is connected directly to dimension tables (which store descriptive information, like product names or regions). The dimension tables are denormalized, meaning all related data is contained in a single table.
• Shape: The design looks like a star, with the fact table at the center and the dimension tables branching off.
• Advantages: Star schemas are simpler, faster for querying, and easier to understand.
• Use Case: Ideal for scenarios with simpler queries and where data redundancy is not a concern.

2. Snowflake Schema

• Design Overview: The snowflake schema is an extension of the star schema, where dimension tables are normalized. This means that the dimension tables are split into additional related tables to eliminate data redundancy.
• Shape: The schema resembles a snowflake because of its branching structure, where each dimension can have multiple levels of related tables.
• Advantages: Reduces data redundancy and storage requirements but can involve more complex queries due to the additional joins between tables.
• Use Case: Suitable for large-scale data warehouses where storage optimization and data consistency are crucial.

Difference Between Star and Snowflake Design

• Star Schema: Simpler, denormalized structure that leads to faster query performance.
• Snowflake Schema: More complex, normalized structure that reduces redundancy but may require more complex SQL queries.

Are there other Snowflake designs?

In the realm of Snowflake data warehousing, star and snowflake schemas are the two primary designs. Beyond these, Snowflake (the platform) itself does not introduce new schema designs but supports modern architectures like Data Lakes, Data Marts, and Data Mesh for more distributed data architectures.

Suggested resources:

• Grokking the System Design Interview - A great resource to understand the differences between star and snowflake schemas and how to design scalable systems.
• Grokking Data Structures & Algorithms for Coding Interviews - Helps with the query optimization techniques needed for complex schemas like snowflake designs.

In summary, the two main Snowflake designs are the Star Schema and the Snowflake Schema, each with its own advantages and use cases depending on data complexity, redundancy, and storage optimization needs.