What are the best practices for designing a star schema or a snowflake schema in Snowflake?

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What are the best practices for designing a star schema or a snowflake schema in Snowflake, and what are the trade-offs between the two?

Daniel Steinhold Answered question August 4, 2023

Designing a star schema or a snowflake schema in Snowflake involves careful consideration of data organization and query performance. Both schema designs are common in data warehousing and analytics, and each has its strengths and trade-offs. Here are the best practices for designing star and snowflake schemas in Snowflake and the trade-offs between the two:

**Star Schema:**

– **Best Practices:**
1. Use Denormalization: In a star schema, denormalize the dimension tables to reduce joins and improve query performance. This means including all relevant attributes within each dimension table.
2. Central Fact Table: Design a central fact table that contains key performance metrics and foreign keys to the dimension tables. The fact table should be highly denormalized for efficient querying.
3. Cluster and Partition: Cluster the fact table on frequently used columns and partition it based on time or other relevant columns to optimize data retrieval and pruning.
4. Keep Hierarchies Simple: Limit the number of hierarchical levels in the dimension tables to maintain query performance and avoid excessive joins.
5. Use Numeric Keys: Prefer using numeric surrogate keys for dimension tables to improve join performance and reduce storage.
– **Trade-offs:**
1. Performance: Star schema usually results in better query performance due to denormalization and reduced joins.
2. Maintenance: Star schema can be easier to maintain and understand compared to snowflake schema as it has fewer joins and simpler hierarchies.
3. Storage: Star schema may require more storage compared to a snowflake schema due to denormalization.

**Snowflake Schema:**

– **Best Practices:**
1. Normalize Dimension Tables: In a snowflake schema, normalize dimension tables to avoid data redundancy and improve data integrity.
2. Use Surrogate Keys: Utilize numeric surrogate keys for dimension tables to improve join performance and maintain referential integrity.
3. Leverage Snowflake Clustering: Use clustering keys on dimension tables to optimize data retrieval during queries.
4. Query Optimization: Optimize queries with appropriate join strategies and indexing on foreign keys in dimension tables.
5. Complex Hierarchies: Snowflake schema is suitable for handling complex hierarchies as it allows for separate tables for different levels of the hierarchy.
– **Trade-offs:**
1. Performance: Snowflake schema may have slightly lower query performance due to increased joins compared to the star schema.
2. Complexity: Snowflake schema can be more complex to design and maintain due to the need for multiple joins across normalized dimension tables.
3. Query Complexity: Complex hierarchies and normalization can result in more complex queries, which may require more optimization effort.

**Trade-offs Comparison:**

– Star schema generally provides better performance and is easier to understand and maintain, but it may require more storage.
– Snowflake schema offers better data integrity due to normalization and is more suitable for complex hierarchies, but it may result in slightly lower query performance and increased complexity.

**Choosing Between Star and Snowflake Schema:**

– Choose a star schema when query performance and simplicity are the primary concerns, and when hierarchies are relatively simple.
– Choose a snowflake schema when data integrity and complex hierarchies are essential, and when query optimization is feasible.

Ultimately, the decision between a star schema and a snowflake schema depends on the specific requirements of your data warehousing and analytics use case, as well as the trade-offs that best align with your data modeling and query performance goals.

Daniel Steinhold Answered question August 4, 2023