Star Schema and Snowflake Schema - 6 Main Difference with Example

The star schema and snowflake schema are two different types of database schemas used in data warehousing and business intelligence applications. Here’s a comparison of the two:

Star Schema:	Snowflake Schema:
1. Structure: – Central fact table surrounded by dimension tables. – Simple and straightforward design.	1. Structure: – Central fact table connected to multiple dimension tables, which are further normalized into sub-dimension tables. – More complex and normalized structure.
2. Normalization: – Denormalized: Dimension tables are not normalized, leading to redundancy.	2. Normalization: – Normalized: Dimension tables are split into multiple related tables to remove redundancy.
3. Query Performance: – Faster query performance due to fewer joins.	3. Query Performance: – Slower query performance due to more joins required.
4. Ease of Use: – Easier to design and understand.	4. Ease of Use: – More complex to design and understand due to normalization.
5. Storage: – Requires more storage due to data redundancy in dimension tables.	5. Storage: – Requires less storage as data redundancy is minimized.
6. Use Case: – Preferred for simpler and smaller data models.	6. Use Case: – Preferred for more complex and larger data models.

Example : Star Schema and Snowflake Schema

Star Schema:	Snowflake Schema:
Star Schema: – Fact Table: Sales (sales_id, product_id, store_id, date_id, amount) – Dimension Tables: Product (product_id, product_name, category), Store (store_id, store_name, location), Date (date_id, day, month, year)	– Fact Table: Sales (sales_id, product_id, store_id, date_id, amount) – Dimension Tables: Product (product_id, product_name, category_id), Category (category_id, category_name), Store (store_id, store_name, location_id), Location (location_id, city, state), Date (date_id, day, month, year, quarter, fiscal_year)

The choice between star and snowflake schemas depends on the specific requirements of the data warehouse, such as the complexity of the data and the need for query performance optimization.

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