Star Schema and Snowflake schema

The Star Schema and Snowflake Schema are two types of dimensional models used in data warehousing to organize data for analytical queries. Both schemas are designed around a fact table that holds measurable data (facts), and dimension tables that provide context or descriptive attributes. However, they differ in the normalization level of the dimension tables.

1. Star Schema

Key Characteristics:

• Fact Table: Contains quantitative data, such as sales, revenue, or quantity, and typically includes foreign keys that reference the dimension tables.
• Dimension Tables: These tables hold descriptive attributes related to the facts, such as customer information, product details, time periods, etc. In a Star Schema, dimension tables are denormalized. That is, each dimension table contains all the relevant attributes (e.g., a single "Product" table contains product name, category, manufacturer, etc.).
• Structure: The fact table is at the center, with dimension tables surrounding it, forming a star-like structure.

Example (Sales Data):

• Fact Table: Sales
  • Columns: Sale_ID, Product_ID, Customer_ID, Date_ID, Amount
• Dimension Tables:
  • Product Table: Product_ID, Product_Name, Category, Manufacturer
  • Customer Table: Customer_ID, Customer_Name, Address, Email
  • Time Table: Date_ID, Date, Month, Year

Advantages:

• Simple and Intuitive: The structure is easy to understand and simple to query. The fact table is connected to dimension tables in a straightforward manner.
• Faster Query Performance: Since dimension tables are denormalized, fewer joins are required, which often leads to better performance in queries.

Disadvantages:

• Data Redundancy: Denormalization leads to data duplication in the dimension tables (e.g., product category or manufacturer details may be repeated for each sale), which can result in higher storage usage.
• Data Integrity: Redundant data increases the chance of inconsistencies and makes updates harder to maintain.

2. Snowflake Schema

Key Characteristics:

• Fact Table: Similar to the Star Schema, it contains measurable data and foreign keys to reference the dimension tables.
• Dimension Tables: Unlike the Star Schema, the dimension tables in a Snowflake Schema are normalized. That is, related attributes are split into separate tables to reduce redundancy. For instance, a “Product” table may be divided into “Product,” “Product Category,” and “Manufacturer” tables.
• Structure: The schema resembles a snowflake shape due to the additional level of normalization in the dimension tables, where each dimension can be split into multiple related tables.

Example (Sales Data, Normalized):

• Fact Table: Sales
  • Columns: Sale_ID, Product_ID, Customer_ID, Date_ID, Amount
• Dimension Tables:
  • Product Table: Product_ID, Product_Name, Category_ID
  • Category Table: Category_ID, Category_Name
  • Manufacturer Table: Manufacturer_ID, Manufacturer_Name
  • Customer Table: Customer_ID, Customer_Name, Address_ID
  • Address Table: Address_ID, Address, City, State, Zip
  • Time Table: Date_ID, Date, Month, Year

Advantages:

• Reduced Data Redundancy: Since the dimension tables are normalized, data is not duplicated, which reduces storage space and improves data consistency.
• Better Data Integrity: With fewer repetitions of data, the risk of inconsistency is lower.

Disadvantages:

• Complex Queries: The normalization means more tables and more joins in queries, which can make queries more complex and slower compared to the Star Schema.
• More Maintenance: The normalization process requires more effort to maintain and update the schema as it involves multiple tables.

Key Differences between Star and Snowflake Schema

When to Use Each Schema:

• Star Schema: Best for performance-intensive applications where speed is crucial (like in reporting and OLAP cubes). It is ideal for data marts where ease of access and simplicity are prioritized over storage optimization.

• Snowflake Schema: Suitable for large-scale data warehouses where data integrity, storage efficiency, and normalization are more important. It is often used when dealing with complex datasets with hierarchical relationships between dimensions, and where the complexity of queries is less of a concern.