Difference between Star schemas and Snowflakes schemas

Snowflake schemas and star schemas are both data modeling procedures used in data repositioning to organize data for efficient querying and analysis. Here’s a comparison of the two:<b>Star Schemas</b><b>Structure</b>: In a star schema, data is organized into one or more fact tables and multiple dimension tables.<b>Central Fact Table</b>: The fact table contains quantitative data (metrics or measures) that business users want to analyze, surrounded by multiple dimension tables.<b>Simplicity</b>: It is relatively simpler and easier to understand and implement compared to snowflake schema.<b>Performance</b>: Queries generally perform well due to fewer joins and denormalization.<b>Usage</b>: Star schemas are commonly used in data warehousing and are suitable for scenarios where denormalization and query performance are critical.<b>Snowflake Schemas</b><b>Structure</b>: A snowflake schema is an extension of a star schema, where dimension tables are normalized into multiple related tables.<b>Normalization</b>: Dimension tables in a snowflake schema are divided into multiple smaller dimension tables, reducing redundancy by separating hierarchies and relationships.<b>Complexity</b>: It is more complex than a star schema due to the normalization of dimension tables, which leads to more joins in queries.<b>Storage</b>: Snowflake schemas may save storage space due to normalization but can suffer from increased query complexity and potentially slower performance.<b>Usage</b>: Snowflake schemas are useful in scenarios where reducing storage redundancy is critical, such as large data warehouses with many dimensions and relationships.<b>Key Differences between Star schemas and Snowflakes schemas</b><b>Structure</b>: Star schema has a single level of dimension tables directly connected to the fact table, while snowflake schema normalizes dimension tables into multiple related tables.<b>Normalization</b>: Star schema is denormalized, whereas snowflake schema is partially normalized.<b>Query Performance</b>: Star schema typically performs better due to fewer joins, whereas snowflake schema may suffer from increased query complexity and potentially slower performance due to more joins.<b>Complexity</b>: Snowflake schema is more complex to design and maintain compared to star schema.In summary, star schemas are simpler and often faster for querying, making them suitable for most data warehousing applications. Snowflake schemas, on the other hand, offer storage efficiency but at the cost of increased query complexity and potentially slower performance due to normalization and increased join operations. The choice between them depends on the specific needs of the data warehouse and the trade-offs between query performance and storage optimization.