Star Schema vs Snowflake Scheme
In many interviews you may get this question "Can you explain when you chose star and snowflake schema and what basis you decide?"
The star schema and snowflake schema are two types of data warehouse schemas that are used for organizing data into a relational database.
Star Schema
In a star schema, a central fact table is surrounded by dimension tables, each of which is directly related to the fact table. The fact table contains the measures, metrics, or facts of a business process, while the dimension tables contain the descriptive attributes related to the fact data.
Snowflake Schema
The snowflake schema is a more complex version of the star schema where the dimension tables are normalized into multiple related tables. This means that the dimension data is broken into additional tables, which can be linked to the main dimension table.
Basis for Decision
1. Query Performance vs. Maintenance Star schemas generally offer better query performance due to denormalization, while snowflake schemas can be easier to maintain due to normalization.
2. Complexity of Data If the data has many complex relationships and hierarchies, a snowflake schema might be more appropriate.
3. Data Volume For large volumes of data, a star schema might be preferred because it simplifies queries and improves performance.
4. User Requirements If the endusers need fast query performance and are not concerned with the underlying complexity, a star schema is often chosen.
5. ETL Complexity The complexity of the ETL (Extract, Transform, Load) process can influence the choice. A star schema might require a simpler ETL process compared to a snowflake schema.
6. Storage Costs If storage costs are a concern, the more space efficient snowflake schema might be chosen.
In practice, the decision between a star and snowflake schema often comes down to a balance between the need for fast query performance and the desire for a normalized data structure that can handle complex data relationships and ensure data integrity. It's also important to consider the technical skill level of the users and the specific requirements of the business intelligence tools that will be used to analyze the data.