In dimensional modeling, there are two main schema designs: Snowflake and Star. Each has its unique advantages and use cases, and the decision to use one over the other depends on the data and the requirements of the business.

Snowflake schema is a normalized design that separates data into multiple related tables. This schema is ideal for organizations with complex data structures and relationships, where data is stored in multiple tables to avoid data redundancy. Snowflake schema can also handle large amounts of data and support advanced analytics.

On the other hand, Star schema is a denormalized design that combines all data into a single large fact table, surrounded by smaller dimension tables. The star schema is simple to understand and query, making it ideal for business intelligence and data visualization applications. Star schema provides faster query performance as it reduces the number of join operations required to retrieve data.

In conclusion, the choice between Snowflake and Star schema in dimensional modeling depends on the requirements of the business and the nature of the data. Organizations with complex data structures and relationships are better suited to use Snowflake schema, while those with simpler data structures and a focus on BI and data visualization should use Star schema.