Which schema design prioritizes analytical data retrieval through denormalization?

The star schema is a database design that prioritizes analytical data retrieval through denormalization. In this schema, a central fact table connects to multiple dimension tables. This structure simplifies queries and enhances performance, making it easier for analysts to retrieve large volumes of data quickly without complex joins, as the dimension tables are straightforward and typically contain redundant data.

Denormalization in this context means reducing the number of tables and the complexity of relationships by combining related data into fewer tables. In the case of the star schema, this approach allows for faster query performance, which is essential in data warehousing and business intelligence applications where quick access to summarized data is critical.

The other schemas mentioned, such as the snowflake schema, involve more normalization, where the data is organized into more tables, leading to more complex queries and potentially slower performance. Relational schemas focus on structured data and the integrity of relationships, while object-oriented schemas are designed to encapsulate data and behavior in objects, which is different from the analytical focused structure of a star schema.