What type of database design should be used in a data warehouse to reduce the data volume of dimensions?

In a data warehouse, a snowflake schema is used to optimize the organization of dimension tables by normalizing them into multiple related tables. This approach effectively reduces data redundancy by breaking down the dimensions into their constituent attributes and creating separate tables for related data. By structuring the data in this way, the overall dimension data volume is minimized, as it eliminates duplicate entries that are often present in a less efficient star schema.

The snowflake schema enhances query performance by allowing for smaller, more manageable tables, especially when dealing with large datasets. Additionally, this normalization contributes to a clearer and more logical organization of data, making it easier to maintain and update. Overall, the snowflake schema aligns well with the needs of a data warehouse by facilitating efficient storage and improving data integrity while reducing the volume of dimension data.

In contrast, other schema types such as star schemas tend to use denormalized dimension tables which can lead to increased data volume and redundancy, while normalized schemas might not be ideal for analytical queries commonly used in data warehousing due to their complexity. Flat schemas, on the other hand, may simplify queries but can drastically inflate the data volume by including repetitive information across dimensions. Thus, the snowflake schema is the most effective choice for addressing the need to