This architectural approach, frequently employed in data warehousing, structures data storage to support analytical workloads. It is characterized by a central fact table connected to multiple dimension tables, some of which are normalized into further sub-dimension tables. A common example is a sales database where the central fact table (sales transactions) connects to dimension tables like customer, product, and date. The customer dimension might be further normalized into sub-dimensions such as customer demographics and customer location.
The significance of this structure lies in its ability to optimize query performance and reduce data redundancy. By normalizing dimension tables, storage space is used efficiently. The resulting schema facilitates complex analytical queries, allowing for in-depth reporting and business intelligence. Historically, this structure arose from the need to balance query speed with storage limitations in traditional relational database systems.
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