Which data model serves as an extension of a conceptual data model?

Which data model serves as an extension of a conceptual data model?

• A. Physical data model
• B. Dimensional data model
• C. Logical data model
• D. Canonical model

Answer: (C)

The logical data model is indeed an extension of a conceptual data model. When developing a logical data model, the primary goal is to add more detail to the high-level concepts established in the conceptual model. The conceptual model outlines the general structure of the data environment, focusing on the entities, their attributes, and the relationships between them without getting into how the data will be implemented. Once the foundational concepts are identified, the logical data model elaborates on these elements by defining the data structures, data types, and the constraints that apply to them. This model does not concern itself with the physical storage of data or the specific technologies that will be used; rather, it keeps the focus on how the data elements interact and are organized logically. In contrast, a physical data model translates the logical data model into a format that can be implemented using specific storage technologies, including considerations necessary for performance and data distribution. Both dimensional and canonical models serve specific purposes in different domains (like data warehousing and integration, respectively) but do not specifically extend the conceptual model in the way the logical data model does. Thus, understanding the progression from conceptual to logical is fundamental in data management practices.

The logical data model is indeed an extension of a conceptual data model. When developing a logical data model, the primary goal is to add more detail to the high-level concepts established in the conceptual model. The conceptual model outlines the general structure of the data environment, focusing on the entities, their attributes, and the relationships between them without getting into how the data will be implemented.

Once the foundational concepts are identified, the logical data model elaborates on these elements by defining the data structures, data types, and the constraints that apply to them. This model does not concern itself with the physical storage of data or the specific technologies that will be used; rather, it keeps the focus on how the data elements interact and are organized logically.

In contrast, a physical data model translates the logical data model into a format that can be implemented using specific storage technologies, including considerations necessary for performance and data distribution. Both dimensional and canonical models serve specific purposes in different domains (like data warehousing and integration, respectively) but do not specifically extend the conceptual model in the way the logical data model does. Thus, understanding the progression from conceptual to logical is fundamental in data management practices.