[Database Design]
There are basic three data models: High-level, Representational, and Low-level.
High-level, also known as Conceptual or Semantic data models: Describes the semantics of the data within its problem domain. For example, if the problem domain of the data is a university, the database description will incorporate the things and propositions that are important to the university. Thus, things (entities) like Students, Professors, Classes, Courses, and Grades would be described by this model as well as how each instance relates to each other (relationships). Please note that, in some cases, this model also describes the data entities characteristics (attributes), but normally the logical data model carries this task in a much detailed manner.
Representational, also known as logical data model: Concerns to the detailed attributes of each individual data entity and how these attributes contribute or affect the data relationships (primary and secondary key groups).
Low-level or Physical data models: Describes in details the physical means used to store the data, which relates to the storage media, data types, data partitioning, processing power, geographical distribution of the data and etc…
Due to the characteristics of each model, it is common to start with the conceptual model (to learn which entities compose the data domain and their relationships), then apply the logical model (to understand the details of the data without actually worrying about its implementation), and complete the whole process with the low-level model (knowing how exactly the data will be implemented, stored, and distributed).
It is said that, sometimes, the low-level model can be discarded, and the logical model is directly implemented. While this is true, the statement may be easily misunderstood. Discarding the physical data model would mean that one would not need to worry about the actual implementation of the conceptual and logical models by using a tool that would absorb/parse these models and implement them for the user. In this case, the user really did not need to know all the details of the implementation. However the tool itself needs to and it does so by applying well-know and understood data patterns in the industry.
Works Cited
Carpenter, Tom. (2010). SQL Server 2008 Administration: Real-World Skills for MCITP
Certification and Beyon (Exams 70-432 and 70-450). Sybex.
Elmasri, R., & Navathe, S. (2011). Fundamentals of Database Systems 6th Edition. Boston: Addison - Wesley.
