ER/Studio - Embarcadero Technologies Product Documentation

ER/STUDIO OVERVIEW > DATA MODELING FUNDAMENTALS
Data Modeling Concepts
A data model represents the things of significance to your enterprise and the relationships between them. At its core, a
data model depicts the underlying structure of an enterprise's data and the business rules governing it. A data model
is comprised of two parts, a logical design and a physical design.
The Logical Model: A logical model is developed before the physical model. It addresses the business and
functional requirements of systems development. The logical design allows you to determine the organization of the
data that you need to store in a database before you create the database; it serves as a blueprint.
The Physical Design: The physical design addresses the technical implementation of a data model, and shows how
the data is stored in the database. For example, you can specify the datatype to be used by each column in the table,
and determine how tables will be stored in the database.
Data Model Design Principals: To design the most effective data model possible, you should focus on the logical
design before developing the physical design. Both the logical and physical design processes are complex, so it is
best to separate rather than to mix the two. A sound logical design should streamline the physical design process by
clearly defining data structures and the relationships between them.
The Purpose of a Data Model: A data model can be useful for other things in addition to creating databases,
although creating a database is generally its primary purpose. In systems development, the goal is to create an
effective database application that can support some or all of your enterprise. However, a data model of your business
can help you define operational aspects of your business that you might otherwise overlook. Also, a well-defined data
model that accurately represents your business, can be helpful in orienting employees to goals and operations. The
data model can also serve as an invaluable communications tool for both internal and external constituents.
The Relational Model : Most early data models were developed to help systems analysts make business data
conform to a physical database or machine architecture. Hierarchical and network models often ran most efficiently on
particular systems. In the early 1970s E. F. Codd developed the relational data model, based on relational algebra.
The relational model stressed data independence, where data independence is defined as independence of data from
the underlying physical structure in which it is stored. Thus, systems that supported relational data models let users
easily migrate data to larger or newer systems with little regard to the physical differences between storage devices.
The power of the relational model lies in its simplicity. In the relational model, data is organized in tables of rows and
columns. Each table represents one type of data. Each row, or tuple, represents one item of data of that type. Each
column, or domain, represents one type of information about the type of data stored in the table.
The Entity-Relationship Model : Peter Chen introduced entity-relationship modeling during the late 1970s. Along
with a number of other theorists, such as Hammer and McLeod with their Semantic Data Model, Chen introduced a
new way of thinking about data. Chen’s ideas stressed that a data model should represent the reality of a business,
without regard for how that model might be implemented in a manual or automated system. Though it can seem
ridiculous today, these ideas were revolutionary at the time, and were instrumental in freeing individuals from the
constraints of a hierarchical business model. The ability to model a business “on paper” let business planners test out
and debug ideas before implementing them, thus saving money, other resources, and aggravation.
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