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What is Data Modeling? Data modeling is the act of exploring data-oriented structures. Like other modeling artifacts data models can be used for a variety of purposes, from high-level conceptual models to physical data models.
From the point of view of an object-oriented developer data modeling is conceptually similar to class modeling. With data modeling you identify entity types whereas with class modeling you identify classes.
Data attributes are assigned to entity types just as you would assign attributes and operations to classes. There are associations between entities, similar to the associations between classes — relationships, inheritance, composition, and aggregation are all applicable concepts in data modeling.
Traditional data modeling is different from class modeling because it focuses solely on data — class models allow you to explore both the behavior and data aspects of your domain, with a data model you can only explore data issues.
However, some people will model database methods stored procedures, stored functions, and triggers when they are physical data modeling. It depends on the situation of course, but I personally think that this is a good idea and promote the concept in my UML data Point essays on architecture profile more on this later.
In fact, my experience is that CRC cards are superior to ORM diagrams because it is very easy to get project stakeholders actively involved in the creation of the model.
Instead of a traditional, analyst -led drawing session you can instead facilitate stakeholders through the creation of CRC cards. Although methodology issues are covered laterwe need to discuss how data models can be used in practice to better understand them. You are likely to see three basic styles of data model: These models, sometimes called domain models, are typically used to explore domain concepts with project stakeholders.
On Agile teams high-level conceptual models are often created as part of your initial requirements envisioning efforts as they are used to explore the high-level static business structures and concepts.
Logical data models LDMs. LDMs are used to explore the domain concepts, and their relationships, of your problem domain. This could be done for the scope of a single project or for your entire enterprise.
LDMs depict the logical entity types, typically referred to simply as entity types, the data attributes describing those entities, and the relationships between the entities.
LDMs are rarely used on Agile projects although often are on traditional projects where they rarely seem to add much value in practice. Physical data models PDMs.
PDMs are used to design the internal schema of a database, depicting the data tables, the data columns of those tables, and the relationships between the tables. PDMs often prove to be useful on both Agile and traditional projects and as a result the focus of this article is on physical modeling.
Although LDMs and PDMs sound very similar, and they in fact are, the level of detail that they model can be significantly different. This is because the goals for each diagram is different — you can use an LDM to explore domain concepts with your stakeholders and the PDM to define your database design.
Figure 1 presents a simple LDM and Figure 2 a simple PDM, both modeling the concept of customers and addresses as well as the relationship between them.
Both diagrams apply the Barker notationsummarized below. Notice how the PDM shows greater detail, including an associative table required to implement the association as well as the keys needed to maintain the relationships. More on these concepts later. A PDM should also indicate the data types for the columns, such as integer and char 5.
A simple logical data model. A simple physical data model. Data models can be used effectively at both the enterprise level and on projects. Enterprise architects will often create one or more high-level LDMs that depict the data structures that support your enterprise, models typically referred to as enterprise data models or enterprise information models.
Enterprise data models provide information that a project team can use both as a set of constraints as well as important insights into the structure of their system.
Project teams will typically create LDMs as a primary analysis artifact when their implementation environment is predominantly procedural in nature, for example they are using structured COBOL as an implementation language.
LDMs are also a good choice when a project is data-oriented in nature, perhaps a data warehouse or reporting system is being developed having said that, experience seems to show that usage-centered approaches appear to work even better.
However LDMs are often a poor choice when a project team is using object-oriented or component-based technologies because the developers would rather work with UML diagrams or when the project is not data-oriented in nature.
As Agile Modeling advises, apply the right artifact s for the job. Or, as your grandfather likely advised you, use the right tool for the job. It's important to note that traditional approaches to Master Data Management MDM will often motivate the creation and maintenance of detailed LDMs, an effort that is rarely justifiable in practice when you consider the total cost of ownership TCO when calculating the return on investment ROI of those sorts of efforts.
When a relational database is used for data storage project teams are best advised to create a PDMs to model its internal schema. My experience is that a PDM is often one of the critical design artifacts for business application development projects.
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