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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 modeling 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.The Online Writing Lab (OWL) at Purdue University houses writing resources and instructional material, and we provide these as a free service of the Writing Lab at Purdue.
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