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. Because of this focus data modelers have a tendency to be much
Data modeling is the process of creating and extending data models which are visual representations of data and its organization. The ERD Diagram (Entity Relationship Diagram) is the most popular type of data model. Data models exist at multiple levels including: • The Conceptual Data Model describes data from a high level. It defines the problem rather than the solution from the business point of view. It includes entities and their relationships. Typically the conceptual data model is developed first. • The Logical Data Model describes a logical solution to a data project. It provides more details than the conceptual data model and is nearly ready for the creation of a database. These details include attributes, the individual pieces of information that will be included. Typically the logical data model is developed second. • The Physical Data Model describes the implementation of data in a physical database. It is the blueprint for the database. Typically the physical data model is
Data modeling is the process of identifying, documenting, and implementing the data requirements for your application. This involves reviewing existing data models and processes to see if they can be reused, and creating new data models and processes to suit your application’s unique requirements. The major events in data modeling are: • Identifying the data and associated processes (such as that sales people in the field need to see the online product catalog and submit new customer orders). • Defining the data (such as data types, sizes, and defaults). • Ensuring data integrity (by using business rules and validation checks). • Defining the operational processes (such as security reviews and backups). • Choosing a data storage technology (such as relational, hierarchical, or indexed). It’s important to understand that data modeling often involves company management in unexpected ways. For example, data ownership (and the implied responsibility of data maintenance, accuracy, and timel
Despite its name, data modeling has nothing to do with getting data all dolled up and ready for a night on the town. Or does it? Actually, that description might not be too far off. Data modeling is a way to structure and organize data so it can be used easily by databases. Unstructured data can be found in word processing documents, email messages, audio or video files, and design programs. Data modeling doesn’t want these “ugly” data; rather, data modeling wants data that is all made up in a nice, neat package for processing by a database. So in a way, data modeling is concerned with how the data looks. Data modeling is routinely used in conjunction with a database management system. Data that has been modeled and made ready for this system can be identified in various ways, such as according to what they represent or how they relate to other data. The idea is to make data as presentable as possible, so analysis and integration can be done with as little effort as necessary. We can a
This is the process of structuring and organizing data. These data structures are then typically implemented in a database management system. In addition to defining and organizing the data, data modeling may also impose constraints or limitations on the data placed within the structure. Managing large quantities of structured and unstructured data is a primary function of information systems. Data models describe structured data for storage in data management systems such as relational databases. They typically do not describe unstructured data, such as word processing documents, email messages, pictures, digital audio, and video. Early phases of many software development projects emphasize the design of a conceptual data model. Such a design can be detailed into a logical data model. In later stages, this model may be translated into physical data model.
