Overview of data modeling

You can use the workbench to create, modify, and generate DDL for physical data models and logical data models. You can also create and work with domain models and glossary models to help you adhere to naming and data type standards. All data modeling objects are stored and edited in a data design project.
The following types of data models are used in this product.
Base data models:
Logical data models

Logical data models are not specific to a database. At a high level, they describe things about which an organization wants to collect data and the relationships among these things. They are organized hierarchically and contain objects such as packages, entities, attributes, and other relationship objects.

Logical data models can be transformed into physical data models or UML models, and they can also be generated from physical data models or UML models. You can use these transformation features to propagate UML model designs throughout the data model life cycle. You can also generate logical data models from existing physical data models, so that you can reuse existing database designs.

Physical data models

These models are database-specific models that represents relational data objects (for example, tables, columns, primary keys, and foreign keys) and their relationships. For some database targets, you can also add storage objects to your physical data model such as table spaces and buffer pools.

Physical data models can be transformed into logical data models, or they can be generated from logical data models. After you have completed your physical data model design, you can generate DDL statements from the model which can then be deployed to a database server.

Dimensional modeling

You can use the workbench to create, modify, and generate DDL for dimensional-physical data models. Dimensional modeling extends logical and physical data models to further model data and data relationship requirements.

Dimensional models map the aspects of each process within your business. Database schemas that are modeling according to dimensional modeling principles work well with applications that must read large amounts of data quickly. This quick, easy access to the data helps you develop applications and queries that enable the enterprise to analyze the data.

Enforcement of enterprise standards and best practices:
Domain models
Domain models describe the domain types that an organization allows and their constraints. Atomic domains can be stored in a domain model or as part of a logical data model. You can associate a domain data model with a data design project so that the domain data types are available for either logical or physical data modeling.
Glossary models
Glossary models describe the names and abbreviations that an organization allows for data objects. You can use glossary models to enforce object naming standards within an organization.

In addition to the use of these two data model types, you can also enforce naming standards and best practices by using data model analysis.

Using the workbench, you can create and modify data models by using the Data Project Explorer, the Properties view, or a diagram of the model. You can also perform the following tasks to communicate data model information to other team members:

In addition to the four data model types described above, you can use the mapping editor to generate mapping models, which describe and map relationships among a variety of data sources. Mapping models can be used to generate scripts that you can use to transform and filter data from mapping model compliant sources to mapping model compliant targets. Mapping models can also be exported as CSV files so that you can communicate mapping model information to other team members.


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