Data modeling is the documenting of data relationships, characteristics, and standards based on its intended use of the data. Data modeling techniques and tools capture and translate complex system designs into easily understood representations of the data creating a blueprint and foundation for information technology development and reengineering.
A data model can be thought of as a diagram that illustrates the relationships between data. Although capturing all the possible relationships in a data model can be very time-intensive, a well-documented models allow stakeholders to identify errors and make changes before any programming code has been written.
Data modelers often use multiple models to view the same data and ensure that all processes, entities, relationships and data flows have been identified.
There are several different approaches to data modeling, including:
Concept Data Model (CDM)
- The Concept Data Model (CDM) identifies the high level information entities, their relationships, and organized in the Entity Relationship Diagram (ERD).
Logical Data Model (LDM)
- The Logical Data Model (LDM) defines detail business information (in business terms) within each of the Concept Data Model and is a refinement of the information entities of the Concept Data Model. Logical data model are non-RDBMS specific business definition of tables, fields, and attributes contained within each information entity from which the Physical Data Model (PDM) and Entity Relationship Diagram (ERD) is produced.
Physical Data Model (PDM)
- The Physical Data Model (PDM) provides the actual technical details of the model and database object (e.g. table names, field names, etc.) to facilitate creation of accurate detail technical designs and actual database creation. Physical Data Models are RDBMS specific definition of the logical model used build database, create deployable DDL statements, and to produce the Entity Relationship Diagram (ERD).
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