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IDEF1X, UML DDL, Information Engineering & ERD Yes Yes Update database and/or update model Multi-user collaboration using File, DBMS or (transfer via XMI, CVS/TFS or Difference Merge). MySQL Workbench: Physical IDEF1X, IE (Crow’s feet), UML, and more Yes Yes Update database and/or update model No Navicat Data Modeler Conceptual, Logical ...
The last step in data modeling is transforming the logical data model to a physical data model that organizes the data into tables, and accounts for access, performance and storage details. Data modeling defines not just data elements, but also their structures and the relationships between them.
An entity, strictly speaking, is an instance of a given entity-type. There are usually many instances of an entity-type. Because the term entity-type is somewhat cumbersome, most people tend to use the term entity as a synonym. Entities can be thought of as nouns. [7] Examples include a computer, an employee, a song, or a mathematical theorem.
Overview of a data-modeling context: Data model is based on Data, Data relationship, Data semantic and Data constraint. A data model provides the details of information to be stored, and is of primary use when the final product is the generation of computer software code for an application or the preparation of a functional specification to aid a computer software make-or-buy decision.
A data structure diagram is a diagram type that is used to depict the structure of data elements in the data dictionary. The data structure diagram is a graphical alternative to the composition specifications within such data dictionary entries. [1] The data structure diagrams is a predecessor of the entity–relationship model (E–R model
A complex data model can involve hundreds of related tables. Computer scientist Edgar F. Codd created a systematic method to decompose and organize relational databases . [ 3 ] Codd's steps for organizing database tables and their keys is called database normalization , which avoids certain hidden database design errors ( delete anomalies or ...
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Normalization splits up data to avoid redundancy (duplication) by moving commonly repeating groups of data into new tables. Normalization therefore tends to increase the number of tables that need to be joined in order to perform a given query, but reduces the space required to hold the data and the number of places where it needs to be updated if the data changes.