How do you choose between conceptual, logical, and physical data models?

1 Conceptual data model

A conceptual data model is the most abstract and high-level type of data model. It focuses on the main entities, attributes, and relationships of your data, without specifying any technical details, such as data types, constraints, or keys. A conceptual data model is useful for communicating the overall scope and vision of your data to stakeholders, such as business analysts, managers, or customers. It can also help you identify the main business rules and requirements of your data. A common way to create a conceptual data model is to use an entity-relationship diagram (ERD).

2 Logical data model

A logical data model is the next level of detail after a conceptual data model. It adds more information and precision to your data, such as the specific attributes, domains, and keys of each entity, and the cardinality and optionality of each relationship. A logical data model is independent of any specific database system or technology, but it follows the rules and standards of a data model type, such as relational, hierarchical, or network. A logical data model is useful for validating and refining your data design, as well as for mapping it to a physical data model. You can also use an ERD to create a logical data model, but with more symbols and annotations.

3 Physical data model

A physical data model is the most detailed and concrete type of data model. It specifies how your data will be stored and implemented in a particular database system or technology, such as MySQL, Oracle, or MongoDB. A physical data model includes all the technical aspects of your data, such as the data types, sizes, formats, constraints, indexes, triggers, and procedures of each table, column, and relationship. A physical data model is useful for creating and optimizing the actual database schema, as well as for generating the SQL code or scripts to execute it. You can use a physical data model diagram (PDM) or a data definition language (DDL) to create a physical data model.

5 Benefits of data modeling

Data modeling is a valuable tool for database development, as it allows for a better understanding and organization of data. It also helps to communicate and collaborate with different stakeholders, providing a common language and visual representation of data. Furthermore, data modeling helps to design and optimize the data structure, ensuring accuracy, completeness, consistency, and efficiency. This ultimately saves time and resources by avoiding errors and redundancies while facilitating the creation and maintenance of the database schema and code.

6 Data modeling tools

To create and manage your data models, you can use various data modeling tools, such as:

- ERDPlus : A web-based tool that allows you to create conceptual, logical, and physical ERDs, and generate SQL code from them.

- Lucidchart : A cloud-based tool that allows you to create and collaborate on various types of diagrams, including conceptual, logical, and physical ERDs and PDMs.

- ER/Studio : A desktop tool that allows you to create and manage complex data models, including conceptual, logical, and physical ERDs and PDMs, and support multiple database systems and technologies.

- SQL Developer Data Modeler : A desktop tool that allows you to create and manage logical and physical data models, and integrate them with Oracle Database.