Types of Data Models

There are three main types of data models:

• Conceptual Data Model: Conceptual Data Model is a representations of data Examine and describe in depth your abstract, high-level business concepts and structures. They are most commonly employed when working through high-level concepts and preliminary needs at the start of a new project. They are typically developed as alternatives or preludes to the logical data models that come later.T he main purpose of this data model is to organize, define business problems , rules and concepts. For instance, it helps business people to view any data like market data, customer data, and purchase data.
• Logical Data Model: In the logical data model, By offering a thorough representation of the data at a logical level, the logical data model expands on the conceptual model. It outlines the tables, columns, connections, and constraints that make up the data structure. Although logical data models are not dependant on any particular database management system (DBMS), they are more similar to how data would be implemented in a database. The physical design of databases is based on this idea.
• Physical Data Model: In Physical Data model ,The implementation is explained with reference to a particular database system. It outlines every part and service needed to construct a database. It is made with queries and the database language. Every table, column, and constraint—such as primary key, foreign key, NOT NULL, etc.—is represented in the physical data model. The creation of a database is the primary task of the physical data model. Developers and database administrators (DBAs) designed this model. This kind of data modelling aids in the creation of the schema and provides us with an abstraction of the databases. This model explains how the data model is specifically implemented. Constraints, RDBMS features, and database column keys are made possible by the physical data model.

Data modelling is a fundamental component that facilitates the organisation, structuring, and interpretation of complicated datasets by analysts.

In this tutorial we’ll dive into the field of data modelling, examining its importance, the procedures involved, and answering common queries.