What is Data Modeling in SQL?

Data Modelling: Conceptual, Logical, Physical Data Model Types

I’m not sure what Data Modelling is.
Data modeling (also known as data modeling) is the process of developing a data model for data that will be stored in a database system. This data model is a conceptual representation of Data objects, the associations between different data objects, and the rules that govern their use and interaction.

Data modeling aids in the visual representation of data as well as the enforcement of business rules, regulatory compliances, and government policies on the data it contains. In addition to ensuring consistency in naming conventions, default values, semantics, and security, data models also ensure the quality of the data.

Data Model

The Data Model is defined as an abstract model that organizes the description of data, the semantics of data, and the consistency constraints that apply to data. Instead of focusing on the operations that will be performed on data, the data model focuses on what data is required and how it should be organized instead. A data model is similar to an architect’s building plan in that it aids in the construction of conceptual models and the establishment of relationships between data items.

Data Modeling Techniques can be divided into two categories:

Uml Entity-Relationship (E-R) Model Entity-Relationship (E-R) Model (Unified Modelling Language)
We will go into greater detail about them later.

For freshmen, beginners, and experienced professionals alike, this Data Modeling Tutorial is an excellent resource. In this data model tutorial, you will learn about data modeling concepts in-depth, including:

Why use Data Model?

The primary objectives of using a data model are as follows:

The accuracy of all data objects required by the database is ensured by the database administrator. The omission of information will result in the creation of faulty reports and the generation of incorrect results.
A data model aids in the design of a database at the conceptual, physical, and logical levels, among other things.
The structure of the data model aids in the definition of relational tables, primary and foreign keys, and stored procedures.
It provides a clear picture of the underlying data and can be used by database developers to create a physical database from the underlying information.
It can also be used to identify data that is either missing or redundant.
Creating a data model requires effort and time upfront, but it will save you money and time in the long run by making your IT infrastructure upgrade and maintenance more cost-effective and faster.
Data Models Come in a Variety of Forms
Data Models are classified into the following categories: It is generally agreed that there are only three types of information modeling: conceptual information modeling; logical information modeling; and physical information modeling. Each of these types serves a specific purpose. Using data models, you can represent your data and determine how it is stored in a database, as well as establish relationships between different data items.

Types of Data Models

This Data Model defines WHAT the system contains. It is common for business stakeholders and data architects to collaborate on the development of this model. The purpose of this document is to organize, scope, and define business concepts and regulations.
It defines HOW the system should be implemented, regardless of which database management system is used. Typically, Data Architects and Business Analysts are the ones who create this model. The goal is to create a technical map of the rules and data structures that will be used.
It is important to understand the Physical Data Model because it describes how the system will be implemented in terms of a specific database management system (DBMS). Developers and DBAs are typically responsible for creating this model. The purpose of this project is to implement the database.
Different Types of Data Models
Different Types of Data Models
Modeling of Conceptual Data
A Conceptual Data Model is an organized view of database concepts and their relationships that are used to design database applications. Establishing entities, their attributes, and relationships are the goals of developing a conceptual data model. At this level of data modeling, there is very little information available about the actual database structure that will be used. In most cases, business stakeholders and data architects collaborate to develop a conceptual data model.

The three fundamental tenants of the Conceptual Data Model are as follows:

Conceptual Data Model

Entity: Something that exists in the real world.
An entity’s characteristics or properties are referred to as its attributes.
Dependency or association between two entities is referred to as a relationship.
An illustration of a data model is as follows:

The terms “customer” and “product” refer to two distinct entities. The Customer entity has two attributes: a customer number and a customer name.
Product entity attributes include the name of the product and the price of the product.
The sale is defined as the relationship that exists between the customer and the product.
Modeling of Conceptual Data
Modeling of Conceptual Data
The characteristics of a conceptual data model are as follows:

Provides coverage of business concepts across the entire organization.
This type of Data Model is intended for a business audience and is designed and developed specifically for them.
In contrast to hardware specifications such as data storage capacity or location, software specifications such as DBMS vendor and technology are not taken into consideration when developing the conceptual model. The emphasis is on representing data in the manner in which a user will see it in the “real world.”
Domain models, which are conceptual data models, establish a common vocabulary for all stakeholders by defining the fundamental concepts and scope of the project.

Logical Data Model

Using the Logical Data Model, you can define the structure of data elements, as well as the relationships between those elements. The logical data model provides additional information to the elements of the conceptual data model. The advantage of using a Logical data model is that it serves as a foundation for the Physical data model, which can then be built on top of it. The modeling structure, on the other hand, remains generic.

Model of Logical Data
Model of Logical Data
Neither a primary nor a secondary key is defined at this level of Data Modeling. At this level of data modeling, you must double-check and make any necessary adjustments to the connector details that were previously defined for relationships.

Characteristics of a logical data modeling environment

Describes the data requirements for a single project, but it has the potential to integrate with other logical data models depending on the scope of the work.
The application was designed and developed independently of the database management system.
Data attributes will be represented by datatypes with precise precisions and lengths, respectively.
Typically, normalization processes are applied to the model until it reaches 3NF.
Model of Physical Data
When describing a Physical Data Model, it is important to note that the model is specific to a database. It provides database abstraction while also assisting in the generation of the schema. A Physical Data Model provides a plethora of meta-data, which makes it a valuable resource. The physical data model also aids in the visualization of database structure by replicating database column keys, constraints, indexes, triggers, and other RDBMS features in the physical data model.

Physical Data Model

Characteristics of a physical data model include the following:
According to the project scope, the physical data model describes the data requirements for a single project or application, though it may be integrated with other physical data models as well.
The Data Model contains relationships between tables that are addressed by the cardinality and nullability of the relationships in the model.
In this case, the project was developed to work with a specific version of a DBMS, location, data storage system, or technology.
Columns should have specific data types, lengths, and default values assigned to them.
There are definitions for primary and foreign keys, views, indexes, access profiles, and authorizations, among other things.
The following are the advantages and disadvantages of using a data model:
The following are the advantages of the data model:

Advantages and Disadvantages of Data Model:

The primary goal of developing a data model is to ensure that the data objects provided by the functional team are accurately represented.
The data model should be comprehensive enough to be used in the construction of the physical database.
Using the information contained in the data model, it is possible to define the relationship between tables, primary and foreign keys, as well as stored procedures.
Data Models assist businesses in communicating both within and across organizational boundaries.
In the ETL process, a data model is used to document data mappings.
Assist in identifying appropriate sources of data to use in populating the model.
The following are the disadvantages of the data model:

To develop a data model, one must first understand the characteristics of the physical data that is being stored.
As a navigational system, it generates complex application development and management processes. Because of this, it is necessary to be familiar with biographical truth.
Even the smallest change in structure necessitates a change in the overall structure of the application.
In DBMS, there is no such thing as a set data manipulation language.


Data modeling is the process of creating a data model for the data that will be stored in a database. It is also known as database modeling.
In addition to ensuring consistency in naming conventions, default values, semantics, and security, data models also ensure the quality of the data.
The structure of the data model aids in the definition of relational tables, primary and foreign keys, and stored procedures.
There are three types of conceptualizations: logical, physical, and conceptual.
The primary goal of a conceptual model is to establish the entities, their attributes, and the relationships between these entities and others.
It is the logical data model that determines how the data elements are organized and how they are related to one another.
A Physical Data Model is a description of the data model’s implementation in a database-specific manner.
The primary goal of developing a data model is to ensure that the data objects provided by the functional team are accurately represented.
The most significant disadvantage is that even minor changes to the structure necessitate the modification of the entire application.
You will learn the fundamentals of data modeling by reading this tutorial, starting with concepts such as What is a Data Model? Introduction to the various types of data models, as well as their advantages and disadvantages, and a data model illustration.