Data Modeling Examples

To store data in a database, we must first construct a data model for it. An abstract representation of data items and their interactions is known as a data model. It is similar to an architect's blueprint in that it aids in the construction of a conceptual model. In this post, we'll look at some real-world instances of data modeling and the many sorts of models that exist.

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The Data Modeling process creates a data model for the data that we want to store in the database. The data model is a theoretical depiction of the data objects and the relationships among them. A Data Model looks like a building plan of an architect, and it assists in building a conceptual model.

In this article, we will study data modeling examples and types of data models.

Table of Content - Data Modeling Examples

What is Data Modeling

Data Modelling is the process of producing a data model for the data that we want to store in the database. A data model highlights the essential data and how we must arrange that data. Data models assure uniformity in the naming conventions, and security semantics while assuring the data quality.

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Importance of Data Modeling

  • A data model assists in designing the database at the physical, logical, and conceptual levels.
  • The data model establishes stored procedures, relational tables, foreign and primary keys.
  • It gives a clear picture of the database, and database developers can use it for creating physical databases.
  • The data model depicts the best understanding of the business requirements.
  • The data model is also useful for identifying redundant and missing data.

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Advantages of Data Modelling

The following are the essential advantages of Data Modelling

  1. The data model assists us in identifying proper data sources to inhabit the model.
  2. The Data Model enhances communication throughout the organization.
  3. The data Model assists in documenting the data mapping in the ETL process.
  4. Data modeling enables us to query the data of the database and obtain different reports according to the data. Through the reports, data modeling helps in data analysis.

[Related Article: Data Modelling Interview Questions for Beginners]

Data Modeling Terminology

  • Entity: Entities are the items of the business environment regarding which we need to store the data. Example: Customers, Orders, Products, etc.
  • Attribute: Attributes give a way of structuring and organizing the data.
  • Relationship: The relationship among the entities explains how one entity is connected to another entity.
  • Reference Table: We can resolve the many-to-many relationships among the entities into one-to-many and many-to-one relationships through a reference table.
  • Database Logical Design: It defines the database inside a data model of a particular database management system.
  • Logical Design: In Logical design, we create all the keys, tables, rules, constraints, etc.
  • Database Physical Design: It defines the file organization, internal database storage design, and indexing techniques.
  • Physical Model: It is the physical depiction of the database.
  • Schema: It is the complete description of the database.
  • Logical Schema: Logical Schema is a theoretical design of a database that we do on a whiteboard or paper, and it is similar to the structural diagram of a house. 

Important Perspectives of a Data Model

1. Logical Model

The logical model tells us how we should implement the model. It contains all types of data that we need to capture like columns, tables, etc. Generally, Data Architects and Business Analysts design the logical data model.

2. Conceptual Model

The conceptual model specifies what should be present in the data model structure to organize and define the business concepts. It mostly concentrates on business-oriented attributes, relations and entries. Generally, Business Stakeholders, Data Architects design this model.

3. Physical Model

The physical model specifies how we implement the data model through the database management system. It summarizes the implementation methodology with respect to CRUD operations, tables, partitioning, indexes, etc. Database Developers and Administrators create the Physical Model.

4. Facts and Dimensions

For learning data modeling, we must understand the Facts and Dimensions:

  • Dimension Table: A dimension Table gathers fields that contain a description of the business elements, and different fact tables to refer to it.
  • Fact Table: The fact Table contains the granularity and measurements of each measurement. Facts may be semi-additive, or additive, For example Sales.

5. Dimensional Modeling

Dimensional Modelling is a data designing method of the data warehouse. It utilizes facts and dimensions and assists in simple navigation. Dimensional data model assists in quick performance query. Generally, dimensional models are also known as star schemas.

[Related Article: Salesforce Data Modelling]

Types of Data Models

1. ER (Entity-Relationship) Model

The ER Model establishes the theoretical view of the database. It works around real-time entities and the relationships among them. In the View level, we consider ER models as the best option to design the databases.


The entity is a real-world object, and we can identify it quickly. For instance, we consider the employee as an entity in an employee database. All these entities contain few properties or attributes that provide them with their identity.

-Entity Set

An entity Set is a group of similar types of entities. Entity sets can have entities in which attributes share identical values. For instance, an Employee set may have all the employees of an organization, similarly, a Student set will have all the students of a school.


We represent the entities through their properties, and these properties are known as attributes. Every attribute will have value.


A Key can be a single attribute or a group of attributes that clearly recognizes an entity in the given entity set. For instance, we can identify an employee among many employees through her/his id.


The Association among the entities is known as a relationship. For example, a student “studies” in a school. Here “Studies” is the relationship between the “Student” and “School” entities.

-Relationship Set

A group of relationships of a similar type is known as a relationship set. A relationship set will have attributes, and these attributes are known as descriptive attributes.

-Binary Relationship and Cardinality

A relationship that involves two entities is known as a Binary relationship. Cardinality is the number of occurrences of an entity set that can be connected with the other entity set through a relationship.

Entities have four cardinal relationships, they are:

  • One-to-One Relationship
  • One-to-Many Relationship
  • Many-to-One Relationship
  • Many-to-Many Relationship

ER Diagram Example:

Data Modeling ER Diagram

In the above ER Model, we have four entities: 1) Publisher 2) Books 3) Subject 4) Author, we also have two attributes, are 1) BookId 2) AID. BookId is the attribute of the “Books” entity, and AID is the attribute of the “Author” entity. “Publish” is the relationship between the “Publisher” entity and the “Books” entity, as publishers can publish many books,  it is a one-to-many relationship.

“About” is the relationship between the “Books” entity and the “Subject” entity, as we can have many books for one subject, it is a many-to-one relationship. “By” is the relationship between the “Books” entity and the “Author” entity.

2. Hierarchical Model

This data model arranges the data in the form of a tree with one root, to which other data is connected. The tree hierarchy begins with the “Root” data, and extends like a tree, by inserting the child nodes to the parent node.

In this model, every child node will have only one parent node.

This model effectively explains several real-time relationships like an index of recipes, or a book, etc. The hierarchical model organizes the data in a tree-shape structure with a single one-to-many relationship between two different kinds of data.

For example, one college can have different departments and many faculties.

In the below hierarchical model, “College” is the Root node and it has two child nodes: 1) Department 2) Infrastructure. “College” has a one-to-many relationship with “Department”.

[Realated Article: Tools of Data Modelling]

Hierarchical Model

3. Relational Model

The relational model is the most common data model. It arranges the data into tables, and tables are also known as relations. Tables will have columns and rows. Every column catalogs an attribute present in the entity like zip code, price, etc.

The attributes of a relationship are known as a domain. We can select a specific attribute or a mix of attributes as the primary key, and we can refer to it in other tables when it is a foreign key. 

Every row is known as a tuple, and it contains data related to a particular instance of an entity. This Model is also responsible for the relationships among those tables, which comprise one-to-many, many-to-many, and one-to-one relationships.



Mr. A
Mr. B

From the above two tables, we will get the following resultant table: 


4. Object-Oriented Database Model

The object-oriented database model defines the database as an object collection, or recyclable software components, with related methods and features. Following are the different types of Object-oriented databases:

Multimedia Database

A multimedia database includes media like images that we cannot store in a relational database. 

Hypertext Database

A Hypertext database enables any object to connect to any other object. It is useful for arranging plenty of diverse data, yet it is not suitable for data analysis. 

An object-oriented database model is the popular post-relational database model, as it includes tables. This model is also known as a hybrid database model. 

Network Model

The network model is an extension of the hierarchical model, and it enables many-to-many relationships among the connected records. In this model, we arrange the data in a graph-like structure, and it can have multiple parent nodes.

According to the mathematical set theory, we construct the network model along with sets of connected records. Every set comprises a parent record or one owner or at least one child record.

A record may be a child or member in multiple sets,  by enabling this model we can reveal difficult relationships. 

The following diagram represents the Network model. An Agent Manages many Entertainers and Represents many Clients. Similarly, a Client makes many Payments and Schedules many Engagements.

So, the Network model enables many-to-many relationships among the data nodes.

[Related Article: Big Data Modeling]

5. Object-Relational Model

The object-relational model is a hybrid database model that blends some advanced functionalities of the object-oriented database model with the ease of the relational model.

At core, it enables the designers to embed the objects into the usual table structure.

Call interfaces and Languages are SQL3, JDBC, ODBC, etc. These languages and call interfaces act as extensions to the languages and interfaces of the relational model.


Data modeling plays a vital role in storing the data as per user requirements. As users deal with vast amounts of data, they have to model it for understanding or using it. So, they will use different types of data models to model the data. I hope this article provides you with essential information about types of data models with examples.

If you have any queries, let us know by commenting in the below section.

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Last updated: 03 Apr 2023
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Viswanath is a passionate content writer of Mindmajix. He has expertise in Trending Domains like Data Science, Artificial Intelligence, Machine Learning, Blockchain, etc. His articles help the learners to get insights about the Domain. You can reach him on Linkedin

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