What is Object-Oriented Programming (OOP)? Principles, Benefits & Examples Explained

A computer programming model that organises software design around data or objects rather than functions and logic is known as Object oriented programming- abbreviated as OOP. Well, an object can be defined as a data field that has unique attributes and behaviour. 

Object-oriented programming focuses on the objects that developers would want to manipulate without applying logic. Honestly, this programming approach is suited for software that is large, complex or requires frequent updates and maintenance. 

Thus, it is majorly used in manufacturing and design and mobile applications. For example, Object Oriented Programming can be used for manufacturing system simulation software.

The object oriented programming method is beneficial for collaborative development, where actually the projects are divided based on the groups. Some of the additional benefits of object oriented programming include: 

  • Code Reusability 
  • Scalability 
  • Efficiency

The very first step in object oriented programming is to collect all the objects a programmer desires to manipulate and identify how they are related to each other. Well, this is popularly known as data modeling. 

For example of object can be from a physical entity- like a human being who is associated by properties like name, and address to small computer games like widgets. 

A once-known object is assigned to a class of objects. The class of objects indicates the kind of data held and all logic sequences capable of manipulating it. There exist different distinct logic sequences in each method. Objects are able to communicate through a well-defined interface referred to as a message.

What are the basics of object-oriented programming?

Here are the 4 basics of object-oriented programming: 

Classes Objects Methods  Attributes 
  • Classes-  A user-defined data type that precisely acts as a blueprint for each object, attribute and method. 
  • Objects- Instances of a class created specifically to define the data. This can be real-world objects or some abstract entity. Initially, when the class is defined the description is the only object that is defined. 
  • Methods- These are the functions that objects can perform. It is defined inside a class and it describes the behaviour of an object. Each method contained in class definitions starts with a reference to an object. Additionally, instance methods are the subroutines contained in an object. Methods are used by programmers for reusability or to keep functionality encapsulated inside one object at a time. 
  • Attributes- It represents the state of an object. It also means that these are the characteristics that distinguish classes. Data is stored in the attributes field in the object. Class attributes belong to the class itself and are also defined by the class template. 

What are the main principles of OOP?

Object-oriented programming is based on the following principles:

  • Encapsulation: This principle of OOP states that all the important information is stored inside an object and only selected information is revealed. This implementation and state of individual objects is privately held inside a defined class. In this case, other objects do not have access or authority to make changes in this class. And since they do not have access to authority to make changes they are only able to call a list of public functions or methods. This feature of data hiding provides greater program security and avoids unintended data corruption. 
  • Abstraction: The internal mechanisms are shown by objects only in case of use for other objects. The unnecessary implementation code will be hidden in this way. The derived class can have its functionality extended. This concept can help developers more easily make additional changes or additions over time.
  • Inheritance: Another principle of OOP is Inheritance. This is classes can inherit code and properties from other classes. Relationships and subclasses between objects can be assigned, enabling developers to reuse common logic, while still maintaining a unique hierarchy. Inheritance forces more thorough data analysis reduces development time and ensures a higher level of accuracy.
  • Polymorphism: Objects are built to share behaviours, and they can be in more than one form. The program determines which meaning or usage is required for each execution of that object from a parent class reducing the need to duplicate codes. A child class then gets created, which expands the functionality of the parent class. It allows different types of objects to pass through the same interface.
  • Syntax: It is nothing but a set of rules that describe the arrangement of words and punctuation in a programming language. Syntax is also one of the important principles of OOP. 
  • Coupling: This describes the extent to which different software elements are interrelated. For instance, given that a class has attributes change, then another coupled class also changes.
  • Association: This is the link between one or more classes. These associations can be one-to-one, many-to-many, one-to-many or many-to-one.

What are the benefits of OOP?

Benefits of OOP include the following:

  • Modularity- Objects can be encapsulated as self-contained, thus helping in troubleshooting and collaborative development.
  • Reusability- Code can be reused via inheritance, thus a group of people do not need to write the same code several times.
  • Productivity- Programmers can assemble new programs fast through many libraries and reusable code
  • Independent- Easily upgradable and scalable. Programmers can implement system functionalities independently.
  • Interface descriptions- Due to message passing techniques, external systems description is straightforward.
  • Security-  Because of encapsulation and abstraction, complicated codes are masked; it will be easy to maintain a software application, as internet protocols will be masked from being disturbed.
  • Flexibility- There is an adaption capability from polymorphism which would result in one function accepting a single class into its placement while passing objects through one interface.
  • Code maintenance- A system can be updated and maintained without requiring a great deal of adjustment.
  • Low cost- Other benefits of OOP, including its maintenance and reusability, is it reduces the development costs.

What are examples of object oriented programming languages?

While Simula is the first object oriented programming language to be credited, many other programming languages are used with OOP today. But some programming languages go well with OOP than others. For instance, programming languages that are considered pure OOP languages treat everything as objects. Other programming languages are designed mainly for OOP but with some procedural processes included. Some of the most popular programming languages are designed for, or with, OOP in mind.

For example, the following are some of the very popular pure OOPs languages:

  • Ruby
  • Scala
  • JADE
  • Emerald

Programming languages whose design is primarily based on OOPs include:

  • Java
  • Python
  • C++

Other programming languages used with OOPs include:

  • Visual Basic.NET.
  • PHP
  • JavaScript

FAQs

FAQ 1: What is the main advantage of using Object-Oriented Programming (OOP)?
The main advantage of OOP is its ability to enhance code reusability, scalability, and maintainability. By organizing code into objects, OOP allows developers to easily modify or update specific sections of the code without affecting the entire system, making it ideal for large, complex applications.

FAQ 2: Which programming languages are best for learning Object-Oriented Programming?
Some of the best programming languages for learning OOP include Java, Python, C++, Ruby, and Scala. These languages support OOP principles like encapsulation, inheritance, and polymorphism, and are widely used in both academic and industry settings.

Conclusion: 

Object-Oriented Programming has revolutionised the way software is developed. It provides modularity, scalability, and efficiency through principles like encapsulation, inheritance, and polymorphism. With such versatility, OOP has remained a basis for developing secure, maintainable, and adaptive software solutions across industries. Popular languages such as Java, Python, and C++ show their lasting relevance in building complex applications.

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How Object-Oriented Programming Powers Big Data Analytics in 2025

The world of Big Data analytics is gradually shifting, which means that moving into 2025, the field will become more interesting than ever. 

But do you ever ask yourself where this change comes from or what drives it? 

It’s Object-Oriented Programming (OOP)—a phenomenon that people mostly link with software engineering—that is driving this revolution.

If you are familiar with coding terminology, then you must have heard and wondered all about object-oriented programming. Think of it as a completely different approach towards software development. 

Why is Object-Oriented Programming Vital for Big Data?

OOP in Big Data is about organising and managing data efficiently. Its principles—encapsulation, inheritance, and polymorphism—help break down mammoth datasets into manageable “objects.” This modular approach is particularly vital as Big Data Tools in 2025 become increasingly sophisticated.

For example, Python and Java, programming languages used in Big data, depend on OOP concepts. It offers a framework, productivity and modularity, so data scientists can work on the signal rather than the noise. Thus, one should demonstrate the strengths of OOP in Big Data when speaking about object-oriented programming. 

This allows a single interface to characterise a broad category of actions, after which differentiated classes of objects may go through the same interface. That means that polymorphism works with different objects of one type, and the type of an object is the base class of the given type. 

Developers encapsulate data and operations as one unit or a defined class. In fact, the principle does not allow getting to other objects in order to prevent changes. This practice offers good security and guards against unwanted changes in data. It also assists the developers in making other extra changes or modifications in the future without much complication.

Transmission of code depends on how the objects behave, thereby making it the most crucial element in OOP. The objects of the programme pass and respond to messages (data) to each other, principally through methods.

Here’s a breakdown:

OOP Feature Application in Big Data Analytics
Encapsulation Protects sensitive data during analysis.
Inheritance Simplifies reusing existing data models.
Polymorphism Enables flexibility in applying algorithms.

What is Big Data Analytics?

Big data refers to data that is beyond the ability of usual data processing software to handle. This is a large volume of structured, semi structured and unstructured data that get produced in a split of a second. 

It includes three Vs:

  • Volume: Some of the key challenges relating to computing include: The sheer size of data generated.
  • Velocity: It means the rate at which data gets generated and analysed.
  • Variety: The options of delivering data with text, images, videos, etc.

Change Management for Effective Information Management through Big Data Analytics

Data Collection

Data acquisition refers to the process of enabling multiple information sources, including social media sites, Internet of Things devices and sensors, and customer interfaces. This data is normally in an unformatted or formatted structure, which needs good data to store it most effectively. Apache Kafka and Flume are the most commonly used tools.

Data Processing

It entails data cleansing, scrubbing or cleaning by removing any duplication or error, normalisation of data and putting them in databases. Tools such as Apache, Hadoop, and Spark are significant in the handling and processing of large datasets.

Data Visualisation

After you collect data, it gets analysed to bring out graphical information in the form of graphs or charts, dashboards, etc. Successful business intelligence tools that are available are Tableau and Microsoft Power BI, which allow decision-makers to gain insights into huge amounts of data and learn about trends or new patterns easily.

The Future of Big Data Analytics

Imagine the bustling streets of Mumbai—full of endless possibilities and a constant buzz. That’s how Big Data tools in 2025 are shaping up. Tools like Apache Spark and Hadoop are evolving to incorporate even more OOP features, enabling seamless scalability and real-time analytics.

Moreover, Big Data programming languages are adapting to meet new challenges. Languages like Scala and Kotlin, which are deeply rooted in OOP, are gaining traction in data science courses across India.

For example, researchers are analysing urbanisation in Indian cities and leveraging OOP principles. By creating objects for data points like population growth, infrastructure development, and migration patterns, they can build predictive models that aid urban planning.

If you’re an aspiring data scientist, learning OOP is no longer optional—it’s essential. Enrolling in a data science course will help you master these principles and gain hands-on experience with the Future of Big Data Analytics.

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