Do you think about AI development and artificial intelligence programming? It might be the best idea to come up during this day and age.

But before starting your artificial intelligence development journey, you must learn the basics of different programming languages.

If you’re an entrepreneur who wants to take a crack in this world of artificial intelligence, below is the list of the best programming languages for AI.

The Best Programming Languages to Learn for AI

AI programmers must be happy because there is an abundance of programming languages readily available to learn.

Below is the list of the ten best programming languages for AI. Check it out!

1.) Python

Python is one of the league leaders in the AI development department.

It is one of the most reliable programming languages because of its pre-designed libraries that are useful in AI development.

It converts user code into machine-readable code, making programmers’ life easy.

With tools like NTLK and SpaCy, it is ahead of the competition in natural language, as they also focused on this department to achieve an amazing user experience.

Most artificial intelligence programmers prefer using Python over any other AI programming language because of its simple syntax, data science, scientific computing, desktop apps, and more.

PROS

• The best option for natural language processing and AI programming
• It has a very large community of AI developers
• Deep neural networks and vast machine learning libraries
• Often used general-purpose programming language for frontend and backend functions

CONS

• Security issues

2.) Java

Java is the native language for android app development. It has been widely used by most mobile app developers for years and remains one of the most stable programming languages in mobile app development languages.

It is a good programming language and has been widely used for many AI projects.

Java is the best programming language because it was used for making FaceApp and Google Assistant, which have become one of the most popular applications today that use artificial intelligence.

PROS

• Widely used programming language for AI
• It has many features, including browser tools, a dynamic interface, and more
• Lots of machine learning libraries for deep learning
• All these features make AI development easy
• It uses an object-oriented programming language
• Functional programming to create different programs that use artificial intelligence

CONS

• Very steep learning curve most programmers don’t want to deal with

3.) JavaScript

JavaScript is almost as similar to Java. The only advantage of this language is it’s more versatile and often used for dynamic and secure websites as it’s considered a scripting language.

It can easily manage frontend and backend functions, from multimedia to buttons and data storage.

Another advantage of this programming language is that an extensive community supports it.

If you want to know some of its machine-learning libraries, check out Underscore, jQuery, and React.js to learn more.

PROS

• One of the most popular programming languages used for web development.
• Cross-platform mobile app development
• Its AI capabilities allow it to interact with other source codes, such as CSS and HTML
• Great for frontend and backend web development
• Widely used in almost all mobile apps

CONS

• It is more challenging to learn this programming language than other programming languages, such as Python.

4.) Scala

Scala followed the concept of the Java Virtual Machine (JVM) and provided a better solution for intelligent programming software.

In addition, it is also compatible with other programming languages, such as Java and JavaScript.

The powerful features that made this programming language for AI popular among developers are its pattern matching, flexible interface, and browser tools.

PROS

• High-performance and safe software development
• Easy to develop AI applications and integrates well with Java
• Flexible interfaces for AI development
• One of the best languages for AI development
• Extensive machine-learning libraries

CONS

• Challenging for beginners to learn this programming language

5.) Lisp

Lisp might be the oldest programming language on our list. The inventor of LISP, John McCarthy, coined the term artificial intelligence.

Despite its age catching up, it still has several features that entice AI programmers to use this language.

Its popular features include rapid prototyping abilities, mandatory garbage collection, dynamic object creation, and many more.

It is not considered the go-to programming language in recent years, and it’s thanks to LISP as it laid the foundation for AI development.

PROS

• It is known for its fast coding process and rapid prototyping and is highly adaptable to any projects
• Tremendous community support
• One of the oldest AI programming languages used

CONS

• Not a lot of people use this due to its expensive libraries and a complex syntax

6.) R

R is becoming one of the more popular programming languages worldwide as it caters to experts and beginners alike.

While some may consider R not the best programming language for AI, it is impressive at complex calculations. Statisticians solely created it to crush massive data sets and perform extensive computations.

AI development often involves extensive data analysis, so R is the preferred choice.

It is one of the strongest programming languages for AI and is used in different fields such as medicine, health, sociology, finance, and many more.

PROS

• One of the most popular statistical programming languages
• Integrates well with other languages for AI development
• Known for its visual interface, powerful graphics, and support across different platforms

CONS

• It is not well-supported due to its small community
• Programmers often have issues with its speed

7.) C++ 

Another programming language that is tried and tested in AI development is C++. It is a low-level programming language that is amongst the oldest.

Although some programmers consider it the simplest programming language, its long history in the world of artificial intelligence and its low-level programming capabilities are more than enough to do simple AI models.

PROS

• Considered the fastest programming language in the world
• It is known for its efficiency and quickness
• Most game developers use this language
• Widely used programming languages for AI

CONS

• It can take a lot of work to learn
• It doesn’t have a lot of quality-of-life features

8.) Julia

Unlike other programming languages on our list, Julia is relatively new. While the community is relatively small, it always ranked as the preferred language for artificial intelligence.

Its impressive features are its dynamic interface, debugging, memory management, and metaprogramming.

It also supports C functions and parallel and distributed computing.

Regarding AI development, Julia has all the tools to make life easier, such as packages for probabilistic programming, premade models, algorithm help, and many more.

PROS

• Great for any machine learning projects
• High performance
• It was primarily developed for scientific computing, making it popular among mathematicians, programmers, and data scientists.

CONS

• It has a relatively small community and followers
• Not the most popular language for web development

9.) Haskell

Haskell’s efficient memory management is one of the reasons why it is considered one of the safest AI programming languages.

This AI programming language is more popular in educational research and is useful for pattern matching, Lambda Expressions, and type classes, to name a few.

The only problem programmers have to deal with Haskell is it is often a hit or miss.

There are circumstances wherein it can provide a very smart process, whereas there are also instances it results in more lagging.

It is one of the reasons why most programmers nicknamed it a lazy programming language because it only evaluates codes as needed.

PROS

• Infinite data structures
• Known for its abstraction capabilities when dealing with errors
• It can reuse code
• Its pure functionality makes it one of the most popular languages to learn

CONS

• It can be not easy to learn this programming language

10. Prolog

Instead of using sequence codes that may be hard for programmers for artificial intelligence development, Prolog uses a more simplistic approach.

It uses a set of basic facts, queries, goals, and rules for a more logical way of programming things.

Many people use Prolog for its simple approach; however, it can be challenging for programmers to use this for AI programming.

PROS

• It is mainly used for AI projects involving computational linguistics
• One of the best languages used for problem-solving for AI applications
• Uses logic programming to provide better solutions
• Great for projects that require symbolic reasoning and natural language processing

CONS

• Not the best AI programming language for programmers because it’s challenging to learn

Best Upstart Alternative Programming Languages and Frameworks for AI

If some of the AI programming languages for AI development above are not the best choice for you, then you might find some better alternatives.

1.) Native iOS Swift

Native iOS Swift is relatively new to the world of programming. It was developed by Apple and is used across all Apple platforms, such as iOS, macOS, tvOS app, and watchOS.

2.) Native Android Kotlin

If you’re making Android apps, Native Android Kotlin is one of the best languages for AI development. Its codes are relatively easy, and a beginner is more than happy to learn this.

3.) Flutter

Flutter is intended for mobile development, and while they are relatively new to this environment, more and more programmers and developers lean towards this language.

4.) React Native

React Native might be new to the world of mobile development. Still, its safe, affordable, and user-experience flexibility makes it a better choice amongst mobile app developers.

5.) React.js

For single-page applications, React.js is more than enough to suit users. It uses the Javascript library, which allows programs to run smoothly on different platforms.

6.) Node.js

Node.js is a convenient choice for users who prefer using less engineering. It is the preferred choice for creating scalable but fast web applications and is today’s most commonly used framework for web technology.

7.) Nest.js

Nest.js is a framework often used as a framework for making server-side applications. It is noted for being a great framework for making scalable web applications that use the progressive JavaScript environment.

8.) Laravel

If you want to understand a robust and reliable framework that uses PHP scripting language, Laravel is your go-to solution.

It uses its abundance of features to simplify a programmer’s life by eliminating the painstaking tasks used in web projects.

9.) ElasticSearch

Elasticsearch was mainly built within the Apache Lucene environment and developed in Java. It is great for analyzing, storing, and searching big volumes of data.

Final Words

AI programming has been widely accessible to the public, and several libraries and learning materials paved the way for people to learn this new subject.

Several programming languages, such as Python and Java, have become one of the easiest ways to enter the world of programming.

They are easy to learn and are one of the best programming languages for AI development. We hope you can use these best languages to start your AI project immediately!

Finding the best language for machine learning is undoubtedly difficult because over 700 different programming languages for machine learning are widely used.

The best programming languages for machine learning and other languages that machine learning engineers use will be covered in this guide.

The Best Programming Languages to Learn for Backend Development

Machine learning applications exist for many programming languages, including statistical computing, scientific computing, predictive modeling, and artificial intelligence.

Several programming languages can be used to build machine-learning models. We have outlined the best language for machine learning:

1.) Native iOS Swift

For iOS, iPadOS, macOS, tvOS, and watchOS, Swift is a potent and user-friendly programming language for machine learning.

Swift includes contemporary features that machine learning developers adore, and writing Swift code is interactive and enjoyable. The simple syntax is short but expressive.

The most recent findings in machine learning programming language research, along with years of expertise developing Apple platforms, led to the creation of Swift.

Even simple built-in terms that specify asynchronous behavior can be used to build concurrent code, making it easier to comprehend and less prone to errors.

If you’re looking for a relatively easy-to-use coding system that doesn’t require a steep learning curve, try using Native iOS Swift.

PROS

• Rapid machine learning language development
• Scaling the team and the product is simpler
• Enhancements to performance, development speed, and safety
• Reduced memory footprint
• Swift is open-source

CONS

• Language for machine learning is still in its infancy.
• A small talent pool
• Compatibility issues

NOTE: Fortran is the oldest programming language. Lisp is the second-oldest programming language still in common use.

2.) Native Android Kotlin

A solution called Kotlin/Native allows Kotlin code to be converted into native binaries that can operate without a virtual machine.

An LLVM-based backend for the Kotlin compiler and a native implementation of the Kotlin standard library are included in Kotlin/Native.

Kotlin/Native is primarily made to enable compilation for machine learning algorithms like embedded devices or iOS, where virtual machines are either undesirable or impractical.

If you are a new developer who wants easier access to the mobile world, try using Native Android Kotlin.

PROS

• Increasing Team Productivity
• Shortened development period
• Improved Error Messages
• More Concise Kotlin
• Appropriate for Java and is object-oriented

CONS

• Not the same as Java
• Less Rapid Compilation
• Lower Demand for Talent

NOTE: Java is regarded as the programming language appropriate for financial analysis, network security, preventing cyberattacks, and fraud detection algorithms by internal development teams of numerous organizations and financial institutions.

3.) Flutter

Using Google’s Flutter open-source framework, a single codebase may create stunning, natively built, cross-platform applications.

Flutter is an open-source programming language that will help you create beautiful, quick machine-learning apps with a productive, extensible, and open development model.

This machine learning program allows designers to express their creative vision entirely, unrestricted by the constraints of the underlying framework.

Because of Flutter’s sophisticated compositing capabilities and layered design, you have complete control over every pixel on the screen.

PROS

• This machine learning program allows you to make changes to the app immediately.
• Flutter-based apps have excellent UX because of their incredibly smooth speed.
• Testing and quality assurance are typically much quicker with a single code base.
• In Flutter, development happens quickly and effectively.

CONS

• Browsers do not currently support machine learning applications built using Flutter. So, no web apps.
• Flutter has not been around long enough to generate a substantial resource base.

4.) React Native

React, a top-notch JavaScript toolkit for creating user interfaces, combines the greatest aspects of native programming in React Native.

You may already use React Native in your existing Android and iOS applications or start from scratch and build a whole new app.

Facebook developed the open-source React Native technology for mobile applications.

It gives developers complete access to the native platform and native UI features when creating machine learning apps for platforms including Android, iOS, Web, etc.

PROS

• Cost and speed of app development
• There are smartphone apps that use React Native.
• Simple to work with
• Keep things simple and admirable.
• A sizable developer community that consistently makes contributions.

CONS

• Ineffective for complicated interfaces
• Absence of a few custom modules
• There might be some updating problems.

5.) React.js

React JS is a library with features of a machine learning model, so it’s okay if you accidentally refer to it that way occasionally or if you come across an article that does.

However, based on the rankings, we can confidently say that despite being unique, React is still valued for what it is.

Many developers choose React as their first choice when creating web apps since it is a great library.

React’s growth may seem overwhelming to some, but that doesn’t change the fact that this JavaScript library is still favored by JavaScript developers worldwide.

PROS

• Simple to use and learn
• It gets simpler to create dynamic web applications.
• Reusable components
• Improvements in performance
• The use of practical tools

CONS

• The high pace of development
• Inadequate documentation

6.) Node.js

Node.js is a cross-platform, open-source runtime environment for server-side and networking programs. Compared to more established web application frameworks, it has several benefits.

Enterprises utilize Node.js to offer web services, which run some of the most popular websites on the internet, like Facebook and LinkedIn.

This machine learning program is designed to construct quick and scalable network apps and is built on Chrome’s JavaScript runtime.

This JavaScript runtime is ideal for data-intensive real-time applications that operate across dispersed devices because it employs an event-driven, non-blocking I/O approach.

PROS

• It provides a simple scalability
• Simple to learn
• Renowned for providing high performance
• Large and active community support
• The benefits of caching

CONS

• API instability
• Has a weak support system for libraries
• Unable to handle CPU-intensive tasks as well

7.) Nest.js

NestJS is a framework that employs the appropriate architectural strategies and establishes rules to simplify the developer’s life.

In this machine learning program, you can use the full platform or its components because everything is packed in a straightforward and lightweight manner.

Before then, development on NestJS on node was done just for fun, and an attempt was made to include typescript and best practices into the well-liked Koa.

It is regarded as the beginning of a trip into the world of cutting-edge web application design theories, including microservice architectures, event sourcing, and domain-driven design.

PROS

• Simple API endpoints are very simple to write.
• Boilerplate code generation and integration CLI tool.
• Extensible: NestJS is adaptable and lets you use different libraries in your project.
• The project architecture of NestJS enables testing, scaling, and maintenance functions with a minimum of work.
• A versatile machine learning program

CONS

• Steep learning curve
• Magic in excess. Obfuscation and magic are similar in that things function until they don’t.
• Lacking functionality compared to frameworks in other machine learning languages

8.) Laravel

Laravel is a web application framework that simplifies web development with its expressive and elegant syntax.

Due to its many built-in capabilities and the fact that it makes web development simple, such as through the use of a modular packaging system, Laravel is well-liked among web developers.

Among other things, a comprehensive authentication system and automatic testing machine learning techniques exist.

Laravel is a flexible framework for various machine-learning projects, including caching and routing. It is primarily a backend development framework but also has front-end features.

PROS

• Easy coding
• One such scalable framework that you may use for small and medium-sized web applications is Laravel.
• There is a secure access control system built into the software.
• Data migration is simple
• Simple to learn

CONS

• It falls under the category of lightweight software and has fewer functions.
• High price
• Limited assistance

9.) Elastic Search

Elasticsearch is a real-time, distributed, open-source restful search and analytics engine built on Lucene. Within one second, quick searches are completed, and results are returned.

Several machine learning programming languages, including Java,.Net, Groovy, and Python, are used to create clients for Elasticsearch.

Web search, application search, logging data analytics, application performance monitoring, and geospatial analysis are a few machine learning models of the use cases.

PROS

• Because Elasticsearch was created in Java, it can run on any platform.
• Being a real-time search engine, it can only be searched for one second after a document is added.
• Elasticsearch provides the gateway idea, which makes it simple to create comprehensive backups.
• Because it is distributed document-oriented, scaling up in big organizations is simple. The developer can quickly integrate it into any sizable organization by scaling it.
• In contrast to Apache Solr, Elasticsearch can handle multi-tenancy with ease.

CONS

• Split-brain issues can occasionally arise in Elasticsearch.
• Elasticsearch lacks the multi-language capability for managing request and response data, in contrast to Apache Solr.
• As opposed to alternatives like MongoDB, Hadoop, etc., Elasticsearch is a poor choice for a data store. Streaming TBs of data daily either chokes or loses the data; however, it works fine for smaller use cases.

10.) Python

Python is the most used general-purpose programming language for sentiment data analysis and natural language processing (NLP).

The burgeoning fields of data science, artificial intelligence, and machine learning can benefit from this language. A gold standard for those interested in scientific computing and ML.

Python is frequently used by machine learning engineers and data scientists, even though 33% of them prioritize using it for development.

This object-oriented programming language is a good example of why this flexible machine-learning language is a cross-platform language.

PROS

• Better for data manipulation and repetitive tasks
• A sizable community
• Extensible and object-oriented
• Numerous Libraries
• Embeddable

CONS

• Problems of design
• Sluggish compared to compiled machine-learning languages
• High memory requirements

NOTE: Algorithms can learn to comprehend the subtleties of human language through natural language processing (NLP). Visualizing web-based dashboards and random number generation are made possible using ML algorithms built-in JavaScript.

What Is Machine Learning?

Making it possible for computers to learn from data is the goal of the rapidly growing data science subject known as machine learning. It deals with the statistical analysis of data.

The main objective of machine learning is to develop operating systems that can recognize patterns, gather knowledge from data, construct decision trees, and draw conclusions.

There are numerous uses for machine learning, including computer vision, neural networks, predictive data analytics, and natural language processing.

A programmer doesn’t write the code that tells machine learning algorithms how to distinguish between an image of a cat and a dog in machine learning.

Instead, by training on large data samples, machine learning models are created that teach how to distinguish between a dog and a cat.

One needs a solid foundation in programming, algorithms, data structures, memory management, and logic to implement ML models.

Artificial intelligence can be categorized as either machine learning or deep learning. Machine learning, in essence, is AI that can autonomously adapt with little assistance from humans.

Artificial neural networks are used in deep learning, a subset of machine learning, to simulate how the human brain learns.

Through a high-level programming interface, the deep learning framework provides the building blocks for creating, training data, and verifying deep neural networks.

TIP: Without a solid foundation in statistics, deep learning, computer systems process, design, etc., you’ll never be able to select the best models or address ML issues.

How Can You Learn Machine Learning?

Data analysis, data mining, data organization, and test and experiment execution are all part of machine learning.

Machine learning processes are typically tracked and improved to create reliable and powerful machine learning systems.

The five steps for learning “machine learning” are listed below so that you can start your new career in this fascinating field.

1) Develop Your Programming Skills

For a profession in machine learning, expertise in various programming languages is required.

Knowledge of one or two of the top programming languages for machine learning is required to complete various projects.

Numerous internet resources exist to help machine-learning practitioners choose a machine-learning programming language to study.

Python is an object-oriented programming language for machine learning that’s most often used by many machine learning engineers and data scientists.

2) Take a Course in Machine Learning

One of the highest-paying careers without a college degree is machine learning. But you still need to finish a boot camp or online course.

Machine learning practitioners can quickly learn and comprehend machine learning thanks to the many well-regarded bootcamps and programs available.

3) Begin Initiatives Utilizing Machine Learning

Examine and build straightforward machine learning projects from Scikit-learn and other corresponding sources to better understand machine learning.

You will have a basic understanding of machine learning after completing this task. You can produce original works that you can publish online or list on your resume.

Try starting from scratch on a machine-learning project that calls for straightforward machine-learning algorithms.

4) Study Data Collection Techniques

Data gathering is crucial to machine learning. You can gather the required statistical data effectively and efficiently if you are familiar with various data collection methodologies.

Machine learning systems can complete machine learning tasks quickly and easily when given the right data.

5) Apply for Internships and Apprenticeships in Machine Learning

To develop the business-specific machine learning skills that many organizations desire, you will need the necessary work experience.

A career in machine learning can be launched successfully through an apprenticeship. One way to gain experience is through internships or entry-level positions in machine learning.

Additionally, you can apply for a junior machine learning engineer position in online job postings.

What to Consider When Choosing the Best Machine Learning Programming Languages

The following are the seven important variables that we take into account while choosing the best language for machine learning for each project:

1) Application Type

What languages are available to us depends greatly on the type of application you’re developing, including web applications, mobile applications, embedded firmware, etc.

A variety of machine learning applications can be created using general-purpose programming languages like Java, JavaScript, Python, and C# on various platforms.

In some circumstances, a particular machine-learning language performs better.

2) The Application’s Complexity

The scope and difficulty of a project are crucial factors in selecting the stack or technology we should employ, affecting the choice of machine learning programming language.

A more general-purpose programming language, like Java or C#, could help simplify the skill set requirements to maintain the products.

Medium-sized projects, such as e-commerce sites, internal enterprise applications, and IoT solutions, typically have various layers, components, and integrations.

3) Business Culture

Every business prefers internal versus external technical support and open source versus proprietary software.

A more scalable machine-learning programming language might be required by one company, while another might prefer an easy-to-learn machine-learning language.

All programming languages have trade-offs, so our main goal is to comprehend your unique business challenges before choosing a machine-learning language that suits them.

4) Time to Market

It’s crucial to consider your current employees’ skill sets. Is it possible to teach your staff new software or technology?

We usually advise leveraging current skill sets to bring new ideas to life more quickly unless there is a compelling reason to introduce new technology.

It is best to reserve a language for machine learning and technology exploration for tasks with lengthier deadlines.

The physical environment you currently operate in or may want to be in also impacts time to market.

5) Maintainability

Every language for machine learning and technology stack has its ecosystem of machine learning libraries and vendor support for those machine learning libraries.

We want to consider the trend and update release cycles to ensure that our choice is current and will remain for some time.

Even though we like to work with our clients over the long term, we are aware that sometimes it may be necessary to hand off a codebase to another team.

When choosing your preferred programming language, it is considered to ensure the codebase is long-lasting.

6) Scalability and Effectiveness

Instead of the machine learning programming language, scalability is more about a technology stack influencing the programming languages.

Some well-known and tried-and-true stacks demonstrating their capacity for such scalability include Java Spring,.NET, LAMP, Ruby on Rails (RoR), MEAN, and Ruby on Rails (RoR).

7) Security

Security requirements vary depending on machine learning applications.

Java is the most appropriate programming language for financial analysis, network security, thwarting cyberattacks, and fraud detection techniques.

Micro-service architectures are being used more frequently so that we can execute different services created using other programming languages and frameworks.

Frequently Asked Questions (FAQs)

We’ve answered frequently asked questions about programming languages for machine learning and other crucial subjects.

What Are the Most Popular Programming Languages?

The most popular programming languages in the world are JavaScript (69.7%), followed by HTML/CSS (62.4%), SQL (56.9%), Python (41.6%), and Java (38.4%).

Additionally, hiring managers in the Americas find Java the most popular programming language (PDF, 2.4 MB).

What’s the Importance of Learning a Programming Language?

By learning computer programming, students can gain entry into the dynamic, creative world that depends on machine connections.

These abilities are transferable to so many various fields and industries. A creative career path for students can include 3D animation, web design, or even branding.

What Are Machine Learning Engineers?

Machine learning engineers use their coding and programming talents to gather, process, and analyze data.

They are a hybrid of software engineers and data analysts. To assist in organizing data, machine learning engineers develop predictive models and algorithms using machine learning.

What Is a Java Virtual Machine Used For?

Java bytecode is loaded, validated, and executed by the Java Virtual Machine, or JVM.

Because it executes Java code, it is referred to as the interpreter or the core of the Java programming language.

What Does an Embedded Computing Hardware Engineer Do?

Engineering for embedded systems includes design, development, production, testing, and maintenance.

Regularly tests systems to weed out potential problems and offers system-level support while working with cross-functional teams.

What Is a Neural Network Library?

A deep learning framework called Neural Network Libraries is designed to be used for both study and production.

We plan to run it on desktop computers, HPC clusters, embedded devices, and production servers, among other things.

Final Words

This blog post about the best language for machine learning perhaps helped you learn something.

Machine learning is in an exciting phase right now, regardless of the language for machine learning you pick.

A machine-learning expert can select the best language for a machine-learning using experimentation, testing, and experience.

Knowing a particular programming language for machine learning is ideal because it will help you stand out from the competition while applying for jobs.

Understanding another machine-learning language is simple once you become proficient in one.

The demand for blockchain technology only grows as it finds more success in different industries.

Blockchain is revolutionizing how we think about software development.

Read on if you’re looking for the ideal blockchain programming language for your next blockchain project!

This article has compiled a list of the BEST programming languages for blockchain development.

The Best Blockchain Programming Languages

Let’s take a closer look at some of the top programming languages used for blockchain development.

1.) Native iOS Swift

Kotlin is a concise multi-paradigm programming language produced by the developers of IDE IntelliJ for Java Virtual machine (JVM) and Android.

It behaves similarly to Java and is compatible with the Java ecosystem, so it’s easy to pick up for Java developers.

We find Kotlin to be a more concise Java language that takes away the repetition in constructing Java variables.

Not only that, but it also removes null references and enables extension functions and coroutines, among other features.

Kotlin also has functional and object-oriented programming features. It can interact with existing platform libraries and interoperate with Java classes seamlessly.

Kotlin is also great with its high-level programming language if you wish to create your own ledger technology.

Pros

• General purpose
• Easy to learn
• Interoperable with Javascript infrastructures
• Reliable and less buggy

Cons

• Limited learning resources
• Different

3.) Solidity

Built by the creators of Ethereum, Solidity is a blockchain programming language optimized to run on the Ethereum virtual machine (EVM).

It’s a domain-specific language that is specifically designed for smart contract development.

Solidity uses a high-level blockchain coding language inspired by the top blockchain coding languages like C++, Python, and Javascript.

Due to its likeness to other programming languages, it’s easy for developers to master. It also comes with several features and tools used in blockchain development.

Renowned for its STABILITY and benefits, it’s one of the fastest-growing blockchain development languages in Ethereum and the blockchain industry.

Pros

• Precise accuracy
• User-friendly
• Stable and reliable
• Accessibility to Javascript infrastructures

Cons

• Used for building blockchain ONLY
• Might lack sufficient code coverage

4.) C++

C++ is one of the oldest and most popular programming languages used in the software industry, introduced in 1985.

Unlike other binary coding languages, C++ uses object-oriented programming that is much easier to work with.

It’s a near-universal programming language used by many operating systems and browsers as their primary language.

Many blockchain developers prefer C++ for its features, such as:

• Runtime polymorphism
• Multithreading
• CPU management and memory control
• Code isolation
• Data hiding

It continues to be a popular blockchain programming language for its data-hiding feature that can secure blockchain solutions.

You can find many programming developers who use it in developing smart contracts and building blockchains.

What programming language is the Bitcoin blockchain written in? It’s coded using C++!

Major blockchain platforms like Ripple, EOS, and Stellar also use this language.

Flutter

Written with C++ and Dart, Flutter is an open-source programming language framework supported by Google.

Known for its smooth web application building, you can integrate blockchain using its effortless framework.

If you’re interested in blockchain app development, Flutter allows FAST and FLEXIBLE application building for decentralized applications (DAPPs) on the Ethereum blockchain.

Flutter makes it incredibly easy to create DAPPs that develop smart contracts in a single codebase.

Pros

• General purpose
• Efficient and reliable
• Rapid execution time

Cons

• Complex and lengthy
• Does not support garbage pickup
• Does not support built-in code threads

5.) Javascript

Javascript is the most popular programming language in the WORLD, used by the likes of Facebook, Twitter, Netflix, and more.

It’s a multi-paradigm language that can get you far. Several protocols and frameworks are already set up for Javascript to create smart contracts.

Because it’s so popular and accessible, Javascript can be a getaway to learn other languages for blockchain development later on.

With its VERSATILITY and INTEROPERABILITY, Javascript is a great option for creating a secure and highly capable blockchain with a rich interface.

Node.js

If we’re talking about Javascript and blockchain, it’s impossible not to mention Node.js.

Node.js is a Javascript blockchain framework that offers several advantages for developers looking into blockchain programming.

In blockchain development, Javascript’s Node.js provides the Hyperledger Fabric SDK for blockchain solutions.

There are other Software Development Kits (SDK) that use Node.js, like the Lisk SDK, that are meant to be easy to use for creating a blockchain.

Node.js is a more accessible framework for both young and older developers to learn to enter the blockchain space.

React Native

Another Javascript framework is React Native, a simple yet powerful language that can create complex cross-platform applications.

Arguably its best advantage, React Native’s code reusability, can help you cut down time and cost with your project.

With its vast open-source library from a community of developers, pre-developed components are accessible for an EVEN FASTER development pace!

React.js

React.js is an open-source Javascript framework and front-end library developed by Facebook for building smoother interactive user interfaces.

It’s currently the most popular programming language library by Javascript for building user interfaces.

Pros

• Easier onboarding due to simplicity
• Interoperability
• Greater scalability
• Easier entry into the market
• Multiple Javascript frameworks

Cons

• Inconsistency on different platforms
• Security issues
• No multithreading properties
• Lacks hash functions

6.) Java

Among the top blockchain programming languages, Java is a platform-independent, object-oriented language with more than 9 million developers worldwide.

Java is the best for creating smart contracts. It has several features that make it ideal for developing decentralized apps.

It has multithreading support and concurrency, allowing you to run multiple tasks, surpassing the usual programming languages regarding asynchronous actions.

Many developers turn to Java for creating blockchain solutions for its POPULARITY and extensive community support.

Elasticsearch

Elasticsearch is a distributed, open search and analytics engine built on the Apache Lucene library using Java.

Released in 2010, it’s quickly gaining popularity for business analytics, security intelligence, and full-text search.

Essentially, this search engine allows large-scale storage and analysis of HUGE volumes of data.

If you want to access system security logs or perform business analytics regarding your network, Elasticsearch can be a powerful addition to your blockchain programming.

Elasticsearch can observe and monitor any applications built on your blockchain framework.

It’s a versatile tool fast becoming an appealing analytics engine for developers.

Pros

• Real-time search engine
• Multilingual
• Compatible with multiple platforms
• Fast performance

Cons

• Difficult to learn

7.) PHP

Introduced in 1995, PHP (Hypertext Preprocessor) is one of the most widely-used general-purpose programming languages for developing interactive websites.

Development companies highly recommend it for creating a diverse range of blockchain solutions.

PHP supports object-oriented programming with a huge library that streamlines the work of developers for blockchain coding.

Thanks to its active open-source community, you can build a simple or complex blockchain with reputable PHP developers.

Laravel

Laravel is an open-source, back-end PHP framework for creating custom web applications.

This framework is a popular choice for businesses from various fields like IT, finance, entertainment, and the like.

It has continued to be among PHP’s most preferred server-side frameworks for over a decade.

Though its use in blockchain networks is not well-documented just yet, it has all the gears to make it an impressive tool for building a blockchain app.

Pros

• Open-source community
• Can work with different operating systems
• Simple
• Fast development time

Cons

• Frequent runtime errors
• Not ideal for huge content-based applications

What Is Blockchain Programming?

First, let’s talk about blockchains.

A blockchain is a decentralized database or ledger managed by computers in peer-to-peer networks.

It stores data in blocks that are then linked to other blocks to create a data chain.

A blockchain’s DECENTRALIZED nature guarantees a tamper-proof way to store digital transactions.

Before a blockchain network is released, certain features and protocols that form its architecture should be written in code.

Blockchain programming uses languages such as C++ and Java for writing smart contracts or decentralized apps (DAPPs).

Popular Blockchain Programming Platforms

Hyperledger and Ethereum allow for building DAPPs that run on blockchain technology.

With the former, it offers tools that support the development of distributed ledgers through an easy-to-use application programming interface (API).

Ethereum is an open-source blockchain platform that supports smart contract development.

Smart contracts are built using the Ethereum programming language, Solidity, which runs on the Ethereum Virtual Machine (EVM).

What Skills Do I Need to Learn Blockchain Programming?

There are several skills that every blockchain developer should have.

Companies may have varying demands on the level of expertise and experience. Regardless, here are the ESSENTIAL skills for a successful blockchain programmer.

1: Programming

Programming is crucial in learning blockchain development. It encompasses a broad category of skills, including coding and web development.

Web developers should have a working understanding of coding in different languages for web and blockchain development.

Coding skills are necessary to properly manage and secure data, keep up with network demands and maintain seamless transactions.

Strong knowledge of an object-oriented programming language is essential to work with the most common programming languages for blockchain.

2: Blockchain Architecture

As an aspiring developer, understanding how the blockchain world operates is a MUST.

There are several key concepts that developers should be familiar with, such as:

• Cryptography
• Hash functions
• Smart contracts
• Distributed ledgers

Whether you’re designing blockchain as a core developer or building blockchain apps as a software developer, knowing blockchain architecture is only the beginning.

On top of that, acquaint yourself with the different types of blockchain architecture. Different types of architecture have different strengths and weaknesses.

Depending on what you’ll be doing, understanding these types is essential for blockchain developers to maximize their coding skills.

3: Cybersecurity

Blockchain is gaining notoriety as one of the new paths to STRONGER SECURITY, offering superior resistance against cyber criminals.

With its layered digital cryptography and decentralized nature, blockchain relies on digital security to protect data and prevent manipulation or hacking.

A developer needs to know the best practices to reduce risks against attacks.

In particular, learning how blockchain’s Distributed Ledger Technology (DLT) works is valuable to understand how to maintain a chain’s security.

As cyber-attacks gradually evolve, so should blockchain developers.

4: Cryptography

Similarly to cybersecurity, cryptography guarantees security protection against unauthorized access in a blockchain network.

You’ll be working with several cryptography concepts, such as:

• Encryption
• Hash functions
• Symmetric and asymmetric keys
• Digital signatures

These concepts form the foundation of the blockchain. They ensure that each block is SECURELY connected to other blocks on the blockchain.

5: Data Structures

Blockchain developers work with different data structures to apply them in the blockchain network.

Each block is a data structure that clusters transactions in a public ledger. It can refer to a single block or as many as an entire blockchain.

These structures serve as the framework of all blockchain networks.

It’s a gateway to learning more about complex skills such as data storage, data defense, and distribution.

6: Smart Contracts

Smart contracts are self-executing contracts that enable two network parties to exchange data without needing a third-party intermediary.

All blockchain solutions and interactions use smart contracts.

Since most blockchain activity involves transactions, smart contracts are INTEGRAL for transparency and network fluidity.

If you’re interested in blockchain programming, learning about smart contracts is necessary to understand how they apply to various networks.

Factors to Consider When Choosing the Best Blockchain Programming Languages

Below are the necessary factors to consider when choosing the ideal blockchain coding language for your needs:

1: Goals

The programming language you pick should align with your plans in blockchain development.

Although there are many languages, they have different tools suitable for different purposes.

Find the best ecosystem for your project to discover the programming language that best fits your plans.

Choose what you want to provide to your users, your potential partners and where you want to see your project.

2: Personal Skills

If you’re already skilled in a language, say Java, you can be sure of its reliability and the tools it offers.

But perhaps you’re new to programming. The other languages that are easy to use, like Swift and Kotlin, are accessible even to budding developers.

Choose the programming language you’re familiar with to ensure the best results.

3: Performance

Of course, the performance of the programming language is also an important aspect to look into.

Check the runtime efficiency of different languages to determine if they can run your tasks more productively with fewer crashes and errors.

Using a high-performing programming language with optimized code will save you time and effort in the development process in the long run.

4: Security

The innovation of blockchain is its security system. Therefore, using a language designed with security will make it more resistant to attacks.

Programming languages like Java and C++ have safety features such as data hiding and secure interfaces to reduce threats.

5: Scalability

If you eventually decide to add more team members to your project, a language that is easy to scale will make onboarding more manageable.

Choose a language with simple syntax and a concise codebase for easier readability for new developers.

Frequently Asked Questions

Below are related questions about programming languages that can help you start your blockchain development.

What Should You Learn to Start Developing on the Blockchain?

Blockchain developers require a broad range of skills.

Familiarize yourself with programming languages and LEARN the fundamentals of blockchain principles.

Getting hands-on experience can give you a technical understanding of the tools and techniques needed for blockchain.

Although some companies may hire blockchain developers with a background in web development, security, and networking, it isn’t a hard-and-fast rule.

Many blockchain communities share online tutorials on blockchain programming languages for FREE.

Hone your skills in blockchain coding languages and make the most of it by creating efficient networks.

Is Coding Required for Blockchain Programming?

YES!

Knowing how to code is the first step in learning blockchain development.

Learn the languages used in blockchain development to help you get started on blockchain coding.

How Much Does a Blockchain Developer Earn?

The average salary for a blockchain developer in the United States is about $89,974 annually.

It can go higher if you have years of experience in software or blockchain technology. It can go as high as $132,000 annually, including cash bonuses, tips, and commissions.

Conclusion

The best programming language for blockchain development will largely depend on the ecosystem of your blockchain projects, as well as your goals, preferences, and plans.

Different programming languages offer a diverse range of tools and training to help you get started.

We believe Swift is the most compelling blockchain language to watch out for in the market.

Flutter and Kotlin are also notable frameworks. They are not only accessible but also concise, making blockchain programming even more efficient.

Whatever you choose will impact the success and performance of your project.

The decision is yours!

The data science industry is an emerging field. It currently has a high demand, especially with the market and the advancing studies regarding our activities with technology.

If you’re starting to get cold feet about taking on this industry, we’re here to help you take the first step: choosing the best data science programming languages for you.

We’ve gathered some of the most popular programming languages commonly used in data science. Pick up a few of these, and you’re on your way to becoming an excellent data scientist.

The Best Data Science Programming Languages

Data analysis and compilation are key ingredients in the field of data science. That’s where programming comes in.

Learning the best programming language will help you progress in data science. Certain programming languages may be able to do a lot in data science, while some may serve as an introduction to more complex programming languages.

Here are the top programming languages and a few of their frameworks you can learn. Note that you’re not limited to just one language. Be open to learning as much as you can.

1.) Native iOS Swift

Developed by Apple, Swift is a programming language used to create and grow the application ecosystem and increase customer retention of iOS.

However, as it was further developed, it expanded from the iOS ecosystem and can now work on Linux. The language has also become compatible with TensorFlow and interoperable with Python.

Its numerous libraries and data analysis features can help scientists use this language for data science through machine learning and AI.

This programming language is best for mobile developers curious about data science.

Pros

• Readable
• Easy to learn
• Open-source

Cons

• Only for newer iOS versions
• Weak cross-platform support

2.) Native Android Kotlin

If you’re looking for an easy code to learn for Android, Kotlin might help you get started. They’re a Google-supported open-source programming language specifically for Android development.

It requires less code to get results, making learning easy for those with zero programming knowledge.

It’s also compatible with Java, making combining existing codes between the two programming languages for data easier.

This object-oriented language can help close gaps between the different niches of data science for clearer and more concise results.

Pros

• Lightweight
• Faster compilation
• Easy to use
• Null Safety

Cons

• Young language
• Difficult to find references
• Library and documentation

3.) Dart Language

Dart is a client-optimized language for developing fast applications.

This programming language is easy to learn for any aspiring data scientist, especially if you already have a background in Java. However, your background may also affect your learning curve as a programmer and a data scientist compared to other languages.

This object-oriented and web-based language can be easily compiled, like JavaScript, which is important for faster and more reliable data analysis.

One of Dart’s famous software development kits is Flutter, a collection of tools, libraries, and frameworks that can help build cross-platform applications.

With the added features from Dart’s packages, you can use Flutter to create deep learning and machine learning algorithms and applications perfect for data analysis and machine learning.

Pros

• Easy to learn
• Null safety
• Reliable and quick compilers

Cons

• Low learning curve for Java users
• Limited courses and books

4.) JavaScript

JavaScript is one of the most popular programming languages to learn among web developers. While it isn’t a primary data science language, it is a great aid in the study.

It’s best known for web development because of its capability to build interactive web pages. However, this skill can still be used by data scientists for visualizing data.

It can be a language for data science, given it’s a versatile language that aspiring data scientists can easily learn.

It’s also a functional programming language, making it great for list-processing applications and symbolic computation.

JavaScript has made many frameworks that are constantly used in data science because of all its capabilities. Some examples are React Native, Nest.js, and Node.js. These can all be used to automate and streamline the complex problems of data science.

Pros

• Visualizations
• Easy with big data
• Easy to learn
• Has different toolsets

Cons

• Built-in functionality
• More for web development
• Lacks data science packages

5.) PHP

PHP is a general-purpose programming language best suited for web development. Its code can easily be embedded into HTML or exported to other languages, like Python.

It’s a simple programming language to learn, especially for beginners. It can also be a good gateway to other programming languages, given how its code is closely related to well-known languages for data science.

In terms of data science, it’s generally used for its many libraries for data analysis.

PHP’s most famous framework is Lavarel, which is commonly used to create virtual assistants and messaging applications for the web. However, it also has features that you can use for data analysis.

The framework can create amazing AI and machine-learning models thanks to PHP’s many libraries and packages.

Pros

• Great support and documentation
• Secure
• Strict Parameters

Cons

• Slow

6.) Java

Java is the most popular programming language for building large AI and machine learning applications. It’s easy to learn for beginners. It’s flexible as it can be both compiled and interpreted language.

Thanks to their Java Virtual Machine (JVM) ecosystem, it’s an easy entry to other languages for data science.

It can easily handle complicated tasks needed by data scientists like natural language processing, data analysis, data mining, and deep learning. It’s why Java is a popular language.

One of Java’s most famous frameworks for data analysis and machine learning is Elastic Search. Thanks to its simplicity and accessibility, any data scientist can pick up this framework.

This framework can easily find patterns and important information through machine learning features for faster analysis and forecast of your data.

Pros

• Fast language
• Portable
• Easy to learn
• Java Virtual Machine ecosystem

Cons

• Disciplined language
• Less support and networking opportunities

7.) R Language

When it comes to data science programming languages, the R programming language is already making a name for itself.

It’s a powerful scripting language that can handle high-volume data sets. Its features make it great for machine learning and big data analysis while allowing for statistical computing and graphics.

R language has statistical models and packages, which include neural networks, nonlinear regression, advanced plotting, and many more. It’s also good for data manipulation and visualization.

Pros

• Easy to learn
• Large amount of support
• Multiple packages
• Machine learning operations

Cons

• Lacks basic security
• Can’t imbed to web application

8.) Structured Query Language

SQL is a domain-specific language for data science best used for handling structured data. This feature makes SQL the perfect programming language for data analysis and statistical analysis.

It’s best used for relational databases for a data scientist to easily relate different data points. It’s also commonly used in business analysis and data manipulation.

Pros

• Non-procedural language
• Easy to learn

Cons

• Difficult interface
• Expensive

9.) Python

Python is known as the top data science programming language best known for its problem-solving and natural data processing.

It’s a high-level programming language best used for automation. It performs well in data analysis, modeling, and visualization, which are necessary for data science programming languages.

It’s great for accessibility compared to programming languages like the Statistical Analytical System (SAS) which may need a license.

Pro

• Popularity
• Support and resources
• Open-source tools

Con

• Speed
• Slow computation

10.) Julia

Julia is a rising multi-purpose programming language for scientific computing and numerical analysis.

It’s great for collecting business intelligence because of its features for time-series analysis, risk analysis, and more.

It’s also one of the programming languages for data science that supports parallel and distributed computing which is essential for the statistics of businesses.

The many functions of Julia for deep learning and numerical computing make it a well-known language for data analysis and science.

Pro

• Easy to learn
• Syntax inspired

Con

• Not a lot of support and resources
• Low talent pool

What Is Data Science?

Data science is the study of information to be used by companies and establishments. All this information is used to make business decisions, create strategies to improve and solve complete problems.

It covers machine learning, artificial intelligence, and deep learning algorithms.

With data science, you can automate and streamline many tasks and data for easy analysis using data science programming languages.

Why Should You Pursue Data Science?

As of now, the data science industry is still growing. That means high demand and many employment opportunities.

The salary of an average data scientist in the USA is significantly higher compared to a typical data analyst.

When you take on data science jobs, you’ll be open to opportunities while learning skills and earning a good income.

How Is Programming Used in Data Science?

Data science deals with big data. You’ll have to find a way to manage this data and make them easier to analyze.

That’s where programming languages for data science come in. By coding, you create an analytical model and algorithm to analyze and solve complex problems.

Your computer will do the job through neural networks and machine learning. You can also recycle analytical models from coding language for similar data management problems.

What Are the Different Types of Data Scientists?

Each data scientist specializes in and explores a different niche within the study.

We’ve gathered a few specific specializations you can look into.

These are only a few of the many types of data scientists. More jobs are opening up in the data science career path with how it grows every day.

1: Statistician

A statistician uses theoretical and applied statistics to improve a company’s goals and how they can improve.

The key skills for statisticians are confidence intervals and data visualization. They should be able to present their statistical analysis for others to understand.

2: Software Programming Analyst

A software programming analyst is responsible for mathematical calculations using programming.

They adopt new programming languages for data visualization and data analysis. They can also automize large data-related activities to reduce computational time.

3: Business Analytic Practitioners

These data scientists need a good background in business intelligence and data analysis to succeed in this niche.

Their job is to gather and analyze data to be used in important decision processes. These include high-level database design, return on investments, optimization, and much more.

4: Data Engineers

Data engineers are responsible for designing, building, and managing the information captured by the organization.

They create a data-handling infrastructure that analyzes and processes data in line with the organization

5: Machine Learning Scientists

Machine learning scientists explore innovative approaches and examine new algorithms for understanding data.

It allows for suggestions on pricing strategies and demand forecasting for businesses, to name a few of its benefits.

How Can I Get Started in Data Science?

Data science doesn’t need a 4-year course for you to get into the field. However, it does help to be highly educated in big data and math. These can all be managed through data science programming language.

Here are a few tips to get you started in building your data science career through programming languages.

1: Learn the Basics of Programming

All data science jobs would require some level of programming.

It would help if you had at least the basics of understanding how to program before getting into the field or a specific niche.

Know what programming languages are involved and the problems programmers face when writing code for applications.

2: Familiarize Yourself With Machine Learning Techniques and Algorithms

Machine learning is the most popular area in data science.

You need to understand how machine learning works and which algorithms are popular. Knowing how these are all used gives you a better understanding of the data science goals.

You don’t need to be a statistician or machine learning expert. But having the fundamentals, such as linear algebra and decision trees, on your belt should help you pick up the job.

3: Learn From Fellow Data Scientist Peers

Since data science is still an emerging career, you’ll find data scientists left and right. You can easily find a mentor to help you get through the first steps of starting your data science journey.

Learning along with others is also a great way to pick up skills and compare notes. It would be like working with your classmates in high school.

Look into projects and classes from nearby companies so you can hone your skills with fellow data scientists.

4: Ramp Up the Difficulty

You don’t stop just as soon as you gain data science skills. You’ll have to look for a challenge sometime soon.

As soon as you’ve got the basics down, try harder problems and techniques.

Once you’re used to general-purpose programming language, try picking up new data science languages. When you’re through with simple linear regression models, bump it up to logistic regression.

There’s always room to grow along with this developing field.

Frequently Asked Questions

You might have a few more questions about programming languages for data.

We’ve looked into the different libraries of questions to collect the commonly asked ones from streamlining your research.

Do You Need Coding Expertise to be a Data Scientist?

While companies don’t require expertise, a background in coding language is necessary to understand data science.

Tasks in data science can be challenging, especially if you don’t have prior experience in programming.

Get the basics and build on your skills as you progress.

Are C and C++ Used in Data Science?

Yes, you can use C and C++ in data science. These are under Objective-C, one of the earliest programming languages.

C and C++ can compile data quickly with a broader command of applications.

This object-oriented programming language is best used for projects with extremely large data and many performance requirements. C and C++ can easily compile a gigabyte of data, making them perfect for this job.

However, given that it’s a low-level programming language, it can be complicated for beginners to learn this language.

How Long Does it Take to Learn Data Science Programming?

The length usually depends on the person and their way of learning data science programming.

The average learning time for someone without experience in mathematical and statistical computing is 7 to 12 months of intensive studies to reach the entry-level.

However, learning doesn’t stop with boot camps and online classes. Those are all theoretical approaches to data science.

You’ll have to continue learning while you work. You might even be able to pick up more programming languages and skills to grow with data science itself.

The best way to master your skills and gain job-ready proficiency is by constantly practicing.

Conclusion

There is no single best programming language for data science. Each programming language can solve certain problems that other languages can’t.

When starting your data science journey, see which programming language would be best for you to learn.

Always be open to learning more languages and continue to build on your skills as you go.

The backend serves as the backbone of software development — and a programming language is one of its most vital components.

However, there are currently hundreds of programming languages, making it confusing for new programmers to decide what to use.

To help narrow down your choices, we’ll talk about the best programming languages to learn for backend development.

The 9 Best Programming Languages to Learn for Backend Development

Below, we’ve listed the top languages that backend developers should consider learning:

1.) Native iOS Swift

Swift is a native iOS language developed by Apple Inc. It’s a general-purpose scripting language that lets a developer build ANYTHING.

While it can be used for full-stack development, it’s still convenient even if you’re using it just for backend development.

The program offers impressive code readability since it resembles the English language.

Another thing that makes it easily readable is the conciseness of the language. This means Swift has fewer lines of code, allowing for a faster development process and better scalability.

This language also has automatic memory management, meaning it can handle coding vulnerabilities (like the “use after free” coding error) on its own.

However, Swift is still relatively new. Because of that, it still has LIMITED native libraries and tools developers can use for their projects.

Apple is also still working on the language’s backward compatibility with its older versions.

Pros

• Simple and concise code
• Easy to scale
• Has automatic memory management

Cons

• Limited native libraries and tools
• Lack of backward compatibility with older Swift versions

2.) Native Android Kotlin

Kotlin has been the official language for Android mobile app development since 2017. However, it’s also a GREAT fit for backend development

It’s a general-purpose, open-source programming language that covers Java’s weaknesses — which Java developers will surely appreciate.

Kotlin has concise and expressive code that lets you build easy-to-use (but powerful) abstractions. It also supports type-safe builders and delegated properties.

It’s fully compatible with Java frameworks, too, and supports gradual migration for Java codes.

This means you can start using Kotlin while still keeping the rest of your system in Java.

Despite that, you might notice that Kotlin has a fluctuating compilation speed, so you likely WON’T get the clean builds you’re used to with Java.

Pros

• Interoperable with Java
• Expressive
• Supports gradual migration for Java code

Cons

• Compilation speed could still be improved

3.) Dart

Dart is a dynamic object-oriented programming language developed by Google. It’s NORMALLY used for frontend interfaces in mobile apps and web development.

However, it’s one of the best backend languages for Flutter developers. That’s because Flutter allows for a fast development cycle for iOS, web, and Android platforms.

Dart is a simple but powerful programming language with concise syntax — similar to C, Java, and JavaScript.

Another thing that makes it beginner-friendly is its ability to help you identify errors early on. The Dart compiler also ignores blank characters, tabs, and line breaks for a more readable structure.

The compiler also allows for different forms of code execution (namely on a native and web platform).

Dart is REACTIVE, thanks to its fast generational garbage collection and object allocation.

Unfortunately, it’s not one of the best programming languages for text-heavy web pages. It also has a relatively small support community and limited resources.

Pros

• Simple but powerful
• Reactive
• Allows different forms of code execution

Cons

• Limited resources
• Not the best for text-heavy websites

4.) PHP

PHP is a WELL-ESTABLISHED open-source scripting language used to create interactive and dynamic websites. In fact, it’s perhaps the most used backend language, both in and out of web development.

The language offers excellent code execution performance thanks to its memory utilization. This gives PHP a boost in performance and processing speed.

It’s an INCREDIBLY flexible language as well. That’s because it’s interoperable with many other programming languages and frameworks — such as HTML, Python, Laravel, and CodeIgniter.

In fact, you can use PHP to add functions to web pages without the need for external files.

PHP also has excellent cross-platform compatibility, letting you work with ALL operating systems.

A new PHP developer will find it very easy to learn. In fact, it would be one of the easiest backend programming languages you can learn.

However, PHP is maintenance-intensive as it lacks advanced error handling and debugging tools. Because of this, it’s not good for large backend web development projects.

Pros

• Easy to learn
• Flexible
• Fast performance

Cons

• Maintenance-intensive
• Not the best for large web applications

5.) Java

Java is one of the most popular programming languages known for its use in desktop applications. However, this object-oriented programming language is also used for IoT, big data, gaming consoles, and web applications.

This language is also one of the most secure ones available. That’s thanks to factors like removing explicit pointers and using public encryption keys for authentication.

Java is VERY flexible.

For one, it’s multi-threaded, letting the computer execute multiple tasks simultaneously.

It’s also platform-independent. But while other languages (like C#) are independent because of Common Language Runtime, Java lets backend developers execute one script on different operating systems.

What’s even more impressive is that Java 8, in particular, lets you execute a script in different languages!

However, Java isn’t the most beginner-friendly. That’s because it requires significant memory to process code and has NO backup functions.

Because of JVM functions and garbage collection, it also tends to be slow.

Pros

• Secure
• Flexible
• Huge community support

Cons

• No backup functions
• High memory consumption

6.) JavaScript

JavaScript is probably THE most popular programming language for backend development.

Node.js, Nest.js, and React Native are examples of excellent Javascript runtime and frameworks to make the most out of the language.

It’s forgiving to beginners but is also very useful in website development.

The language is impressively fast, thanks to the fact that it runs the code within the browser rather than relying on third-party sources.

If you’re a JavaScript developer, then you’ll enjoy dynamic typing for its data structures. This saves memory space and makes your code more efficient.

But perhaps its best feature is asynchronous processing — which lets different code blocks be processed at the same time, effectively improving the code’s efficiency too.

Unfortunately, JavaScript’s debugging opportunities could still be improved.

Alongside that, different browsers tend to interpret JavaScript DIFFERENTLY. Some older browsers don’t even support some of the language’s new functions at all.

Pros

• Fast
• Dynamic data
• Asynchronous processing

Cons

• Needs more debugging support
• Varying browser support

7.) SQL

Structured query language, or SQL, is the most common backend language for relational database management systems — such as Microsoft SQL Server and MySQL.

That means it can be used for tasks like retrieving or updating data on a database.

Because of what’s required from SQL, it’s impressively efficient in handling large amounts of data. Coding skills aren’t needed for data retrieval either, so it’s very user-friendly.

Relational database tables are EXTREMELY structured, letting SQL be ACID-compliant. ACID properties pertain to the Atomicity, Consistency, Isolation, and Durability of transactions.

However, this rigidity also makes scaling SQL-based database platforms expensive.

The language also has a steep learning curve, partly because of how complex query structures can be.

Pros

• Doesn’t require coding skills
• Efficient
• ACID compliant

Cons

• Expensive to scale
• Steep learning curve

8.) Python

Python is a high-level programming language that has impressive code readability.

It’s an open-source language used for data analytics, machine learning, and web development.

There’s a very active community of Python developers that provide libraries and resources.

Python is excellent for data-sensitive projects since the language regularly sanitizes data and deletes anything delicate from outer sources.

The language is also VERY easy to learn since it was created to have a syntax that’s as close to English as possible. In fact, even without basic knowledge of any programming language, you can learn it in as fast as two months!

Despite that, Python needs a large memory capacity to do advanced tasks like deep learning algorithms. It’s still slow in processing code compared to other languages.

It’s also better suited for web applications rather than mobile apps.

Pros

• Very easy to learn
• Secure
• Great community support

Cons

• Slow in processing code
• High memory consumption

9) Ruby

You might know Ruby as Ruby on Rails.

However, Ruby is the language, while Ruby on Rails is a powerful backend framework.

As a language, Ruby has been designed to be a user-friendly general-purpose language. In fact, it’s one of the easier programming languages to learn.

It’s very useful for tasks like web development, automation, and web scraping.

Like other scripting languages, Ruby doesn’t directly talk to the hardware. Instead, it has to be written in text files and run through an interpreter before it becomes code.

Despite that, it’s VERY flexible. For one, it lets you mix classes and modules — which is easier to use and maintain compared to inheritance.

Ruby is also extremely secure thanks to in-built properties that lets the language itself protect an application in cases of attacks.

However, Ruby has limited community support. It also has slow performance and processing speed compared to other programming languages like Python and JavaScript.

Pros

• User-friendly
• Extremely secure
• Very flexible

Cons

• Slow performance
• Limited community support

What Is Backend Development?

Backend development, also known as server-side development, connects the frontend of any interface with technologies like web servers and other third-party services.

Backend technology is also responsible for things like business logic, data collection, and cookie reception.

As such, a backend developer will need to:

• Write code for APIs
• Manage databases and data models
• Create software architecture
• Implement server-side scripting
• Ensure communication protocols

This side uses a backend framework or server-side languages for faster development. Of course, backend developers will need to work with frontend developers, architects, and project testers to ensure that this “data access layer” works well.

What Are the Types of Backend Development?

A backend language can either be a functional or an object-oriented language.

We’ll differentiate the two below:

Object-Oriented Programming (OOP)

OOP essentially represents stored data through objects — and these objects can be manipulated by using different methods or functions.

There are four characteristics that make up OOP:

• Abstraction – This hides some of an object’s properties and methods to make it easy to make changes to the program.
• Encapsulation – This is essentially when you group related variables and functions into objects.
• Inheritance – This is when an object gets (or inherits) some of another object’s properties and methods.
• Polymorphism – This refers to when objects respond to the same function in different ways.

These characteristics make programming with OOP languages quite convenient.

Unfortunately, the main downside is that the structure becomes INCREASINGLY complex as your script becomes longer.

Functional Programming (FP)

As opposed to OOP, which turns data into objects, FP does not change the state of data. Instead, data is immutable — meaning the only way to change a value is to make a new variable.

Other characteristics of FP include:

• First-class citizens – Functions support all other generally-available operations since they can be:
  • Passed as an argument
  • Stored in a variable
  • Returned from a function
• Pure functions – Pure functions essentially don’t have side effects, improving a program’s execution time.
• Recursion – This makes a code simpler and more robust by avoiding mutable objects.

Backend Development vs. Frontend Development: What’s the Difference?

Frontend development deals with the user-facing side of web apps or any other project. These developers are in charge of ensuring that visitors will be able to interact and navigate the interface EASILY.

Frontend developers are also responsible for the visual aspects of a project.

Meanwhile, backend development is called server-side programming since it deals more with the project’s structure, system, data, and logic.

Because of that, backend developers need to design databases, security measures, data-processing mechanisms, and others.

What to Consider When Choosing the Best Backend Programming Language for You

There’s no one best programming language that will do well in all projects. However, there are ARE programming languages that can be better in certain situations than other languages.

As such, below are some factors to consider regarding which backend languages are best for you:

#1 Product

The first thing you should think about is what you’re using the language for.

If you’re working on web development, then you can pretty much choose ANY language.

But not every language is suited for mobile applications, including Python. However, Python is excellent for machine learning and deep learning algorithms.

Other products like IoT, booking systems, and network programming would also require different languages and stacks.

#2 Size of the Project

Web development with many functions will have different needs from simple apps.

Another thing you should keep in mind here is whether you’ll be upscaling in the future. If the answer is yes, then you should look for a more flexible stack, such as Swift.

If you’re handling relational database management systems, you’ll have to decide right from the start how big the project is since scaling SQL is expensive.

SQL is the most loved programming language for that kind of project, so it might be hard to look for a cheaper (but still reliable) alternative for that.

#3 Other Components of Your Stack

Backend developers need to collaborate with other people who are working on the same project. Alongside that, backend programming typically comprises two or more languages.

Because of that, you want to ensure that the backend languages you use are compatible with each other and the rest of the stack.

For instance, Objective C is a pretty outdated language already.

But if you need to improve or scale a project that uses Objective C, you can still use the same language or use Swift. That’s because Swift has great interoperability with Objective C.

If you’re starting a project from scratch, you can choose Kotlin and Java, for instance. Other backend languages that have great interoperability include C# and JavaScript.

Swift, just by itself, is already excellent for full-stack development.

#4 Cost

Some programming languages are open-source and free, drastically reducing costs. Some examples are PHP, Python, and Java.

However, other languages are licensed and paid — and rates also differ depending on the scale of your project.

Aside from that, the costs don’t end when you’re done with the app or web development. Instead, you’ll also need to maintain, update, and possibly upscale your project.

Frequently Asked Questions

If you have more questions on backend programming languages, we’ve answered a few below:

Which Is the Most Powerful Backend Language?

Java is a very powerful language since it’s simple while also being secure, economical, and supported by a HUGE community.

However, there’s really no single most powerful language that works with ALL backend technologies.

Which Is the Easiest Language for Backend Development?

PHP can be regarded as the easiest backend programming language. In fact, you can learn it within a week.

Python is also one of the easiest backend programming languages, with beginners being able to learn it in as little as two months.

Is Backend Coding Harder Than Frontend?

It CAN be harder depending on your skill set and experience.

We’ll list down a few reasons why either part of software development is difficult.

Backend coding can be harder than frontend because:

• It has a steeper learning curve.
• It’s not as visual as frontend.
• It requires more knowledge of arithmetic.
• Backend web development gets harder the more the website expands.

Meanwhile, frontend coding can be harder than backend because:

• You need to learn more principles, such as UX, design, and programming.
• It requires more tools.
• Testing is more rigorous than the backend.
• The frontend landscape is rapidly changing with new tools and trends.

Final Words

With so many programming languages out there, it can be hard to choose which you learn to start your backend programming career.

Python and PHP would be two of the easiest languages to start, but you can also consider the other backend programming languages on this list.

While not every one of them is easy, they’ll certainly be very useful since the demand to have them on developers’ skill sets is high.

Do you wish to explore microservices development? You’re probably in search of the best programming languages for microservices architecture.

There may be several languages or frameworks you already use in programming. But it’s important to utilize ones that suit your current project.

Stick with us as we walk you through the top 9 best programming languages for microservices architecture.

We guarantee that this list will be the bread and butter of your blueprint.

The 9 Best Programming Languages to Build Microservices Architecture

Microservices have become WIDELY POPULAR as companies aim to expand their business capabilities and services.

With such desires among entrepreneurs, more complex systems are required to run applications.

Unlike the traditional monolithic architecture, microservices can house and operate several functionalities under one UI (user interface).

Programmers can develop microservices with these 9 best programming languages and frameworks:

1.) Native iOS Swift

Swift is a native programming language in iOS, macOS, and other Apple applications. So, if you plan on developing microservices on these platforms, Swift is the easiest way to go.

Swift is a good language for microservices, given that it offers modern features and performs FASTER than other frameworks.

Additionally, this programming language can handle large connections (which is highly required on microservices) and has low memory footprints. Hence, it doesn’t require many resources to fit into existing projects.

Pros

• Easy to code and contains modern features
• Faster than existing languages
• Handles large connections
• Low memory footprints

Cons

• Limited cross-platform support
• Doesn’t support earlier iOS versions

2.) Native Android Kotlin

If you’ve already learned to write microservices and other programs with Java, you’ll have no problem using Kotlin!

It will be easier for you to learn and adapt to this language since they have similar intricacies.

This further means it can operate with famous Java libraries and systems like Spring Boot, Jackson, and Reactor.

It’s also a good language for microservices development because it allows programmers to utilize modern idioms that can imply the method of developing microservices.

Additionally, Kotlin has first-class support from Google in the Android ecosystem. Hence, it’s guaranteed to stay a mainstream language for a long time.

Pros

• Interoperability with Java, like Spring Boot
• Utilize modern idioms
• First-class Google support

Cons

• Limited learning resources
• Slow compilation speed

3.) Flutter

Flutter is a platform or framework rather than a language. Google created it as an open-source programming kit to develop native-looking applications for iOS and Android.

It has grown in popularity over the years because it can be used to develop apps quickly and easily for several operating systems.

Primarily, it uses Dart as its programming language in cross-platform development. Its operability is also SIMILAR to Java, JavaScript, and TypeScript, so you won’t have any difficulties adapting to it.

With Flutter and Dart, it is possible to create separate modules or mini-apps that can function independently. However, despite this, the module can’t be delivered one at a time. 

Pros

• Supports cross-platform development
• Easier to fix bugs
• Fast and efficient programming

Cons

• Mechanisms are not lightweight
• Dart is not as popular as other languages

4.) React Native

Like Flutter, React Native is a mobile application framework supporting cross-platform development. 

This is a great option, especially if you want to SAVE MONEY when developing microservices architecture while still having native-level performance.

Developers love how they can reuse the codes and recycle the components they coded and developed in the React Native ecosystem.

Above that, sharing and delivering codebases within the program is possible to ease the workflow.

Pros

• Quick and cheap to develop
• Cross-platform support
• Reusable codes and components

Cons

• Slower performance than native
• Doesn’t have custom modules

5.) React.js

One of the good frameworks that support microservices development is React.js. – an open-source JavaScript library popularly used in developing mobile and web applications.

It’s also relatively simple to use, as it doesn’t require developers to follow strict rules, and it’s EASY to integrate plug-ins or libraries into an existing project or codebase.

By utilizing Virtual DOM, it can also make quick updates and dynamic front-end. It’s also high performing and offers a good user experience.

Pros

• Easy plug-in integrations
• Quick updates
• High-performing user experience

Cons

• Only covers UI
• Poor documentation

6.) Node.js

Microservices Node JS is said to be beneficial for startups and organizations who want to start microservices architecture since it’s based on V8 runtime.

Microservices Node JS is famous among developers because developing apps for web services is simple and easy. Plus, it offers HIGH PERFORMANCE for the program, cheaper development and maintenance costs, and increased productivity.

Pros

• High performance
• Cost-effective
• Increase developer’s productivity

Cons

• Not scalable
• Unstable API

7.) Nest.js

Nest.js is another microservices framework that allows developers to code Node.js. It’s a good microservices framework since it’s a group of small services interacting with each other.

This general-purpose development methodology is highly adaptable, especially for C# and Java developers.

It allows for the utilization of various technologies and tools, enabling programmers to choose the right tool for a specific function or business skill.

Pros

• Easy to adapt
• Has a library of tools and technology

Cons

• Doesn’t suit complicated API

8.) Laravel

Laravel is commonly used in developing custom applications. It offers various tools to create a reliable platform that’s easy to deploy and maintain.

This is one of the microservices frameworks that is lightweight and fast. It also has STRONG module boundaries, which is essential for large teams.

Additionally, since it’s versatile, programmers can choose whichever language they wish to use in the development.

It also has API support, per RPC or REST architecture model, and works with different data storage technologies and frameworks.

Pros

• Lightweight and fast
• Strong module boundaries
• Versatile with any language

Cons

• Costly
• Limited support

9.) Elasticsearch

Elasticsearch is an open search and analytics engine central to the ELK Stack. It’s greatly known for its SPEED and SCALABILITY. 

Elasticsearch offers several search options and delivers fast search responses using indexes.

It also reduces the probability of data loss in multiple nodes while allowing data management and storage of huge files. 

Pros

• Quick research response
• Scalable
• Store huge files
• Reduce chances of data loss

Cons

• Not as good as MongoDB and Hadoop in data storage

Other Programming Languages and Frameworks for Microservices

Golang

This is known for its concurrency and API support for microservice architecture.

This is primarily designed to handle and develop large and complex apps and in creating web services, and it supports increased productivity of machines.

It also allows developers to write robust tests and instantly embed them in the workflow.

Furthermore, Golang provides access to the RPC framework, GoMicro, and Go Kit, which work perfectly with microservice architecture.

.Net Microservices

.Net microservices provide built-in support to build and deploy microservices with Docker. It also comes with APIs that easily consume already developed microservices architecture.

This means that developers no longer need to revamp the application entirely to migrate.

Besides this, it also allows gradual migration to .Net core technology with new and existing microservices architecture since it can be mixed with other apps written in other languages. 

Java Microservices Framework

The Java programming language is known to be a great option due to its annotation syntax.

Java microservices are easy to develop, especially when coupled with microservices frameworks.

Additionally, it offers UI, model components, and connectivity to back-end resources. Java microservices offer service discovery solutions that are also easy to connect to microservices.

It provides several frameworks for this purpose, including Spring Boot (for aspect oriented-programming), Dropwizard, Restlet, and Spark (supports creating web apps).

Redis

This is a high-speed, NoSQL database that facilitates elastic data structures and quick replies for solving complex coding challenges. It also supports values in addition to a core data model.

What Is Microservices Architecture?

Microservices architecture is a technique or development process that allows a program to host several applications under a single UI.

It is an extensive concept implemented to disintegrate the application to create fragments of smaller services that can function INDEPENDENTLY while interacting in lightweight mechanisms.

Businesses primarily utilize microservices architecture. As they seek to expand their presence in the industry and gather a larger customer base, they’re also creating complex apps.

Instead of using the traditional monolithic architecture developed under a single code base, larger projects requiring smaller entities migrate to the more advanced structure.

What Are the Benefits of Microservices Architecture?

While monolithic structures are cheaper and easier to launch, microservices architecture still has a bigger edge that would easily overthrow the former.

There are many reasons why you should start writing microservices:

• Simple to develop – Given that each service is divided into smaller components, it’s easier for developers to input codes and web protocols intended for a specific program.
• Independent – Each service functions and operates independently. Hence, when one module fails, it doesn’t hurt the entire application.
• Modifiable – Adding features, interfaces, and other inputs to the program is also relatively easy since each service is grouped according to business skill.
• Scalable – Developers can straightforwardly scale the program up or down depending on business needs since its architecture is built into individual-versioned parts.
• Easy to maintain – Given that each service operates independently, it’s also easy for developers to autonomously maintain, test, implement, and deploy one functionality after the other without compromising other model components.

Why Should You Migrate to Microservices Architecture?

Migrating to microservices architecture from the traditional monolith is a HUGE STEP for businesses.

While the latter is cheaper, the former is in the position of catering to complex applications.

Here are several reasons why you should consider migrating to single, independent microservice architecture:

• Monolithic codes are complicated to handle and maintain. Services also lack distinction since they run as a single code base.
• Developers are FREE to use a diverse programming language for each service, given that they are not built under one system.
• It’s simpler to scale up and down and modify inputs in each service with microservices architecture.
• Given that monolithic codes are built under the same system, modifying one component in the model would COMPROMISE the others. This could also happen when technical failures occur in a component.

Choosing the Best Programming Language for Microservices: What to Look For

If you’ve finally decided to switch to microservices architecture, you must choose the appropriate programming language based on business needs.

Here are the things to look for:

1. Observable and independent deployment process
2. Supports automation and operates in a customer-centric manner
3. Supports continuous integration
4. Has component decentralization
5. Suited for a business domain

Frequently Asked Questions

If you have more questions about the perfect programming language for your microservices model, read on! We’ll answer them carefully in the section below:

Is Python a Good Choice for Microservices?

The Python microservices framework is a recommended tool for programmers in microservices. It’s a high-level programming language that offers easy integration into different technologies. It also uses RESTful API, which is great for utilizing web protocols. 

It also allows for FAST and EASY prototyping than other languages and frameworks. Additionally, it’s compatible with legacy languages like PHP and ASP.

Other leading frameworks that are great for web development are:

• Flask
• CherryPy
• NameKo
• Falcon
• Bottle

Is C++ Good for Microservices?

C++ is also one of the good languages for microservices. It’s a complex programming language that’s QUICK and FLEXIBLE. It offers a good service registry through the libraries available.

This language is similarly used in automotive applications, robotics, and database services, and it’s also widely used as a REST SDK cross-platform programming language.

What Is the Best Way to Communicate Microservices?

There are different ways to communicate between microservices. But, the most used method is single-receiver communication with synchronous protocols.

Synchronous protocols include HTTP/HTTPS for Web API HTTP service. But similarly, you can also use asynchronous messaging protocols, like AMQP.

Final Words

Microservices software systems are GREAT to consider to expand business capacity.

This software development approach solves most problems caused by monoliths and makes the programmer’s life easier, especially with its automated deployment mechanisms. 

We hope this article guided you in choosing a great programming language for functional programming. Wait no further, and try microservices in your next project!

If you want to get into Android development, you’ve probably spent a long time polishing up your Java knowledge. For years, Java was the main language for making Android apps.

But this has changed recently, with the adoption and rapid support of Kotlin. This NEW programming language has become increasingly prevalent for mobile apps.

You may not have heard of this new language or wonder, Why is it worth learning? After all, Java still exists.

We’re here to cover everything about Kotlin, so read on!

What Is Kotlin Programming Language?

Kotlin is an open-source programming language whose first iteration was released in 2011. Despite being relatively young, Kotlin enjoys a large following of newer developers.

In 2017, Google announced that Kotlin would replace Java and become the preferred language for Android development. This led to a boost in its popularity and quick adoption.

Thanks to its object-oriented and functional programming capabilities, Kotlin can cover Java’s shortcomings.

Kotlin also allows developers to create cross-platform apps even without an IDE. This language’s developer JetBrains is also behind the IntelliJ IDEA IDE, which fully supports Kotlin.

What Are the Features of Kotlin?

Here’s a small preview of what to expect when you begin learning the Kotlin language:

• Statically typed language for functional programming
• Fully compatible with the Java Virtual Machine (JVM)
• Can create code usable across different platforms
• Has safeguards to reduce any potential crashes
• Supports lazy-loading for faster startup
• Explicitly-defined data class members

What Is Kotlin Used For?

Kotlin is a substitute programming language for Java, but note that this doesn’t mean Java has become obsolete. Java is still receiving regular support from its developer.

Newer app developers use Kotlin for their mobile application development, but this programming language can be extended to server-side web development and client-side web development.

This language also shows its worth in server-side web development because of its integration potential with Java.

Large companies have slowly begun moving portions of their apps to Kotlin, file-by-file. This is only possible because their code is so similar.

Kotlin and Mobile App Development

Since Kotlin is now Google’s preferred language for mobile development, it’s easier than ever for newer developers to get into the mobile world.

If you’ve ever made a single parameter mistake in Java and need to fix it quickly, you know how stressful it can get. Whether you’re developing for web or mobile, Kotlin’s got your back.

This new language makes it easier to access web APIs with less writing and with only one programming language.

Kotlin’s developer JetBrains also makes Android Studio, which means Kotlin gives you full support for the Android platform!

If you don’t want to let go of the languages you already know, that’s okay! Other mobile app development languages are available, like Python or C#.

What Are the Benefits of Using Kotlin Code?

Switching to Kotlin from Java may disorient you if you don’t know too many programming languages. Fortunately, you can keep using both languages.

But if you decide to make the switch, here are some benefits of switching to Kotlin code:

#1 Quickly-Growing Community

Since its adoption by Google, the number of Kotlin developers has grown recently. New developers have more resources to fall back on if they encounter any issues.

This new language’s large community is a good safety net and source of help for newer developers. However, note that Kotlin is still fairly young.

Kotlin can’t match the long and concise documentation that Java can offer, but its rising popularity in both web and Android development means it may soon catch up.

#2 Cleaner Code

Java’s age can be seen through some of its older requirements, like long blocks of code that need lots of repetition. This “boilerplate code” is necessary but painful to write and write.

Kotlin handles this issue by having less redundant code by default, letting developers do more with less work and reducing the time spent copying and pasting boilerplate.

Take the main function, for example: In Kotlin, it reads ad “fun main(),” but it’s main() in Java. Despite both functions meaning the same thing, it’s a little more clearly defined in Kotlin.

#3 Time-Saving

Kotlin saves time by being inherently simpler than Java. It’s everything a sequel should be because it keeps everything good with Java but makes up for its blind spots.

Thanks to its clearer nature and ease of use, you can access powerful browser functions with this language using less code than a similarly-designed Java app.

While it won’t replace the original, Kotlin’s multi-platform uses make it flexible. A dev team can bypass having to rewrite their program for multiple embedded systems with Kotlin.

#4 Removes the Risk of Null Variables

This is one feature of Kotlin that Java veterans will appreciate. Kotlin has null safety, meaning every Kotlin variable is non-nullable by default.

So even if you accidentally leave a nullable expression in your code, your program won’t crash after you launch it!

Kotlin reduces the potential of null reference exceptions when running your code, like null pointer exceptions in Java. Add a safe call operator to keep your program safe from crashes.

#5 Faster Asynchronous Programming and Extension Support

Sometimes you’ve mistyped a function, but you only catch it after the current program version’s already gone live.

While Java offers some support for asynchronous programming, it’s often difficult and tedious to pull off properly. Kotlin takes the burden off you with the help of coroutines.

In case you’ve misdefined a class and need to add an extension function in Java, it’s fairly difficult, but Kotlin can clearly define the new class without problems!

Kotlin vs. Java Code: What Are the Differences?

Despite being similar, Kotlin and Java are still different languages. Here are some ways they differ:

• Java has more extensive Android documentation than Kotlin
• Kotlin can achieve the same results as Java with less boilerplate
• You don’t need semicolons in Kotlin, unlike Java
• Kotlin’s simpler and easier to work with

Kotlin’s Interoperability

Kotlin as a language focuses on interoperability with its predecessor, which means it’s possible to rewrite older apps into Kotlin slowly.

Thanks to its interoperability, any standard Java libraries that your program requires are easily accessible through Kotlin.

Kotlin Being Interoperable With Java Code

Native code written in Java can be quickly moved over to Kotlin because these languages are fully interoperable.

This lets developers reuse their pre-written work instead of having to rewrite it in multiple languages whenever they move platforms.

Kotlin Being Interoperable With JavaScript

So we know that Kotlin’s usable with Java, but is it with JavaScript? Fortunately, yes!

Any code you’ve initially written in Kotlin or libraries used can be transpiled into JavaScript with kotlin.js and kotlin.multiplatform plug-ins.

Why Should You Learn Kotlin Code?

This object-oriented and functional language is worth learning whether you want to develop cross-platform mobile applications or enter the field of web development.

It’s easy to pick up, has a growing base of developers, and is fully supported by Android Studio and Google. Kotlin is future-proof and heavily supported, so it’s worth learning.

Note that it’s strongly typed, so variables like the var keyword or val keyword must be clearly defined before they can be used properly.

Frequently Asked Questions (FAQs)

We’ve discussed the basics of this programming language, but if you have more questions, you might find them below:

Is Kotlin the Same as Java?

You’d be forgiven for assuming that Java and Kotlin are the same language since Kotlin’s being pushed as the “replacement” for Java. They’re similar but not the same.

Kotlin encourages speed and precision over Java, plus it has some features that Java lacks, like the ability to create extension functions and Lambda expressions.

Java’s enduring prevalence makes it hard to get away from this programming language, but Kotlin has been slowly gaining ground over the last few years.

Anyone who wishes to support Kotlin will find enough resources to get started but won’t find the same amount of resources as Java devs.

Is Kotlin the Same as Python?

Python is one of the “other” languages often touted as easy for beginners to pick up, so it’s usually compared to Kotlin.

These programming languages are different at a fundamental level because Kotlin offers greater support for functional programming in contrast to Python.

Python has more support for object-oriented programming than Kotlin, and it’s more popular than Kotlin for now. This may change in the future.

Is Kotlin Difficult to Learn?

Despite its powerful functions and increasing prevalence in Android development, Kotlin isn’t too difficult to pick up, as long as it’s not your first programming language.

This is mainly because Kotlin has one notable improvement over Java. It eliminates the need to create longer code blocks so developers can do more with less written work.

However, note that it’s important to learn Java before Kotlin to fully appreciate the ease of the newer language because, otherwise, Kotlin code may confuse you.

Is Kotlin Frontend or Backend?

Though typically used to write front-end code, Kotlin is ideal for full-stack web development.

Because it’s so often praised for its front-end usability, Kotlin is sometimes mistaken as unusable for back-end code.

However, Kotlin’s full integration with Java is also ideal for back-end web development. Note that your app may need extra work for server-side development if you use Kotlin.

Developers who use more than one language for their websites will be fine, too, since Kotlin compiles both languages into either Java or JavaScript. This common code is usable cross-platform!

Conclusion

Kotlin has quickly become one of the most popular programming languages, thanks to its support base. Android developers no longer have to stick with Java for their coding needs!

This modern programming language is ideal for multiple platforms and will produce powerful and flexible programs.

We hope we answered your questions about “what is Kotlin?” and what it’s all about.

Take the plunge and join the Kotlin team!

Did you know that behind Apple’s showpiece is software with a bunch of codes? 

Before you can even send iMessages, take selfies, do social media, and other stuff on your devices, programmers first boot them to run their features. 

For the Apple ecosystem, they used the Swift programming language to command the platforms to perform their functions. 

If you’re curious about how the Swift code works, stick with us! We’ll walk you through the basic facts you must know about this popular language before you start coding. 

Swift Programming Language: What Is It?

Swift is a programming language developed by Apple. It’s a relatively easy-to-use coding system that doesn’t require a steep learning curve. 

The Swift code based its techniques on Objective C. Yet, it was able to include shorter syntax and easier readability. It even promotes uncomplicated maintenance and safety. 

Its modern practices are also similar to those that are seen in other programming languages like JavaScript, Kotlin, and Ruby but have more modern features developers love. 

Mainly, this language is designed to support the native iOS development process, specifically with iPadOS, watchOS, tvOS, macOS, and iOS. 

However, developers also used this language for clean, safe, easy-to-code, and concise iterations for other operating systems, including Linux and Windows. 

Swift has grown in popularity fast across different platforms because it is safer and easier to understand than older programming languages like Objective C.

The code’s format also appears very clean, aiding programmers to commit lesser mistakes. 

History of Swift Language

Chris Lattner and other Apple programmers started the development process of the Swift code in 2010.

As previously mentioned, they integrated techniques and language ideas from Objective-C, Rust, Haskell, Ruby, Python, C#, CLU, and other languages.

In 2014, Swift was formally introduced as a new programming language during the Worldwide Developers Conference (WWDC).

Registered Apple developers were able to get access to the beta version of the language during the conference. They also got the 500-page programming manual through iBooks and the Apple website.

The first version came out in September of the same year, together with the introduction of the Gold Master of Xcode 6.0 for iOS. This was then followed by version 1.1, together with Xcode 6.1.

The programming language quickly matured over time as they released version after version in short intervals. To date, there are over 56 versions of Swift, and the latest iteration is Swift 5.6.3.

Development for Swift still continues, but programmers slowed down their pace in terms of setting a release date for version 6.

The language truly had its humble beginnings. Yet, we can’t deny that since its launch, developers have learned to love Swift.

In the year 2015, it was recognized as the Most Loved Programming Language. It was also able to clinch the second spot the following year.

With its ever-growing popularity, Swift was even considered by Google as their primary language for Android, which is still running on Google OS.

Main Features of Swift

What makes Swift special and distinct from other languages are its features. These enabled Swift to offer a great coding experience and results to many programmers. 

Automatic Reference Counting (ARC) 

Memory usage management can sometimes be a hassle for app developers. But when you write Swift code, it’s the least to be worried about.

The Automatic Reference Counting (ARC) functions as a memory management tool, which aids in AUTOMATICALLY tracking and managing the app’s memory usage.

Closures Unified With Function Pointers

Closures in Swift are like the blocks in Objective C or lambda in other languages. These are self-contained functionality blocks that can be passed on within your code.

Function pointers, in this case, carry specific behavior or address of a function and point to code vs. data.

Developers find this feature helpful because they’re able to signify behaviors without having to write the code.

Tuples and Multiple Return Values

With Swift, it’s easier to return multiple values compared to getting only one value at a time.

Specifically, Tuples enable programmers to share and create value groupings. Default values can be grouped as a single entity from a function, appearing as prearranged elements following a sequence.

Whenever there’s a need to return multiple values, Tuples can be utilized to do so as a single value.

Generics

This feature allows programmers to locate mistakes in the Swift code by enabling types as parameters.

With Generics, developers can come up with reusable functions and types that are flexible enough to work with any type based on the defined requirements.

Fast and Concise Iteration Over a Range or Collection

Developers don’t have to worry about their data being all over the place.

Swift is able to identify the data used in arrays. Hence, it can assign elements in an array, which are groups containing similar values. 

This is very helpful with referencing and indexing.

Structs that Support Methods, Extensions, and Protocols

When coding with the Swift ecosystem, developers are given the freedom to include additional functionalities with extensions. They can also extend types without having to access the original source code.

Structures or classes can also be defined in a single file. The external interface, on the other hand, can be made available for other code use.

Additionally, Swift can also take care of defining protocol behaviors instead of having them as global functions or individual conformance.

Powerful Error Handling Built-in

Error handling in Swift is IMPRESSIVE. It helps programmers to easily determine the consequences when errors occur.

In this case, Swift supports error handling through throwing, catching, propagating, and manipulating errors that are still recoverable in a runtime.

Advanced Control Flow with Do, Guard, Defer and Repeat Keywords

Swift uses control flow statements in controlling how a program functions, including control transfer statements, loop statements, and branch statements.

Besides these, there are also advanced statements you can use: do, guard, defer, and repeat. Do statements are used to introduce scope, guard is for managing errors, defer for clean-up actions, and repeat to allow block codes to run multiple times.

Functional Programming Patterns

By taking an array, the function is able to compute a value repeatedly. This creates a pattern that can be returned to the code requiring such action.

Swift Playgrounds and Swift REPL (Read-Eval-Print-Loop)

To learn Swift the fun and simple way, you can download Swift playgrounds on your iPad, and you can also access it in Xcode.

Whenever a programmer types a code, they can view the results from the side of the screen. Outcomes from the code can be viewed as graphics, lists, or graphs of values.

There’s also a timeline assistant where a complex view of the code’s translation can be accessed. This is a great feature, especially for doing new UI.

When the code already runs smoothly in Swift playgrounds, it can simply be exported to the same project where it must be integrated.

Interaction with Swift code is also enhanced when used in Terminal or within the debugging console of Xcode’s LLDB. 

Additionally, Swift syntax can be used to evaluate a running application or when writing code in a script-like development environment. 

Swift Package Manager

The Swift package manager is a tool used to build, run, test, and package Swift libraries. It’s utilized to distribute the libraries as well as the Swift source code to the Swift community.

Swift packages include custom commands. These are handy for programmers, especially in providing additional tools for a project.

These configuration packages are mainly developed in Swift. Hence, configuring targets, declaring products, and managing dependencies become easier. 

Objective-C Interoperability

The Swift code co-exists with existing Objective C files within the same project. This allows developers to easily adapt and switch to Swift in implementing features and functionalities.

In doing so, programmers only need to get full access to their Objective C API.

Swift Version History

Despite being a young language, Swift was able to mature fast with several iterations in a span of 8 years. To date, there are already 56 versions of the language that offer new features and improvements from its initial iteration. 

But, out of the 56, Swift underwent major revisions from 2014 to 2022 in 21 versions. They constantly add and change functionalities and dynamics for a smoother Swift experience. 

A LOT of changes were made from Swift 1.0 in August 2014 to the latest version, which is Swift 5.7. The last update was made in June of 2022. 

All these were made possible with the developer’s years of experience in innovating Apple platforms. 

Swift 2.0

During the WWDC in 2015, version 2 of Swift was introduced, along with additional features and improvements.

Developers made Swift an open-source programming language for Linux, plus they improved its error handling mechanisms with keywords.

They also made this version safer with the #available blocks. This allows you to wrap the codes to be executed on systems. 

At the same time, generics and nillability for existing Objective C frameworks were introduced to make the coding interface better in Swift.

Swift 3.0

In 2016, another major release was done for Swift. Developers were able to integrate new features like additions to the Linux port and package manager for easy-to-manage dependencies.

They also REFINED the core language and standard library to eliminate NS prefixes, as well as holdovers from Objective C.

Swift 4.0

The beta version of Swift 4.0 was introduced during the 2017 WWDC together with Xcode 9. In this iteration, developers added a new codable protocol for easy data serialization.

They also enabled string literals to break down into multiple values or lines using the “”” declaration. These are used to open and close multi-line strings.

Additionally, strings were made to become a collection of characters compared to previous versions.

Swift 5.0

The major improvement seen in Swift 5.0 is the integration of ABI Stability and Binary Compatibility. With this, the Swift libraries are already incorporated in macOS, tvOS, watchOS, and iOS releases.

They also added improved standard library additions that are meant for performance improvements.

The Pros of Swift

• Safe – Being designed for safety, Swift eliminates entire classes identified as unsafe code. It also utilizes value types, including arrays and dictionaries, allowing programmers to copy the type without the risk of modification. Default swift objects are never nil with the use of a Swift compiler. It stops the creation or usage of nil with a compile-time error. This makes writing code safer and cleaner and, conversely, prevents runtime crashes in iOS apps. But, in cases where nil is appropriate, the options functionality operates. The Swift syntax forces the programmer to safely use “nil” with “?” to signify that they understand the behavior.
• Fast – Even from its older versions, Swift already has lightning-speed performance. The assembly language is transformed into a machine code that utilizes every feature of modern hardware. This language is also a predecessor of C and Objective C-based languages with more improved functionalities, and it provides object-oriented programming with classes, protocols, and generics. Hence, you can expect a top-notch performance that caters to most software demands.
• Modern – Swift is the latest programming language developed by programmers through their experiences in developing Apple platforms. Compared to other languages from previous generations, Swift is easier to maintain and read. A Swift project is also cleaner and less prone to errors with the support of inferred types and modules. Memory management is also more automatic than in traditional languages.
• Easy to Learn – Beginners can learn swift code even if they have zero knowledge about coding. First-timers can download Swift playgrounds on their iPad to get a brief introduction and feel about what the language is like. It’s also a fun and interactive way to learn Swift. This language is also made to be intuitive and user-friendly with all its features – lightweight closure syntax, inferred types, modules, and automatization.
• Accessible – Swift is a free language, and it’s widely available for anyone to use; plus, it’s also open source. Swift, as an open-source programming language, can be navigated using third-party tools. There’s also a community, as well as resources, to help beginners explore the language.
• Enables Cross-Platform Support – By saying that the language is open source, Swift does not only support Apple platforms. It can also support other major platforms like Linux and Windows.

The Cons of Swift

• Frequent Updates – Given that Swift is not as mature as previous languages, it’s expected that there are still many things to improve. Although Swift is already functional and can top off its predecessors, its resources and capabilities are not as robust as the others.
• Weak Support on Other Platforms – Even if developers declared that it doesn’t solely support Apple’s platforms, it’s still best to utilize it in developing iOS applications rather than Linux and Windows.
• IDE Support – IDES lack support for earlier iOS versions. It also falls short in providing support for syntax highlighting, compiling, autocomplete, and refactoring.

What Is Swift Coding Language Used For?

Primarily, Swift codes are used in programming the software of Apple platforms. It’s tagged as an intuitive language used for developing applications in iOS, macOS, tvOS, and watchOS.

As previously mentioned, Swift was developed to replace Objective C in the development of Apple’s platforms.

The Swift code is the primary language utilized in the following:

• iPhone and iPad application development
• MacOS desktop application development
• Apple Watch development
• Apple TV development

Swift is also an open-source language. Hence, aside from Apple, it also provides development support for other major platforms like Linux and Windows.

Since its 2.2 version was released in 2016, it has been able to function in the Linux operating system. In 2020, with version 5.3, Swift started to operate with Windows.

Swift and iOS Mobile App Development

One of the popular uses of Swift is mobile app development for iOS. Previously, applications in iOS were made through Objective C.

But, since the introduction of Swift, MANY developers have opted for it, given its modern features, easy-to-use interface, and user-friendliness.

What Makes Swift Code So Great?

There is A LOT to love about the Swift code. Mainly, its features are the major takeaways when this language is compared to its predecessors.

The main features of Swift that were discussed in the previous sections make writing code easy. It also makes it uncomplicated to understand and manage, unlike other languages.

Yet, despite its simplicity, it still allows for maximum control for the programmer to achieve what they need in a true system language.

Furthermore, Swift supports inferred types to make the codes appear cleaner and reduce the probability of mistakes, while modules eliminate headers to make way for namespaces.

Semicolons are also things you can ditch with this language, given that Swift offers automatic memory management.

Generally, using Swift is key to reducing the development efforts required to suffice complicated codes. Yet, it still offers GREAT results and a smooth programming experience.

Swift vs. Objective-C: What Are the Differences?

Objective C was the language used in developing OS X and iOS applications back when Swift was not yet introduced in 2014.

But, after the creation and introduction of Swift to the world of programming, Objective-C’s popularity declined. However, there are still developers who use Objective C to maintain C-based platforms.

It’s believed that Swift is a more reliable option than Objective C, given that the former is safer, faster, and easier to maintain and code.

If you’ve started coding with C-based languages, there’s nothing to worry about. Its interoperability allows Swift to co-exist with C-based files, gaining complete access to its APIs.

Is Swift the Same as Objective-C?

Swift and Objective C are two DIFFERENT languages, even if they have common usage. They both have distinct mechanisms and functionalities that differ from each other.

In navigating Swift, programmers have the option to use Swift playgrounds, XCode, Cocoa Touch, and other tools. But, with Objective C, development only takes place in X Code. 

Recent versions of Swift are relatively FASTER than Objective C, given its modern language with a simpler syntax. It also receives constant updates signifying that it’s still being improved by developers.

Objective C-based applications, however, can support any version of iOS, even the previous ones. On the other hand, Swift requires at least iOS 7 iPhones and iPads. 

How Can I Start Using Swift Programming Language?

If you’re looking for a beginner-friendly mobile app development programming language, Swift is a great place to start!

This language is probably one of the easiest to learn and navigate, especially for people with zero knowledge of coding. 

The first thing you need to start using Swift is a Mac, plus the device where you’ll develop the Swift apps. For example, if you’re going to create a program for iOS, you’ll need an iOS device. 

You also need to download and install Xcode, Objective C, and the Swift compiler, then proceed to launch Xcode and choose the language you’ll be using. 

Open the Swift playgrounds and REPL to start learning and exploring its features. This is a GREAT environment to start coding with Swift. 

Once you’ve gotten the hang of it, you’re all set and ready to develop your applications! 

Frequently Asked Questions

Swift has become helpful to many developers, especially in reducing development efforts. It’s also a great language to learn for both beginners and experts. 

In this article, we’ve discussed many things you’ll need to know about Swift. But if you still have questions, don’t worry! We’ll answer a few more for you:

What Kind of Programming Language Is Swift?

Swift is an open-source, general-purpose language. It’s engineered to have modern features which are safer and easy to navigate and maintain for many programmers. 

Is Swift Frontend or Backend?

Swift can be used in developing the front end (user’s screen) and back end (server) of your programs. Using the language for both allows for extensive code sharing, which is helpful for many programmers. 

Is Swift in Demand?

Swift is pretty much in demand, given that Apple platforms remain popular. Developers using Swift are also sought by many IT leaders.

Should I Learn Python or Swift?

If you’re a beginner who just started with coding and you want to develop Apple platforms, Swift is the best language to learn. 

But, if you’re more interested in artificial intelligence, prototyping, and backend development, Python is the top choice. 

Does Facebook Use Swift?

Facebook started using Swift to make it easier for programmers to develop and improve their iOS apps, especially the UX. 

Does Google Use Swift?

Primarily, Google uses JavaScript and TypeScript for frontend development, while C++, Python, Golang, and PHP are for the backend. 

Final Thoughts

Despite being a young language, Swift still proved its worth in the world of programming. It has a lot to offer that other languages can’t.

If you’re planning to develop applications for Apple devices, it’s high time to learn coding with Swift. It can also cater to other operating systems like Linux and Windows. 

Transitioning from Objective C to Swift is also a no-brainer! It can co-exist with C-based files, making it easier for you to operate in both.

Explore Swift and start your mobile app development journey!

The Java programming language is a multiuse, multithreaded language. It has also been one of the foremost computer programming languages since Sun Microsystems developed it in 1995.

The Java development platform is a workhorse of the digital world. It gives would-be Java developers the ability to do almost anything they set their minds to, as long as they know how to use it.

Let’s answer the question: “What is Java?“ We’ll review the Java platform and the different Java programs and see how the Java language compares to other programming languages.

What Is Java?

To answer the question, “What is Java?” we need to look at what it was made to do, who it helps, and what services the programming language offers to Java developers today.

Java is an object-oriented programming language with millions of users across the globe. It is well-known for being a platform-independent language and a computing platform.

Java software is a network-centric language connected and shared across networks like the Internet. It’s not tied to a specific software platform. Rather, it works on any system that can support Java.

Java is a means by which human programmers can communicate with machine code. Java is powerful because it works as both a compiled language and an interpreted language.

Java is well-loved because it is reliable, secure, and quick to understand and use. This makes Java source code ideal for game-developing software, cloud computing, big data analytics, and even artificial intelligence.

Users looking to run the Java programming language can rely on the Java Platform, a collection of Java programs that allow programmers to work with Java code and make it work on their computers.

By understanding the Java platform, programmers can run Java programs across all web browsers. They can do front-end (Java API) and back-end (Java Virtual Machine) programming.

The History of Java

The more than 30-year history of Java began when a team of Sun Microsystems engineers, led by James Gosling, the father of Java, initiated the Java programming language in 1991.

Java 1.0 was revealed to the public in 1996, offering no-cost run times on popular computing platforms. After stringent rewriting of the Java code, Java 2.0 was released for widespread use.

The Java program continued the next year when Sun Microsystems formalized Java. While the Java software development was proprietary, Java implementations were free for the first few years.

Sun generated revenue by selling licensed products like the Java Enterprise System. This allowed them to release the Java Virtual Machine (JVM) as free, open-source software.

This process was completed in 2007 when the JVM’s core code was completed, and the Java Virtual Machine was ready for full, open-source distribution.

These developments allowed the Java language to spread across mobile devices, web applications, operating systems, and enterprise software.

The Oracle Corporation currently owns the Java program. Today, it is one of the most accessible and widely-used programming languages on the planet.

Java Version History

To answer the question, “What is Java?” we need to look at what Java was.

Like other programming languages, Java has undergone several tweaks, iterations, and versions to get to its current form.

This software development has allowed this language to become the backbone of machine code and web services across the Internet. Let’s revisit some of its past incarnations:

Java Versions	Release Date
JDK Alpha & Beta	1995
JDK 1.0	Jan 23, 1996
JDK 1.1	Feb 19, 1997
J2SE 1.2	Dec 8, 1998
J2SE 1.3	May 8, 2000
J2SE 1.4	Feb 6, 2002
J2SE 5.0	Sep 30, 2004
Java SE 6	Dec 11, 2006
Java SE 7	July 28, 2011
Java SE 8	Mar 18, 2014
Java SE 9	Sep 21, 2017
Java SE 10	Mar 20, 2018
Java SE 11	Sep 25, 2018
Java SE 12	Mar 19, 2019
Java SE 13	Sep 17, 2019
Java SE 14	Mar 17, 2020
Java SE 15	Sep 15, 2020 (latest Java Version)

Main Features of Java

Java has plenty of features that make it stand out compared to other similar languages. Let’s look at some of them below:

• Java is an object-oriented programming language, which makes it one of the easiest languages to learn. It works best for creating object-oriented applications.
• You only need to write the code once, which works across multiple platforms. Code written on one machine will work effortlessly on another.
• It is network-centric and flows across the Internet.
• The Java compiler allows for premier error detection, saving you time, effort, and energy. It can save a project in danger of failing due to bugs.
• Java is secure and hard to exploit because it works independently of the operating system it’s used on.
• Java compiler has the power to compile source code to Java bytecode. Java Virtual Machine then turns this into machine code your computer can understand.

Java is extremely useful and offers accessibility like no other language. It makes it possible to do whatever you need to do digitally.

What Is Java Programming Language Used For?

Java can be used for a whole host of applications. However, its main uses are in the Internet of Things (IoT), big data, cloud computing, artificial intelligence, and video game development.

OS systems like Android are built on Java. This makes it indispensable for programmers working on Android devices and those looking to make and sell Android apps and services.

Java is famous for being WORA (Write Once, Read Anywhere), which allows it to be used on cloud-based applications across the Internet. It lets you view your data on any laptop, tablet, or smart device.

The system also processes mind-boggling amounts of data in real time. This allows it to be used for real-time and satellite-based apps and to create AI programs and robotics.

Java in Mobile App Development

Java is most famous to casual tech fans because of its ability to develop languages for mobile applications. It allows you to write code for a website, a mobile app, a social media platform, and more.

In fact, Java application servers are used by megacorporations like Meta, Google, and Spotify to code their mobile apps and ensure that they work every time, delivering your searches in nanoseconds.

Java application development is serious business for Java, a major part of what makes it great. Thanks to Java, even casual programmers and beginners in mobile app development can create professional-level apps for mobile devices.

The Components of Java Programming Languages

A Java application is made of a computer language the machine can read and the source code, which the human programmer can read. It’s Java’s job to translate your orders.

These 3 components are designed to help you, the programmer, instruct the computer on what needs to be done and how to do it. They ensure that the program works and that you experience a successful run.

Java API

Java APIs (Application Programming Interfaces) are the front-end communications between the programmer and the platform. They define the syntax and semantics of the Java language.

These act as a database for pre-existing code that users can cut and paste into theirs to ensure functionality. It helps automate the process and helps guarantee that your code runs smoothly.

Java Virtual Machine (JVM)

The Java Virtual Machine handles the back-end communication between Java and the hardware. It is an intermediary between your code and the machine you wrote it on.

This code, otherwise known as the Java byte code, is translated into machine code by the JVM. This allows Java bytecode to be run on different systems and has multiple frameworks for users.

Here are some steps that show how the JVM works:

1. The user’s source code is saved as a .java file.
2. Java compiler converts the code into bytecode as a .class file.
3. The code can now be understood by the JVM.
4. The JVM, which is found in the RAM of your OS, then converts the bytecode into machine code, which the computing device then knows how to process and execute.

Java Runtime Environment (JRE)

Java Runtime Environment is designed to run Java programs. It is essential for programmers who need access to as many pieces of Java as possible, such as Java applets.

This is because it contains the JVM, loader class, and class libraries needed to make the Java suite work across devices.

Java Development Kit (JDK)

The Java Development Kit is used to make Java applications and applets. It helps you write and execute Java-based programs and works on all major OS systems.

If you plan on working heavily with Java as a programmer or an app developer, you will need this kit to run the entire Java platform.

The Different Types of Java Platforms

Here are the four main types of Java programming platforms. Let’s look at what makes them stand out and what they work best on.

Java Platform, Standard Edition (Java SE)

The Java SE is the basic version of this programming language, equipped with the essentials you need to start coding. It sets the definitions of objects and comes with the necessary functionality.

The Java SE is useful for developing graphical user interface (GUI), database access, and networking.

Java Platform, Enterprise Edition (Java EE)

The Java EE offers runtime environment and API for scalable network applications. This makes it ideal for use across departments and businesses to increase interoperability.

Java Platform, Micro Edition (Java ME)

Java ME offers an API and virtual machine designed to run on mobile phones, tablets, and smaller devices. It is a specially-designed version of the Java platform.

Java FX

Java FX allows for internet application development using high-level media engines and graphics. It’s designed for modern, higher-end clientele looking to connect network data sources.

The Benefits of Java Programming Language

Every Java programmer knows how beneficial Java can be. The differences between them may be what kind of benefits they see. After all, a versatile language can mean different things to different people.

Java offers plenty of technical benefits to users, from its interoperability to its scalability, to its adaptability. It works across many different systems, devices, and platforms, and works well.

It allows you to migrate to any computer platform you choose to work on. You do not need to make cross-platform code adjustments.

It also works in the business setting, providing the shortest learning curve among languages and increasing smart connectivity. You also have a large pool of Java programmers to recruit from.

This accessibility makes Java an undeniable asset. It’s low-risk, high-reward, offering users who understand it the ability to make and create any program or platform they set their minds to.

Why Should You Use Java Language?

Java remains one of the most enduring programming languages on the planet because of its broad range of app development tools, ease of use, widespread availability, and more.

Java technology can be felt everywhere, from the apps on our mobile phones to our favorite websites and search engines. Even operating systems like Android can be managed using Java.

Individual users and amateur coders can benefit greatly from Java thanks to its built-in functions, support and learning materials, security, and platform independence.

No matter what platform you use or the program you plan on making, Java has the tools to help you make your dreams come true. It handles both back and front ends and is only limited by your understanding of the language.

Frequently Asked Questions

Still have questions regarding how useful Java is for non-coders? Wondering how influential Java can really be? Well, we’ve got some answers for you.

Let’s examine some common questions concerning Java applications and see why this language is so popular:

What Type of Language Is Java?

Java is a high-level general-purpose, class-based, object-oriented language. This means that well-defined boundaries make each object easier to implement and the language easier to code with.

Java Virtual Machine defines its binary format and is compiled to the byte code instruction set.

Java’s syntax is similar to C++ but is easier to work with. It is free to download for personal and professional use.

Where Is Java Used in the Real World?

Java is the 5th Most Popular Programming Language in the world, with almost 4% of the Internet being made up of Java applications. It’s used by almost every major digital giant and megacorporation.

In addition, Java has its uses in almost every field of research and business, with agencies like NASA and Google relying on it. Even day-to-day programs like Google Docs use Java code.

Is Java Easy for Beginners?

Creating Java applications is quite easy, as the system is well-structured and works across platforms. Java libraries offer code examples that ensure you don’t have to start from scratch.

Whatever isn’t automated in the process is easy to master. This is thanks to a passionate community that collaborates and shares answers, meaning that you have access to a wellspring of Java knowledge.

Why Is Java Popular Today?

Java’s popularity can be attributed to its easy and safe use. Its longevity means it’s been dissected and studied, which means there are plenty of study materials you can use to learn it.

It’s also got an active community that’s always willing to share findings with new users. Combined with its quality development tools, new users know they’re in good hands and that the platform is setting them up for success.

Which Companies Use Java?

Support developers and software engineers across Silicon Valley swear by Java. This is because it’s understood by almost every operating system and works excellently as machine code.

Some companies that use Java are sales companies like Amazon, digital platforms like Google, Meta Platforms (Facebook), Microsoft, and streaming and service companies like Netflix, Uber, and Airbnb.

These are some of the largest companies in the world using Java for their web services. The language has proven itself indispensable in shaping the modern Internet and advancing technology as a whole.

Which Apps Use Java Code?

Because most end-users have no clue what programming language their favorite apps use, they’re often pleasantly surprised to know their mobile applications were made using Java.

Many of our favorite social media apps like Spotify, Twitter, and even Minecraft, one of the biggest video games ever made, were built using Java source code.

Enterprise applications like CashApp and ThinkFree Office were designed using the Java program. An operating system like Opera Mini can be made on the Java platform.

Conclusion

Java is an object-oriented application development language that allows users to create the code necessary for application development on everything from enterprise applications to natural language processing.

Java development continues to this day, as more and more companies and individuals abide by its processing power and ease of use.

It will continue to stay popular and relevant for decades to come.

JavaScript and HTML/CSS are very familiar programming languages to people even outside the coding world.

However, other programming languages deserve some recognition, too — one of them being Objective-C.

Basically, this language is used for any iPad and iPhone app.

In this article, we’ll talk more in-depth about what it is and why it’s something you might be interested in trying to learn.

What Is C and Object-Oriented Programming Language?

Let’s backtrack for a bit first.

The first C programming language was originally developed in the 1970s. But it was only in the 1980s when it was implemented in the first mainframes and microcomputers.

Since then, it has influenced other modern programming languages. In fact, you can even say C is the grandparent of object-oriented programming.

Object-oriented programming involves a group of languages that gives you a clearer structure to work with since you’re mimicking real-world building.

Other languages will give a computer a list of tasks to execute. Meanwhile, object-oriented programming languages let you handle what you can call a “virtual set of legos.”

You can group these legos and give each group an instruction to carry out.

For instance, you can use the first group to make just the stem of the flower, then use another group to make the flower itself.

What’s great about this is you can isolate the “groups of legos” from one another. This makes it EASIER to find and fix bugs. It’s also simpler to reuse codes in future programs.

C vs. C++ vs. C# vs. Objective-C: What’s the Difference?

The C family of programming languages is different from one another. We’ll discuss more below:

C

C is a procedural programming language based on ALGOL, BCP, and B.

It quickly grew popular because it was much easier to understand and use compared to other programming languages in those times. C allowed developers to work with hardware at a low level while still using readable syntax.

This is important since low-level languages are more complicated since they use more symbols.

But C became the FIRST language to create an operating system (Linux’s version 4) without using Assembly — the then-lowest-level language that can be used to interact with computer hardware.

Aside from the other C languages, C has served as a guide for other languages too! In fact, JavaScript, Java, Swift, and Golang even use the same syntax that C started.

C++

C++ is a general-purpose language derived from the C programming language. It was created to add instance methods and objects to C.

This language experienced widespread adoption because programmers believed that object-oriented programming is more effective and efficient to use when dealing with big software projects.

Because of that, C++ is the most common replacement for C — in fact, it’s usually a direct replacement.

Programmers typically use C++ when they need low-level control over a system’s resources while maintaining high performance.

Thanks to that, the language is mainly used for creating operating systems, embedded systems, and web browsers. Some modern desktop applications that use C++ are Adobe programs and Microsoft Windows OSX.

Most C++ developers actually work in the game development industry.

Because of the large user base, C++ has a huge number of frameworks and libraries that can allow developers to use the language for more advanced projects — like high-performance graphics and audio digital signal processing!

C#

C# was inspired by C rather than being directly derived from it. It’s actually a combination of C and Java.

Microsoft initially made C# a closed-source language to develop programs within their .NET framework.

But the company eventually released it as a free and open-source language that can be used for Linux and Mac OSX. 

C# is excellent for general object-oriented programming projects, regardless of their scale.

Nowadays, C# is widely used in game development and web service development.

Objective-C

C and Objective-C are very similar. In fact, Objective-C can do EVERYTHING that C can — although it creates a thin layer of a different syntax for creating and handling objects.

This object-oriented language can dynamically load code. Thanks to that, you can create programs of any scale.

It’s the foundation of OS-X and iOS and was mostly exclusive to Apple products.

The way Objective-C executes a code referenced by a method is different from other C family languages.

In all these languages, the method that you call becomes connected to the part of the class where it’s located when you compile the codes. Meanwhile, in Objective-C, the message is sent to the class during runtime and resolved while the app is running.

When Swift was released, the two languages became interoperable. This means that the two can run alongside each other in the same application.

What Is Objective-C Programming Language?

C and Objective-C are almost the same — except Objective-C programming language is the object-oriented language version of C language.

Despite that, developers Brad Cox and Tom Love used class libraries and the Smalltalk language as a guide for their concepts. 

NeXT first used it to make the custom programming platform OpenStep. Apple acquired NeXT later on and utilized OpenStep for their operating system.

Because of that, Objective-C became the main programming language for Apple products.

Main Features of Objective-C

It’s thanks largely to the Objective-C language features that have appealed to developers since the 1980s.

Below are some of the main features of this language:

• Creation of data structures – There are two kinds of data structures: classes and objects. There’s no such thing as a “factory system” here. Every class is a meta-class that’s automated and managed by runtime. If you want to create an object, you just need to send a message to the class that you want to instantiate. Aside from that, you can extend classes WITHOUT using subclassing, thanks to categories.
• Inheritance – Inheritance lets you create classes even when you already have an existing one! This feature also allows you to reuse codes. You’ll also be able to autonomously prolong the original program and create a modification while keeping the same features.
• Encapsulation – This feature lets you limit other users from having DIRECT access to state values of the variables. You can use it to adapt to new requirements and set different variables as to write-only or read.
• Data hiding – Data hiding is a safety feature that PROTECTS data from unwanted access. It can help prevent the risk of changes (whether accidental or deliberate) to the program.
• Polymorphism – This includes static and dynamic typing. If you’re unsure which objects will be used at run time, dynamic typing lets you declare a variable that can be held in reference to an object. This allows for increased flexibility when coding. Meanwhile, if you’re sure about the objects that will be used at run time, static typing can be a better option.

The Components of the Objective-C Programming Language

There are two major parts of every code: the Data Types and Variables.

Below, we’ll talk about what you can specifically expect to see in an Objective-C code:

Basic Data Types

Common data types include:

Primitive Data Type

These are simple and straightforward data. Some examples are:

• int – Short for “integer value,” this type is a whole number, which includes zero and negative numbers.
• float – A floating point value has as many decimal places as it can hold. These values aren’t accurate, so if you need precise decimals, it’s better to use NSDecimalNumber.
• char – This is any single character, from letters to symbols. Lowercase and uppercase letters are read as two different characters.
• BOOL – This type is short for “boolean,” which is a 1-bit true-or-false value. Keywords like YES/NO, TRUE/FALSE, true/false, 1,0 can represent these values.

Object Data Type

These are more complex than primitive data types, giving them more complex properties and methods too. Some examples are:

• NSString – This is essentially a group of characters strung together to make text. Instances of this type are immutable. That means that string manipulation is impossible once you’ve created and initialized an NSString object.
• NSNumber – This is a lightweight “wrapper” class that lets a primitive data type have object-oriented features.

Variables

You can think of variables as a way to hold data together.

When you create a variable in Objective-C code, you usually do it through static typing. This involves variable declaration, where each type of data that the variable will hold is mentioned at the BEGINNING of the code.

Object variables are a bit trickier than scalar variables since you’ll need to de-reference a pointer using asterisks.

Advantages of Objective-C

Objective-C is by no means a new programming language. But it’s lasted for more than 40 years because of the following advantages:

• Easy to use – A big reason why the language took off is it’s VERY user-friendly. It doesn’t have complex features (like templates and overloading) that other languages tend to have. Aside from that, Objective-C has a fast program feature, making programming efficient and quick.
• Compatible with any iOS version – Because of how ingrained Objective-C is in Apple platforms, it has backward compatibility.
• Has function-rich libraries – Objective-C’s libraries have a lot of built-in features that can make programming easier. You can also create your own features and add them to the library! This makes Objective-C programs easy to extend too without much alteration.
• Has plenty of tools – Because of its history, Objective-C has been equipped with an abundance of tools. These include the XCode and Cocoa frameworks, and those from third-party contributors and Apple itself.

Disadvantages of Objective-C

• Complex syntax – Modern languages have syntaxes that look like plain English, making them easier to understand. But if you compare Objective-C to any new language, you’ll notice that the lines of code are more complex.
• Time-consuming – Aside from the complex syntax, the lines of code are LONG as well. There’s a lack of namespacing, too — for instance, variable names shouldn’t contain spaces. Instead, a variable name should only have letters, numbers, and underscores. So, you’ll need to carefully reread the whole thing to ensure that your code is correct.
• Requires more maintenance – Objective-C is an outdated language, so you’ll need to keep updating apps developed using it.

What Is Objective-C Used For?

Objective-C is one of the oldest object-oriented languages. However, it also remains one of the MAIN programming languages in OS-X and iOS development and their applications.

Objective-C and Mobile App Development

Objective-C has been used to develop software for MANY Apple products.

It relies on behavior offered by framework classes from Cocoa and Cocoa Touch. XCode is also important in building Apple apps.

Objective-C is compatible with several mobile app development languages — such as the C family, JavaScript, and Swift. Admittedly, you’ll sometimes need to use a “bridge” (such as with JavaScript) to make things work smoothly.

Because of that, it’s also very possible to develop Objective-C programs for non-Apple operating systems or those that aren’t dependent on Apple APIs. You can do this by compiling the programs for platforms supported by GNU GCC or LLVM/Clang.

Though Objective-C isn’t a modern programming language, the scope of the things you can do with it still makes it a beginner-friendly programming language for mobile app development.

Objective-C vs. Swift Programming Language

In 2014, Apple introduced the Swift programming language.

Initially, it worked alongside Objective-C, but they eventually encouraged developers to use Swift more.

Is Objective-C or Swift Easier?

Swift is a higher-level programming language based on the C family. It’s open-source and can easily be used on different platforms — such as Linux, Windows, and Android.

Swift code is MUCH easier to write and understand. It also REDUCES the chances of making an error. This is possible thanks to runtime crash, which is triggered by possible bugs so you can fix an issue immediately.

Other Swift features include:

• Automatic Reference Counting (ARC) – ARC automatically handles memory management, meaning you don’t have to worry if you have enough memory for your project.
• Concise code syntax – You’ll need to type down FEWER lines of code for string manipulation, repetitive statements, and such.
• Interactive playgrounds – You can download these playgrounds on your Apple devices to learn how to write code or be familiar with the Swift algorithm.
• Compiler feature – This reduces classes of unsafe code, also lowering the amount of runtime crashes an app will experience.

Swift also has dynamic libraries that can help improve performance when you build apps for iOS.

With Swift, coding for any type that conforms to protocol WITHOUT knowing the specific type is possible.

Meanwhile, for Objective-C, categories run directly in compile time and can only be used to extend a certain type’s functionality.

Frequently Asked Questions

We’ve answered a few more questions on Objective-C below:

Is Objective-C Still Used?

Yes, Objective-C is still used. Of course, that’s because it’s a huge part of the foundation of Apple’s operating system.

Apple still supports it since the legacy code is still fully functional, but it WON’T be getting updates anymore.

Instead, Apple encourages their developers (or aspiring developers) to learn Swift.

Is Objective-C Hard to Learn?

Despite being an old programming language, Objective-C is surprisingly NOT hard to learn.

In fact, it’s one of the easier languages beginners can learn!

That’s because it has a very fluid and dynamic runtime. The syntax might look overwhelming at first, but you’ll be able to get the hang of it easily.

While Swift is easier to write and understand when you’re used to it, it’s NOT very beginner-friendly.

This is because it has additional features that call for a lot of nuances — for instance, you can expect different behavior from classes and structures.

What Language Is Objective-C Similar To?

Objective-C is a mix of the original C programming language and Smalltalk.

Since Objective-C is syntactically an extension of C, you can say that C and Objective-C are exactly the same in some parts.

If we’re talking about alternatives you can learn, Python would be the best one.

Is Objective-C Faster Than Java?

C embedded into Objective-C is faster than Java of the same version.

But many factors go into how fast a code works — the biggest factor being the piece of hardware you’re running the code on.

Final Thoughts

The Objective-C programming language is one of the oldest programming languages with an object-oriented paradigm.

Newer programming languages have features such as memory management and easier syntax.

Despite that, Objective-C remains an important language that serves as the foundation of Apple’s operating system and inspiration for other C code families.

It’s a surprisingly beginner-friendly language to learn, too. So, if you’re interested in entering the world of programming languages, this certain one is highly recommended.