VPN technology is getting popular all over the world due to its characteristic of provide privacy and counter restrictions on access of applications and websites. The requirement of VPN varies on circumstances around user such as Government policies.

IKEV2 protocol is most secure and fast protocol among other protocols. In this blog we tell you how to develop Android VPN app with IKEV2 protocol. But Android does not provide build-in support for IKEV2 protocol so we will use StrongSwan (the OpenSource IPsec-based VPN Solution) libraries for this purpose.

Getting Started

Scope of this blog is to configure the StrongSwan and integrates in AndroidApp. There are three major parts of this app.

• StrongSwan libraries  (libstrongswan, libcharon etc.)
• Application in Java (Android)
• Library to glue these two parts

The Java part and the libraries communicate by means of the Java Native Interface (JNI).

To achieve this there are three major steps need to implement.

1. Configure StrongSwan
2. Integrate StrongSwan in Android App
3. Java code to use connect VPN using StrongSwan

1. Configure StrongSwan:

I am working on windows platform. For configuring StrongSwan there are some shell commands, as windows cmd does not support shell commands for this I have used CENTOS virtual machine. Download VMWare or Vitual box to host your virtual machine on windows and then open .vmx file

In CENTOS you need the following tools:

• a recent GNU C compiler (>= 3.x)
• automake
• Autoconf
• Libtool
• pkg-config
• gettext
• perl
• Python
• lex/flex
• yacc/bison
• gperf

Now follow the steps to configure StrongSwan

a. Clone StrongSwan

Clone StrongSwan using command:

Git clone https://git.strongswan.org/strongswan.git

After a successful check out, give the autotools a try

b. Go to StrongSwan directory

First go to the Strongswan directory that you have cloned by the following command.

cd strongswan/

c. Create source files

Then run these commands one by one after each command done successfully:

•	./autogen.sh
•	./configure
•	Make
•	Make install

This creates several pre-build source files. Next go to JNI directory by running the following command:

cd src/frontends/android/app/src/main/jni

And run this command

Git clone https://git.strongswan.org/android-ndk-boringssl.git -b ndk-staticopenssl

Now copy the code from CENTOS to window and run the app in android studio the code for the App can be found in the source: strongswan/src/frontends/android directory of our repository. To build it the Android SDK and NDK are required.

2. Integrate StrongSwan in Android App:

Now we integrate StrongSwan libraries in Android app. Here we use sample android app given by StrongSwan as front-end app. For this purpose we need .so files for native classes to communicate with Java classes. Download the Strong project from Github and copy JniLibs folder from this Github project and past it in your project that have copied from CENTOS in the following path:

strongswan/src/frontends/android /app/src/main

Now build the project, if there is NDK path problem try to replace this

task buildNative(type: Exec) {
    workingDir 'src/main/jni'
commandLine "${android.ndkDirectory}/ndk-build", '-j', Runtime.runtime.availableProcessors()
}

with this

task buildNative(type: Exec) {
    workingDir 'src/main/jni'
commandLine "${android.ndkDirectory}\\ndk-build.cmd", '-j', Runtime.runtime.availableProcessors()
}

and sync now.

3. Java code to use connect VPN using StrongSwan:

To connect with VPN using StrongSwan in this app you need to replace some piece of code as below:

In file path

strongswan\src\frontends\android\app\src\main\java\org\strongswan\android\logic/CharonVpnService.java

You will see the code

SettingsWriter writer = new SettingsWriter();
writer.setValue("global.language", Locale.getDefault().getLanguage());
writer.setValue("global.mtu", mCurrentProfile.getMTU());
writer.setValue("global.nat_keepalive", mCurrentProfile.getNATKeepAlive());
writer.setValue("global.rsa_pss", (mCurrentProfile.getFlags() & VpnProfile.FLAGS_RSA_PSS) != 0);
writer.setValue("global.crl", (mCurrentProfile.getFlags() & VpnProfile.FLAGS_DISABLE_CRL) == 0);
writer.setValue("global.ocsp", (mCurrentProfile.getFlags() & VpnProfile.FLAGS_DISABLE_OCSP) == 0);
writer.setValue("connection.type", mCurrentProfile.getVpnType().getIdentifier());
writer.setValue("connection.server", mCurrentProfile.getGateway());
writer.setValue("connection.port", mCurrentProfile.getPort());
writer.setValue("connection.username", mCurrentProfile.getUsername());
writer.setValue("connection.password", mCurrentProfile.getPassword());
writer.setValue("connection.local_id", mCurrentProfile.getLocalId());
writer.setValue("connection.remote_id", mCurrentProfile.getRemoteId());
writer.setValue("connection.certreq", (mCurrentProfile.getFlags() & VpnProfile.FLAGS_SUPPRESS_CERT_REQS) == 0);
writer.setValue("connection.strict_revocation", (mCurrentProfile.getFlags() & VpnProfile.FLAGS_STRICT_REVOCATION) != 0);
writer.setValue("connection.ike_proposal", mCurrentProfile.getIkeProposal());

Replace it with

initiate(mCurrentProfile.getVpnType().getIdentifier(),
mCurrentProfile.getGateway(), mCurrentProfile.getUsername(),
mCurrentProfile.getPassword());

Now it should work

Add StrongSwan as a Module in Android App:

If u want to use strongswan in your app, add android folder from this path strongswan\src\frontends\android in your app as a module and use this project in your app.

Got to File->New->import module

Select android folder from the strongswan project directory

It will give error that the app module is already exist so change the module name from “app” to “strongswan” you can write what u want. And click finish.

Right click on app and click open module settings

Select Dependencies tab from side menu, click on “+”and select module dependency

Select strongswan and click ok.

Now you can see strongswan module is added

Conclusion:

The basic purpose of this blog is to summarize the strongswan(the OpenSource IPsec-based VPN Solution) configuration and intergration in android project to build up the VPN app using IKEV2 protocol.

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.