Top Programming Languages for Every DevOps Engineer
The world of DevOps and software testing is witnessing a tectonic shift. The advent of newer technologies like Artificial Intelligence, Augmented intelligence, Cognitive reasoning, Robotic automation, No-code and Virtualization have made huge differences to the DevOps world. The real force behind these developments is the galaxy of programming languages. In recent times, different top programming languages have been used to identify , analyze and solve complex problems. It is often said that one programming language can’t solve all the problems of the world, and so we have analyzed a sample of 100k research documents and websites that discuss the role of programming languages in various fields such as Medical research, Parallel Computing, AI and data science, IT and software testing, Cloud deployment, and robotics.
But, before we head into the world of top programming language, let’s understand the need to use these platforms in the first place.
Top Programming Languages Make Business Intelligence More Powerful and Relatable
Programming is a very specialized activity that requires high-end skills in computer science, mathematics, logical reasoning, and statistics. The traditional plane of top programming languages always involved JAVA, FORTRAN, Pascal, COBOL, Algol, Miranda and Oberon. However, in the last 3 decades, with better access to computer hardware systems, internet penetration and enhanced data storage capabilities, many more platforms have been added to this list of top programming languages. The basic premise of selecting these programming languages is very simple– they can compute very fast, and when we have big data and business intelligence projects in our hands, having an in-depth knowledge of these top programming languages becomes a winning formula for any modern day DevOps team.
Agility, Real-time analytics and AI Ops: The World of Data Science and Programming Languages
Students as young as 10 years old are now taking up programming courses. This is correlated to STEM courses that have begun to understand the importance of coding for beginners. The popularity of top programming languages can be evaluated from the way these are taught and further refined for students and professionals who come with little or no coding experience.
So, our criterion to select programming language included ease of coding, simplicity, market demand, reliability and data management. Whether it’s non-IT or IT organization, learning these languages could provevery beneficial for you. Companies that are facing considerable difficulties in delivering superior services with agility and precision are looking out for programmers with data science skills. Incorporating DevOps and computing skills into the business process management have simplified IT operations and accelerated digital transformation of various departments.
DevOps solutions have grown in popularity in recent years due to their ability to bring together all areas of an organization and create reliable software with improved quality and faster delivery. Operational automation is one of the key benefits of DevOps, but it requires strong programming and scripting abilities on the side of engineers.
DevOps technologies have risen in popularity in recent years as a result of their capacity to bring all functions of an organization together to provide more dependable software with higher quality and faster delivery. Operational automation is one of the key benefits of DevOps, but it requires strong programming and scripting abilities on the side of engineers.
So, here’s the list of the top programming languages that DevOps teams should consider deploying in your product development project in 2022.
Python has wrapped the world of programming and DevOps in its tight coil. From medical research to fundamental marketing analytics, Python programmers are in a huge demand everywhere. Between 2016 and 2021, Python programming language emerged as the most popular programming language for AI and machine learning development. It is now almost on the verge of becoming the greatest open source coding platform for DevOps in Business analytics, AI Ops, SaaS and Cloud industries as well. What makes Python is universally known! It supports almost every internet protocol — HTML / XML, JSON, Email processing and so on. Moreover, it also aligns with major frameworks, and micro-frameworks like Django, Pyramid, Flak, Bottle and modern CMS platforms.
DevOps teams use Python for web development, GUI development, scientific and numeric modeling, system administration, and Big Data project management.
Trivia: One of the world’s biggest and oldest Python programming events – EuroPython is slated for next week. The event will be organized at Dublin, Ireland between 11 and 17 July.
Apart from being an open source language, Python is more user-friendly than other languages of comparable complexity. Python in DevOps is a concept that aims to incorporate automation that might improve overall performance when integrated with C++ or Java.
Python is created by combining the greatest features of C and C++. It is an extremely powerful high-level programming language that can structure both tiny and large-scale projects with ease. Python integration can reduce reliance on manual labor and effectively and seamlessly automate activities. With high-level dynamics and iterative approaches achieved through libraries, working in Python environment is very satisfying and productive.
Why Python for beginners?
Python is easy to learn and adheres to standard object-oriented programming principles. Unlike its closest competitor, R isn’t well adapted to serve as a full data science tool. Python, as a clear and practical tool, fills in such gaps. A data scientist should be familiar with deep learning, a hot topic in the machine learning world.
Scikit-learn, which is the native machine learning algorithm, also makes it possible to run machine learning algorithms. Python is used by applications such as YouTube, Instagram, Amazon, Spotify, Netflix, Reddit, and others.
Python learners also look out for opportunities in other programming domains, and Julia ranks as one of the best Python alternatives. Known for its “slimness” in AI and machine learning, Julia outperforms many existing Machine Learning frameworks. Recently, it was speculated that Julia can operate at 5x faster computing speed than PyTorch.
Though it is still far from replacing Python in DevOps projects, Julia’s rise in recent years in AI ML operations, data visualization and machine learning applications has taken everyone in data science by awe. Morever, Julia can be easily matched with libraries from Python, C++, Fortran and Java, making it a decent programming platform in Python-based projects.
- Vue JS
- Backbone JS
- React JS
Java’s motto of “write once, execute anywhere” is enticing to programmers. Java is an all-purpose companion that can make your life easier whether you’re developing an application or hoping to become a data scientist or a mobile app developer. Because Java is used by so much of Android, it’s a good idea to learn how to develop in it.
Uber, Google, Netflix, Pinterest, Spotify, Airbnb, and Amazon are some of the most well-known companies that use Java for their workloads.
Ruby is very similar to Python, although it has a few additional advantages. It’s an interpreted language, therefore it’s easy to use in a variety of industrial settings. This functionality also makes it simple for Ruby to create and implement the scripts required for DevOps workflows. The programming language is widely utilized in web development and plays an important role in infrastructure management.
The GoLang (Go) language, sometimes known as the Google Programming language, is largely based on C. It gained a lot of traction in 2016, and it’s likely to gain even more in 2017.
The language was created to address some of the problems that plague many other current languages, making it a clean and straightforward alternative. BBC, SoundCloud, IBM, and Klout are among the companies that use it.
Microsoft’s C# computer language is a general-purpose, multi-paradigm programming language. C# is still one of the most popular programming languages after two decades. Because it uses Microsoft Visual C++ as an IDE, it is one of the most capable languages for the.NET framework and is best suited for Android, iOS, and Windows.
Slack, Insightly, Pinterest, and Tableau are all written in C#. Many Microsoft desktop apps, such as Photoshop and Microsoft Office, use C# as well. Many reputed website use C# in their backend. For example, Dell, Bing, and Visual Studio.
PHP is a server-side scripting language. This language has been around for some time now and DevOps teams primarily rely on PHP for web development. PHP is one of the most widely used programming languages in the world, with millions of websites using it.
For beginners, it is a reasonably simple language to learn, and there are numerous resources available online. PHP is used to create content management systems such as WordPress, Joomla, and the e-commerce platform Magento. WordPress-powered websites power more than 30% of the internet.
PHP comes with various libraries and is used as a web-based scripting interpreter on Linux systems. It can handle all internal systems to the end of the implementation process.
C/C++ are conventional programming languages that serve as the foundation for several key technologies. It has several advantages over other languages, including increased agility and speedier implementation. C is a traditional low-level programming language, while C++ is a superset of C with object-oriented capabilities added on top.
Did you know PERL is an abbreviation for Practical Extraction and Report Language?
Since its conception in 1987, PERL has made massive upgrades in database integrations, cross-platform compatibility and embedded analytics.
PERL has gained massive reputations as a high-level, general-purpose programming language for GUI development and text manipulation. It’s used for GUI development, text interpretation and processing, online applications, and database integration. It’s also utilized for low and high-level applications. PERL can be used in both small and large applications for simple tasks.
SQL, or Structured Query Language, is a programming language for storing, manipulating, and querying data in relational databases. The language has been around for a long time and is mostly utilized in DevOps scenarios because of its container support. SQL is supported by a Linux-based server, while containers can be designed and operated on Windows, Mac, and Linux platforms.
Bash is a scripting language with a command-line user interface for interactive command language and scripting. It is a widely used Unix shell that has paved the way for the creation of thousands of Linux systems around the world.
Due to its resemblance to Java, Scala has a lower learning curve, with the exception that the complexity associated with running Java applications has been removed in Java. All of Java’s modular constructs are included in the programming language.
Scala is the language that combines the best of both functional and object-oriented ideas. Support for big data and REPL are two other excellent features. The disadvantage is that its compiler is quite similar to that of a Pentium 5 processor. It may use some work, but its flaws are forgiven in the end.
R has become the lingua franca of data science, as data is the fuel of the twenty-first century. It is currently experiencing increased demand, indicating that the future looks brighter. It’s an open-source statistical programming language that’s free to use. R’s source code is developed in C and FORTRAN and is distributed under a broad public license. Its ability to solve extremely complicated problems is admirable.
For beginners, R may be frightening, but give it some time and it will grow on you. R makes data manipulation and complicated data handling a breeze. It has a thriving community, and new features are added regularly. It is a widely spoken language in many parts of the world.
Swift is an Apple-developed programming language. The Apple ecosystem used to revolve around Objective C. Apple, on the other hand, launched Swift, its programming language, in an attempt to make things easier for developers.
Furthermore, because it is open-source, developers can work on Windows or Linux systems, create their compilers, and be confident that their programs will run on Apple devices.
Interoperability with Java is a game-changer in DevOps domain. Kotlin is one of the most advanced programming languages that boasts of the IntelliJ IDEA, Android Studio, and the automated Java-to-Kotlin converter. JetBrains introduced Kotlin as a programming language in 2011. It’s a statically typed programming language that runs on the Java Virtual Machine (JVM) and was created as a superior alternative to Java.
Kotlin is a popular mobile programming language that has been embraced by organizations such as Square and Airbnb.
Kotlin is a popular choice for businesses since it is a versatile language that can be used for a variety of purposes. Its shortcode and ease of use make it a suitable candidate for mobile app development, while its compatibility with the JVM makes it a viable choice for corporate apps. DevOps teams can use from a host of Kotlin-supported frameworks for advanced API management and console development. It supports:
- Javalin, and many more.
Kotlin keeps updating the features and applications for superior user experience. For example, last month, Kotlin 1.7.0 was released on GitHub. The feature release upgraded to Kotlin K2 compiler in Alpha for JVM, stabilized language features, performance improvements, and evolutionary changes such as stabilizing experimental APIs.
Rust is a Mozilla Research-sponsored general-purpose, multi-paradigm, compiled programming language. It’s a “safe, concurrent, practical language” that supports pure-functional, imperative-procedural, and object-oriented programming methods. The goal of Rust was to eliminate common programming problems like null pointers.
MATLAB is a MathWorks’ product. The same company also owns Simulink, the most-advanced No Code block diagram-based environment for embedded multi-domain simulation frameworks.
We will discuss MATLAB and Simulink in this article.
MATLAB brings in iterative analytics and data visualization to mathematics and statistical science. Its ability to scale on GPUs, clusters and Cloud environment is phenomenal. MATLAB has stupendous popularity among big data engineers and AI analysts working on software, hardware and sensors for advanced engineering and simulation-based projects.
With close to five decades of programming mind behind its rise to fame in the data visualization and big data intelligence domains, MATLAB is a every data scientists favorite project.
MATLAB is a programming language that is used to create machine learning and deep learning applications in wide range of domains. These include Control Systems, Signal Processing, Robotics, Testing and Maintenance and Telecom / 5G. MATLAB-based applications analyze data, construct algorithms, analyze photos, and double-check findings for parallel computing, website deployment, and Cloud management.
As discussed above, Simulink is part of the MathWorls family. It is a fascinating programming language for DevOps teams looking to build simulation in agile manner using Low/ No code deployments. This very property allows Simulink users to expand the scope of their Agile software development. Not only does it accelerate delivery to customers using short iteration cycles but also brings in no code benefits with continuous integration / continuous development with an enhanced team collaboration. Teams can perform embedded simulation, automated testing, and code generation — all in one step, thus responding to changing requirements quickly or in Agile.
Popular engineering projects in Control Systems, Telecom, IoT / 5G, Signal Processing, Digital Twins, AI and much more, specifically use Simulink for development process. Scania, a world leader in the driver-less technology for heavy commercial vehicles, uses Simulink.
You can refer to the MathWorks website to understand how to use both Simulink and MATLAB programming languages in private and public Cloud environments to boost model-based deployments in Cloud and Parallel Computing.
These languages were chosen based on their pace of growth, demand, and salary. We’ve also acquired data from several developer polls to better understand it from a developer’s perspective. We hope this information provides you with some useful programming language predictions for 2022.
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