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Top Ten Popular Programming Languages

programming languages

Programming languages are the means to communicate with a computer. The instructions written in a programming language are converted into a machine code that computers can understand. Just like there are so many human languages, there are a number of programming languages. Each language is suitable for certain requirements and is able to solve specific problems. There are quite a few programming languages that are in demand today.

In the modern digital world, the success of a company depends on the right selection of architecture and design, which in turn depend on the underlying programming languages.

The world is rapidly moving towards low-code and no-code platforms, which require minimal coding knowledge and skills. These are suitable for non-tech people. The rapid rise of cloud-based application development also augurs well for the low-code/no-code paradigm.

Applications built using low-code/no-code platforms have short turnaround times. Another pattern that is seen recently is that tools like ChatGPT are being used for coding tasks.
All these methods generate boilerplate code that is mostly generic, has inherent security risks, and may need heavy customisation. Needless to say, these are not a replacement for traditional software development and unlikely to replace skilled coders. However, they can augment the software development process by providing the necessary inputs.

Programming skills in demand

Let’s now look into the various programming languages that are popular in 2023 from the standpoint of their usability and business significance. Note that all the languages listed below are equally popular.

1. Python

Most startups are using Python based frameworks (Django/Flask). Python is an interpreted, high-level, general-purpose language that supports rapid application development. It is highly readable, intuitive and flexible, and has a good ecosystem of support modules. Python can be used for:

  • Web development
  • Data analysis
  • Data visualisation
  • Machine learning

2. JavaScript

If you are interested in a dynamic language that provides interactive behaviour and manages the user experience for web applications by allowing you to write client-side scripts, then you should use JavaScript. There are a number of powerful JavaScript based frameworks and libraries, which include Angular, React, Vue, jQuery and Backbone. More than 97 per cent of the world’s websites use JavaScript for client-side programming and browser-based applications. Node.js (which is also a JavaScript language) is being used in server-side applications. JavaScript also provides libraries that are used for visualisation.

3. Java

If one wants a platform-independent, robust, enterprise-grade programming language, Java is the answer. This high-level, object-oriented programming language is used for building platform-independent applications that can run in any environment. It follows WORA (write once, run anywhere), where it can be written in one system and expected to run in any other Java-compatible system. It is widely used in web and application development, and Big Data. Java frameworks like Spring, Struts, Hibernate are quite popular.

4. PHP

If one wants to build web applications quickly, PHP could be the answer. More than 80 per cent of websites in the world are built using PHP. It is used for writing server-side scripts. It can also be used for writing command-line scripts and desktop-based applications. However, these are generally considered less secure. PHP is not suited for large applications, and is relatively poor at handling errors.

5. SQL

If the requirement is to interact with database systems, then SQL is the de-facto answer. This language is the industry standard for creating, defining, modifying and updating database objects. It allows you to write queries and scripts to retrieve data from back-end systems. SQL is also used in Big Data environments like Spark. This declarative language specifies the desired results without specifying “how” to get the results. SQL is a very powerful tool for accessing and manipulating data.

6. R

If one needs a language for statistical processing, then R is preferred. It is used for scientific computing, as well as data analysis and data visualisation. Application areas of R include linear and non-linear modelling, calculation and testing. Applications coded using R can interface with a number of databases, and process both structured and unstructured data.

7. C#

C# is the go to language if you are working in Microsoft technologies. It is also a general-purpose, component-oriented, object-oriented language used for web development, enterprise applications and mobile development, as well as other applications.

8. C++ and C

If you need a statically typed language, where fast rendering and performance is of essence, then C++ and C are the preferred languages. They are compiled languages. C++ basically adds object-oriented features to the C language. Both the languages are widely used in computer science. These high performance languages are used for video games and other client-server applications including embedded systems.

9. Swift

Swift is the language used for iPhone, iPad and MacOS based app development. It is an open source, general-purpose programming language that caters to performance, safety and design patterns. It is a general-purpose language suitable for a wide range of use cases. However, it has an incomplete cross-platform support and poor interoperability with third-party tools; these limit its versatility.

10. NoSQL

Handling different types of data is extremely important with the rapid rise of unstructured and semi-structured data, and the expertise of NoSQL databases comes handy. NoSQL or Not only SQL are non-relational databases designed to provide high performance and scalability. They typically follow the ‘eventual consistency’ paradigm and adhere to the CAP theorem (consistency, availability, partition tolerance). NoSQL databases are vastly used to store data in a distributed fashion in Big Data systems.

The different categories of NoSQL databases are:

  • Key-value stores (e.g., Redis, Memcached)
  • Columnar stores (e.g., Cassandra, HBase)
  • Document stores (e.g., MongoDB)
  • Graph databases (e.g., Neo4J)
  • Time-series databases (e.g., InfluxDB)

Data is usually replicated and stored in clusters. The different nodes in the cluster store some amount of data so that even if there is a breakdown of nodes, the data is not lost. They allow horizontal scalability. However, NoSQL databases are generally less mature than SQL databases, and lack some of the advanced features and optimisations offered by SQL databases.

Each programming language discussed above has its own significance and application across industries. Organisations need to choose their application environment and the business use case, and then select the right language for their needs.



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