The programming language Python has come a long way since its inception in the 1990s. Little did Guido Van Rossum knew when he developed Python that it would become one of the most popular languages in the world. Today, Python is one of the most widely used programming languages on the planet and implemented for more than a few applications. Be it enterprise-level applications, machine learning and artificial intelligence models, or data science jobs, Python is excessively being used in almost every industry and field that is thriving.

Python’s Current Scenario

There are more than 8 million Python developers across the world who use Python religiously for a variety of purposes. Due to its dynamic nature and ease of scalability, Python has already turned into the developer’s preferred language. This is also the reason why Python has been able to beat JAVA, which has for the longest time been the developer’s favourite language. But it can also be due to the natural ageing process of a language that JAVA is nearing the end. Most new languages are designed to solve modern challenges. While languages developed long ago are most efficient in the problems of their age, it becomes extremely difficult for them to stay relevant to changing industries and scenarios.

But, Python being an open-source language with such a large and supportive community, continues to stay relevant and at its peak even today. Its abundant libraries and in-built functions make it a popular choice among organizations, enterprises, developers and data scientists. Even though JAVA is still being used for enterprise development, it’s relevancy in other fields is close to none. If you look around, you won’t find a machine learning expert designing and training models on JAVA. But, in spite of this fact. JAVA stands as the second most popular language among developers across the globe.

Taking Over JAVA

Python has been successfully able to take over JAVA in most of the spheres. When it comes to enterprise development, JAVA is facing threats from Google’s new programming language Go. However, as we progress into the future the need for high-performance computing keeps on increasing more than ever. It is the need of the hour for data science and artificial intelligence models. Even though one might think that the deployment of extreme GPU might help gain speed and efficiency, the reality is far off. It doesn’t serve the purpose of processing needs. Instead, cutting edge applications need other dependencies to perform optimally and help scientists and developers accomplish the desired goals. Ultimately, this is ushering organizations and research institutions to look for robust programming languages, designed for a niche task and deliver speed.

Entering the World of Julia

Having said, the world is entering an age where everyone’s favourite Python is facing threats from a new entrant in the world of programming languages- Julia. Viral Shah, the CEO of Julia Computing point out that in the early 2000s, developers preferred to use C language for system programming, JAVA development for enterprise applications, SaaS for analytics and MATLAB for scientific calculations. However, today’s developers are using Rust for system programming, Go for enterprise development, Python/R for analytics along with Julia for scientific calculations.

However, this wasn’t the exact scenario a few years earlier. With Julia nowhere in the picture, the transition from MATLAB was to Python. Since machine learning started being used in almost every application that we know and Python libraries facilitated the much easier implementation of ML models, people switched to Python. Earlier, MATLAB was the best option for the task and helped in analytics as well as scientific calculations. But, it was obvious that people looked fit easy to implement solutions that were easily understood, fast, high-performing and scalable. Thus, Python filled into both JAVA’s and MATLAB’s shoes perfectly.

Where Does Julia Stand?

One of the key difference between Julia and Python has been the way both approach a particular problem. While Julia is purposefully built to mitigate the challenges around high-performance computing, Python has evolved into this role. Even though Python has till now been able to assert to the challenges of the industry, let’s accept it that it wasn’t designed for the job. Developers and researchers have been lucky to let and watch Python evolve into a fast computation langauge. On the other hand, Julia is quintessentially designed with high speed in mind. It’s barely a few months old and has already started generating buzz among researchers and data scientists.

A stable version of Julia called 1.2 was released only two months ago and has already been further improved to effectively handle resource-intensive data science projects. Right now the language has over 800 developers who are contributing on Github and helping it become the go-to language.

Conclusion

Being a resource and speed intensive, two months old Julia is already giving the three-decade-old Python a tough battle. Even though it might be difficult to say whether it will completely take over Python or not, it will surely have an impact on the world with its features that are designed to handle complex computations. Moreover, as problems keep on becoming resource-intensive and require rigorous computations, Julia might be able to become everyone’s favourite due to its high-performance capabilities. Unless Python wants to have a fate like JAVA, it would have to up the game and try to optimize its libraries for speed and efficiency. It might not have to do with just launching new updates but completely transforming the engine to make it a more CPU friendly language. An advantage that Python already has over Julia is its abundant libraries. Since it is just in its infancy stage, it will take Julia a long time to come up with efficient and dynamic libraries and functions like Python. The fight between the two languages has just begun, but it is already turning into an advantage for researchers and scientists who require fast and efficient tools to achieve their goals.