quickBytes | Julia Developer Survey results indicate Julia may be the language of the future

> Julia Developer Survey results indicate Julia may be the language of the future

The Julia community recently took part in the Julia Developer Survey following JuliaCon 2019, where developers gathered to discuss the future of the language and its growing impact on the world of data science. Julia is generating serious developer interest: according to the TIOBE index, a method for measuring the popularity of programming languages, Julia is now ranked 35th after first breaking the top 50 languages just three years ago. Unsurprisingly, Julia was one of the fastest growing languages in 2018.

The most popular features of Julia were its performance, ease of use, and open source license. When asked why they first tried Julia, most developers stated that they believed Julia is the language of the future.

Julia is a high-performance language that works particularly well for scientific computing. Stylistically similar to Python but with the performance of lower-level languages like C++, Julia combines high-level code and an intuitive syntax with production-grade performance. In doing so, Julia solves the infamous two language problem, a common issue in the data world where data scientists prefer to code with high-level languages like R and Python, but performance-critical parts of the codebase must be rewritten in C or C++. Rewriting code wastes time and developer resources and is a costly duplication of effort. By operating across the entire development stack, Julia can noticeably accelerate the development of complex data-driven software.

While Julia may be the language of the future, it must also overcome a number of hurdles before seriously competing with Python, the data science behemoth. Julia’s most significant problem is its package ecosystem: the language’s packages aren’t as mature or well-maintained as required. Conversely, Python benefits from a rich ecosystem of tools and relies on a massive community of developers to extend its functionality.

Python is likely to be the default data science language for many years, but Julia will continue to expand its influence as machine learning and complex data projects become mission critical for more companies. If successful, Julia can radically streamline the relationship between software development and data science and begin a new era of robust data engineering.