Sadly, the only news coming out of PyCon last week was Donglegate. In a sad case of he-said-she-blogged, two people lost their jobs and the technology industry took a deep look inside and discovered that (surprise!) it’s a male-dominated, sometimes misogynistic bro-fest. Lost in all the finger pointing of the past week, however, was a central fact:
Python is doing exceptionally well.
This is evident from PyCon attendance alone, which saw 2,500 people attend (up from a mere 20 in 1994), some of them the beneficiaries of the $100,000 in sponsorship money raised. Importantly, 20% of the attendees were women (which makes the inappropriate “dongle” comments even more frustrating). Another $10,000 was raised in an auction for PyLadies.
Python’s popularity is particularly interesting in light of programming language fragmentation. Whereas once enterprise IT had to choose sides between Java or .Net, today’s empowered enterprise developers are spoiled for choice, as Redmonk analyst Stephen O’Grady opines. Despite a myriad of programming languages from which to choose, Python continues to more than hold its own, ranking fourth overall in terms of adoption.
And when it comes to relative job growth, Python is rocking, even compared to other top languages:
Big Data Tailwind For Python?
Interest in languages like Java and PHP has fallen over the last few years while Python has remained steady. But there’s reason to believe Python is about to see an upsurge in interest.
Part of Python’s popularity stems from how easy it is to learn, especially for enterprise developers coming from a C/C++ or Java background. Developers also turn to it because of its general purpose nature. Developers and the enterprises that employ them often turn to technologies that fit multiple use cases, particularly when they’re easy for newbies to pick up.
But Big Data may well be Python’s big selling point.
As AppNexus Director of Optimization and Analytics David Himrod tells Computerworld:
Key to Python’s usefulness is its simplicity…One of the biggest challenges that [AppNexus] faces is how to get a diverse set of employees working on the same technology stack. Python provides employees with different backgrounds–notably engineers, mathematicians and analysts–a common, easy-to-understand language that can be used to prototype new functionality for the company.
As anyone that has been around open source over the years knows, “prototype” today turns to “production” tomorrow. “Complex but powerful” tends to be a losing formula for new technologies that depend upon developer adoption. “Powerful but easy to use,” however, wins most every time, particularly in an area like Big Data, which has enterprises experimenting with their data.
Python’s Big Data ambitions also recently got a cash infusion from DARPA (U.S. Defense Advanced Research Projects Agency), which invested $3 million in Continuum Analytics to help improve Python’s data processing and visualization capabilities.
A Bright, Big Data Future For Python
None of which means Python has won the language war. As O’Grady notes, fragmentation is here to stay, given that developers increasingly determine the tools they use, and opt for a languages tailored to specific applications. But given Python’s ready-made fit for Big Data, Big Data’s importance, and investments in Python to make it even better for Big Data projects, it seems safe to project a healthy future for Python.
With or without juvenile and demeaning dongle comments.
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