4 Biggest Developer Trends of 2016

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Whatever your role in the tech industry, you know that technology trends evolve at lightning speed. Today’s emerging Big Data platform or programming language is tomorrow’s industry standard. As the year progresses, it’s worth pausing for a moment to consider which trends are dominating the tech conversation.

Apache Spark vs. Hadoop

People have long debated whether Apache Spark will become the next big thing in Big Data. An open-source cluster computing framework originally developed at the University of California, Berkeley, Spark has gained its share of fans over the past few years, all of whom are quick to highlight the platform’s speed advantage over Hadoop MapReduce, which has long served as the standard for open-source cluster management.

Cloudera and other vendors have all invested in developing Spark, suggesting a good deal of industry traction going forward. While Google Cloud Dataflow may present a long-term threat to the dominance of Spark and Hadoop, it seems unlikely that businesses will give up their current cluster infrastructure anytime soon.

Responsive Design

Given the increasing prevalence of mobile devices, any Website that can’t auto-adjust for a smaller display is at a disadvantage. In response to this, more tech schools and boot camps are teaching the fundamentals of responsive design. For those Web developers who spend every day wrestling with designs, new suites of tools have made it easier to implement and test mobile layouts, even on a desktop; for example, Safari 9 in OS X El Capitan features a Responsive Design Mode.


Container technology just gets more popular as time goes on. Although their functionality can become incredibly complex, containers are quite simple in concept: they’re lightweight virtual machines, utilized to help developers optimize software development. The most prominent example of container tech, arguably, is Docker.

Developers appreciate Docker for its flexibility and safety. Let’s say you’re running multiple containers on a server, and one of those containers dies. Because containers are partitioned off from one another, you only need to restart the individual container, instead of the whole server. It’s an effective way to run multiple software packages (in multiple containers) on a single server without a whole lot of risk.

Machine Learning

Facebook, Google, and other major tech firms are ramping up their machine-learning capabilities, and it’s easy to see why: machine-learning frameworks, cognitive computing, and other A.I. tools are perhaps the only way to effectively deal with an increasingly complex data landscape.

Outside of tech, industries as diverse as healthcare and banking are looking to machine learning to deal with their computational issues. But designing an effective A.I. platform is much easier said than done; as Andrew Moore, the dean of computer science at Carnegie Mellon University, recently told Fortune magazine, it takes a lot of human work to ensure that self-learning algorithms can seamlessly integrate with a specific application.

Unlike some of the other trends on this list, machine learning hasn’t yet seen widespread adoption. If you believe the tech pundits, however, that will change over the next few years.

Image Credit: Shutterstock.com/ronstik

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