The mustaches of Movember may be growing, but it’s also the time of year to look ahead and into the digital transformation of 2017. More specifically, how will big data continue to make waves? 2016 has shown a surge of companies adopting big data and IoT, and then realizing the intelligence behind it. Not only that, but there is huge opportunity to find valuable insights within social data and the ever-increasing community of mobile applications. So, what are some of the exciting big data trends we should be on the lookout for next year?
Floating in the Cloud…Continues
The chance of us coming down from the cloud is slim to none. Hybrid and public cloud services continue to increase and we stand at the crossroads of a marriage between data storage and analytics, unleashing new possibilities of managing massive amounts of data sets. A main driver to the success of big data and the cloud is running both Spark or Hadoop on an elastic infrastructure. You don’t want to be stuck with the inability to scale your business as it continues to grow and require larger data workloads.
APIs play a large role in scalability as well. There is huge potential to unlock systems that have previously untapped data through the increased adoption of cloud-based services, as well as tying together a multiple platform ecosystem that will be fast and flexible.
Unleashing More Spark
Apache Spark is proving to be a powerhouse of speed in the big data platform space, with faster processing times than traditionally found with Hadoop’s MapReduce (i.e. Hadoop’s processing component). According to an article from InfoQ, Spark enables applications in Hadoop clusters to run up to 100 times faster in memory and 10 times faster even when running on disk. The only downside is that Spark does not have its own file management system, like Hadoop, so it needs to be integrated with one, which is a primary reason for Spark not replacing Hadoop but rather complementing it. When both Spark and Hadoop work together, the output is powerful big data analytics and storage capabilities.
Learning Goes Deeper
One of the trends we’ve seen in 2016 has been cognitive computing, which will continue to grow and evolve in 2017. Deep learning is getting deeper, becoming more in-sync with how neural-networking takes place in the human brain. Smart machines are getting smarter and giving more sophisticated data outputs and higher-level generalizations on previously untapped data, like dark data. This, in turn, also has the ability to increase processing power — hugely beneficial for things like parallel processing of large amounts of data to allow more precise outputs for things like sound and visual recognition and diagnostics.
The future of big data looks good and we’ve only just begun. As we increase the amount of technology and applications and demand more accuracy in areas like health diagnosis or manufacturing, big data will have significant impact on the success or failures of enterprises and the movement forward of society.