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10 Data Analytics Predictions for 2016: Page 2

By Ann All     Feedback
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NoSQL Goes Mainstream

With the rise of Web, mobile and IoT applications, use of NoSQL is becoming more popular, said Bob Wiederhold, CEO of Couchbase, provider of NoSQL database technology. "In 2016, we'll see more enterprises re-platform their data management systems using NoSQL to overcome the limits of their 30-year old legacy relational systems," he said.

While NoSQL databases are a better fit than traditional relational databases for supporting Web, mobile and IoT applications, said Ravi Mayuram, Couchbase's senior vice president of products and engineering, there is an IT skills gap for building new data management platforms.

"It is incumbent that database developers evolve their skills in order to meet these new platforms, but the technology innovators must remove much of the friction to make the transition from relational databases to NoSQL as easy as possible by extending traditional tools and languages," Mayuram said. "This will be done in 2016 in both the private sector and in academia, further fueling the growth of enterprise NoSQL deployments."

Data Storage Innovation

While spinning disks help companies scale data growth, it takes too much time to get the data off the disk, said Splice Machine's Zweben, who predicts a huge future for solid-state drive (SSD). "With SSD, there are no moving parts, much like being in memory, so the process of getting the exact data you need is extremely fast. In 2016, all new applications will use SSDs and spinning disks will become a thing of the past, he said.

As consumer demand for flash memory continues to drive down its cost, we will see more flash deployments in Big Data in the enterprise, said MapR's Schroeder. "The optimal solution will combine flash and disk to support both fast and dense configurations. In 2016, this new generation of software-based storage that enables multi-temperature solutions will proliferate so organizations will not have to choose between fast and dense; they will be able to get both."

Distributed Data Workloads

Technology cycles have swung back and forth from centralized to distributed data workloads, noted MapR's Schroeder. "Big Data solutions initially focused on centralized data lakes that reduced data duplication, simplified management and supported a variety of applications including customer 360 analysis. However, in 2016, large organizations will increasingly move to distributed processing for Big data to address the challenges of managing multiple devices, multiple data centers, multiple global use cases and changing overseas data security rules."

All about the Algorithm

As companies become increasingly interested in using data analysis to detect and mitigate cyber attacks, they will realize that effective algorithms, not the data itself, is key, said Hitesh Sheth, CEO of Vectra Networks, a provider of automated threat management software.

"To combat cyber attacks that evade perimeter security, enterprises are collecting petabytes of flow and log data in hopes of detecting attacks," Sheth said. "These systems turn into unwieldy analysis projects that typically detect an attack only after the damage is done, wasting valuable time and money. Threat detection algorithms will play a significant role in making Big Data more useful and actionable."

Ann All is the editor of Enterprise Apps Today and eSecurity Planet. She has covered business and technology for more than a decade, writing about everything from business intelligence to virtualization.

This article was originally published on January 4, 2016
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