Cloud and appliances to be biggest drivers of big data in 2016

The coming year will see a new wave of adoption for big data technologies, with cloud computing and the need to gather data from a growing number of appliances among the key drivers of this.

Predictions for the industry in 2016 by Ovum note that there will be a “rising tide of IT spending” that will boost investment in big data analytics.

Principal analyst at Ovum and author of the report Tony Baer said: “The next wave of big data investment will target more of the enterprise mainstream that will have more modest IT and data science skills compared with the early adopters.”

Despite the increasing interest in tools such as Spark – which Ovum noted will be the fastest-growing set of workloads in 2016 – SQL will remain a key first step for organisations looking to make the most of big data.

“Don’t count SQL out,” Mr Baer said. “SQL-on-Hadoop remains a potent draw for Hadoop vendors who are aiming to reach the large base of enterprise SQL developers out there.”

He added that Spark will be complementary to SQL, providing businesses with additional paths to insights, such as through the streaming of graph analysis. this can then be queried using language that enterprise database developers are very familiar with.

Another key prediction for 2016 will be the emergence of data lakes as a key priority for mature Hadoop users. Enterprises that have already successfully put analytics into production across multiple lines of business and stakeholder groups will drive increased demand for tools to govern the data lake and make it more transparent.

As a result, Ovum forecast significant growth in tools that build on emerging data lineage capabilities to catalogue, protect, govern access, tier storage, and manage the lifecycle of data stored in data lakes.

“Governance of data lakes will not be built in a day. While some of the tooling exists today, capabilities such as managing the lifecycle of multi-tiered storage will have to be extended to cover the growing heterogeneity of Hadoop clusters,” Mr Baer said.

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