Within the next two years, around 65 percent of analytics packages will feature the Apache Hadoop framework for handling massive datasets, according to a new research note from Gartner.
Hadoop, an open-source framework for running data applications on large hardware clusters, has become a favorite of not only Facebook, but also IBM and other large firms that wrestle with enormous amounts of data. IT vendors have already begun integrating Hadoop into their products—for example, SAP’s recent HANA-based analytics bundle.
“While IT organizations conduct trials over the next few years, especially with Hadoop-enabled database management system (DBMS) products and appliances,” read Gartner’s note, “application providers will go one step further and embed purpose-built, Hadoop-based analysis functions within packaged applications.”
Other research firms have predicted a similar rise for Hadoop. Over the summer, Market Research Media suggested the framework would power a $2.2 billion market by 2018. While other research firms have pegged that market at a somewhat lower valuation, many are likewise optimistic about Hadoop’s future prospects.
But Hadoop’s open-source nature could hinder IT vendors seeking to make some money off proprietary platforms. Research firm IDC believes that competition between open-source and commercial platforms could force vendors to lower product-licensing fees, which could depress revenues.
“The Hadoop and MapReduce market will likely develop along the lines established by the development of the Linux ecosystem,” Dan Vesset, vice president of Business Analytics Solutions for IDC, wrote in a statement back in May. “Over the next decade, much of the revenue will be accrued by hardware, applications, and application development and deployment software vendors.”
However popular Hadoop becomes over the next two years, it could very well lag behind natural language and spoken-word capabilities as a “must have” for analytics platforms. Gartner believes that, within the next four years, 70 percent of BI vendors will integrate tools that allow users to speak to analytics applications. “Ultimately, ‘personal analytic assistants’ will emerge that understand user context, offer two-way dialogue, and (ideally) maintain a conversational thread,” the note read.
Indeed, some Big Data platforms—most notably IBM’s Watson—are already exploring the intersection of natural language and analytics. But as Watson recently proved, there’s also a point where a super-smart system can learn language that’s a bit too natural.