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What the Hortonworks IPO could mean for the future of big data

14/11/2014 by


Enterprise Hadoop vendor Hortonworks recently filed for an initial public offering. It could change the game in the coming months for big data and Hadoop. 

There's no doubt that the Apache Hadoop open source framework is one of the foundational elements of the recent rise of big data. Hadoop, which allows users to store and process large data sets, is the cornerstone of many big data software stacks.

Companies such as Hortonworks, Cloudera, and MapR are leading the charge in providing commercial distributions of Hadoop. While they were all started at different times, all of these companies have remained private until now.

On Monday, November 10, California-based Hortonworks filed a Form S-1 with the U.S. Securities and Exchange Commission in preparation for an initial public offering of common stock. Put simply, Hortonworks is going public.

The impending IPO of big player like Hortonworks has potentially huge implications for Hadoop and for big data as a greater trend. It's important to examine some of the likely ramifications of this deal.

When considering big data and Hadoop's critical role in many big data deployments, it's essential to note that while Hadoop and big data are definitely not synonymous, they are affected by each another.

Mike Gualtieri, an analyst at Forrester, believes there is a legitimate market for Hadoop vendors and an IPO from Hortonworks was expected. He also noted that competitor MapR said it would be considering a play at an IPO sometime next year.

"Forrester believes Hadoop is a must-have for large enterprises because of what Forrester calls "Hadooponomics" -- its ability to linearly scale both data storage and data processing at a dramatically reduced cost compared to other platforms," Gualtieri said.

If there is a real demand for big data and Hadoop, an IPO by Hortonworks could help further legitimize the big data space and grab the attention of apprehensive corporate IT decision-makers who have avoided big data so far.

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