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New Hortonworks Service Addresses Cloud Migration

June 12, 2017

The cloud is touted for its ability to enable businesses to avoid upfront equipment costs, offload operational expenditures, and scale as needed. And many organizations have embraced the cloud for these very reasons.

But making the transition from on-premises to cloud-based environments can sometimes be challenging. So is managing both cloud- and on-premises-based environments – as many businesses that opt for hybrid strategies do.

Hortonworks Inc. is working to help its customers simplify all that through a new offering called Hortonworks Flex Support Subscription. It provides a single Hortonworks Data Platform support subscription that is transferable between the cloud and on-premises deployments.

Already 25 percent of Hortonworks customers use the company’s solutions in the public cloud. And more companies are transitioning more of

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their applications to the cloud. Indeed, moving to the cloud is the top priority for many data and analytics technology decision makers around the world, according to the global Business Technographics Data and Analytics Survey, 2016, from Forrester.

“Our customers are moving to the cloud in unprecedented numbers and they need a support model that matches today's reality of hybrid deployments with variable load factors,” said Jamie Engesser, vice president of product management at Hortonworks. “For the first time, Hortonworks customers can purchase one support subscription that gives them the flexibility to deploy HDP in the cloud, on-prem or in hybrid architectures as needed.”

Hortonworks, which went public in December 2014, was founded in 2011 to expand on Hadoop.

Hadoop is an open source technology that runs on commodity hardware, and allows users to do compute on disc instead of on network storage – and unnecessarily using lots of network processing. Both of these aspects of Hadoop make it more affordable than alternative solutions. Hadoop and various tools designed to work with it can allow for sophisticated clustering, data mirroring and more. Such functionality can be used in a variety of applications. For example, some companies are using Hadoop to identify anomalies in credit card transactions to identify fraud.

Edited by Alicia Young

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