Nimbula, MapR want to give Hadoop a home on your private cloud

We all know that Hadoop is the darling of the big data crowd. However, the data-processing platform typically runs on big, brawny dedicated clusters of physical hardware. And although it’s massive and powerful, it’s also messy and hard to manage. But that’s starting to change.

Witness Tuesday’s news that private-cloud startup Nimbula worked with MapR Technologies to put MapR’s Hadoop distribution on Nimbula’s private cloud infrastructure. The resulting offering pairs Nimbula Director — with its management and scaling capabilities — with MapR’s high-speed Hadoop product, the two companies said.

The promised benefit is that Nimbula brings its elastic, multi-tenant technology, Hadoop brings its big data capabilities, and together they offer an infrastructure that promises to be easier to scale and manage by mere mortals.

According to the company’s statement about the news:

Nimbula Director … takes bare metal servers with local disks and turns them into a large multi-tenant pool of compute with a self-service provisioning interface enabling the repeated provisioning and deprovisioning of Hadoop and non-Hadoop workloads. The automation of the Hadoop services from MapR and the automation of the underlying instances from Nimbula work together to maintain a fully-functional and highly-available Hadoop cluster.

VMware’s plan for highly available Hadoop

Making Hadoop more manageable, more available and more flexible is a huge opportunity and one that is being attacked by many tech companies. VMware’s Serengeti project (s vmw) aims to put Hadoop on VMware’s vSphere hypervisor, as Derrick Harris reported in June.

And MapR is definitely playing the field, trying to entrench its Hadoop distribution on as many cloud platforms as possible — it already has partnerships with Amazon Web Services (s amzn) and Google Compute Engine, as well.

One thing seems clear: The company or companies that can solve the problem of making big data jobs less of a big management problem, are in for a lot of business.

Feature image courtesy of Shutterstock user 1971yes.