The collaboration between IBM, Hortonworks and Red Hat aims to accelerate hybrid cloud architectures. [shutterstock: 722088316, iDEAR Replay]

The collaboration between IBM, Hortonworks and Red Hat aims to accelerate hybrid cloud architectures. [shutterstock: 722088316, iDEAR Replay]

Red Hat and Partners Collaborate To Accelerate Hybrid Architectures

Hortonworks, IBM and Red Hat announced an Open Hybrid Architecture Initiative, a new collaborative effort the companies can use to build a common enterprise deployment model. It is designed to enable big data workloads to run in a hybrid manner across on-premises, multi-cloud and edge architectures.

As the initial phase of the initiative, the companies plan to work together to optimize Hortonworks Data Platform, Hortonworks DataFlow and IBM Cloud Private for Data for use on Red Hat OpenShift, an enterprise container and Kubernetes application platform.

This can enable users to develop and deploy containerized big data workloads. As a result, it would be easier for customers to manage data applications across hybrid cloud deployments. In addition, IBM and Hortonworks will extend their joint work to integrate key services offered through Hortonworks DataPlane Service with IBM Cloud Private for Data.

Enterprises are undergoing massive business model transformations. The ability to process and analyze new types and tremendous amounts of data power them. As a result, many are moving to hybrid cloud environments that leverage lightweight microservices in the most efficient manner possible.

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What the initiative is about

Hortonworks and IBM previously announced a collaboration to help businesses accelerate data-driven decision-making. The collaborative effort with Red Hat builds upon that foundation. It has the intent to bring big data workloads to a modern and container-based foundation.

On the whole, this will enable customers to deploy Hortonworks and IBM platforms into a hybrid cloud environment powered by Red Hat OpenShift. Moreover, the initiative includes the following:

  • Hortonworks plans to certify Hortonworks Data Platform as a Red Hat certified Container on Red Hat OpenShift.
  • In addition, Hortonworks will enhance HDP to adopt a cloud-native architecture for on-premises deployments. It is planning to achieve this by separating compute and storage and containerizing all HDP workloads. This allows customers to more easily adopt a hybrid architecture for big data applications and analytics. The common security features, data governance and operations that enterprises require remain untouched.
  • IBM has begun the Red Hat OpenShift certification process for IBM Cloud Private for Data. The move will help provide the vast OpenShift community of developers and users fast access to robust analytics, data science and more. Red Hat will fully support all of the processes across hybrid clouds.

“The work that Red Hat, IBM and Hortonworks are doing to modernize enterprise big data workloads via containerization is important. Above all, it aims at helping customers to take advantage of the agility, economics and scale of a hybrid data architecture,” said Rob Bearden, chief executive officer of Hortonworks. “The innovations resulting from this collaboration can enable a seamless and trusted hybrid deployment model. Enterprises that are undergoing significant business model transformation need this.”

Moving away from public cloud

In addition to competitive challenges, organizations are also scrambling to bring applications once designed for public cloud behind the firewall. The reasons for this are greater control, lower costs, greater security and easier management.

In fact, in a recent IDC Cloud and AI Adoption Survey, more than 80 percent of respondents said they plan to move or repatriate data and workloads from public cloud environments. They want to move it behind the firewall to hosted private clouds or on-premise locations. The plans are realized over the next year. This is because the initial expectations of a single public cloud provider were not realized.

“As these dynamics continue, they’ll work to slow innovation and hinder companies’ progression to enterprise AI,” said Rob Thomas, general manager, IBM Analytics. “Scaling the ladder to AI demands robust data prep, analytics, data science and governance, all of which are easily scaled and streamlined in the kind of containerized, Kubernetes-orchestrated environments that we’re talking about.”

 

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