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Datera Universal Data Fabric Simplifies Stateful Application Provisioning on Kubernetes

Integration with Google's open source container system
This is a Press Release edited by StorageNewsletter.com on 2016.09.30

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Datera, Inc., the hyperscale data company, announced a new integration with Google's open source container system Kubernetes.

The Datera universal data fabric seamlessly complements the Kubernetes orchestration framework and allows for automatic provisioning and deployment of stateful applications.

With the Kubernetes integration, Datera translates service-level objectives such as performance, durability, security and capacity into its universal data fabric.

This integration allows enterprises and service providers to build private clouds at any scale for any kind of application.

Google recently released a Kubernetes update that supports stateful applications to run in containers, enabling developers to run thousands of traditional enterprise workloads. However, traditional storage is cumbersome and expensive to deploy and manage, especially at scale. With this integration, Datera expands Kubernetes to automatically tailor runtime storage capabilities for each stateful application, automatically scale applications, and automatically isolate and protect them with dedicated storage segments. This brings public-cloud-like infrastructure capabilities to any data center and allows businesses to manage applications of any kind at a fraction of the cost and time.

"As data centers are re-imagined for cloud, there's a need for a universal platform that can orchestrate workloads and data anywhere across private and public clouds," said Marc Fleischmann, CEO and co-founder, Datera. "Google Kubernetes contributes the necessary orchestration framework and Datera supplies the complementary universal data fabric. Kubernetes and containers are emerging key technologies, and foundational integrations like this will take them mainstream to create tomorrow's autonomic multi-cloud datacenter."

Demo of the integration

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