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For all the numbers showing explosive growth of the public cloud, there remains a stubborn segment of the market where policies and regulations prohibit the moving of data into any kind of public cloud.
Robin Systems is middleware that creates application-defined infrastructure. That includes levering Docker containers to isolate the application, but more importantly, applying software-defined storage to make it “application aware.” That’s the layer that Kubernetes lacks. The smarts of the Robin platform comes at the management piece where all the configurations and commands to deploy and manage failover and recovery are abstracted to a single click. The idea is making your do-it-yourself private cloud have the same automatic deployment that you get from public cloud database as a service PaaS offerings.
Robin Systems is not the first provider to traverse this path. Portworx provides a software-defined storage solution for containerized workloads that supports Kubernetes and other orchestrators such as Marathon, Nomad, and Swarm. It makes storage look and operate like cloud storage. Mesosphere is the original player in this space, which originally supplied the Marathon orchestrator that was part of the original Apache Mesos spec but now also supports Kubernetes.
Also: What is Docker and why is it so darn popular?
By being the most recent player to the market, Robin is differentiating itself by zeroing in on Kubernetes and incorporating automation. It touts its Hadoop friendliness with its certification from Hortonworks. Robin Systems wants to automate container deployment for enterprises seeking the best of both worlds for private cloud deployment: avoid the vendor lock-in and avoid at least some of the baggage.
Previous and related coverage
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Containers vs. virtual machines: A simplified answer to a complex question TechRepublic
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What does ‘hybrid cloud’ mean? It depends on whom you ask
A new survey shows C-level executives define hybrid cloud slightly differently than IT specialists; level of cloud adoption also influences views.
AI chips for big data and machine learning: GPUs, FPGAs, and hard choices in the cloud and on-premise
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Related Topics:
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