Kubernetes Leftovers
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With its Kubernetes bet paying off, Cloud Foundry doubles down on developer experience
More than 50% of the Fortune 500 companies are now using the open-source Cloud Foundry Platform-as-a-Service project — either directly or through vendors like Pivotal — to build, test and deploy their applications. Like so many other projects, including the likes of OpenStack, Cloud Foundry went through a bit of a transition in recent years as more and more developers started looking to containers — and especially the Kubernetes project — as a platform on which to develop. Now, however, the project is ready to focus on what always differentiated it from its closed- and open-source competitors: the developer experience.
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Kubernetes in the Enterprise: A Primer
As Kubernetes moves deeper into the enterprise, its growth is having an impact on the ecosystem at large.
When Kubernetes came on the scene in 2014, it made an impact and continues to impact the way companies build software. Large companies have backed it, causing a ripple effect in the industry and impacting open source and commercial systems. To understand how K8S will continue to affect the industry and change the traditional enterprise data center, we must first understand the basics of Kubernetes.
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Google Cloud rolls out Cloud Dataproc on Kubernetes
Google Cloud is trialling alpha availability of a new platform for data scientists and engineers through Kubernetes.
Cloud Dataproc on Kubernetes combines open source, machine learning and cloud to help modernise big data resource management.
The alpha availability will first start with workloads on Apache Spark, with more environments to come.
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Google announces alpha of Cloud Dataproc for Kubernetes
Not surprisingly, Google, the company that created K8s, thinks the answer to that question is yes. And so, today, the company is announcing the Alpha release of Cloud Dataproc for Kubernetes (K8s Dataproc), allowing Spark to run directly on Google Kubernetes Engine (GKE)-based K8s clusters. The service promises to reduce complexity, in terms of open source data components' inter-dependencies, and portability of Spark applications. That should allow data engineers, analytics experts and data scientists to run their Spark workloads in a streamlined way, with less integration and versioning hassles.
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