Operations | Monitoring | ITSM | DevOps | Cloud

Amazon’s Elastic Kubernetes Service (EKS) is the company’s managed option for Kubernetes clusters. We have several articles on using AWS and Kubernetes on our blog, and felt there was a need to highlight some of the key features that AWS EKS offers. Many of these features have been rolled out or updated over the last year. We have mentioned some of these features in other posts, such as our comparison of EKS with AKS and GKE.

In an effort to provide a comprehensive comparison of our tool against other Git clients on the market, we have explored how GitKraken fares against some formidable competitors: And now, we’re taking on the GitKraken vs Fork argument.

Red Hat OpenShift is a Kubernetes-based platform that helps enterprise users deploy and maintain containerized applications. Users can deploy OpenShift as a self-managed cluster or use a managed service, which are available from major cloud providers including AWS, Azure, and IBM Cloud. OpenShift provides a range of benefits over a self-hosted Kubernetes installation or a managed Kubernetes service (e.g., Amazon EKS, Google Kubernetes Engine, or Azure Kubernetes Service).

In Part 1, we explored three primary types of metrics for monitoring your Red Hat OpenShift environment: We also looked at how logs and events from both the control plane and your pods provide valuable insights into how your cluster is performing. In this post, we’ll look at how you can use Datadog to get end-to-end visibility into your entire OpenShift environment.

In Part 1 of this series, we looked at the key observability data you should track in order to monitor the health and performance of your Red Hat OpenShift environment. Broadly speaking, these include cluster state data, resource usage metrics, and information about cluster activity such as control plane metrics and cluster events. In this post, we’ll cover how to access this information using tools and services that come with a standard OpenShift installation.

Kubeflow v1.0 was released on March 2, 2020 Kubeflow and there was much rejoicing. The banner announcement, “Cloud-Native ML for Everyone,” while clearly hyperbole, is evidenced by the streamlined command-line interface (CLI), informative and intuitive dashboard and comprehensive cloud provider documentation. Compounded with a best-in-class product suite supporting each phase in the machine learning (ML) lifecycle, Kubeflow stands unrivaled in the arena of ML standardization.

Google, the original developer of Kubernetes, also provides the veteran managed Kubernetes service, Google Kubernetes Engine (GKE). GKE is easy to set up and use, but can get complex for large deployments or when you need to support enterprise requirements like security and compliance. Read on to learn how to take your first steps with GKE, get important tips for daily operations and learn how to simplify enterprise deployments with Rancher.