Operations | Monitoring | ITSM | DevOps | Cloud

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.

Are you currently dealing with complex and fast-changing Cloud & Container environments? If your answer to that question is yes, then you are probably looking for an easy solution that gives you complete control to make sense of all these fast and complex IT environments. In the dynamic world of microservices and containers, traditional monitoring solutions are no longer sufficient to provide needed visibilities to maintain healthy and happy environments.

The first question we always get from developers before trying out our product is - “What’s the difference between your platform and Heroku?”. We think it’s very important to answer your questions clearly and this article is meant to be our answer.‍

Most Kubernetes deployments live on the cloud. According to the CNCF, while 63 percent of companies use AWS for Kubernetes, 29 percent are deploying Kubernetes on Azure, and the number is rapidly growing. Microsoft Azure provides mature Kubernetes tools, primarily the Azure Kubernetes Service (AKS). In this post, we’ll show you how to run your first Kubernetes cluster on AKS, and how to manage enterprise Kubernetes deployments on AKS with Rancher.

eBPF is a hot topic right now; most of the infrastructure-focused conferences and events have included talks on eBPF over the past year, which is creating a lot of interest in the technology. You might be wondering what eBPF is. eBPF stands for “extended Berkeley Packet Filter” which is a feature in modern Linux kernels that allows you to write mini-programs that are attached to low-level hooks in the Linux kernel, that execute based on certain events (e.g. filtering network traffic).

What is Podman? Well, we will start by saying that Podman is a container engine developed by RedHat, and yes, if you thought about Docker when reading container engine, you are on the right track. Podman wants to be the alternative to the well-known container Docker engine, but you may wonder: what does RedHat offer through Podman? Why should I switch to Podman? Is Podman the replacement for Docker or just another competitor?

Rancher is the enterprise computing platform to run Kubernetes on-premises, in the cloud and at the edge. It’s an excellent platform to get started with containers or for those who are struggling to scale up their Kubernetes operations in production. However, in a world increasingly dominated by public infrastructure providers like Google Cloud, it’s reasonable to ask how Rancher adds value to services like Google’s Kubernetes Engine (GKE).