Operations | Monitoring | ITSM | DevOps | Cloud

In this post we will talk about using image scanning on admission controller to scan your container images on-demand, right before your workloads are scheduled in the cluster. Ensuring that all the runtime workloads have been scanned and have no serious vulnerabilities is not an easy task. Let’s see how we can block any pod that doesn’t pass the scanning policies before it even runs in your cluster.

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.

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.