Operations | Monitoring | ITSM | DevOps | Cloud

This article will show you how to leverage the Sysdig Terraform provider to follow Secure GitOps principles and use security as code in your infrastructure to define Alerts, Rules, and Policies.

AI and Machine Learning are becoming critical differentiators in the technology landscape. By their nature, AI and ML are computation hungry workloads. They require best-in-class distributed computing environments to thrive. AI and ML present a perfect use case for Kubernetes, the distributed computing platform engineered at Google to run their massive workloads.

Many companies are moving their infrastructure and web applications to the cloud. Along with moving to the cloud, organizations are finding new ways to remain agile and nimble, especially when it comes to software. By utilizing Amazon AWS and Amazon ECS, companies are decoupling their monolithic applications and taking advantage of microservice architecture.

Norwegian leader in fleet management, equipment and vehicle tracking, ABAX is one of Europe’s fastest-growing technology businesses. The company provides sophisticated fleet tracking, electronic mileage logs and equipment and vehicle control systems to more than 26,500 customers. ABAX manages over 250,000 active subscriptions that connect a variety of vehicles and industrial equipment subscriptions.

Container technologies have taken the infrastructure world by storm. Ideal for microservice architectures and environments that scale rapidly or have frequent releases, containers have seen a rapid increase in usage in recent years. But adopting Docker, containerd, or other container runtimes introduces significant complexity in terms of orchestration. That’s where Kubernetes comes into play.

As explained in Part 1 of this series, monitoring a Kubernetes environment requires a different approach than monitoring VM-based workloads or even unorchestrated containers. The good news is that Kubernetes is built around objects such as Deployments and DaemonSets, which provide long-lived abstractions on top of dynamic container workloads.

In the previous post in this series, we dug into the data you should track so you can properly monitor your Kubernetes cluster. Next, you will learn how you can start inspecting your Kubernetes metrics and logs using free, open source tools. In this post we’ll cover several ways of retrieving and viewing observability data from your Kubernetes cluster.

This blog will examine how Rancher improves the life of DevOps teams already invested in AWS’s Elastic Kubernetes Service (EKS) but looking to run workloads on-prem, with other cloud providers or, increasingly, at the edge. By reading this blog you will also discover how Rancher helps you escape the undeniable attractions of a vendor monoculture while lowering costs and mitigating risk.

Approximately one year ago, Kubernetes 1.14 made support of Windows containers running on Microsoft Windows Server nodes generally available. This was a declaration that Windows node support was stable, well-tested, and ready for adoption, meaning the vast ecosystem of Windows-based applications could be deployed on the platform.