Operations | Monitoring | ITSM | DevOps | Cloud

Patching systems is not a new problem; this is something that organizations are very familiar with. Organizations have tried solving this issue in various ways by cobbling together multiple tools, processes, different teams, and more. Organizations are constantly trying to stay secure and keep their systems up to date, but patch management isn’t a simple process. In fact, applying the patch itself is just one part of the overall workflow, which involves multiple teams and various steps.

As the industry leader in the DevOps space, we’re excited to share recent updates to the Puppet Enterprise platform that give infrastructure operations teams the flexibility to manage and automate more infrastructure and complex workflows in a simple yet powerful way.

Client libraries are collections of code that make it easier for developers to write flexible and efficient applications that interface with APIs. Datadog provides client libraries so you can programmatically interact with our API to customize dashboards, search metrics, create alerts, and perform other tasks. We’re pleased to announce that we’ve developed and open-sourced two new client libraries for Java and Go in addition to our existing Ruby and Python libraries.

MicroK8s was first released in late 2018 and has seen significant adoption rates from developers and enterprises alike ever since. Taking increasing demand and curiosity around the topic, we have already given an introduction to MicroK8s as well as covered how to deploy MicroK8s locally in previous blog posts. This time, we’ll take a look at MicroK8s’ applied value, by examining common MicroK8s use cases. Among others, this includes AI/ML workflows.

The Cloud Is a No-Brainer… Most companies by now have a “to the cloud” initiative that’s at one stage or another of maturity. It’s a great way to make better use of all your resources—people, time, and money. And it gives you more flexibility. You don’t have to size your entire application architecture for important but short-lived peaks, such as Black Friday for retailers or year-end ERP closing for large manufacturers, to name just a couple examples.

Get Better Observability With Machine Learning Anomaly Detection. DevOps teams today are challenged with the rapid growth and complexity of infrastructure. Managing those environments only through static thresholds becomes insufficient, so to address this issue, modern DevOps teams rely on advanced ML/AI algorithms.

As the Kubernetes ecosystem grows, new technologies are being developed that enable a wider range of applications and use cases. The growth of edge computing has driven a need for some of these technologies to enable the deployment of Kubernetes to low-resource infrastructure to the network edge. In this blog post, we are going to introduce you to one method of deploying k3OS to the edge. You can use this method to automatically register your edge machine to a Rancher instance as a control plane.