Operations | Monitoring | ITSM | DevOps | Cloud

At Moogsoft we use Jenkins to implement our CICD Pipelines. We run Jenkins where we run most everything else; Kubernetes, but you don’t need to have Jenkins running on Kubernetes to use this plugin. This is made possible by the community maintained Kubernetes plugin. Recently we had the need to not only run agents local to the same cluster that Jenkins runs in, but in other clusters across different regions.

In our first post we went over setting up the Kubernetes Plugin. This described the basic setup of getting the plugin configured, and set with the proper perms to function. In this post we will go over how to leverage the plugin to generate agent pods. At Moogsoft most of our pipelines are scripted and are built inside of, or from parts of, Jenkins shared functions library we maintain.

Kubernetes workloads are highly dynamic, ephemeral, and are deployed on a distributed and agile infrastructure. Application developers, DevOps teams, and site reliability engineers (SREs) often require better visibility of their different microservices, what their dependencies are, how they are interconnected, and which other clients and applications access them. This makes Kubernetes observability challenges unique.

In a previous post, we described how we envision cloud-native initiatives reaching the 2.0 phase, where phase 1 was centered around providing clusters and running its underlying infrastructure effectively. Now that teams are starting to move some of their existing services to a microservices architecture, developers and platform engineers are being tasked with implementing the right policies and governance controls to ensure applications are running as securely as possible.

It has been 6 months since our engineering team started to work on what we decided to call Qovery v2. But what makes this next release so cool? Here are the top 4 features that make this release so appealing.

Microsoft recently reported two widespread cryptomining attacks targeting Kubeflow, a popular cloud-native platform for machine learning (ML) workloads on Kubernetes. Attackers targeted Kubeflow installations using either the Kubeflow central dashboard interface or Kubeflow Pipelines interface for scheduling crypto-mining workloads.