Starting with Argo CD 2.4, creating config management plugins or CMPs via configmap has been deprecated, with support fully removed in Argo CD 2.8. While many folks have been using their own config management plugins to do things like `kustomize –enable-helm`, or specify specific version of Helm, etc – most of these seem to have not noticed the old way of doing things has been removed until just now!

Gremlin's Detected Risks feature immediately detects any high-priority reliability concerns in your environment. These can include misconfigurations, bad default values, or reliability anti-patterns. A common risk is deploying Pods without setting a CPU request. While it may seem like a low-impact, low-severity issue, not using CPU requests can have a big impact, including preventing your Pod from running.

The OpenTelemetry Collector is a useful application to have in your stack. However, deploying it has always felt a little time consuming: working out how to host the config, building the deployments, etc. The good news is the OpenTelemetry team also produces Helm charts for the Collector, and I’ve started leveraging them. There are a few things to think about when using them though, so I thought I’d go through them here.

Shared development environments have long been the norm at a time when agility and collaboration reign supreme. But this once-effective approach now reveals its limitations, giving rise to productivity and collaboration bottlenecks, often leading to conflicts and resource contention that stall projects and hinder development teams. In this article, we will explore the key challenges posed by collaboration bottlenecks and introduce the ephemeral environments solution that redefines how teams operate.

Since its inception, Amazon Elastic Container Service (ECS) has emerged as a strong choice for developers aiming to efficiently deploy, manage, and scale containerized applications on AWS cloud. By abstracting the complexities associated with container orchestration, ECS allows teams to focus on application development, while handling the underlying infrastructure, load balancing, and service discovery requirements.

One of the most critical aspects of a developer's workflow is reducing deployment time. While Qovery does a lot of heavy lifting to make your deployments faster and more efficient, there are still ways to fine-tune the process on your end. This guide will help you optimize deployment time across three essential phases: Build, Deployment, and Runtime.

In this tutorial, you’ll see how to deploy Solr on Kubernetes. You’ll also see how to use the Solr Operator to autoscale a SolrCloud cluster based on CPU with the help of the Horizontal Pod Autoscaler. Let’s get going! 🙂

In the ever-evolving landscape of software development, where agility and reliability are paramount, Kubernetes has emerged as a game-changer with its container orchestration capabilities. However, as applications become more complex and distributed, managing Kubernetes workflows presents a unique set of challenges.

You’ve loaded data into Densify and after reviewing recommendations your developers don’t want to take the recommended action – they aren’t trusting, yet. This is one of the top issues identified by the FinOps Foundation and many of our customers of Densify – but not all. Let’s explore what some customers do differently to build organizational trust in taking optimization recommendations.

Containers and microservices have revolutionized the way applications are deployed on the cloud. Since its launch in 2014, Kubernetes has become a de-facto standard as a container orchestration tool. Helm is a package manager for Kubernetes that makes it easy to install and manage applications on your Kubernetes cluster. One of the benefits of using Helm is that it allows you to package all of the components required to run an application into a single, versioned artifact called a Helm chart.