The latest release of the D2iQ Kubernetes Platform (DKP) represents yet another significant boost to DKP’s multi-cloud and multi-cluster management capabilities. D2iQ Kubernetes Platform (DKP) 2.6 features the new DKP AI Navigator, an AI assistant that enables DevOps to more easily manage Kubernetes environments. As Forbes noted in Addressing the Kubernetes Skills Gap, “The Kubernetes skills shortage is impacting companies across sectors.”

Most vendor trials take quite a bit of effort and time. Now, with Mezmo’s new Welcome Pipeline, you can get results with your Kubernetes telemetry data in just a couple of minutes. But first, let’s discuss why Kubernetes data is such a challenge, and then we’ll overview the steps.

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.

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.

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.

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! 🙂

Cloud Tagging should be automated, not left to humans. Optimization in the cloud is actually really simple. Here’s how we get our customers thinking differently which in turn makes them successful. “To your developers the cloud is like a candy store is to a kid where all the candy is free”. Just as parents need to teach their kids the value of money, you need to teach your developers the value of cloud spend.

Welcome to the Calico monthly roundup: August edition! From open source news to live events, we have exciting updates to share—let’s get into it!