Automation has become more than just a “nice to have” choice. It’s an essential part of the modern business landscape, promising increased efficiency, reduced costs, and improved accuracy. However, despite its potential benefits, many organizations struggle when trying to implement automation. In this article, we’ll explore some of the most common implementation mistakes we’ve encountered and how to navigate them effectively.

Discover the powerful capabilities of SharePoint Online, a cornerstone for modern collaboration and document management. Our free eBook, “Mastering SharePoint Online,” offers a deep dive into maximizing productivity and collaboration within your organization. Download it now to embark on your journey toward SharePoint mastery.

This article is part of our series “Codefresh in the Wild” which shows how we picked public open-source projects and deployed them to Kubernetes with our own pipelines. We will use several tools such as GitHub, Docker, Codefresh, Argo CD, Kubernetes. This guide chronicles how we integrated all those tools together in order to build an end-to-end Kubernetes deployment workflow.

The performance, scalability, and flexibility that Kubernetes offers are big reasons for its rapid adoption. At the same time, however, it’s not simple or easy to manage Kubernetes clusters, which means third party tools are practically a requirement as you scale. I have been reminded of this a lot lately. While attending three major tech conferences in recent months, I spoke with a number of companies at varying phases of their Kubernetes journey.

Even the most reliable and well-designed software systems experience failures. Tracking incident response metrics helps teams strengthen both organizational preparedness and system resilience by uncovering trends, gaps, and opportunities for improvement. In short, important metrics for incident management are: Understanding these metrics helps engineering leaders improve service uptime, meet SLAs, and align operational capacity.

For a lot of teams, incident management can be a bit of a headache. It's stressful. It's not optimized. The whole process can feel like it's being held together with tape. Worst of all? Responders are the ones feeling the brunt of it. But in reality, your customers are, too. Think about it: But honestly, the situation doesn't even have to be so dire. Things can be, generally speaking, totally fine.

We are thrilled to share the exciting news that Qovery has once again been recognized as a Momentum Leader for DevOps in the Winter 2024 Grid Report. This marks the third consecutive time we've received this prestigious acknowledgment, and we couldn't be more grateful for the ongoing support of our incredible community of users 🙏

Efficient code can be defined by numerous factors, in addition to what others deem necessary based on their experiences. Per this article, we will define it as: Measuring and comparing your code on its design, optimization, and performance; while considering its use cases, compute resources, quality, scalability, and structure. Along with our definition, it is important to understand some key concepts before we dive further into some best practices and additional support to contextualize our definition.

Edge AI is transforming the way that devices interact with data centres, challenging organisations to stay up to speed with the latest innovations. From AI-powered healthcare instruments to autonomous vehicles, there are plenty of use cases that benefit from artificial intelligence on edge computing. This blog will dive into the topic, capturing key considerations when starting an edge AI project, main benefits, challenges and how open source fits into the picture.

In Kubernetes management, the ability to efficiently monitor resource utilization is very important for cluster owners. Have you ever heard about the kubectl top command and wondered how it could revolutionize your Kubernetes management experience? If so, you're in the right place. The kubectl top command – a powerful tool that offers snapshots of resource metrics for pods and nodes within a Kubernetes cluster.