Operations | Monitoring | ITSM | DevOps | Cloud

Fleet, also known as “Continuous Delivery” in Rancher, deploys application workloads across multiple clusters. However, most applications need configuration and credentials. In Kubernetes, we store confidential information in secrets. For Fleet’s deployments to work on downstream clusters, we need to create these secrets on the downstream clusters themselves.

In this article, we will deploy a clustered Prometheus setup that integrates Thanos. It is resilient against node failures and ensures appropriate data archiving. The setup is also scalable. It can span multiple Kubernetes clusters under the same monitoring umbrella. Finally, we will visualize and monitor all our data in accessible and beautiful Grafana dashboards.

Have you heard about eBPF? It’s the technology that’s set to transform the Kubernetes landscape. In this article, we’ll explore what eBPF is and why it’s poised to become the next big thing in Kubernetes. But here’s the catch – despite its game-changing potential, it seems that few people are truly aware of its impact. Let’s delve into the details and discover why you should care.

Kubernetes has emerged as the go-to platform for container orchestration, empowering organizations to efficiently deploy and manage applications at scale. As the demands on applications continue to grow, scaling Kubernetes becomes essential to ensure high availability, optimal performance, and seamless expansion. Today, we will delve into the intricacies of scaling Kubernetes on AWS, exploring best practices, popular tools, performance optimization strategies, cost considerations, and future trends.

If you are looking into the cloud-native world, then the chances of coming across the term “Kubernetes” is high. Kubernetes, also known as K8s, is an open-source system that has the primary responsibility of being a container orchestrator. This has quickly become a lifeline for managing our containerized applications through creating automated deployments.

Building Docker images efficiently is crucial for developers and organizations seeking streamlined development and deployment processes, and it’s a real topic for the 50k developers on Qovery; a few weeks ago, a topic was even open on our forum about it. In this article, we will explore three valuable tips gathered along the way to improve the build time of your Dockerfiles, allowing you to optimize your workflow and save valuable time.

The benefits of going cloud-native are far reaching: faster scaling, increased flexibility, and reduced infrastructure costs. According to Gartner®, “by 2027, more than 90% of global organizations will be running containerized applications in production, which is a significant increase from fewer than 40% in 2021.” Yet, while the adoption of containers and Kubernetes is growing, it comes with increased operational complexity, especially around monitoring and visibility.

In the grand tapestry of software engineering, our journey often winds through labyrinthine layers of application logic. Here, bugs play a compelling game of hide-and-seek, and features dance in an unpredictable ballet. During these instances of fervent exploration, we find ourselves longing for a reliable compass—a secret weapon—to help us decipher the riddles that lie ahead. Cue execution tags, our luminous lighthouse cutting through the dense fog of complexity.