Operations | Monitoring | ITSM | DevOps | Cloud

Quickly deploy a production-ready Amazon EKS cluster in just 10 minutes. This step-by-step guide covers essential setup and best practices for running scalable, secure Kubernetes workloads on AWS.

Accelerate development & testing with ephemeral environments. Explore their benefits, key tools (Docker, Kubernetes, CI/CD), and how Qovery simplifies setup for on-demand, production-like dev environments.

The best problems are the ones you never have to deal with. That's why smart teams catch issues early on, before they impact production. Cycle gives great visibility to spot troublesome workloads, control resource usage, and take action before things go sideways.

In this blog series we’ve examined Five Myths of Apache Spark Optimization. But one final, bonus myth remains unaddressed: I’ve done everything I can. The overprovisioned resources that lead to underutilized CPU and memory in my workloads is just the cost of doing business.

As organizations scale their Kubernetes infrastructure, one truth becomes clear: no single team can optimize it alone. Efficiency, resilience, and cost-effectiveness in Kubernetes environments depend on the collective effort of multiple personas, each bringing essential knowledge and responsibility. But it’s not just about division of labor. It’s about active collaboration across roles to unlock the full potential of the platform.

Kubernetes gives you the amazing power to deploy and manage containerized applications. But this power comes with a trade-off. Instead of letting you focus only on writing code and delivering features, Kubernetes also shifts the burden of resource optimization i.e., cost control, performance, and scalability, directly onto your shoulders. The answer to these challenges is automation. Automated optimization takes the guesswork out of resource allocation.

Elasticsearch stands as one of the most robust open-source search engines available today. Built on Apache Lucene, it handles complex search operations, real-time analytics, and large-scale data processing with impressive speed and accuracy. Kubernetes has transformed how we deploy and manage containerized applications. This orchestration platform automates deployment, scaling, and operations of application containers across clusters of hosts.

As Kubernetes environments grow in scale and complexity, platform teams face increasing pressure to secure workloads without slowing down application delivery. But managing and enforcing network policies in Kubernetes is notoriously difficult—especially when visibility into pod-to-pod communication is limited or nonexistent. Teams are often forced to rely on manual traffic inspection, standalone logs, or trial-and-error policy changes, increasing the risk of misconfiguration and service disruption.