Operations | Monitoring | ITSM | DevOps | Cloud

Organizations using entry-level tools face limited visibility, slow issue response, and scalability challenges that increase downtime risks. ManageEngine solves this with its enterprise-grade, AI-powered platform, delivering end-to-end digital experience monitoring in cloud and On-Premise versions. Switching isn't only easy, it brings predictive intelligence, global precision, and seamless growth support to your workflows—protecting your revenue while improving your operational excellence.

On February 17, 2026, YouTube went down for users worldwide. Starting around 8:00 PM ET, the platform's homepage, Shorts feed, sign-in system, smart TV apps, YouTube Music, and YouTube Kids all stopped working. Over 21,000 reports were logged on IsDown alone. The error message was the same everywhere: "Something went wrong." For consumer users, it was an inconvenience. For businesses that depend on YouTube — content teams, advertisers, media companies, live streamers — it was a blind spot.

How database indexes work, when to use B-tree vs hash indexes, clustered vs non-clustered indexes, and how to tell if your indexes are actually helping.

AI coding agents are excellent at writing code. Ask Claude Code, Codex, or Cursor to add a feature, and it just works. At Checkly, we were ready for the new agentic world from the start! Monitoring as Code means your entire monitoring setup lives in your repository. API Checks, Browser Checks, alert channels, status pages; everything is defined in code, managed with the Checkly CLI, and version-controlled like any other part of your stack.

SREs and application developers rely on telemetry data to understand and improve their systems. As organizations scale and evolve, those systems generate an ever-growing volume of metrics, logs, and traces. But more data alone does not make it easier to improve performance or reliability: Identifying meaningful optimizations still requires careful investigation and analysis.

Previously I have written about how useful public cloud storage can be when starting a new project without knowing how much data you will need to store. However, as datasets grow over time, the costs of public cloud storage can become overwhelming. This is where an on premise, or co-located, self-hosted storage system becomes advantageous: it provides the greatest range of benefits, including cost, performance, security, and data sovereignty.

The explosion of AI has created a continuous demand for computing power. At the heart of this need sits one critical resource: GPUs. They have become the hardware of choice for AI and machine learning, particularly deep learning workloads that operate on enormous data sets. However, as organisations race to train larger models and deliver faster inference, many are discovering that owning and operating GPU infrastructure isn’t always practical.

Last time, we explored the concept of variable scopes in Icinga 2, which help you manage and organize your DSL configurations effectively. As promised, today we’ll dive into another, how shall I say, advanced topic: Namespaces in Icinga 2.

Kubernetes operators extend the Kubernetes API with custom logic, automating tasks like provisioning, configuration, and policy enforcement. Instead of managing these tasks manually or with ad hoc scripts, Operators codify your workflows into controllers that run natively inside the cluster. In this tutorial, you’ll build a simple operator using Go and Kubebuilder; a framework that scaffolds much of the boilerplate so you can focus on core logic.

AI has made it easier than ever to write code. Shipping it is a different story. Today we released the 2026 State of Software Delivery report, sponsored by Thoughtworks. In it, we analyzed more than 28 million CI/CD workflows across thousands of engineering teams. The picture that emerged is clear: teams are producing more code than ever, but fewer of them are able to turn that activity into software that actually reaches customers.