Operations | Monitoring | ITSM | DevOps | Cloud

To ensure that your organization’s cloud spend is efficient, you need detailed and granular visibility to understand what comprises your costs, what causes them to change, and how the cloud services and resources you use are enabling your business goals. Extending your visibility and more closely monitoring your cloud costs can position you to successfully adopt FinOps, which provides a framework that can help you maximize the value you get from your cloud spend.

It’s a story many network administrators dread: leaving the office on a Friday afternoon with everything running smoothly, only to return Monday morning to a nightmare of system-wide failure. If you’re a network administrator, you know the quiet comfort in logging off on Friday, satisfied that all servers are operational, backups are complete and the network is running efficiently. It’s the moment when you finally let out a sigh of relief, looking forward to a stress-free weekend.

For today’s network operations teams, change is a constant. Applications, app delivery chains, software-defined and physical infrastructures, cloud services, and more are in continuous flux. Further, as organizations continue to pursue ever more strategic digital transformation efforts, the pace of this change only accelerates. These days, about the only constant is the demand being placed on network operations teams.

Outages are unexpected events that can suddenly stop an organization's operations. Whether it's a network issue, a key application going down, or a system crash, these problems can cause confusion and disrupt work. Teams scramble to identify the problem, while employees are left in the dark, uncertain about the impact or duration of the issue. A lack of real-time communication can lead to frustrated employees, delayed responses, and prolonged recovery times.

In the first post, we introduced system tables and how to use them to inspect your cluster. In this follow up, we’ll explain some techniques to improve the speed of system table queries.

Keeping a Kafka cluster optimized can feel like a balancing act. Every piece—brokers, partitions, producers, and consumers—has to work in harmony, or you’ll start running into bottlenecks. To get Kafka to run smoothly and handle growing traffic loads, balancing load across the system is key. Let’s go over practical load-balancing techniques that can improve Kafka performance, keep everything running efficiently, and prevent data slowdowns from building up.

You’ve found some Long Animation Frames (LoAFs) impacting your site, now you need to fix them! LoAFs can make animations feel sluggish, delay user interactions, and generally reduce your site’s responsiveness, all of which contribute to a frustrating experience for users. Fortunately, by analyzing LoAF data and addressing common performance bottlenecks, you can dramatically improve how smoothly your site runs.

Though you might not immediately notice it the next time you log in, Grafana’s frontend has undergone a major upgrade. We recently migrated our dashboard architecture to utilize the Grafana Scenes library, enabling the creation of more stable, dynamic, and flexible Scenes-powered dashboards. Yes, the UI is pretty much the same, but under the hood, the engine responsible for visualizing the dashboards used by millions of people around the world has largely been rewritten.

An organized inventory for monitoring using ManageEngine OpManager is essential for resolving network issues and optimizing performance. Configuring and updating monitoring settings is easier and more efficient when devices and interfaces are organized into subgroups and supergroups. For instance, say you’re monitoring 500 or more devices.

‍ While Scout provides powerful monitoring tools (try it now!) mastering logging is an awesome complement to these skills. In this post, we’ll see how to create readable, actionable logs for both humans and machines. You’ll improve your logging strategy, drastically reduce troubleshooting time, and put yourself in the best possible position for maximum observability. As a starting example, let’s take this error log.