I run a small Kubernetes cluster at home, which I originally set up as somewhere to experiment. Because it started as a playground, I never bothered to set up monitoring. However, as time passed, I’ve ended up dropping more production-esque workloads onto it, so I decided I should probably put some observability in place. Not having visibility into the cluster was actually a little odd, considering that even my fish tank can page me.

Grafana is a widely used open-source platform for monitoring and visualization. Grafana has a lot of built-in functionality and also provides a large amount of community templates that can improve your overall experience. However, Grafana requires quite a lot of configuration and the documentation can be a bit overwhelming for beginners. In this article, we explore seven alternatives that can be simpler to use and can provide seamless integration of traces, logs, and metrics.

At this point, it’s almost passé to write a blog post comparing events to the three pillars. Nobody really wants to give up their position. Regardless, I’m going to talk about how great events are and use some analogies to try to get that across. Maybe these will help folks learn to really appreciate them and to depreciate a certain understanding of the three pillars. Or maybe not.

Canonical, the publisher of Ubuntu, today announced the general availability of Data Science Stack (DSS), an out-of-the-box solution for data science that enables ML environments on your AI workstation. It is fully open source, free to use and native to Ubuntu. It is also accessible on other Linux distributions, on Windows using Windows Subsystem Linux (WSL), and on macOS with Multipass.

Today, we’re excited to unveil the Anomaly Detection feature. It will enable users to create smarter alerts based on dynamic metrics, moving beyond traditional fixed-threshold alerts. It will soon be available to all our users and is currently undergoing beta testing with select users. By detecting deviations from expected patterns, Anomaly Detection will help you stay informed about critical issues without getting overwhelmed by irrelevant alerts. Let’s dig in deeper.

In an increasingly digital world, organizations face complex challenges in managing their security data that’s growing at a relentless pace. With the rapid growth of cyber assets and the ever-present threat of sophisticated attacks, legacy security tools often struggle to keep up.

Remember the Tabs vs. Spaces arguments? It seems that observability has grown up enough that we are arguing over which signals are the “best” signals for observability. Often referred to as the Pillars of Observability, Metrics, Logs, and Traces (sometimes adding Events for MELT) each provide a unique perspective on a system. What happens when we change our perspective from finding the “best” telemetry format to finding the telemetry that aligns with the problems we need to solve?

Apple has once again redefined the possibilities of mobile technology with the release of iOS 18. With our commitment to stay at the forefront of innovation, we've seamlessly integrated iOS 18's powerful features into Site24x7's mobile app to deliver an unparalleled experience for DevOps and IT teams.

As organizations increasingly rely on complex and ephemeral infrastructure to drive business outcomes, the need for faster, more accurate, and automated IT operations has never been greater. Enter AIOps (Artificial Intelligence for IT Operations), a transformative approach that leverages AI and machine learning to automate and enhance IT operations management. These new learning systems can analyze massive amounts of network and machine data to find patterns not always identified by human operators.