Operations | Monitoring | ITSM | DevOps | Cloud

Top tips is a weekly column where we highlight what’s trending in the tech world and share ways to stay ahead. This week, we’re talking about something every IT team knows too well—incidents. Whether it’s a sudden server crash, a network outage, or a system slowdown right before an important client call, incidents always seem to strike at the worst possible time. No matter how strong your IT setup is, issues are bound to happen.

AWS did not intend to build Amazon EKS; it simply had to. Kubernetes adoption beamed light years ahead of AWS’s own managed container orchestration service. This forced AWS to develop a managed service to accommodate customers who wanted to use upstream Kubernetes but did not want to manage it themselves. As soon as AWS got around to it, it knocked the Kubernetes-based container management service out of the park. Not only is Amazon EKS simpler than Kubernetes, but EKS pricing may also be worth it.

At Civo Navigate London 2025, we hosted a panel discussion with Josh Mesout, James Faure, Abdul Hummaida, Jonas Vermeulen, and Daniel Miodovnik to discuss the latest trends and challenges in AI adoption. Through this conversation, the panelists covered topics such as the current state of AI adoption to the challenges of scaling AI, and the future of work.

The best way to minimize the impact of an incident is to catch it early, before small issues snowball into major disruptions. That requires maintaining healthy systems and ensuring sufficient resources are available when problems arise. But developers and IT operations pros working in large enterprises face a challenge: Complex systems operate in an inherently degraded state. In his essay “How Complex Systems Fail,” Dr.

The engineer you pay $200,000 a year just spent an hour copy-pasting data between dashboards. Again. Software engineers have critical skills that are in the highest demand. And yet, many world-class engineers are currently spending too much of their time clearing tickets, routing alerts, and responding to the same types of incidents over and over again. This operational toil is costing you.

Modern software applications are increasingly complex. Microservices, cloud infrastructure, and distributed architectures make it challenging for developers, DevOps engineers, and SREs to maintain high performance and a seamless user experience. Traditional Application Performance Monitoring (APM) provides critical insights into how applications perform, but alone, it often leaves blind spots when it comes to diagnosing issues or understanding the full system behavior.

In a microservices architecture, a single user request can pass through multiple services before completing. When performance drops or an error occurs, tracing that journey is the only way to locate the source. Distributed tracing provides that visibility. At its core are OpenTelemetry Spans — units of work that capture what each service does during a request.

The challenge of storing, processing, and alerting on your time series data is only part of the battle when it comes to deriving value from time-stamped data. While InfluxDB 3 addresses those hurdles with the database and Python processing engine, data analytics teams still need to be able to visualize their data and build dashboards to complete the time series story.

In my previous blog, we demonstrated how the FrogML SDK streamlines the process of integrating custom-built or publicly sourced models from your IDE into JFrog Artifactory. Now that your models are securely stored, versioned, and managed, the natural next question arises: “Ok, so you have some models in JFrog Artifactory, now what?” This is where the real power of the JFrog Platform comes into play.