Operations | Monitoring | ITSM | DevOps | Cloud

As engineering organizations scale, project visibility becomes a real challenge. Engineering managers lose track of what's actually happening across multiple teams. Executives ask "are we on track?" and get conflicting answers. Status meetings multiply but clarity doesn't improve. The root problem isn't lack of data, modern engineering teams generate tons of project information across JIRA, GitHub, CI/CD pipelines, and project management tools.

Microservices make it tough to understand how applications behave end-to-end. Most teams already rely on an Application Performance Monitoring (APM) tool to track system health. But as requests move across many services, you also need distributed tracing. Jaeger gives you that visibility. The real value comes from connecting the two. Instead of running APM and Jaeger in silos, you can combine their strengths, metrics from your APM, and traces from Jaeger, to get a clearer view of performance.

The OpenTelemetry community and the contributors to the Java Special Interest Group (SIG) have spent a great deal of time integrating core Java technologies into the project. An integration that is particularly useful is Java Management Extensions (JMX). It has been around since J2SE 5, and has been mature for some time. Many of the most widely used Java applications have adopted it over time and support this extension.

In this post, we will talk about OpenTelemetry exporters. OpenTelemetry exporters help in exporting the telemetry data collected by OpenTelemetry. OpenTelemetry frees you from any kind of vendor lock-in by letting you export the collected telemetry data to any backend of your choice. In modern distributed systems, efficiently collecting, transmitting, and analyzing telemetry data from diverse sources poses a significant challenge.

Bob did—until he realized there was a smarter way. With real-time insights from Nexthink, he stopped guessing and started making data-driven decisions: keeping the high performers, replacing the real troublemakers, and upgrading the underperformers. The result? Happier employees, optimized budgets, and devices refreshed based on need—not age.

Even a simple upgrade can cause unexpected issues. Here’s what happened when a PVS upgrade broke XenServer licensing, and how we fixed it.

Level 1 engineers face incoming tickets without real-time visibility into endpoints. The result? Endless tool-switching, guesswork diagnostics, missed SLAs, and unnecessary escalations. Critical issues remain hidden until they impact productivity.⁠ Then came Nexthink.⁠ Now engineers see issues in real time, fix faster, and even prevent problems users don’t notice.

We love to talk about IT revolutions. We celebrate the leaps in innovation that change how we work and live. We look at the 1980s and see the personal computer, which turned computing from a command-line chore into an intuitive experience for everyone. We point to the 1990s as the decade the internet connected the world, the 2000s as the era when virtualization and the cloud broke the chains of physical hardware, and this decade as the dawn of mainstream AI. Each of these moments was transformative.