Operations | Monitoring | ITSM | DevOps | Cloud

Modern software systems are complex, with multiple services interacting across different environments. Understanding how they behave—tracking performance, identifying bottlenecks, and diagnosing failures—requires more than just collecting data. OpenTelemetry provides a standardized way to gather logs, metrics, and traces, but the real value comes from making that data easy to interpret through visualization.

Until recently, Checkly was telling you what broke in your app. Now, it can also tell you why it broke. We're excited to announce the general availability of Checkly Traces, a new addition to our synthetic monitoring platform that bridges the gap between frontend monitoring and backend observability. By combining synthetic monitoring with distributed tracing, Checkly Traces empowers development teams to detect, diagnose, and resolve issues faster than ever before.

What happens if we put OpenTelemetry, Prometheus 2.x, Prometheus 3.x, and vmagent together for comparison in scraping and pushing data to remote storage?

Understanding how your applications perform is crucial. OpenTelemetry has emerged as a powerful observability framework, offering a standardized approach to collecting telemetry data such as metrics, logs, and traces. For Java developers, the OpenTelemetry Java SDK provides the tools necessary to instrument applications effectively. This guide is all about the OpenTelemetry Java SDK, exploring its components, configuration, and advanced features to help you harness its full potential.

We’ve all worked on that app that hangs just a little too long in weird places, or had that query we could never get to perform just right. The network waterfall in Chrome DevTools can’t quite show us what’s going on behind the scenes, and tracing with OTel (and honestly, tracing in Sentry) was just… hard. Today that changes.

In the rapidly evolving IT environment, observability at scale has become a critical challenge for organizations aiming to maintain operational excellence. The proliferation of telemetry collection agents across diverse infrastructures often increases complexity, resource strain, and configuration inconsistencies.

Most developers are familiar with Opentelemetry core components—Traces, Metrics, and Logs. But there’s one part of the OpenTelemetry ecosystem that doesn’t always get the spotlight: processors. These behind-the-scenes operators shape your data pipeline, helping you filter, enrich, and fine-tune telemetry data before it reaches your backend systems. Processors play a key role in making sure your data is cleaner, more useful, and just the way you need it.

OpenTelemetry has emerged as the gold standard fueling o11y and SIEM platforms, but transitioning an existing telemetry stack requires careful planning and execution. This guide outlines a practical approach to evaluating, implementing, and scaling OpenTelemetry in production environments.