Operations | Monitoring | ITSM | DevOps | Cloud

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.

When it comes to monitoring and observability, two of the most discussed standards are OpenMetrics and OpenTelemetry. While both are designed to collect and transmit metrics, they have distinct goals, use cases, and communities driving their development. In this guide, we'll break down what each of these projects is, how they compare, and how they fit into your monitoring stack.

When working with OpenTelemetry, environment variables play a crucial role in configuring and customizing your setup. These variables provide a flexible and convenient way to adjust settings without needing to change code, allowing you to fine-tune your OpenTelemetry installation across different environments.

Monitoring and observability have become the backbone of reliable software systems. OpenTelemetry, a CNCF project, has gained immense traction as the go-to framework for collecting and exporting telemetry data. But what makes it even more powerful is its Collector—a vendor-agnostic tool that simplifies data processing. Combine that with Docker, and you’ve got a robust, portable, and scalable observability solution.

In our era of cloud-native applications, robust observability is critical to maintain performance, identify issues, and enhance user experiences. With its advanced capabilities, DX Operational Observability (DX O2) integrates seamlessly with OpenTelemetry, a leading open-source observability framework. In this blog, we explore how to onboard the OpenTelemetry Demo Application to DX O2. The demo application provides a hands-on introduction to combining these powerful tools.

In software development, making sure your apps perform well is key. Performance issues, hidden delays, and wasted resources can quickly hurt user experience and increase costs. That’s where OpenTelemetry profiling steps in to help. In this blog, we’ll break down what OpenTelemetry profiling is, why it’s important, and how you can use it to optimize your applications.

We recently released Refinery 2.9, which came with great performance improvements. Reading through the release notes, I felt the need to write a piece on this improvement, as it’s quite important but easy to overlook: collect loop taking too long. This is the story of how we used distributed tracing to find the slowdown in this loop.

Managing observability in cloud-native environments can feel like juggling a thousand things at once. OpenTelemetry makes this easier by becoming a favorite among developers for collecting, processing, and exporting telemetry data without breaking a sweat. Now, let’s talk about the OpenTelemetry Helm Chart. It’s like having a shortcut button for deploying OpenTelemetry in Kubernetes.

In formal terms, OpenTelemetry is an open source framework used for instrumenting, generating, collecting, and exporting telemetry data for applications, services, and infrastructure. It provides vendor-neutral tools, SDKs and APIs for generating, collecting, and exporting telemetry data such as traces, metrics, and logs to any observability backend, including both open source and commercial tools.