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.

Rust’s strong memory safety and efficient code execution make it a top choice for building robust, high-performance systems. But even with its powerful guarantees around memory management and thread safety, Rust applications in production environments can still face challenges such as latency spikes, resource contention, and unexpected bottlenecks. For this reason, monitoring Rust applications is essential to ensure they meet performance expectations and remain reliable under load.

Observability isn’t just a nice-to-have—it’s essential. OpenTelemetry steps in as a unified framework that helps you collect, process, and export telemetry data across distributed systems. The OpenTelemetry Collector Contrib extends this framework, offering extra components that make it even more powerful and flexible, helping developers and operators monitor and optimize systems with ease.

The promise of observing your application without writing code (i.e., auto-instrumentation) is not new, and it’s extremely compelling: run a single command in your cluster and suddenly application telemetry starts arriving at your observability backend. What else could you ask for? The OpenTelemetry Operator aims to fulfill such a dream for Kubernetes environments by using a set of well known patterns such as operators and custom resources.

Jaeger, a renowned distributed tracing system, has been a trusted companion for developers and operations teams seeking to unravel the complexities of microservices architectures. However, as the landscape continues to evolve, the time has come to explore Jaeger alternatives that offer distinct features and advantages.