Operations | Monitoring | ITSM | DevOps | Cloud

The latest News and Information on Distributed Tracing and related technologies.

Declarative Configuration in OTel (Grafana OpenTelemetry Community Call #1)

We’re kicking off a brand-new Grafana OpenTelemetry Community Call! Join us as we dive into getting observability into your apps and infrastructure with Grafana, powered by OpenTelemetry. In this session, we’ll dive into Declarative Config — the new way to make OpenTelemetry onboarding simple and powerful. Instead of juggling environment variables or boilerplate in your startup code, declarative config gives you a clean, language-agnostic approach that works across SDKs and unlocks future possibilities like remote configuration. Join us with Marylia Gutierrez (OTel JavaScript approver & core contributor) to explore.

What is Open Telemetry? The Future Is Here

Watch SolarWinds tech evangelist, Sascha Giese, dive into OpenTelemetry (OTel) and explain why a vendor-agnostic standard is the future of observability and application performance monitoring (APM). If you’ve ever wondered, what is OpenTelemetry? Sascha’s presentation is a great start or restart to diving back into the topic.

Authentication Model in OpenTelemetry

In any type of software that involves the movement of data or information, there is a pressing need to make the passage of data secure. One way of achieving this is by authentication. You must have experience authenticating API calls or other data streams. In modern systems, where even a small mishap can wreak havoc and you might wake up to a $$$ bill the next day, we should do whatever is within our capacity to secure our systems.

Implement Distributed Tracing with Spring Boot 3

A slow checkout request. A background job stuck waiting on another service. A log message that looks fine — until performance drops. In a Node.js microservices setup, these are the moments that test your observability. You know something's wrong, but tracing the request across dozens of services feels impossible. Distributed tracing changes that. It connects every span in the request's journey, showing exactly where time is spent and where things start to break down.

How OpenTelemetry Auto-Instrumentation Works

Most developers use auto-instrumentation as it’s meant to be used — run the Java agent, add NODE_OPTIONS, and telemetry starts flowing. When it stops, though, figuring out why can be tricky. Maybe the agent didn’t load, maybe there’s a framework version mismatch, or something else entirely. Understanding how auto-instrumentation works makes it easier to spot and fix these issues.

Improving browser tracing step by step

Browser tracing has always been one of those things that feels invisible until it isn’t. When it works well, you get clear, actionable insights into how your app is performing in the wild. When it doesn’t, you’re left staring at noisy data, gaps in traces, and spans that don’t quite tell the story. Over the last few months, we’ve been chipping away at that problem.

Complete guide to OpenTelemetry Tracing (with code examples)

Distributed tracing is an essential technique for monitoring modern, cloud-native applications. It provides a holistic view of a request's entire journey as it propagates through a multi-service architecture, making it invaluable for performance optimization and root cause analysis. But how do you generate and collect this trace data in a standardized, vendor-agnostic way? That's where OpenTelemetry comes in.

OpenTelemetry + ignio: The Foundation for Intelligent, Unified Observability

In the previous post, What is OpenTelemetry?, we went over the What, Why, and the How of OpenTelemetry. We also went over the telemetry data lifecycle (data generation à collection à storage à usage) and how telemetry data (MELT) could be put to use to troubleshoot a representative web application scenario.

Cloud Microservices Monitoring on AWS and Azure with OpenTelemetry

Your checkout flow starts in an AWS Lambda function, calls a payment service running on EKS, then triggers notifications through Azure Functions. Three different compute platforms, two cloud providers, one distributed trace that you can't see. Cloud providers want you to use their native monitoring tools. AWS pushes X-Ray and CloudWatch. Azure promotes Application Insights and Azure Monitor. These tools work well within their ecosystems but lock you into vendor-specific implementations.