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

How to Install and Configure an OpenTelemetry Collector

Originally published June 2024. Updated May 2026. A lot has changed since the first version of this guide. In May 2026, OpenTelemetry officially graduated within the CNCF, the highest maturity level a project can achieve. All three core signals (metrics, logs, and traces) are now stable across every major language SDK. Collector adoption has never been higher, and the ecosystem around it, particularly OpAMP for remote management, has matured significantly. This update walks through three things.

You don't need to pick one: how Sentry and OpenTelemetry work together

You already instrumented the backend with OpenTelemetry. Your services emit spans. Your teams know the OTel APIs. Maybe you already run a Collector. So when you start evaluating Sentry, the obvious question is: Do you need to replace your OpenTelemetry setup with the Sentry SDK? No. The practical answer is usually: keep OpenTelemetry where it already works, add the Sentry SDK where it gives you more application context, and send OpenTelemetry Protocol (OTLP) events to Sentry.

Explore for Spans: One View with Infinite Depth

It’s 20 minutes into a P0 incident, and you have already switched between four different tools, re-authenticated twice, and translated queries across three incompatible syntax languages. The root cause you are searching for. Well, that is still out there somewhere. The reality of investigative latency is that most engineering teams face navigation problems, not data problems. During high-pressure incidents, teams lose cognitive momentum due to context switching between disconnected telemetry silos.

Anthropic Monitoring & Observability with OpenTelemetry and SigNoz

Learn how to implement end-to-end monitoring and observability for Anthropic (Claude) API-based applications using OpenTelemetry and SigNoz. In this video, we walk through instrumenting your Anthropic API calls, collecting traces, metrics, and logs, and visualizing everything in SigNoz to gain real-time visibility into performance, failures, and bottlenecks. You'll see how to move from basic logging to production-grade observability, so you can debug faster, optimize latency, and confidently run Claude-powered AI systems at scale.

Using AI to Instrument Applications with OpenTelemetry

OpenTelemetry is one of the best things that’s happened to observability in the last decade. It’s open. It has SDKs for every language that matters. It’s vendor neutral. The OTel community has been doing the hard work of standardizing how applications emit telemetry, so that you, the engineer, don’t have to learn five different agent formats to monitor five different services.

Building a CloudWatch metrics pipeline: parsing OpenTelemetry data

AWS delivers CloudWatch metrics in OpenTelemetry format via Firehose, but AppSignal uses its own internal format. Building the parser to bridge these two formats presented several technical challenges. The metrics arriving through this pipe power AWS automated dashboards. When AppSignal detects metrics from a supported AWS service, it creates a dashboard for it automatically, with pre-built charts grouped by category: compute, databases, networking, messaging, storage, and others.

Contributing Distributed Partition Ownership to the Azure Event Hub Receiver

If you're running OpenTelemetry collectors against Azure Event Hubs, distributed partition ownership and checkpointing just got significantly better. Your fleet now self-organizes. Failover is automatic. Restarts don't lose data. Here's how we got here.

OpenTelemetry Fleet Management: Scalable Control

OpenTelemetry has turned observability pipelines into production infrastructure, but managing them at scale often creates a massive operational burden. In this demo, we show how Coralogix Fleet Management acts as the central control plane for your OTel ecosystem, providing the governance and orchestration required for modern DevOps. Stop the "manual marathon" of PRs and Helm upgrades. Move toward a safer, more predictable operating model where telemetry is consistent, audited, and scalable.

Making Semantic Conventions Work for You With OpenTelemetry Weaver

Your dataset has hundreds of attributes. Some are self-explanatory: http.response.status_code, server.address. Others are not: meta.refinery.reason, dataset.slug, sli.latency_target_ms. If you don't know what an attribute means, you can't write a good query. And if an AI agent doesn't know what it means, it guesses.