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If you’re reading this, chances are you’re already familiar with OpenTelemetry (OTel)—the open-source standard for collecting observability data. But what about OpenTelemetry agents? How do they work, and why do they matter? This guide unpacks everything you need to know about OTel agents—where they fit in your stack, how to set them up, and common pitfalls to watch out for. Let’s get into it.

OpenTelemetry is the go-to open-source standard for observability, but when it comes to tracking frontend performance and user interactions, things get a little tricky. Unlike backend services, browsers introduce challenges like CORS restrictions, asynchronous execution, and limited access to certain telemetry data. This guide covers everything you need to know about using OpenTelemetry in the browser, from setup to best practices, advanced configurations, and real-world debugging techniques.

Aerospike is a high-performance, real-time NoSQL database built for speed, scale, and low-latency transactions—think millions of reads/writes per second without breaking a sweat. When you're dealing with high-throughput applications, keeping an eye on Aerospike’s performance isn't just a good idea—it's mission-critical to avoid bottlenecks, connection issues, or unexpected slowdowns.

OpenTelemetry (often abbreviated as OTel) is the golden standard observability framework, allowing users to collect, process, and export telemetry data from their systems. OpenTelemetry’s framework is organized into distinct signals, each offering an aspect of observability. Among these signals, OpenTelemetry metrics are crucial in helping engineers understand their systems.

We all know that Checkly is a ‘secret weapon’ for engineering teams who want to shorten their mean time to detection (MTTD). With Checkly, you can know within minutes if your service is unavailable for users, or acting unexpectedly. In this article we’ll talk about how Checkly traces can help you expand on the benefits of Checkly, adding insights that will help you diagnose root causes, and further reduce your mean time to resolution (MTTR) for outages and other incidents.

Observability is essential for understanding how modern applications perform and behave in production. OpenTelemetry has emerged as the industry standard for collecting, processing, and exporting telemetry data—traces, metrics, and logs—without vendor lock-in. This guide will walk you through OpenTelemetry’s core components, how it works, and why it’s a game-changer for observability.

Modern applications are becoming increasingly complex, and debugging distributed systems can feel like searching for a needle in a haystack. This is where OpenTelemetry (OTel) comes in. If you're using NestJS, integrating OpenTelemetry can provide deep insights into your application's behavior, helping you track performance, troubleshoot issues, and understand service interactions.

Logs play a critical role in observability, but they do come with their own challenges. Grafana Loki, our horizontally scalable, highly available, multi-tenant log aggregation system, addresses these challenges head on, giving you an open source tool that’s both cost effective and easy to operate.

OpenTelemetry is an open-source observability framework designed to instrument, generate, collect, and export telemetry data, including traces, metrics, and logs. It is vendor-agnostic, allowing developers to send data to multiple backend services like Last9, Prometheus, Datadog, or Jaeger without vendor lock-in. For Next.js applications, OpenTelemetry is particularly useful due to the framework’s hybrid rendering approach.

OpenTelemetry is a big, big project. It’s so big, in fact, that it can be hard to know what part you’re talking about when you’re talking about it! One particular critique I’ve seen going around recently, though, is about how OpenTelemetry is just ‘three pillars’ all over again. Reader, this could not be further from the truth, and I want to spend some time on why.