Fleet management is a derivative term. Originally used in the automotive industry, it’s now used in a span of domains. It’s being used in data telemetry since the introduction of OpAmp, which is a part of the Open Telemetry project. Now, fleet management has broader implications. It simplifies telemetry data collection by automating agent deployment, and configuration, and providing insights into the real-time health and performance of your sprawling agent infrastructure.

This article compares OpenTelemetry and traditional APM tools with their strengths, weaknesses, and ideal use cases to help you choose the right solution for your application performance monitoring needs.

If you’re already familiar with distributed tracing, you know that spans are the building blocks of traces. But are you sleeping on what span events can do for you? First, you may need a wake-up call as to what a span event even is. While spans represent units of work or operation within a trace, a span event is a unique point in time during the span’s duration.

This guide covers types of data that can be redacted and step-by-step instructions for configuring the Attribute Processor.

OpenTelemetry and OpenTracing are open-source projects used to instrument application code for generating telemetry data. While OpenTelemetry can help you generate logs, metrics, and traces, OpenTracing focuses on generating traces for distributed applications. If you’re thinking of choosing between OpenTelemetry and OpenTracing, go for OpenTelemetry. OpenTracing is now deprecated, and users of OpenTracing are advised to migrate to OpenTelemetry.

OpenTelemetry and DataDog are both used for monitoring applications. While OpenTelemetry is an open source observability framework, DataDog is a cloud-monitoring SaaS service. OpenTelemetry is a collection of tools, APIs, and SDKs that help generate and collect telemetry data (logs, metrics, and traces). OpenTelemetry does not provide a storage and visualization layer, while DataDog does.

When it comes to monitoring, diagnosing, and optimizing the performance of complex systems today, you can’t really go wrong with tracing tools. And while OpenTelemetry has become the go-to choice for instrumenting apps and collecting traces, there are several other options in the backend that can effectively store, manage, and analyze traces sent by OpenTelemetry. Two of these open-source tools are Jaeger and Grafana Tempo. In this article, we’ll compare and contrast the two.

While investigating some issues users raised around the OpenTelemetry Collector running in AKS, I found a few nuances that are worth noting. In this article, I'll go over some changes you have to implement in your values.yaml to make it work for you.