The scenario: you want to see distributed traces, maybe for your web app. You’ve set up an OpenTelemetry collector to receive OTLP traces in JSON over HTTP, and send those to Honeycomb (how to do that is another post, and we’ll link it here when it’s up).

Over the last few years, Microsoft has built excellent tooling around different technologies. Today, everything is available and achievable through the Azure Portal, which helps manage complex solutions. However, this brings in challenges when it comes to managing the distributed resources. The struggle to keep track of messages or know the flow of messages through these distributed resources is growing day by day.

Systems and applications alike have become progressively distributed as microservices, open-source tools, and containerisation have gained traction. In order to actively monitor and respond quickly to issues that arise in our environment, distributed tracing has proven to be vital for businesses such as Uber, Postmates, Hello Fresh and TransferWise. It is, however, important to clarify what distributed tracing actually means.

SAP Hana monitoring support is now available in the open source OpenTelemetry collector. You can check out the OpenTelemetry repo here! You can utilize this receiver in conjunction with any OTel collector: including the OpenTelemetry Collector and observIQ’s distribution of the collector. Below are quick instructions for setting up observIQ’s OpenTelemetry distribution, and shipping SAP Hana telemetry to a popular backend: Google Cloud Ops.

Cloud native and microservice architectures bring many advantages in terms of performance, scalability, and reliability, but one thing they can also bring is complexity. Having requests move between services can make debugging much more challenging and many of the past rules for monitoring applications don’t work well. This is made even more difficult by the fact that cloud services are inherently ephemeral, with containers constantly being spun up and spun down.

Today we’re excited to announce BindPlane OP – the first observability pipeline built for OpenTelemetry – is out of beta and now generally available. You can download the latest version here. Two months ago we released BindPlane OP in beta, and while we were confident we had something special, the response surpassed all of our expectations.

Telemetry data is a powerful tool for understanding the behavior of complex systems. OpenTelemetry provides a platform-agnostic, open-source way to collect, process, and store telemetry data. This post explores the OpenTelemetry collector architecture, specifically focusing on the Collectors component. We'll look at how collectors work and how they can be used to process telemetry data from any system or application. We'll also discuss some benefits of using OpenTelemetry for your telemetry needs.

OpenTelemetry (OTEL) is a big data platform that enables the collection and analysis of large-scale telemetry data. Many companies have adopted it for use in their products. In this post, we’ll discuss semantic conventions in OpenTelemetry and how they are used to make data processing easier. We’ll also discuss the different types of semantic conventions and their importance.

The growth of technology has led to more efficient and relevant digital experiences, and customers continue to expect more out of those interactions. That’s true no matter their location and no matter which device they choose to use. Companies that cannot provide these kinds of personalized interactions for their customers find themselves falling behind the competition as technology continues to advance.

The OpenTelemetry Collector is an application written in Go. The GitHub readme does a great job of describing it: So the OpenTelemetry collector is a Go binary that does exactly what its name implies: it collects data and sends it to a back-end. But there’s a lot of functionality that lies in between. What a neat service! A local destination for data that handles the final sending of Open Telemetry information to your back end.