Oftentimes users of open source are told to go download it and figure it out… or pay for a managed solution in the cloud. So the typical choice is free and do-it-yourself or expensive and easy. With our new changes to Grafana Cloud, we are making it both free and easy to have a real, composable observability solution.

OpenTelemetry has evolved so much since the 2019 KubeCon North America in San Diego, where I live-demoed OpenTelemetry on the keynote stage and highlighted our alpha to the world. We’re excited to be entering general availability of our Collector and Tracing SDKs soon. While I missed the energy from the packed crowd of over 10,000 technologists cheering me on, it was wonderful that this year’s event was more accessible than ever to a worldwide audience.

At the recent Illuminate conference, Dave Sudia from GoSpotCheck shared the process that led them to choosing Sumo Logic for their observability needs.

Today, AWS announced enhancements for AWS Distro for OpenTelemetry. We’re working with AWS to build in additional support from partners. In tandem with that launch, Honeycomb is announcing support for event ingestion using OpenTelemetry Protocol (OTLP). With that change, you can simplify management overhead and configuration by no longer needing to maintain a separate OpenTelemetry exporter.

There is more to Distributed Tracing with Jaeger than just capturing machine data as with metrics, or tailing log files. To start, you should read this primer. In this article, I will walk you through the initial principles you’ll need before executing anything within your codebase. This is going to focus on Node.js, as slight differences and concerns exist for browser applications.

Grafana Tempo, the recently announced distributed tracing backend, relies on integrations with other data sources for trace discovery. Tempo’s job is to store massive amounts of traces, place them in object storage, and retrieve them by ID. Logs and other data sources allow users to quickly and more powerfully jump directly to traces than ever before. Previously we investigated discovering traces with Loki and exemplars.

This post aims to provide a very simple beginner’s guide to Jaeger + OpenTracing instrumentation for Go applications (the terms “application” and “service” is used interchangeably in this document) via a working example. If you are new to instrumentation, I recommend that you first read this post for a practical introduction to instrumentation for Jaeger and OpenTracing. You can also get more info on using logs in Go.

Every journey in distributed tracing starts with instrumenting an application to emit or extract trace data from each service as they execute. There are many ways to instrument, including the use of SDKs and pre-configured frameworks, and many protocols for transmitting the trace data to the analysis tool.

We at Sumo Logic believe in an open, flexible, community-driven approach to collecting observability data. Those reasons are outlined in one of my recent blogs. In that publication, I share the belief that an application’s observability gains traction from the fact that telemetry signals are designed, composed, and produced by an application developer/vendor in compliance with industry standards, and are not a proprietary, black box component of the monitoring vendor.