OpenTelemetry (or “OTel”) helps you get your instrumentation started quickly, and it helps you get the most out of that telemetry data by providing flexible exporting options. As a result, it’s emerging as the new standard for instrumentation. To that end, today we’re sharing more insight into the work we’ve done (and are doing) to enable a path for all Honeycomb users toward OTel adoption. We hope you’ll be as excited as we are to embrace these open standards!

Welcome to Part 1 of our multipart series on Distributed Tracing for Full Stack Developers. In this series, we’ll be learning the ins-and-outs of distributed tracing and how it can assist you in monitoring the increasingly complex requirements of full stack applications.

Use this guide to write a golang application emitting traces using the OpenTelemetry Protocol (OTLP) Specification. All you need is a basic understanding of developing applications in golang.

In one of my previous blogs I explained how important it is for a modern observability platform to provide “the observers” full, flexible access to all raw telemetry. Observability’s promise to find unknown unknowns relied directly on the ability of fast, powerful and multidimensional high-cardinality analysis of raw data, to uncover previously unknown patterns that have not yet been visualized as a metric, dashboard panel or an alert or anomaly event.

Running and troubleshooting production services requires deep visibility into your applications and infrastructure. While basic logs and metrics are available out of the box with Google Cloud Compute Engine (GCE), capturing advanced data used to require the installation of both a metrics agent and a logging agent.

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.

Slack experienced meteoric growth between 2017 and 2020—but that level of growth came with growing pains. In his talk at the 2021 o11ycon+hnycon, Frank Chen (LinkedIn), a Slack Senior Staff Engineer, detailed one of Slack’s biggest pain points in that period: flaky tests. A flaky test returns both a passing and failing result despite no changes in the code. At one point, between 2017 and 2020, Slack’s flaky test rate reached as high as 50%.

If you’re not already familiar with it, Grafana Cloud is the easiest way to get started observing metrics (Prometheus and Graphite), logs (Grafana Loki), traces (Grafana Tempo), and dashboards. Here are the latest features you should know about!

This blog series is focused on observability into Kafka based applications. In the previous blogs, we discussed the key performance metrics to monitor different Kafka components in "Monitoring Kafka Performance with Splunk" and how to collect performance metrics using OpenTelemetry in "Collecting Kafka Performance Metrics with OpenTelemetry." In this blog, we'll cover how to enable distributed tracing for Kafka clients with OpenTelemetry and Splunk APM.

Today, we’re excited to announce enhancements to the VMware Tanzu Observability by Wavefront platform, which helps teams scale their observability practices and shorten the feedback loops between development and operations. The new features give more flexibility and functionality to any open source investments; help operations, development, and SRE teams resolve problems faster; and extend observability more efficiently into DevOps workflows. Here’s a quick rundown of what’s new.