When you want to direct your observability data in a uniform fashion, you want to run an OpenTelemetry collector. If you have a Kubernetes cluster handy, that’s a useful place to run it. Helm is a quick way to get it running in Kubernetes; it encapsulates all the YAML object definitions that you need. OpenTelemetry publishes a Helm chart for the collector. When you install the OpenTelemetry collector with Helm, you’ll give it some configuration.

Customer-facing applications request and process many types of sensitive data, such as API keys, credit card numbers, and email addresses. As your application scales in size and complexity, it becomes harder to keep track of this sensitive data moving across more services, increasing the risk of data leaks.

Today, I’d like to share with you a new community-contributed integration that helps you optimize and debug your Gradle builds. This new Gradle plugin is available today, is free to use, and you can use it immediately with a free Honeycomb account.

Data enrichment is the process of adding additional context or attributes to telemetry data at the source that increases its value during analysis. OpenTelemetry, a collaborative open source telemetry project with the largest organizations in the observability space, can be configured to enrich logs and metrics from dozens of sources. This blog will show you the basics of how to use BindPlane OP to easily deploy and configure OpenTelemetry to enrich data from a source.

Just a few short months ago, we talked about a bunch of updates to Honeycomb’s support for OpenTelemetry. To the surprise of no one, we’ve got more updates to share!

OpenTelemetry is an open source, vendor-neutral observability framework that provides tools, APIs, and SDKs to collect and standardize telemetry data from cloud-native applications and services. One of OpenTelemetry’s key components is the OpenTelemetry Collector, which receives and processes data before using exporters to route it to the destinations of your choice.

OpenTelemetry is an open source set of tools and standards that provide visibility into cloud-native applications. OpenTelemetry allows you to collect metrics, traces, and logs from applications written in many languages and export them to a backend of your choice.

Modern application architectures are complex, typically consisting of hundreds of distributed microservices implemented in different languages and by different teams. As a developer, site-reliability engineer, or DevOps professional, you are responsible for the reliability and performance of these complex systems. With observability, you can ask questions about your system and get answers based on the telemetry data it produces.

This article will give you a quick overview of some of the key attributes you should know in order to get started with leveraging the OpenTelemetry collector for your next telemetry project. As an integral component of any project that involves distributed tracking, the OpenTelemetry Collector plays an important role. Simply put, it is helpful to know that the collector itself is a data pipeline service that collects telemetry data.

Many developers don’t know what instrumentation really is, and those who do don’t really understand the black magic that takes an application and makes it emit telemetry, especially when automatic instrumentation is involved. On top of that, each programming language has its own tricks. I wanted to unwrap this loaded topic on my podcast, OpenObservability Talks. For this topic I invited Eden Federman, CTO of Keyval, a company focused on making observability simpler.