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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.

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.

When we set out to trace applications running outside of AWS Lambda, there was little doubt in our minds that building on top OpenTelemetry was by far the best course of action. There are many reasons for this, but chiefly, it is a question of coverage. At its most fundamental level, achieving coverage requires as-wide-as-possible support for technologies, and interoperability among instrumentations.

HashiCorp Consul began as an open-source project for service discovery. It has evolved to provide other valuable functionality like secure service mesh to help secure microservice architectures based on service identity, but also the ability to achieve repeatable application deployment lifecycles via Network Infrastructure Automation and control access to the service mesh via Consul API Gateway.These features are considered the four core pillars of Consul service networking.

Comprehensive observability starts with good instrumentation. OpenTelemetry, aka “OTel,” sets a unified standard, enabling you to instrument your applications once, then send that data to any backend observability tool of choice. OpenTelemetry’s standard for generating and ingesting telemetry data is slated to become as ubiquitous as current container orchestration standards. Because of this, development teams are increasingly adopting OpenTelemetry to their applications.

Distributed tracing helps you track requests across services and understand issues affecting your application performance. In distributed cloud architecture, debugging performance issues is complicated. Distributed tracing gives visibility to teams on how a user request performs across different services, protocols, and cloud infrastructure. Let’s start with a brief overview of distributed tracing.