Jaeger and OpenTelemetry are essential technologies that greatly improve the observability of software applications. OpenTelemetry is a vendor-neutral platform that makes it easier to create and collect telemetry data, including logs, traces, and metrics. Its extensive backend integration adaptabilities allow it to fit into a wide range of infrastructures. However, Jaeger is an expert in distributed tracing within microservice environments.

The OpenTelemetry Transformation Language, or OTTL for short, offers a powerful way to manipulate telemetry data within the OpenTelemetry Collector. It can be leveraged in conjunction with OpenTelemetry processors (such as filter, routing, and transform), core components of the OpenTelemetry Collector. It caters to a range of tasks from simple alterations to complex changes.

If you want to see if OpenTelemetry helps you become a better Python developer — or if you just want to know how to add OpenTelemetry to your Python service — you’ve come to the right place. In this blog, we’ll show you how to instrument your Python application using OpenTelemetry and how to visualize your OpenTelemetry data using Application Observability in Grafana Cloud. We’ll walk you through the following steps.

As OpenTelemetry rises in popularity, more organizations are implementing, or planning to implement, the open source project to monitor their applications — and, meanwhile, more vendors are offering OpenTelemetry support. In fact, a quick Google search for “OpenTelemetry support” shows results ranging from legacy APM vendors to newer, cloud native solutions like Grafana Cloud.

In today's rapidly evolving landscape of distributed systems and microservices, understanding how applications behave in production environments has become increasingly complex. Traditional monitoring tools often fall short when it comes to providing comprehensive insights into the performance and behavior of these modern architectures.

Choosing an OpenTelemetry backend depends on several factors, such as your monitoring and observability requirements, current infrastructure, scalability needs, and budget constraints.

MongoDB’s flexibility and document-oriented nature have always stood out to me as its most compelling features, setting it apart from the strict schema constraints of traditional relational databases. This adaptability is a boon for application development, allowing for more dynamic data interactions that mirror real-world information complexities and freeing table schemas’ constraints.

In the dynamic world of observability and analytics, everyone’s looking for smarter, more efficient, and interoperable ways to handle their data. That’s where Cribl steps in, bringing you an exciting update to our product lineup. We’re thrilled to introduce the OTLP Metrics Function to Cribl Stream 4.5! This Function converts metrics into the OpenTelemetry Protocol (OTLP) format with ease!