Learn how to get started with OpenTelemetry Logging, streamline your observability, and enhance debugging with structured, context-rich logs.

As we wrap up an incredible year, it’s the perfect time to celebrate Cribl’s progress and innovation in 2024! This year brought many exciting features designed to solve real-world problems and make life easier for our customers. In the spirit of reflection and festivity, I’ll highlight twelve game-changing product features, releases, and enhancements— each a testament to listening, learning, and delivering value to you, our users.

When you have a piece of data tucked into your logs or span tags, how do you dig for that bounty of insight today? Commonly this sort of data will be numeric, like a purchase total or number of units. Wouldn’t it be nice to easily turn that data into a metric timeseries? The Sum Connector in OpenTelemetry does just that, allowing you to create sums from attributes attached to logs, spans, span events, and even data points!

Angular applications often grow in complexity, making it challenging to monitor performance and troubleshoot issues effectively. Enter OpenTelemetry: a powerful, vendor-neutral framework for telemetry collection. This guide will walk you through implementing OpenTelemetry in your Angular projects, enhancing your ability to observe and optimize your applications.

Cribl Edge can send data to OpenTelemetry in several different ways. In this blog post, we’ll focus on the OpenTelemetry Metrics. In the blog, we’ll talk about Cribl Edge, but what we say applies to Cribl Stream, too! We will cover how to use Cribl Edge to collect Linux System Metrics, transform them into the OTLP Metrics format, and deliver them to an OTLP Destination.

Boost observability with Fluent Bit and OpenTelemetry! Collect, process, and export logs and metrics easily for smarter monitoring.

I’m a big fan of tasting menus. In the culinary world they let us sample a variety of dishes in small portions, helping us understand and appreciate different flavors and options. Inspired by this concept and a talk I gave earlier this year, I have crafted a “tasting menu” of OpenTelemetry Collector configurations in Grafana Cloud.

It’s very popular to push logs, in a formatted way, to the console output of an application (sometimes referred to as stdout). Although using a push-based approach like OTLP over gRPC/HTTP is preferred and has more benefits, there are many legacy systems that still use this approach. These systems typically use a JSON output for their logs. So, how do we get these JSON logs into a backend analysis system like Honeycomb that primarily accepts OTLP data?

Migrating from other vendors becomes a lot easier with OpenTelemetry and Grafana Alloy, our distribution of the OpenTelemetry Collector. But when you come from platforms that use different temporalities, such as Datadog or Dynatrace, you face a challenge integrating with a Prometheus-like ecosystem such as Grafana Cloud: Your metrics still mean the same as before, but they just don’t look right.

Yuri Shkuro· Follow Published in JaegerTracing · 6 min read· 1 day ago -- Listen Share Jaeger, the popular open-source distributed tracing platform, has had a successful 9 year history as being one of the first graduated projects in the Cloud Native Computing Foundation (CNCF). After over 60 releases, Jaeger is celebrating a major milestone with the release of Jaeger v2.