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.

The aim of this article is to demonstrate how you can instrument a Java application using Opentelementry and Jaeger. In this example, we will be instrumenting our Java application using OpenTelemetry and the OpenTelemetry Java client, and the tracing data will be exported and visualized using Jaeger. We will use the Logz.io Jaeger backend as it is compatible with common tracing standards like Zipkin, OpenTelemetry, and OpenTracing.

Serverless applications streamline development by allowing you to focus on writing and deploying code rather than managing and provisioning infrastructure. To help you monitor the performance of your serverless applications, last year we released distributed tracing for AWS Lambda to provide comprehensive visibility across your serverless applications.

Previously, I wrote a Beginner’s Guide to Jaeger + OpenTracing Instrumentation for Go providing guidance on manually instrumenting Go services. This is useful for cases where we want fine-grained tracing of specific functions. However, what if all we want is to trace a service’s inbound and outbound calls with little to no additional code?

Like a bratty teenager, traditional monitoring answers your questions, but does so in a terse, unhelpful manner: Why is my page slow? Guess it’s the API call. It’s a 504 thing — you wouldn’t understand. Ok, so why is the API call slow? Ask your DB query. Gosh! You need a better conversation with your code — one which gives you contextual clues about your application’s performance.

In a previous blog post, "Monitoring Kafka Performance with Splunk," we discussed key performance metrics to monitor different components in Kafka. This blog is focused on how to collect and monitor Kafka performance metrics with Splunk Infrastructure Monitoring using OpenTelemetry, a vendor-neutral and open framework to export telemetry data. In this step-by-step getting-started blog, we will.

If you already had some experience with Kamon, you probably saw Kamon create Spans automatically for a lot of stuff, including HTTP server requests, database calls, actor messages, and more. But what happens when you want to create Spans for methods or code blocks that Kamon doesn’t instrument automatically? Let’s look at the two simplest ways to create Spans programmatically with Kamon.