Turning logs into metrics isn’t a new concept. A version of this functionality is implemented in most agents, visualization tools, and backends. It’s everywhere because converting logs to metrics has many practical applications and is one of the fundamental mechanisms for controlling log volume in a telemetry pipeline. In this post, I’ll briefly overview log-based metrics, explain why they matter, and provide examples of how to build them using OpenTelemetry and BindPlane OP.

Grafana Beyla, an open source eBPF auto-instrumentation tool, has been able to produce OpenTelemetry trace spans since we introduced the project. However, the traces produced by the initial versions of Grafana Beyla were single span OpenTelemetry traces, which means the trace context information was limited to a single service view. Beyla was able to ingest TraceID information passed to the instrumented service, but was unable to propagate it upstream to other services.

The.NET framework is a powerful platform for creating various applications, from web-based services to comprehensive enterprise solutions. Its extensive libraries, support for multiple programming languages, and powerful development tools enable the creation of high-performance, scalable applications that can be customized to suit various needs. This framework continuously evolves to meet the demands of modern software development with a complete ecosystem of add-ons created by an enthusiastic community.

Continuing our series on tracing with OpenTelemetry in AWS Kinesis, we now explore a refined approach to overcome the challenges faced in our initial attempt with the PartitionKey parameter.

In the first article of our series, we explored the importance of trace headers and the complexities involved in their propagation. Now, we shift from theory to practice. This second installment will take you through a hands-on baseline scenario and our initial strategy of propagating the OpenTelemetry trace context in AWS Kinesis by using the PartitionKey parameter.

Welcome to our series on navigating the complexities of trace header propagation with OpenTelemetry in AWS Kinesis. In this 3-part exploration, we'll dive into the critical role of trace headers in distributed systems, discuss the unique challenges presented by AWS Kinesis, and explore innovative solutions that keep your data tracking robust and consistent.

Java has long been a foundational pillar in application development, its versatility and robustness serving as key drivers behind its widespread adoption. Since its inception, Java has evolved to meet the ever-changing demands of scalable deployments, offering a reliable platform for creating everything from web applications to complex, server-side systems.

Today, we’re making it even easier to find and solve user-impacting issues with three new Sentry Performance features.

DevOps professionals know that ensuring optimal application performance is paramount. More and more customers and prospects interact with companies online, and any hiccup can impact your bottom line. What’s more, companies continue to leverage cloud-native apps for improved flexibility and resource optimization. All of which means that Application Performance Monitoring (APM) tools need to evolve.

Imagine you're leading a team of engineers responsible for monitoring and optimizing the performance of a cloud-based application used by millions of users worldwide. As the application continues to scale, you recognize the pressing need for a robust observability solution to learn about its distributed architecture. In this scenario, you're faced with an essential decision: choosing between OpenTelemetry and Datadog for distributed tracing and observability.