Operations | Monitoring | ITSM | DevOps | Cloud

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

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.

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.

With the release of.NET 8, Microsoft released a new framework called.NET Aspire that’s shaking up the way distributed applications are crafted. Aspire makes it painless to configure and deploy distributed apps in.NET. You can check out the Aspire docs for a full rundown.

What do we gain when we integrate OpenTelemetry with FastAPI? Integrating OpenTelemetry with FastAPI offers many benefits that greatly improve the observability and monitoring capabilities of applications built on this high-performance web framework. By integrating OpenTelemetry's instrumentation capabilities into FastAPI projects, you can understand your applications' inner workings, enabling them to monitor, analyze, and optimize performance.

For years, we’ve been installing what vendors have referred to as “agents” that reach into our applications and pull out useful telemetry information from them. From monitoring agents, to full-blown APM tools, this has been the standard for many decades. With OpenTelemetry though, the term “agent” isn’t used as much, and in most scenarios means something slightly different.

OpenTelemetry is a popular open source project to instrument, generate, collect, and export telemetry data, including metrics, logs, and traces. OTel, however, does not provide a monitoring backend — and this is exactly where the Grafana stack comes in. Here at Grafana Labs, we’re fully committed to the OpenTelemetry project and community.

OpenTelemetry is rapidly becoming the most expansive project within the Cloud Native Computing Foundation (CNCF), boasting as many commits as Kubernetes and garnering widespread support from customers. Numerous companies are adopting OpenTelemetry and integrating it into their applications. Elastic® offers detailed guides on implementing OpenTelemetry for applications. However, like many applications, pinpointing and resolving issues can be time-consuming.

Profiling is emerging as a core pillar of observability, aptly dubbed the fourth pillar, with the OpenTelemetry (OTel) project leading this essential development. This blog post dives into the recent advancements in profiling within OTel and how Elastic® is actively contributing toward it. At Elastic, we’re big believers in and contributors to the OpenTelemetry project.