The topic of continuous profiling has been an ongoing discussion in the observability world for some time. I said back in 2021 that profiling was set to be the next major telemetry signal in observability, and in fact, since then there’s been growing interest in profiles. Startups and large observability vendors have gotten into this domain. A significant recent step was when the OpenTelemetry project decided to add profiles to its core signals and formalized the open unified specification for that.

The Uptrace team is pleased to present the latest v1.7 release, which introduces dark mode, revamped navigation, dashboards list, system tracing metrics, and much more.

Every day, we hear about new vulnerabilities or exploits that underline the importance of application security in today’s connected world. Such incidents put sensitive user information at risk and threaten applications’ infrastructure. Securing applications is therefore crucial not only from a technical standpoint but also to maintain user trust and ensure service reliability. The challenge lies in identifying and mitigating potential security threats before they can be exploited.

Telemetry data is growing exponentially, but the business value isn’t increasing at a similar pace. Getting the right telemetry data is hard, so I recently had a conversation with Matt Aslett, Director of Research at Ventana Research, now a part of ISG, about five myths and realities in telemetry data handling.

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.

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.

Standardized frameworks play a fundamental role in leveling the playing field and setting the standard within the tech industry, ensuring that everyone has access to the same tools and practices. These frameworks promote best practices and foster innovation and collaboration across different sectors. One example of such a framework is OpenTelemetry, a project that has rapidly gained traction and continued to flourish as an open-source initiative under the Cloud Native Computing Foundation (CNCF).

In the fast-pace world of technology, keeping an eye on how well our applications are doing is crucial. Indeed, opentelemetry offers a comprehensive framework designed to capture the nuances of software applications. At the core of this framework lies the opentelemetry Collector, responsible for aggregating, processing, and exporting telemetry data. Why is this important?

At observIQ, we are big believers and contributors to the OpenTelemetry project. In 2023, we noticed project awareness reached an all-time high as we attended trade shows like KubeCon and Monitorama. The project’s benefits of flexibility, performance, and vendor agnosticism have been making their rounds; we’ve seen a groundswell of customer interest.

Naming things, and specifically consistently naming things, is still one of the most useful pieces of work you can do in telemetry. It’s often overlooked as something that will just happen naturally and won’t cause too much of an issue—but it doesn’t happen naturally, it does cause issues, and you end up having to fix the data in pipelines or your backend tool.

According to Akamai, 83% of web traffic is through APIs. Microservices, servers, and clients constantly communicate to exchange information. Even the Google search you made to reach this article involved your browser client calling Google APIs. Given APIs govern the internet, businesses rely on them heavily. API health is directly proportional to business prosperity. This article covers everything about API monitoring, so your API infrastructure’s health is always in check ✅.

See the results of our 2024 survey of over 500 observability decision-makers to find out where the industry is headed As technology evolution continues at its rapid pace, so does observability. Observability is becoming critical to driving positive business outcomes, and we wanted to understand how users are evaluating trends and their impact over the coming years.

In this post, I describe how to send OpenTelemetry (OTel) data from an AWS Lambda instance to Honeycomb. I will be showing these steps using a Lambda written in Python and created and deployed using AWS Serverless Application Model (AWS SAM).

Welcome to the 34th edition of our monthly product newsletter - SigNal 34! Last month was full of action. We did our first launch week, and we were thrilled to see the response. We have shipped some amazing features recently. Let’s see what humans of SigNoz were up to in the month of February 2024.

At observIQ, we’ve seen continuous customer interest in scalable and performant observability solutions for Windows environments. As of 2023, Windows is estimated to be deployed to 75% of desktops worldwide. Unsurprisingly, we commonly speak to CTOs, DevOps, and IT managers responsible for managing fleets of thousands of Windows-based end-user and point-of-sale systems in the Financial, Healthcare, Insurance, and Education sectors.

Distributed tracing tools are essential in modern software development and operations for monitoring, troubleshooting, and optimizing complex distributed systems. The best tracing tools can help you eliminate performance bottlenecks and recover from incidents faster. Use this guide to pick the right one for you.