Grafana Tempo 2.4 is here and comes with a stack of new features and enhancements to help improve performance and operational capabilities. Check out the video above, which highlights the new experimental TraceQL metrics feature that creates metrics from traces, and continue reading to get a quick overview of all the latest updates in Tempo. If you’re looking for something more in-depth, don’t hesitate to jump into the Grafana Tempo 2.4 release notes or the changelog.

The early days of e-commerce on the internet resembled a digital Wild West, characterized by unencrypted form inputs and clear-text storage of sensitive information. Fast forward to today, and the landscape of online payments has transformed dramatically, bolstered by industry-driven guide rails like the Payment Card Industry Data Security Standard (PCI DSS). These standards ensure that consumer details are stored appropriately and handled with the utmost care and security.

There is so much good work that OpenTelemetry has done in the software industry, specifically around the domain of observability, in the last five years. Bringing users and vendors together to define the future of telemetry? Check! Unify logs, traces, and metrics under a completely vendor-neutral API? Check! Deprecate other standards by bringing their collaborators to the table to ensure their use cases are met? CHECK!

At observIQ, we’re big believers and contributors to the OpenTelemetry project. In 2023, we saw project awareness reach an all-time high as we attended tradeshows 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.

Jaeger and OpenTelemetry are essential technologies that greatly improve the observability of software applications. OpenTelemetry is a vendor-neutral platform that makes it easier to create and collect telemetry data, including logs, traces, and metrics. Its extensive backend integration adaptabilities allow it to fit into a wide range of infrastructures. However, Jaeger is an expert in distributed tracing within microservice environments.

The OpenTelemetry Transformation Language, or OTTL for short, offers a powerful way to manipulate telemetry data within the OpenTelemetry Collector. It can be leveraged in conjunction with OpenTelemetry processors (such as filter, routing, and transform), core components of the OpenTelemetry Collector. It caters to a range of tasks from simple alterations to complex changes.

If you want to see if OpenTelemetry helps you become a better Python developer — or if you just want to know how to add OpenTelemetry to your Python service — you’ve come to the right place. In this blog, we’ll show you how to instrument your Python application using OpenTelemetry and how to visualize your OpenTelemetry data using Application Observability in Grafana Cloud. We’ll walk you through the following steps.

As OpenTelemetry rises in popularity, more organizations are implementing, or planning to implement, the open source project to monitor their applications — and, meanwhile, more vendors are offering OpenTelemetry support. In fact, a quick Google search for “OpenTelemetry support” shows results ranging from legacy APM vendors to newer, cloud native solutions like Grafana Cloud.

Choosing an OpenTelemetry backend depends on several factors, such as your monitoring and observability requirements, current infrastructure, scalability needs, and budget constraints.

In today's rapidly evolving landscape of distributed systems and microservices, understanding how applications behave in production environments has become increasingly complex. Traditional monitoring tools often fall short when it comes to providing comprehensive insights into the performance and behavior of these modern architectures.

MongoDB’s flexibility and document-oriented nature have always stood out to me as its most compelling features, setting it apart from the strict schema constraints of traditional relational databases. This adaptability is a boon for application development, allowing for more dynamic data interactions that mirror real-world information complexities and freeing table schemas’ constraints.

In the dynamic world of observability and analytics, everyone’s looking for smarter, more efficient, and interoperable ways to handle their data. That’s where Cribl steps in, bringing you an exciting update to our product lineup. We’re thrilled to introduce the OTLP Metrics Function to Cribl Stream 4.5! This Function converts metrics into the OpenTelemetry Protocol (OTLP) format with ease!

Tracing provides indispensable insights into the state and performance of distributed applications, but it can often be difficult to determine the root cause or ultimate business impact of issues indicated by traces. Translating visibility of individual microservices into broader performance insights often requires drawing complex correlations between spans. This can be a laborious process, which can complicate everything from troubleshooting and triage to tracking KPIs and managing costs.

For any software company, a log monitoring tool is a must for collecting, storing, and providing a centralized view of all logs from different applications and hosts for faster anomaly detection, incident resolution, and troubleshooting. They can also help detect security threats and provide audit trails. They are effective in capacity planning, decision-making, and ensuring optimized performance.

In this article, we will use OpenTelemetry to instrument a sample Flask app for traces. Flask is one of the most popular web application frameworks of Python. It consists of Werkzeug WSGI toolkit and Jinja2 template engine. Instrumentation is the biggest challenge engineering teams face when starting out with monitoring their application performance. OpenTelemetry is the leading open-source standard that is solving the problem of instrumentation.

Falcon is a minimalist Python web API framework for building robust applications and microservices. It also compliments many other Python frameworks by providing extra reliability, flexibility, and performance. Using OpenTelemetry, you can monitor your Falcon applications for performance by collecting telemetry signals like traces. Instrumentation is the biggest challenge engineering teams face when starting out with monitoring their application performance.

Are you looking for a Dynatrace alternative? Then you have come to the right place. In this article, we will go through the top 9 Dynatrace alternatives. First, let's briefly discuss what Dynatrace offers and why you might consider other solutions. Dynatrace is a leading observability and application performance management (APM) platform, providing deep insights into application performance and reliability.

Django is a popular open-source python web framework that enables rapid development while taking out much of the hassle from routine web development. It also helps developers to avoid common security mistakes. As such, many applications are built with Django. Django is very popular among web developers and has a huge community behind it. It gives web developers ready-to-use components for common things that you will need to accomplish for a web application.

Developing a native Android app is a challenging task that requires a deep understanding of the Android SDK, as well as programming languages such as Java or Kotlin. The process requires navigating various tools, frameworks, and APIs, each with its own rules. On top of that, you need to ensure compatibility and optimal performance across the diverse Android ecosystem, with its multitude of devices, screen sizes, and OS versions.

In today’s economic and regulatory environment, data sovereignty is increasingly top of mind for observability teams. The rules and regulations surrounding telemetry data can often be challenging to interpret, leaving many teams in the dark about what kind of data they can capture, how long it can be stored, and where it has to reside. In the past, addressing these issues at scale was a costly endeavor.

In today’s complex data landscape, continuous profiling has become essential for detailed insights into application resource usage. Grafana Labs is now advancing this field with the introduction of Span Profiles in Grafana 10.3. The Span Profiles feature represents a major shift in profiling methodology, enabling deeper analysis of both tracing and profiling data. Traditional continuous profiling provides a system-wide view over fixed intervals.

Nestjs is a Nodejs framework for building scalable server-side applications with typescript. It makes use of frameworks like Express and Fastify to enable rapid development. It has gained wide popularity in recent times, and many applications are making use of the Nestjs framework. Using OpenTelemetry client libraries, you can monitor your Nestjs application. Monitoring your Nestjs application is critical for performance management.

Spans are fundamental building blocks of distributed tracing. A single trace in distributed tracing consists of a series of tagged time intervals known as spans. Spans represent a logical unit of work in completing a user request or transaction. Distributed tracing is critical to application performance monitoring in microservice-based architecture. Before we deep dive into spans, let's have a brief overview of distributed tracing.

FastAPI is a modern Python web framework based on standard Python type hints that makes it easy to build APIs. It's a relatively new framework, having been released in 2018 but has now been adopted by big companies like Uber, Netflix, and Microsoft. Using OpenTelemetry, you can monitor your FastAPI applications for performance by collecting telemetry signals like traces. FastAPI is one of the fastest Python web frameworks currently available and is really efficient when it comes to writing code.

Modern digital organizations have rapidly adopted microservices-based architecture for their applications. Distributed tracing tools help monitor microservices-based applications. Choosing the right distributed tracing tool is critical. How do you know which is the right one for you? In this post, we will cover the top 13 distributed tracing tools in 2024 that can solve your monitoring and observability needs.

Late last year, during an engaging Twitch livestream by AWS Developer Advocate Darko Mesaroš, I was introduced to a new framework Id seen a few people talking about called Winglang. What really got my attention was this wasn’t just another entry in the list of cloud frameworks, Winglang was on a mission to redefine how we interact with cloud environments. Its ambition to serve as an expansive framework that could effortlessly connect different cloud platforms instantly caught my attention.

Welcome to the first SigNal of 2024! It is a year that we’re looking forward to accomplishing great things. We recently crossed 16,000+ GitHub stars as we continue to be amazed by the support of the developer community in our mission of open-source observability. Let’s see what humans of SigNoz were up to in January 2024.

When you have questions about your software, telemetry data is there for you. Over time, you make friends with your data, learning what queries take you right to the error you want to see, and what graphs reassure you that your software is serving users well. You build up alerts based on those errors. You set business goals as SLOs around those graphs.