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.

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.

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

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.

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.