Operations | Monitoring | ITSM | DevOps | Cloud

The digitalization of the end user experience is leading to increasingly rapid changes for both customers and business owners alike. Customers demand and depend on fast, reliable web services whether for informational purposes or to conduct e-commerce transactions. That’s why regardless of the device that is used, slow digital experiences aren’t easily forgiven. If they happen often enough, they will reflect poorly on your brand and will eventually impact your bottom line.

Here at Grafana Labs, when we’re building integrations for Grafana Cloud, we’re often thinking about how to help users get started on their observability journeys. We like to focus some of our attention on the different technologies you might come across along the way. That way, we can share our tips on the best ways to interact with them while you’re using Grafana products.

In this tutorial, we will go through a working example of a Python application auto-instrumented with OpenTelemetry. To keep things simple, we will create a basic “Hello World” application using Flask, instrument it with OpenTelemetry’s Python client library to generate trace data and send it to an OpenTelemetry Collector. The Collector will then export the trace data to an external distributed tracing analytics tool of our choice.