OpenTelemetry is an an open-source observability framework that aims to standardize the generation, collection, and management of telemetry data(Logs, metrics, and traces). It is incubated under Cloud Native Computing Foundation(Cloud Native Computing Foundation), the same foundation which incubated Kubernetes.

OpenTelemetry is quietly becoming the default standard for generating, transmitting and managing observability data and new-age companies are embracing it for future-proof instrumentation of their applications.

In this guide, you will learn how to implement OpenTelemetry in Python Applications. Following lessons cover everything you need to know about using OpenTelemetry to implement observability.

Lesson 1: Setting up a basic Flask application

Lesson 2: Setting up SigNoz

Lesson 3-1: Auto-instrumentation with OpenTelemetry

Set up automatic traces, metrics and logs collection in our Flask application.

Lesson 3-2: Manual instrumentation with OpenTelemetry

Learn how to implement manual instrumentation with OpenTelemetry for more granular controls.

Lesson 4: Create spans manually in your Python application

Learn how to create manual spans and add metadata and attributes to them.

Lesson 5: Create custom metrics with OpenTelemetry

Create custom metrics like counter, gauge, histogram in your application.

Lesson 6: Configure OpenTelemetry logging SDK in Python

Learn how to configure OpenTelemetry logging SDK in Python.

Lesson 7: Customize metrics streams produced by OpenTelemetry SDK using views

At the end of this tutorial series, you will be able to use OpenTelemetry effectively to monitor your Python application.