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 the Environment Set up a basic Flask application.

Lesson 2: Setting up SigNoz Set up SigNoz to receive data collected from OpenTelemetry.

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 Learn how to create custom with OpenTelemetry

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