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@ -8,27 +8,34 @@ In this guide, you will learn how to implement OpenTelemetry in Python Applicati
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### Lesson 1: Setting up a basic Flask application
[Lesson 1: Setting up a basic Flask application](/lesson-1)
In this tutorial, you will create a simple Flask to-do application with MongoDB.
### 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 2: Setting up SigNoz](/lesson-2)
OpenTelemetry does not provide a storage and analytics layer. In this tutorial, you will set up SigNoz to send your OpenTelemetry data.
### Lesson 3-2: Manual instrumentation with OpenTelemetry
[Lesson 3-1: Auto-instrumentation with OpenTelemetry](/lesson-3-1)
Set up automatic traces, metrics, and logs collection in the Flask application.
[Lesson 3-2: Manual instrumentation with OpenTelemetry](/lesson-3-2)
Learn how to implement manual instrumentation with OpenTelemetry for more granular controls.
### Lesson 4: Create spans manually in your Python application
[Lesson 4: Create spans manually in your Python application](/lesson-4)
Learn how to create manual spans and add metadata and attributes to them.
### Lesson 5: Create custom metrics with OpenTelemetry
[Lesson 5: Create custom metrics with OpenTelemetry](/lesson-5)
Create custom metrics like counter, gauge, histogram in your application.
### Lesson 6: Configure OpenTelemetry logging SDK in Python
[Lesson 6: Configure OpenTelemetry logging SDK in Python](/lesson-6)
Learn how to configure OpenTelemetry logging SDK in Python.
### Lesson 7: Customize metrics streams produced by OpenTelemetry SDK using views
[Lesson 7: Customize metrics streams produced by OpenTelemetry SDK using views](/lesson-7)
In this tutorial, learn how to:
- how to configure to change the default aggregation using the name of the instrument
- how to have multiple exporter with different temporalities
- how to limit the number of attributes that are output for a metric
- how to drop a metric entirely
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