From f80401ea73e1c6b40b578a490f6fff8e14de1ad4 Mon Sep 17 00:00:00 2001
From: ankit01-oss <>
Date: Tue, 28 May 2024 16:24:04 +0530
Subject: [PATCH] Updated readme

---
 lesson-3-2/README.md        |  2 +-
 lesson-6/README.md          |  4 +---
 lesson-7-how-to-x/readme.md |  4 +++-
 readme.md                   | 31 ++++++++++++++++++++++++++++++-
 4 files changed, 35 insertions(+), 6 deletions(-)

diff --git a/lesson-3-2/README.md b/lesson-3-2/README.md
index b5f21c0..bd8d045 100644
--- a/lesson-3-2/README.md
+++ b/lesson-3-2/README.md
@@ -1,4 +1,4 @@
-# Lesson 3.2: OpenTelemetry Manual Instrumentation of Python Application
+# Lesson 3.2: Manually configure OpenTelemetry agent 
 
 In the previous tutorial, we set up auto-instrumentation for our Flask application with OpenTelemetry without any code changes. In this tutorial, we will manually configure the agent.
 
diff --git a/lesson-6/README.md b/lesson-6/README.md
index 773b5b7..da8dfa7 100644
--- a/lesson-6/README.md
+++ b/lesson-6/README.md
@@ -1,6 +1,4 @@
-url - `opentelemetry-python-manual-instrumentation`
-
----
+# Lesson 6: Configure OpenTelemetry logging SDK in Python
 
 In the previous tutorials, we have seen how to manually create spans in a Python application. In this tutorial, we will look at how to configure OTel logging SDK in Python application.
 
diff --git a/lesson-7-how-to-x/readme.md b/lesson-7-how-to-x/readme.md
index 98061c7..be8adfc 100644
--- a/lesson-7-how-to-x/readme.md
+++ b/lesson-7-how-to-x/readme.md
@@ -1,4 +1,6 @@
-The programs show how to customize the metric streams produced by the SDK using Views. 
+# Lesson 7: Customize metrics streams produced by OpenTelemetry SDK using views
+
+The above code tutorials show how to customize the metric streams produced by the SDK using Views. 
 
 * change_aggregation.py: Shows how to configure to change the default aggregation using name of the instrument
 * configure_temporality.py: Shows how to have multiple exporter with different temporalities
diff --git a/readme.md b/readme.md
index e899b53..f64c24e 100644
--- a/readme.md
+++ b/readme.md
@@ -1,5 +1,34 @@
+# Guide on implementing OpenTelemetry in Python Applications
 
+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
-Lesson 3-1: Auto-instrumentation with OpenTelemetry
\ No newline at end of file
+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
+