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-# Module 2: Change Data Capture: Enabling Event-Driven Architectures
-
-
-## Introduction
-Change Data Capture, commonly known as CDC, is an efficient way to track changes in a database.
-
-The purpose of CDC is to capture insertions, updates, and deletions applied to a database and to make this change data available in a format easily consumable by downstream applications.
-Why do we need CDC pattern?
-
-    1. Real-time Data Syncing: CDC facilitates near-real-time data integration and syncing.
-    2. Efficient Data Pipelines: It allows incremental data loading, which is more efficient than bulk load operations.
-    3. Minimized System Impact: CDC minimizes the impact on the source system by reducing the need for performance-intensive queries.
-    4. Event-Driven Architectures: It enables event-driven architectures by streaming database events.
-
-
-## Contents
-- data_flow module:  Singleton class to manage RabbitMQ connection.
-    - mq.py
-- db module: Singleton class to connect to MongoDB database.
-    - mongo.py
-- cdc.py :
-- **Functionality**: This script sets up a CDC pipeline. It monitors a MongoDB database for changes and forwards these changes to a RabbitMQ message queue.
-- **MongoDB Connection**: It connects to a MongoDB database (specifically the "scrabble" collection), utilizing a class from `db.mongo`.
-- **Change Tracking**: The script watches for insert operations in MongoDB. When a change is detected, it processes and serializes the change details.
-- **Data Processing**: Each change's metadata is extracted and serialized using `json_util`. The `_id` field is converted to a string and additional metadata is added.
-- **RabbitMQ Integration**: After serialization, the script publishes the data to a RabbitMQ queue named "test_queue".
-- test_cdc.py
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