Certainly, Venkatesh! Here's a comprehensive README.md tailored for your AWS_internals repository. This README is designed to provide clarity and ease of understanding for anyone exploring your project.

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AWS Internals – Coderrange Edition

🔍 Deep Dive into AWS Services with Python Simulations

Welcome to the AWS Internals repository! This project offers an in-depth exploration of core AWS services by simulating their internal mechanisms using Python. It's crafted to aid learners, developers, and educators in understanding the foundational concepts of AWS without the need for actual AWS infrastructure.

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🚀 Project Overview

This repository emulates the internal workings of various AWS services, providing a hands-on approach to grasp their functionalities. By recreating these services in Python, users can:

• Understand the underlying principles of AWS services.
• Experiment with service behaviors in a controlled environment.
• Enhance their knowledge without incurring AWS costs.

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🧰 Simulated AWS Services

1. Amazon S3 (Simple Storage Service)

• Features:

  • Bucket creation and management.
  • Object storage with metadata handling.
  • Access control simulations.

• Learnings:

  • How S3 stores and retrieves data.
  • Understanding object storage principles.

2. AWS Lambda

• Features:

  • Function invocation simulation.
  • Cold start and warm start behaviors.
  • Event-driven execution models.

• Learnings:

  • Serverless computing fundamentals.
  • Execution lifecycle of Lambda functions.

3. Amazon DynamoDB

• Features:

  • Table creation with partition and sort keys.
  • CRUD operations with consistency models.
  • Indexing and query simulations.

• Learnings:

  • NoSQL database design.
  • Data retrieval and storage strategies.

4. Amazon SQS (Simple Queue Service)

• Features:

  • Message queuing with visibility timeouts.
  • Dead-letter queue simulations.
  • Message retention and delivery policies.

• Learnings:

  • Asynchronous communication patterns.
  • Decoupling of microservices.

5. AWS IAM (Identity and Access Management)

• Features:

  • User and role creation.
  • Policy definition and enforcement.
  • Permission evaluation mechanisms.

• Learnings:

  • Access control and security best practices.
  • Policy evaluation logic.

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🛠️ Getting Started

Prerequisites

• Python 3.x installed on your system.
• Basic understanding of AWS services.

Installation

1. Clone the Repository:

   git clone https://github.com/venkatesh-db/AWS_internals.git
   cd AWS_internals

2. Set Up Virtual Environment (Optional but Recommended):

   python -m venv venv
   source venv/bin/activate  # On Windows: venv\Scripts\activate

3. Install Dependencies:

   pip install -r requirements.txt

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📂 Repository Structure

AWS_internals/
├── s3_simulation/
│   └── s3.py
├── lambda_simulation/
│   └── lambda_function.py
├── dynamodb_simulation/
│   └── dynamodb.py
├── sqs_simulation/
│   └── sqs.py
├── iam_simulation/
│   └── iam.py
├── README.md
└── requirements.txt

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📸 Visual Aids

For a better understanding, refer to the diagrams/ directory containing architecture diagrams for each simulated service.

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🤝 Contributing

Contributions are welcome! If you have suggestions or improvements:

1. Fork the repository.
2. Create a new branch: git checkout -b feature-name.
3. Commit your changes: git commit -m 'Add feature'.
4. Push to the branch: git push origin feature-name.
5. Open a pull request.

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📄 License

This project is licensed under the MIT License.

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📬 Contact

For any queries or feedback:

• Email: venkatesh.db@gmail.com
• LinkedIn: https://www.linkedin.com/in/venkatesh-db/

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Feel free to customize this README further to align with your project's specifics. If you need assistance with any particular section or further enhancements, let me know!