[Feature]: Containerize the ETL Pipeline with Docker

[Satvik-Singh192]

So, what is it about?

I propose we containerize the ETL pipeline using Docker and Docker Compose.

This will create a consistent, isolated, and reproducible development environment for all contributors. Currently, the project setup relies on a contributor's local Python environment, which can lead to setup friction (OS differences, Python versions, package conflicts).

A containerized setup means anyone can clone the repo and get the pipeline running with a single command (docker-compose up) without worrying about local Python setup.

Acceptance Criteria

• Create a Dockerfile
  • Use a lightweight base image (e.g., python:3.10-slim).
  • Copy and install dependencies from requirements.txt.
  • Set the default command to run the pipeline (e.g., CMD ["python", "main.py"]).
• Create a docker-compose.yml file
  • Define a single service (e.g., etl).
  • It should build from the local Dockerfile.
  • It must use a volume to mount the local code directory into the container (e.g., volumes: ['.:/app']). This is the most important part, as it allows contributors to fix the TODOs in the code and see their changes reflected inside the container without rebuilding.
• Create a .dockerignore file
  • Should ignore common files like venv/, __pycache__/, .git, .vscode/, .idea/, etc., to keep the build context clean and fast.
• Update README.md
  • Add a new "Running with Docker" section with the new setup instructions.

Adding Docker is a valuable learning opportunity in itself, as it's a core tool in modern data engineering and lowers the barrier to entry for new contributors.

Code of Conduct

• I agree to follow this project's Code of Conduct

[Satvik-Singh192]

@Dheerajyadav1 please assign this issue to me sir