Scalable Microservices with Django, FastAPI & Kubernetes

Welcome to the documentation for the Scalable Microservices with Django, FastAPI & Kubernetes

Project Overview: PDF Converter with Microservices

This project is part of a hands-on course where we build a PDF Converter powered by a modern microservices architecture. The goal is to teach you how to design, develop, and deploy scalable, production-ready systems using today’s most in-demand technologies.

Tech Stack Highlights

Backend Microservices: Django & FastAPI Databases: PostgreSQL & MongoDB Messaging Queue: RabbitMQ for async communication Containerization & Orchestration: Docker & Kubernetes Architecture: Fully containerized, scalable, and cloud-ready microservices setup

What You’ll Learn

How to break down a monolith into microservices Designing APIs using Django & FastAPI Handling async tasks with RabbitMQ Managing multiple databases in a microservice environment Deploying and scaling services using Kubernetes & Docker

Real-World Project

By the end of this project, you’ll have a production-ready PDF Converter application and the skills to build similar systems in your own projects or workplace.

Introduction

This repository contains the source code for the course website.

🛠️ Getting Started

To get started with the project, please follow the instructions below:

Before starting, make sure you have the following installed:

• Python 3.x

Project Setup

To set up the project, follow these steps:

1. Clone the repository:

   git clone https://github.com/okotdaniel/microservices-course-docs.git

2. Navigate into the project directory:

   cd microservices-course-docs

3. Create a virtual environment:

   • On macOS/Linux:

     python3 -m venv venv

   • On Windows:

     python -m venv venv

4. Activate the virtual environment:

   • On macOS/Linux:

     source venv/bin/activate

   • On Windows:

     venv\Scripts\activate

5. Install the required dependencies:

   pip install -r requirements.txt

6. Run the application:

   mkdocs serve

Your application should now be up and running within the virtual environment.