Project Description
I am building a production-grade web application that leans heavily on a Python backend (FastAPI or Django preferred) and needs to speak fluently with both MySQL and MongoDB. Beyond the usual CRUD endpoints, the system must support real-time data processing streams and a predictive-analytics layer that surfaces insights directly inside the UI.
Here is what I need from you:
• Architect and code the backend services in clean, test-covered Python, setting up robust connections to MySQL for transactional data and MongoDB for unstructured / high-volume datasets.
• Design the data models and migration scripts so switching between environments is painless.
• Implement a real-time pipeline (Celery, Redis, Kafka—use what you are strongest with) that ingests incoming events, performs on-the-fly calculations, and pushes updates to the front end via WebSockets or server-sent events.
• Build a small predictive module (scikit-learn or similar) that can be trained on historical records and queried through an API endpoint for forecasts.
• Wrap everything in well-documented REST endpoints, add Swagger / OpenAPI docs, and containerise the whole stack with Docker for easy deployment.
Acceptance criteria
• One-click Docker compose brings up MySQL, MongoDB, and the Python service with seeded test data.
• Hitting /realtime-demo emits live updates within <1 s latency.
• POST /predict returns forecast JSON with confidence scores and unit tests cover at least 80 % of the codebase.
If this sounds in your wheelhouse, let’s get started—happy to give you Git repo access and iterate quickly on milestones.