Python & FastAPI
FastAPI has quietly become the default choice for new Python backends. It is fast enough for most real workloads (on par with Node and Go for I/O-bound work), it generates OpenAPI docs for free, its dependency-injection system makes testing pleasant, and Pydantic v2 gives you validation with almost no ceremony. This track treats FastAPI as a production framework — not as a toy for tutorials — with a focus on the deployment, persistence and operational patterns that keep a Python service healthy under real traffic.
What this track covers
Building REST APIs with FastAPI end-to-end: Pydantic v2 models, async endpoints, dependency injection, SQLAlchemy 2.x for persistence, authentication, containerising with Docker, and deploying on managed platforms. Also covers the boundary decisions — when to reach for FastAPI vs Django REST vs Flask — and the gotchas specific to running Python under a real load (GIL, worker sizing, cold start).
Who this is for
Python engineers building their first production API, Django developers evaluating FastAPI for a new service, and polyglot backend engineers coming from Spring Boot or Node who want a fast on-ramp to the Python ecosystem.
Prerequisites
- Comfortable with Python 3.10+ syntax and type hints
- Basic SQL and an intuition for what a query costs
- HTTP fundamentals — verbs, status codes, JSON
- Docker basics
Recommended learning order
// beginner
Start with 'Building REST APIs with FastAPI' to get a service running end-to-end with Pydantic and SQLAlchemy. Add authentication and validation. Deploy in a container.
// intermediate
Add async patterns properly (async DB drivers, background tasks), a caching layer, and integration tests. Study Uvicorn vs Gunicorn+Uvicorn worker configurations — this is where most Python services hit their first performance wall.
// advanced
Streaming responses, WebSockets, structured concurrency, observability, and the harder deployment questions — Kubernetes, autoscaling, cold-start-sensitive workloads. Compare notes with the Java Spring Boot track: the abstractions map cleanly and the trade-offs sharpen when you see both.
Common mistakes to avoid
- Blocking the event loop with synchronous database drivers inside async endpoints
- Skipping Pydantic validation on request bodies and handling type errors deep in business logic
- Running a single Uvicorn worker in production and being surprised by throughput
- Reaching for Celery for a task that could be a FastAPI BackgroundTask
- Confusing async performance with parallelism — Python's GIL still applies to CPU-bound work
Career relevance
FastAPI is now the fastest-growing Python backend framework and is standard at ML-adjacent companies where the API layer often sits in front of a Python-native model. Fluency with FastAPI + SQLAlchemy 2 + Pydantic v2 is a distinct hiring signal separate from generic 'Python' on a CV.
Featured tutorials in this track
FastAPI vs Spring Boot — Which Backend Framework Should You Choose?
An honest, side-by-side comparison of FastAPI and Spring Boot — performance, developer experience, async capabilities, ecosystem, scalability and real-world fit.
FastAPI Testing Strategy — Unit, Integration and API Testing
A pragmatic FastAPI testing strategy with pytest — unit tests, async TestClient, dependency overrides, real Postgres via testcontainers and CI integration.
Deploy FastAPI Applications to Kubernetes
A complete guide to deploying FastAPI on Kubernetes — Deployment, Service, Ingress, ConfigMaps, Secrets, HPA and zero-downtime rollouts.
FastAPI + Kafka — Build Real-Time Event Systems
Build event-driven systems with FastAPI and Apache Kafka using aiokafka — producers, consumers, schemas, retries and exactly-once-ish semantics.
JWT Authentication in FastAPI — Secure APIs Properly
Implement JWT authentication in FastAPI the right way — OAuth2PasswordBearer, password hashing, access + refresh tokens and role-based access control.
FastAPI + Redis Caching — Make Your API Faster
Add Redis caching to FastAPI the right way — async Redis client, decorator-based caching, TTL strategies and cache invalidation patterns.
CI/CD Pipeline for FastAPI with GitHub Actions and Docker
Build a complete CI/CD pipeline for a FastAPI app — pytest, linting, Docker image builds, container registry push and deployment from GitHub Actions.
Dockerizing a FastAPI Application the Right Way
Build small, fast, secure Docker images for FastAPI — multi-stage builds, Gunicorn + Uvicorn workers, non-root users, and production-ready Dockerfiles.
+ 2 more tutorials in this track — browse the full archive.