Building Microservices with Python is a hands-on course in building microservices with FastAPI, from splitting services and connecting PostgreSQL to running them with Docker Compose behind Nginx, communicating over HTTP and RabbitMQ, and adding JWT and tracing. It suits Python developers and IT staff, and you leave able to run a multi-service system with Docker Compose.
Microservices split a system into several small services, each handling its own work with its own database, and each developed, tested and deployed separately while still working together as one system. This lets several teams work in parallel and scale only the parts under heavy load, but it also brings new things to manage, such as communication between services, data spread across many places and tracking problems across the system. Python with FastAPI is a popular choice because it is quick to write, easy to read and supports async work.
This course builds microservices with FastAPI, from the ideas behind them and splitting services to REST APIs validated with Pydantic, PostgreSQL through SQLAlchemy 2.0 and choosing data management patterns. Learners run services with Docker Compose, connect them over HTTP and through RabbitMQ with FastStream, use Nginx as the single entry point, add JWT authentication, health checks, logging and tracing, write tests and bring the whole system together in a workshop. (3 days, 6 hours per day, 18 hours in total, Intermediate level.)
Course Details
This course is the 2026 edition of Building Microservices with Python. It runs for 3 days, 6 hours per day (18 hours in total, 09:00-16:00), as lectures with labs. Intermediate level. This edition grows the original Movies and Casts services behind Nginx into a complete microservice system. It uses current FastAPI with lifespan handlers, async SQLAlchemy 2.0 with Alembic and projects managed by uv, and adds calls between services with httpx, timeouts and retries, messaging through RabbitMQ with
FastStream, sagas and the outbox pattern, JWT, OpenTelemetry, testing and GitHub Actions. It keeps the original core of microservice concepts, REST APIs with FastAPI and PostgreSQL, database per service, shared databases, API composition, Docker Compose and Nginx as the single entry point. The course focuses on Python and differs from Building Microservices with NestJS and Microservices with Spring Boot. Every lab uses open source or free tools. Learners take home a lab guide and the source code of the whole system.
Section 1: Introducing Microservices
Section 2: Lab: Setting Up Your Tools
Section 3: Lab: REST APIs with FastAPI
Section 4: Lab: Using PostgreSQL with FastAPI
Section 5: Data Management Patterns for Microservices
Section 6: Lab: Microservices on Docker
Section 7: Lab: Calling Services over HTTP
Section 8: Lab: Nginx as an API Gateway
Section 9: Lab: Messaging through RabbitMQ
Section 10: Transactions across Services
Section 11: Lab: Authentication across Services
Section 12: Lab: Monitoring the System
Section 13: Testing Microservices
Section 14: Build and Deployment
Section 15: Workshop: A Cinema Ticket Booking System
No public rounds are open right now. Join the waiting list and we will contact you first when the next round opens, or ask us on LINE. Or call 02-570-8449 or 088-807-9770
A monolith keeps every function in one application that is deployed as a whole. Microservices split the system into several small services, each handling one area and talking through APIs or a message broker. They can be developed and deployed separately and only the busy parts need scaling, but the system becomes more complex: you must manage the network, data spread across databases, transactions across services and tracing problems between them. Small systems often start as a well-structured modular monolith.
With database per service, each service has its own database and others reach its data only through APIs or events, so each team can change its data structure freely. Reading data across services then needs techniques such as API composition, and you accept eventual consistency. With a shared database, several services use one database, which is easy to start with and allows direct joins, but changing one table can affect many services.
Add Nginx as another service in compose.yaml and expose a single port. In nginx.conf, write location blocks by path, such as /api/movies and /api/casts, and use proxy_pass to point at each service name and internal port, since Docker Compose creates a network where services reach each other by name. If a service runs several instances, an upstream block spreads the load. Then run docker compose up, and users reach every service through one host.
It suits Python developers, IT staff, system administrators, academics and teams planning to split a system into microservices. You should be able to write Python, understand web applications and HTTP at a basic level, have used databases and SQL, and bring a laptop that can run Docker Desktop.
You will be able to split a system into microservices and choose suitable data patterns, build REST APIs with FastAPI and PostgreSQL, run several services with Docker Compose behind Nginx on one host, make services communicate over HTTP and through RabbitMQ, authenticate with JWT, monitor the system with logs and tracing, and write tests and a pipeline that builds images. Participants who complete the training receive a Certificate of Completion with its own number, which can be verified online.
The course uses Python 3.14 with projects managed by uv, FastAPI, Pydantic v2, SQLAlchemy 2.0 and Alembic for the APIs, httpx for calling other services and FastStream for messaging, with PostgreSQL 18, RabbitMQ, Nginx and Jaeger running on Docker Desktop, pytest for testing and GitHub Actions. All are open source or free. A laptop with 16 GB of RAM or more is recommended, as several containers run at once.
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