Python · FastAPI · Data Pipelines

One language, end-to-end: API, scheduling, external data, observability.

Difficulty: intermediate
Lessons: 7
Total time: 185 min

Working with data flows in Python

Python is the most-agreed-upon language for data work. With FastAPI you build APIs, with APScheduler you run scheduled jobs, and with PostgreSQL you store the result.

By the end:

Build a small FastAPI server
Split folders by domain
Connect PostgreSQL with a real pool
Run scheduled jobs with APScheduler
Call external APIs ethically (rate-limit, robots.txt)
Build an observable service

Prerequisite — Python 3.13 + uv installed.

Lessons

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Python · FastAPI · Data Pipelines

Working with data flows in Python

Lessons

Step 1 — Why Python · FastAPI

Step 2 — Folder structure

Step 3 — PostgreSQL connection

Step 4 — APScheduler

Step 5 — External APIs · crawler ethics

Step 6 — Data pipeline

Step 7 — Observability

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