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
• Routers, validation, errors, CORS — FastAPI patterns that don't break in practice

Flow

[1] Why Python ──▶ [2] Folder philosophy ──▶ [3] postgres ──▶ [4] APScheduler
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                [8] FastAPI in practice ◀── [7] Observability ◀── [6] Pipeline ◀── [5] Crawler ethics

Steps 1–4 build the service skeleton (language · structure · DB · schedule). Steps 5–8 complete the data-flow story (external calls · pipelines · operations).

Prerequisite — Python 3.13 + uv installed.