Python · FastAPI · Data Pipelines

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

Completing a data service

Language and structure

Define the problems suited to Python and assign folder responsibilities.

Storage and schedule

Implement PostgreSQL and scheduler contracts for retries and concurrent runs.

Collection and processing

Respect upstream boundaries while building an idempotent data pipeline.

Operational API

Expose processing state through metrics and explicit FastAPI failure responses.

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.

Python · FastAPI · Data Pipelines

Flow

Completing a data service

Lessons

Step 1 — Why Python · FastAPI

Step 1 — Why Python · FastAPI

Step 2 — Folder structure

Step 2 — Folder structure

Step 3 — PostgreSQL connection

Step 3 — PostgreSQL connection

Step 4 — APScheduler

Step 4 — APScheduler

Step 5 — External APIs · crawler ethics

Step 5 — External APIs · crawler ethics

Step 6 — Data pipeline

Step 6 — Data pipeline

Step 7 — Observability

Step 7 — Observability

Step 8 — FastAPI in Practice

Step 8 — FastAPI in Practice

Other courses

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

Step 8 — FastAPI in Practice