APIs for Data Engineers: Requests, Pagination & Retries in Python

Music: 'Anticipation' by ENDELØS on #Uppbeat https://uppbeat.io/c/endelos License code: AOFPXCL9KPJOSADK Toy API examples work in 10 lines of code. Production extractors are built differently: they walk through paginated data, handle rate limits gracefully, survive network drops, and protect API keys from leak bots. In this video, you'll learn the 3 essential patterns that separate a beginner API script from a production data pipeline using Python's requests library. By the end, you'll know how to build polite, retry-safe extractors that fail loudly when something breaks instead of swallowing errors. All the code is in the course GitHub repo. 📌 In this video: Setting mandatory timeouts on every request raise_for_status(): Why failing loudly saves pipelines Walking page-based pagination with .extend() Retries with exponential backoff using requests.RequestException Reading rate limits via response headers (GitHub API) Securing API keys using .env, python-dotenv, and .gitignore 🔗 Links: Course code + exercises: https://github.com/Darsh-Joshi-10/dat... Full playlist: • Data Engineering requests: https://requests.readthedocs.io/ ⚙️ Setup: run pip install -r requirements.txt from the repo root, then run python module-01-python/03_apis/api_basics.py to follow along. 📝 Homework: three challenges in exercises/03_api_exercises.py covering timeouts, pagination, and the Wi-Fi drop retry challenge. Solutions go up 24 hours after this video. ✅ Subscribe + hit the bell — next up is 1.4: Pandas Part 1 (The core operations you'll use every day). ☕ Enjoying the course? You can support it here (keeps the course 100% free): https://ko-fi.com/dcodeit 💬 Hit an error following along? Drop a comment and I'll help. #DataEngineering #Python #APIs #Requests #LearnDataEngineering #DataEngineeringCourse #PythonForBeginners