Every data engineering project starts with a simple Python script. Maybe it pulls data from an API, cleans it up, and writes it out. It works the first time, and the second time. Then one day it does not. It breaks quietly. It corrupts a file. It fails at 2 am. There are no logs or backups. Suddenly, what felt like a tiny utility script becomes a critical pipeline. A small patch here and there will work for now until the loop continues.
This tutorial walks through that journey step by step. We start with the kind of script most engineers have written and grow it into a well-designed pipeline. We clean up the structure, break work into components, handle errors, add retries and logging, manage configuration, and introduce automation. Finally, introduce an orchestration layer that naturally fits the pipeline scripts.
If you are someone aspiring to step into the data world, this workshop will pave the way for that. We will explore how simple scripts evolve into full-on ETL pipelines and use Apache Airflow as our orchestration engine