Build A Real Pyspark Pipeline From Scratch

Master PySpark with a real dataset: schema design, joins, window functions & the ‘why’ behind every technical decision.

What you’ll learn
Build a complete PySpark data pipeline from scratch.
Explain and justify core PySpark architectural decisions.
Read and interpret the Spark UI.
Understand why Parquet outperforms CSV for analytical workloads.

Requirements
Motivation
Python

Description
This course contains the use of artificial intelligence. AI tools were used to help produce input data and some visual materials, while all technical content, code, and teaching are entirely my own.Are you stuck at pandas?You know Python, you’ve used pandas — but the moment a project involves millions of rows or a job description mentions PySpark, things feel like a different world. A different mental model, a different syntax, and most tutorials don’t help. This course bridges that gap.What you’ll buildStarting from raw CSV files, you’ll build a complete PySpark pipeline: clean and enrich the data, aggregate it across age groups, gender and app categories, compute a behavioral evolution index using window functions, and write production-ready Parquet output. Real dataset, real questions, real pipeline — something you could show in a technical interview tomorrow.What makes this differentThis course doesn’t just teach you the syntax — it teaches you the why. Every technical choice is explained so you can justify it on the job and in interviews. It’s based on a hands-on workshop tested with students at an engineering school in France.What’s inside5 modules covering Spark fundamentals, schema design, data cleaning & joins, window functions & moving averages, and Parquet optimization — with quizzes, starter code, and full solutions included.Who this is for: Python developers, data engineers, data scientists and data analysts ready to move beyond pandas into real distributed data processing.

Beginner Python developpers curious about Data Engineering

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