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Build A Real PySpark Pipeline From Scratch
Rating: 4.5 out of 5(12 ratings)
401 students

Build A Real PySpark Pipeline From Scratch

Master PySpark with a real dataset: schema design, joins, window functions and the why behind every technical decision.
Created byRahma GARGOURI
Last updated 3/2026
English
English [Auto],

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.

Course content

5 sections • 14 lectures • 1h 28m total length

  • What is MapReduce?7:08

    Explore MapReduce model by splitting data across workers, applying map in parallel, then using reduce to combine results, while Spark keeps data in memory and builds a DAG for optimization.

  • Spark Architecture8:01

    Explore how Spark implements mapreduce with a driver and executors, and learn the roles of Spark context, DAX scheduler, and task scheduler in lazy, DAG-based execution.

  • Setting Up Your Environment5:08

    Set up your PySpark environment by installing pyenv, Python 3.11, PySpark 3.5, and Java 17, then run first-session.py to confirm Spark version 3.5.1 and a local Spark UI at localhost:4040.

  • Written Guide Step-by-Step Setup Guide (macOS & Windows)13:46
  • The Spark UI12:49

    Learn to read the Spark UI and diagnose performance by examining jobs, stages, and tasks, focusing on whole-stage codegen, shuffle boundaries, and cache effectiveness through a live PySpark demo.

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 build

Starting 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 different

This 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 inside

5 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.

Who this course is for:

  • Beginner Python developpers curious about Data Engineering

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