← Course Overview

Getting Started

What is Apache Spark?
Spark vs Pandas — When to Use Which
Installing PySpark
Your First SparkSession
Understanding the Spark UI

DataFrames Deep Dive

Creating DataFrames from Lists, Dicts, and Files
Schemas and Data Types — Why Explicit Schemas Matter
Selecting, Filtering, and Sorting Rows in PySpark
Adding, Renaming, and Dropping Columns in PySpark
GroupBy, Aggregations, and Window Functions in PySpark
Handling Null Values and Dirty Data in PySpark
Reading CSV, JSON, and Parquet Files in PySpark

Spark SQL

Running SQL Queries on PySpark DataFrames
Temp Views, Global Views, and When to Use Each
PySpark Joins — Inner, Left, Right, Anti, and Cross
Subqueries and CTEs in Spark SQL
UDFs — Writing Custom Functions (And Why to Avoid Them)

Spark Internals

Lazy Evaluation and the DAG — Why Spark Waits
Partitions — How Spark Splits Your Data
Shuffles — The Most Expensive Thing in Spark
Catalyst Optimizer — How Spark Rewrites Your Queries
Repartition vs Coalesce — When to Use Which

Real-World Patterns

Building an ETL Pipeline from Scratch
Caching and Persistence — Speed Up Repeated Queries
Broadcast Joins — Eliminating Shuffles for Small Tables
Data Skew — Diagnosing and Fixing Uneven Partitions
Writing Production-Quality PySpark Code

Interview Prep & Next Steps

Top 20 PySpark Interview Questions (With Code Answers)
PySpark Performance Tuning Cheat Sheet
Where to Go Next — Your PySpark Learning Roadmap

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PySpark for Absolute Beginners

Master Apache Spark with Python — from zero to confident.

6 modules·30 lessons·Free forever

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Curriculum

Module 1

Getting Started

5 lessons

01What is Apache Spark?→
02Spark vs Pandas — When to Use Which→
03Installing PySpark→
04Your First SparkSession→
05Understanding the Spark UI→

Module 2

DataFrames Deep Dive

7 lessons

01Creating DataFrames from Lists, Dicts, and Files→
02Schemas and Data Types — Why Explicit Schemas Matter→
03Selecting, Filtering, and Sorting Rows in PySpark→
04Adding, Renaming, and Dropping Columns in PySpark→
05GroupBy, Aggregations, and Window Functions in PySpark→
06Handling Null Values and Dirty Data in PySpark→
07Reading CSV, JSON, and Parquet Files in PySpark→

Module 3

Spark SQL

5 lessons

01Running SQL Queries on PySpark DataFrames→
02Temp Views, Global Views, and When to Use Each→
03PySpark Joins — Inner, Left, Right, Anti, and Cross→
04Subqueries and CTEs in Spark SQL→
05UDFs — Writing Custom Functions (And Why to Avoid Them)→

Module 4

Spark Internals

5 lessons

01Lazy Evaluation and the DAG — Why Spark Waits→
02Partitions — How Spark Splits Your Data→
03Shuffles — The Most Expensive Thing in Spark→
04Catalyst Optimizer — How Spark Rewrites Your Queries→
05Repartition vs Coalesce — When to Use Which→

Module 5

Real-World Patterns

5 lessons

01Building an ETL Pipeline from Scratch→
02Caching and Persistence — Speed Up Repeated Queries→
03Broadcast Joins — Eliminating Shuffles for Small Tables→
04Data Skew — Diagnosing and Fixing Uneven Partitions→
05Writing Production-Quality PySpark Code→

Module 6

Interview Prep & Next Steps

3 lessons

01Top 20 PySpark Interview Questions (With Code Answers)→
02PySpark Performance Tuning Cheat Sheet→
03Where to Go Next — Your PySpark Learning Roadmap→

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