📚 PySpark & Big Data Engineering — Syllabus

Course Overview

This module covers the transition from legacy Hadoop architectures to modern, in-memory Apache Spark processing, ending with cutting-edge Lakehouse architectures.

💡 All notes are designed for beginners using simple analogies, real-world examples, and the Problem → Solution framework.

✅ Chapters

🐘 01 - Big Data & Hadoop Fundamentals

Characteristics of Big Data (Volume, Velocity, Variety)
Batch vs Real-Time Processing
Hadoop Ecosystem & MapReduce limitations
Data Ingestion with Sqoop

⚡ 02 - Apache Spark Fundamentals

Spark Architecture (Driver, Executors, Cluster Manager)
In-Memory Computing Advantages
Spark Deployment Modes (Local, Client, Cluster)

🧩 03 - Spark RDDs (Resilient Distributed Datasets)

What is an RDD? (Immutability, Lineage, Fault Tolerance)
Transformations (Lazy Evaluation) vs. Actions
Narrow vs. Wide Dependencies

04 - Advanced RDD Concepts

RDD Persistence Levels (Cache vs. Persist)
Partitioning for Parallelization (repartition vs coalesce)
Shared Variables (Broadcast, Accumulators)

🗃️ 05 - Spark SQL & DataFrames

The Catalyst Optimizer & Tungsten Engine
Data Abstractions (DataFrames vs Datasets vs Schema RDDs)
File Formats (JSON vs Parquet)
Python UDFs & Performance Hits

📊 06 - Advanced SQL & Transformations

Advanced Grouping (ROLLUP, CUBE)
Common Table Expressions (CTEs) for readable code
Pivoting Data (Long to Wide)
Materialized Views

🧊 07 - Modern Data Architectures (Delta Lake & Iceberg)

Problems with traditional Data Lakes
Delta Lake Fundamentals (ACID, Time Travel, Schema Evolution)
Apache Iceberg features and massive scale

Course Content: