A Crash Course In PySpark Course Syllabus

A Crash Course in PySpark is a hands-on, beginner-friendly program designed to equip data professionals with foundational skills in scalable data processing using PySpark. Over approximately 6 hours, learners will progress from setting up their environment to building end-to-end data pipelines involving batch processing, streaming, and machine learning. The course blends core concepts with practical implementation, emphasizing performance optimization and real-world use cases. Each module includes interactive exercises and coding examples to reinforce learning.

Module 1: Getting Started with Spark & PySpark

Estimated time: 0.5 hours

• Installing Spark and configuring PySpark locally
• Setting up the PySpark interactive shell
• Integrating PySpark with Jupyter Notebook
• Overview of Spark architecture: driver, executors, and cluster modes

Module 2: RDDs & Core Transformations

Estimated time: 0.75 hours

• Creating RDDs from files and in-memory collections
• Applying transformations: map, filter, flatMap
• Using key-value pair transformations like reduceByKey
• Executing actions: collect, count, take

Module 3: DataFrames & Spark SQL

Estimated time: 1 hour

• Creating DataFrames from CSV, JSON, and Parquet files
• Performing DataFrame operations: select, filter, groupBy, join
• Running SQL queries on Spark DataFrames
• Working with structured data using Spark SQL

Module 4: Performance Tuning & Optimizations

Estimated time: 0.75 hours

• Understanding the Catalyst optimizer and Tungsten engine
• Repartitioning data for balanced workloads
• Caching strategies for iterative operations
• Using broadcast variables and broadcast joins

Module 5: Advanced Data Processing

Estimated time: 1 hour

• Working with window functions for analytical queries
• Creating and using User-Defined Functions (UDFs)
• Handling complex data types: arrays and structs
• Writing efficient pipelines and handling data skew

Module 6: Spark Streaming Essentials

Estimated time: 0.75 hours

• Introduction to real-time data processing with Structured Streaming
• Applying streaming transformations
• Configuring output sinks for streaming data

Module 7: Machine Learning with MLlib

Estimated time: 1 hour

• Building ML pipelines with Spark MLlib
• Data preprocessing and feature engineering
• Model training for classification and regression
• Evaluating models and hyperparameter tuning

Module 8: Putting It All Together

Estimated time: 0.5 hours

• End-to-end ETL pipeline: ingest, transform, analyze, persist
• Debugging Spark applications
• Logging and monitoring best practices

Prerequisites

• Familiarity with Python programming
• Basic understanding of data processing concepts
• Introductory knowledge of Spark architecture recommended

What You'll Be Able to Do After

• Install and configure PySpark in local and cluster environments
• Load and manipulate large datasets using Spark DataFrames and SQL
• Perform complex data transformations using RDDs and DataFrame APIs
• Optimize Spark jobs using partitioning, caching, and broadcast variables
• Build and evaluate machine learning pipelines using Spark MLlib