PySpark Certification Course Online Syllabus

Overview: This PySpark Certification Course is a comprehensive, hands-on program designed to take learners from the basics to advanced data engineering techniques using Apache Spark and PySpark. Spanning 8 modules, each requiring approximately 6–8 hours of effort, the course totals around 60 hours of learning. You’ll gain practical experience with Spark’s core components—RDDs, DataFrames, Spark SQL, MLlib, and deployment workflows—while building real-world data pipelines and optimizing performance. The curriculum concludes with a capstone project that integrates all concepts, preparing you for real-world data engineering challenges.

Module 1: Introduction to Spark & PySpark Setup

Estimated time: 7 hours

• Spark architecture and components
• Cluster modes: local, standalone, YARN, and Databricks
• Installing and configuring PySpark
• Launching a local Spark session and running basic operations

Module 2: RDDs and Core Transformations

Estimated time: 7 hours

• RDD creation and properties
• Transformations vs. actions
• Map, filter, and other core operations
• Building word-count and log-analysis pipelines using RDDs

Module 3: DataFrames & Spark SQL

Estimated time: 7 hours

• DataFrame API and schema inference
• Loading JSON and CSV data
• Running SQL queries with Spark SQL
• Creating and using temporary views

Module 4: Data Processing & ETL

Estimated time: 7 hours

• Joins and data merging techniques
• Window functions for ranking and aggregations
• Handling complex data types
• Creating and applying UDFs (User Defined Functions)

Module 5: Machine Learning with MLlib

Estimated time: 7 hours

• Introduction to MLlib and Spark ML pipelines
• Feature engineering and data transformation
• Building classification models with logistic regression
• Clustering and model evaluation techniques

Module 6: Performance Tuning & Optimization

Estimated time: 7 hours

• Data partitioning strategies
• Caching and persistence options
• Broadcast variables and shuffle optimization
• Profiling and tuning slow Spark jobs

Module 7: Deployment & Orchestration

Estimated time: 7 hours

• Submitting jobs using spark-submit
• Integrating with YARN cluster manager
• Using Databricks notebooks for development
• Scheduling and monitoring ETL workflows

Module 8: Capstone Project

Estimated time: 10 hours

• Design and implement an end-to-end data pipeline
• Ingest raw log data and perform transformations
• Apply analytics and store processed results

Prerequisites

• Basic knowledge of Python programming
• Familiarity with SQL syntax and queries
• Understanding of fundamental data processing concepts

What You'll Be Able to Do After

• Understand and apply core Spark and PySpark concepts
• Process large datasets using RDDs and DataFrames
• Build and run SQL queries on Spark
• Develop and optimize ETL pipelines
• Deploy and manage PySpark jobs in cluster environments