PySpark Programming

Course Overview

PySpark Programming is a hands‑on, performance‑focused course that teaches learners how to process, analyze, and engineer large‑scale datasets using Apache Spark’s Python API (PySpark). The course covers distributed computing fundamentals, Spark architecture, DataFrames, RDDs, SQL, optimization techniques, and real‑world ETL/ELT pipeline development. Learners work with real datasets to build scalable data‑processing workflows used in analytics, machine learning, and big‑data engineering.

Target Audience

This course is ideal for:

• Aspiring data engineers and big‑data developers

• Data analysts or scientists working with large datasets

• Python developers transitioning into distributed data processing

• Students or career switchers entering data engineering or cloud roles

• Professionals working with Spark on AWS, Azure, or GCP

Course Outcomes

By the end of this course, learners will be able to:

• Understand Spark architecture, cluster components, and execution model

• Use PySpark DataFrames and SQL for large‑scale data manipulation

• Work with RDDs for low‑level distributed processing

• Build ETL/ELT pipelines using PySpark on local and cloud environments

• Optimize Spark jobs using partitioning, caching, and the Catalyst optimizer

• Handle structured, semi‑structured, and unstructured data

• Integrate PySpark with cloud platforms, Delta Lake, and data‑lake architectures

• Apply PySpark for machine‑learning workflows using Spark MLlib