Apache Spark: Design & Execute ETL Pipelines Hands-On

Overview

This hands-on course equips learners with the skills to design, build, and manage end-to-end ETL (Extract, Transform, Load) workflows using Apache Spark in a real-world data engineering context. Structured into two comprehensive modules, the course begins with foundational setup, guiding learners through the installation of essential components such as PySpark, Hadoop, and MySQL. Participants will learn how to configure their environment, organize project structures, and explore source datasets effectively. As the course progresses, learners will develop Spark applications to perform full and incremental data loads using JDBC integration with MySQL. Through practical examples, they will apply transformation logic using Spark SQL, filter data based on business rules, and handle common pitfalls such as type mismatches and folder structure issues during Spark deployment. By the end of the course, learners will be able to construct, execute, and optimize Spark-based ETL pipelines that are scalable and production-ready, empowering them to contribute effectively in real-world data engineering roles.

Syllabus

Setting Up the Foundation

This module introduces learners to the fundamentals of building an ETL framework using Apache Spark. It begins by providing an overview of the Spark ecosystem and its advantages in big data processing. Learners will be guided through the installation and configuration of essential software packages, setting up the development environment, and understanding the structure of a Spark-based ETL project. The module also covers how to work with real-world datasets and prepare configuration files for database interactions—laying a strong groundwork for scalable data processing workflows.

Building ETL Workflows in Apache Spark

This module guides learners through the practical implementation of Extract, Transform, and Load (ETL) processes using Apache Spark. Learners will explore full data loads into MySQL, apply transformation logic using Spark SQL, and handle incremental loading scenarios by tracking and managing new records. The lessons include error handling, filtering strategies, data type compatibility, and database integration using JDBC—all within a hands-on PySpark environment. This module reinforces applied knowledge of Spark for real-world data engineering tasks.