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.