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.
Apache Spark: Design & Execute ETL Pipelines Hands-On
Apache Spark: Design & Execute ETL Pipelines Hands-On
This course is part of Spark and Python for Big Data with PySpark Specialization
Instructor: EDUCBA
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Gain insight into a topic and learn the fundamentals.
3 hours to complete
Flexible schedule
Learn at your own pace
What you'll learn
Install and configure PySpark, Hadoop, and MySQL for ETL workflows.
Build Spark applications for full and incremental data loads via JDBC.
Apply transformations, handle deployment issues, and optimize ETL pipelines.
Skills you'll gain
Details to know
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Recently updated!
August 2025
Assessments
6 assignments
Taught in English
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This course is part of the Spark and Python for Big Data with PySpark Specialization
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There are 2 modules in this course
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.
What's included
5 videos3 assignments
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.
What's included
6 videos3 assignments
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