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PySpark & Python: Hands-On Guide to Data Processing

EDUCBA via Coursera

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Overview

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Build a strong foundation in PySpark and Python for distributed data processing with this beginner-friendly, hands-on course. You will explore how distributed computing supports modern data analysis while developing the Python programming skills needed to create PySpark applications. Starting with Python syntax, control flow, and functional programming concepts, you will learn to work with Resilient Distributed Datasets (RDDs), apply core Spark transformations and actions, and build scalable data processing workflows. As you progress, you will perform DataFrame transformations, execute join operations, integrate MySQL data using JDBC, and construct a Word Count pipeline to reinforce distributed processing techniques. Designed for beginners interested in big data, data processing, and PySpark, this course combines practical coding exercises with clear explanations to help you understand both the concepts and their real-world application. Throughout the course, you will practice analyzing, debugging, and evaluating PySpark programs while gaining experience with distributed data workflows. By the end of the course, you will be able to build and analyze PySpark applications, process distributed datasets efficiently, integrate external data sources, and apply essential data engineering concepts that prepare you for more advanced big data analytics.

Syllabus

Fundamentals of PySpark and Python

This module introduces learners to the foundational concepts required for working with PySpark, beginning with the evolution of data and the relevance of distributed computing frameworks. It establishes the basics of Python programming, emphasizing syntax, structures, and control flow needed for developing PySpark applications. By the end of this module, learners will be equipped with essential programming knowledge and a clear understanding of how to initiate PySpark-based data processing.

Advanced Data Handling and Joins in PySpark

This module builds on the foundational knowledge of PySpark by introducing learners to advanced operations including DataFrame manipulation, join operations, and external data integration with MySQL. Through hands-on examples, students will explore how to process, combine, and analyze distributed datasets effectively. The module culminates with practical application through the classic Word Count problem, reinforcing transformation pipelines and aggregation techniques in a distributed environment.