PySpark & Python: Hands-On Guide to Data Processing
By External Provider
About this course
This beginner-level course is designed to introduce learners to the powerful combination of Python and Apache Spark (PySpark) for distributed data processing and analysis. Through structured lessons and real-world examples, learners will recall foundational Python syntax, identify key elements of PySpark, and demonstrate the use of core Spark transformations and actions using Resilient Distributed Datasets (RDDs). As the course progresses, learners will apply advanced data handling techniques such as joins and data integration using JDBC with MySQL, and construct scalable data pipelines like word count using transformation chains. Each module emphasizes a blend of conceptual understanding and practical coding experience, enabling learners to analyze, debug, and evaluate their PySpark applications efficiently. By the end of the course, learners will have gained hands-on proficiency in building distributed data workflows and be prepared to advance toward more complex data engineering and big data analytics challenges.
What you will learn
- Recall Python syntax and identify key PySpark components for data processing.
- Apply RDD transformations, joins, and JDBC integration with MySQL.
- Build scalable pipelines like word count and debug PySpark applications.