Hands-On PySpark: Practical Data Engineering and Machine Learning

As datasets grow, traditional data processing tools struggle to keep up with the scale. Learning PySpark allows you to leverage the power of distributed computing using Python, opening up new possibilities for data engineering and data science. This text-based course takes you from a beginner to confidently writing PySpark code. You will start with core distributed computing concepts, transition from Resilient Distributed Datasets (RDDs) to the modern DataFrame API, and learn how to apply machine learning algorithms to large datasets. What you'll learn: - Understand the core architecture of Spark and how PySpark coordinates distributed data processing - Master the transition from low-level RDDs to the highly optimized Spark DataFrame API - Write clean, maintainable PySpark code using modern Python practices like type hints - Apply Spark MLlib to build and evaluate machine learning models, including clustering algorithms - Process, filter, and clean large-scale datasets using built-in Spark functions and SQL queries You will start with fundamental terminology and local environment setup before moving on to practical data manipulation. Through structured written explanations and code walkthroughs, you will progress from basic data loading to building a machine learning workflow. This course is designed for aspiring data engineers, data scientists, and analysts who are new to distributed computing. No prior experience with Spark is required, though a basic understanding of Python is helpful. Begin your journey into big data and start writing efficient PySpark code today.