Apache Spark Foundations for Data Engineering and Machine Learning

Data is growing faster than ever, and traditional tools often struggle to process it at scale. Apache Spark provides the framework needed to handle massive datasets for both engineering and predictive modeling. This course takes you from the basics of distributed computing to implementing real-world data workflows. You will gain a solid understanding of how to transform raw data into actionable insights using industry-standard Spark components. What you'll learn: - Understand the core architecture of Apache Spark and distributed data processing - Perform Extract, Transform, Load (ETL) operations on large-scale datasets - Build machine learning pipelines using the Spark MLlib library - Implement Spark Structured Streaming for real-time data processing - Apply modern data patterns like Delta Lake for reliable data storage - Practice data cleaning and feature engineering for scalable machine learning models The course starts with foundational concepts and terminology before moving into practical applications of Spark SQL, streaming, and machine learning. You will work through written explanations and code exercises designed to simulate professional data engineering tasks. This course is designed for beginners interested in data science or engineering; no prior experience with Spark is required. Start building scalable data solutions with Apache Spark today.