Data Engineering with PySpark and Dataproc on Cloud Platform

As organizations generate massive volumes of data, the ability to process and analyze this information efficiently is a highly sought-after skill. This written course guides you through the fundamentals of distributed computing using PySpark and managed cloud infrastructure. You will transition from understanding basic big data concepts to designing, optimizing, and deploying robust data pipelines. Through clear written explanations, practical code snippets, and real-world scenarios, you will master how to run scalable batch and real-time streaming jobs on Cloud Platform. What you'll learn: - Understand core distributed computing concepts, Spark architecture, and foundational PySpark DataFrame APIs. - Configure and manage Spark clusters using Dataproc on Cloud Platform. - Build scalable batch processing pipelines using SparkSQL and modern DataFrame transformations. - Implement real-time data processing using Spark Structured Streaming and cloud messaging integration. - Apply modern data engineering practices, including PySpark type hinting and performance optimization techniques. - Design a machine learning recommendation system pipeline using Spark MLlib. This course begins with essential big data terminology and Spark architecture before moving on to hands-on DataFrame operations. You will then progress to deploying real-world pipelines on Dataproc, concluding with streaming patterns and professional data engineering interview strategies. This course is designed for aspiring data engineers, analysts, and developers who want to learn big data processing from scratch. No prior experience with Spark or cloud platforms is required, though a basic understanding of Python is helpful. Start reading today to build your foundation in modern cloud data engineering.