Serverless Data Processing with Dataflow

Modern data engineering requires processing massive datasets quickly and reliably without the overhead of managing complex server infrastructure. This text-based course introduces you to the power of serverless data processing using Dataflow. You will learn how to design, write, and deploy scalable data pipelines that handle both historical batch data and real-time streaming data. By understanding the underlying Apache Beam programming model, you will transition from manual data handling to automated, production-grade data workflows. What you'll learn: - Understand foundational serverless data processing concepts and pipeline architectures. - Build batch pipelines to clean, transform, and load large-scale historical datasets. - Design streaming pipelines to process real-time data feeds with windowing and triggers. - Write reusable pipeline code using the Apache Beam SDK. - Configure and monitor pipeline execution, scaling, and resource allocation. - Apply best practices for error handling, debugging, and optimizing pipeline performance. The course begins with core definitions and the fundamentals of pipeline design before moving into practical pipeline construction for both batch and real-time streaming scenarios. You will progress through written explanations, architectural breakdowns, and structured code exercises. This course is designed for beginner data engineers, analysts, and developers who want to learn serverless data workflows; no prior experience with Dataflow is required. Start building scalable, serverless data pipelines today.