Foundations of Serverless Data Processing with Dataflow

In today's data-driven world, processing massive datasets quickly and efficiently is a critical skill for any developer or data professional. Managing complex server infrastructure can slow you down, but serverless data processing allows you to focus entirely on your code and analysis. This text-only course guides you through the foundational concepts of serverless data architectures, showing you how to build pipelines that scale automatically to handle any workload. You will start with key terminology and foundational definitions before progressing to practical pipeline design. Through written explanations and clear code examples, you will learn how to write, test, and run your own serverless workflows. What you'll learn: Understand the core architecture of serverless data processing and Dataflow; Write robust data pipelines using the Apache Beam SDK with modern Python type hints; Apply transformations to process both historical batch data and real-time streaming data; Configure windowing and triggering strategies to manage late-arriving data; Practice writing unit tests for your data pipelines to ensure code reliability; Execute and monitor pipelines on fully managed serverless infrastructure. This course is designed for beginner data engineers, developers, and analysts who want to learn serverless data processing from scratch. No prior experience with Dataflow or Apache Beam is required, though a basic familiarity with programming concepts is helpful. Start reading today to build your foundation in serverless data engineering.