Developing Serverless Data Pipelines with Dataflow

Modern data engineering relies on efficient, scalable pipelines to process massive datasets without managing complex server infrastructure. This text-based course guides you through building serverless data processing pipelines using Dataflow and the Apache Beam programming model. You will transition from understanding basic data concepts to designing, writing, and executing your own robust pipelines. Through clear written explanations, practical code examples, and structured logic walkthroughs, you will gain the skills needed to process both batch and streaming data efficiently. What you will learn: Understand the core concepts of serverless data processing, including runners, pipelines, and collections; Write Apache Beam pipeline code to transform, filter, and aggregate datasets; Implement windowing and triggering strategies to handle late-arriving streaming data; Configure pipelines to read from and write to various data sources and sinks; Optimize pipeline performance and troubleshoot common execution bottlenecks. The course begins with foundational definitions of pipeline architectures and core programming primitives. You will then progress through step-by-step written tutorials covering transforms, windowing, and best practices for production deployments. This course is designed for aspiring data engineers and developers who are new to Dataflow. No prior pipeline development experience is required, though a basic familiarity with programming concepts is helpful. Start reading today to build scalable, production-ready serverless data pipelines.