Serverless Data Processing with Dataflow and Apache Beam

In the era of big data, processing massive datasets efficiently without managing complex server infrastructure is a game-changer. This text-only course guides you through the core concepts of serverless data processing using Apache Beam and Dataflow. You will transition from understanding basic data pipelines to designing scalable batch and streaming pipelines. Through clear written explanations, practical code examples, and structured exercises, you will learn how to write pipeline code in Apache Beam and execute it reliably on a fully managed serverless runner. What you will learn: 1. Understand the fundamentals of serverless data processing and pipeline architectures. 2. Write robust batch and streaming data pipelines using the Apache Beam SDK. 3. Execute and monitor pipeline runs on Dataflow without managing underlying servers. 4. Apply windowing and triggering strategies to handle late-arriving streaming data. 5. Implement modern schema management and data transformation best practices. 6. Debug and optimize pipeline execution for performance and cost efficiency. The course begins with foundational concepts of unified data processing, moving step-by-step from local Apache Beam execution to deploying production-ready pipelines on Dataflow. You will read through detailed code walkthroughs and practice with real-world architectural scenarios. This course is designed for beginning data engineers, software developers, and analysts who want to learn serverless data engineering. No prior experience with Apache Beam or Dataflow is required, though basic programming familiarity is helpful. Start reading today to build your first serverless data pipeline.