Serverless Data Processing with Dataflow and Beam

Modern data engineering requires the ability to process massive datasets efficiently without managing complex infrastructure. This text-based course guides you through the foundational concepts of serverless data processing using Dataflow and the Apache Beam SDK. You will start with core terminology, understanding how pipelines are structured, before moving into practical pipeline development. By the end of this course, you will be able to design, write, and run your own serverless data pipelines for both batch and streaming data. What you'll learn: Understand core Beam concepts including PCollections, transforms, pipeline runners, and the execution model; Write custom data processing pipelines using the Beam SDK to clean, transform, and aggregate data; Configure pipelines to handle both historical batch files and real-time streaming data sources; Implement modern pipeline patterns including windowing, triggers, and side inputs; Deploy pipelines to Dataflow to take advantage of auto-scaling and serverless execution. The course begins with essential theoretical fundamentals, explaining how distributed processing works, and then guides you step-by-step through writing pipeline code, handling errors, and optimizing performance. This course is designed for beginners to data engineering, software developers, and data analysts who want to learn serverless data processing. Basic programming knowledge in Python or Java is helpful, but no prior cloud data engineering experience is required. Start reading to master serverless data pipelines today.