Serverless Data Processing with Dataflow

Modern organizations generate massive volumes of data every second, making traditional, server-managed processing pipelines difficult to scale and maintain. Serverless data processing allows you to focus entirely on writing pipeline logic while the underlying infrastructure scales automatically to meet your data demands. In this written course, you will learn how to design, write, and execute robust data pipelines using Dataflow and the Apache Beam SDK. By understanding the core principles of unified batch and stream processing, you will be able to transform raw data into actionable insights without worrying about provisioning or managing servers. You will learn to: Understand the foundational concepts of serverless data processing and the Apache Beam programming model; Build batch and streaming data pipelines to transform, filter, and aggregate large datasets; Configure autoscaling and pipeline monitoring to optimize resource utilization and performance; Apply modern design patterns for connecting pipelines to cloud storage and data warehouses; Practice writing clean pipeline code with step-by-step written walkthroughs and conceptual exercises. The course starts with essential big data terminology and foundational pipeline architectures before guiding you through hands-on code examples. You will progress from simple data transformations to complex, multi-stage serverless processing workflows. This course is designed for aspiring data engineers, developers, and analysts who are new to Dataflow and want to learn serverless data engineering from scratch. No prior experience with big data frameworks is required, though a basic understanding of programming concepts is helpful. Start reading today to build scalable, maintenance-free data pipelines.