Serverless Data Processing with Dataflow

Processing massive datasets often requires managing complex cluster infrastructure, which distracts from writing actual data logic. This text-based course introduces you to serverless data processing, allowing you to run scalable pipelines effortlessly. You will transition from understanding basic data concepts to designing and deploying your own auto-scaling pipelines. You will gain the skills to write unified code that handles both historical batch data and real-time streaming data sources smoothly. What you'll learn: - Understand the core concepts of serverless data processing and pipeline architectures. - Build unified batch and streaming pipelines using the Apache Beam SDK. - Deploy and execute scalable data pipelines on managed serverless infrastructure. - Implement windowing, triggers, and accumulation modes for real-time streaming data. - Configure pipeline observability, logging, and performance monitoring to troubleshoot bottlenecks. - Apply cost-optimization strategies to manage resource allocation efficiently. The course starts with foundational definitions of pipeline design, data transforms, and serverless execution. You will then progress through step-by-step written guides and conceptual explanations covering windowing, state management, and production-ready pipeline operations. This course is designed for aspiring data engineers, developers, and analysts who want to learn serverless data engineering. No prior experience with complex cloud infrastructure is required. Start reading today to master the fundamentals of modern, scalable data processing.