Data Engineering with Apache Beam and Dataflow: Build Batch Pipelines

Modern data engineering requires scalable, reliable pipelines to process massive datasets. Apache Beam offers a powerful, unified model for defining data pipelines that you can run effortlessly on managed cloud services like Dataflow. This written course guides you through the foundational concepts of data processing, taking you from local pipeline development to cloud-scale deployment. You will learn how to write clean, maintainable pipeline code using the Apache Beam Python SDK, apply transformations, and orchestrate your data workflows in a cloud environment. What you'll learn: - Understand the core concepts of data pipelines, including PCollections, Transforms, Pipeline Runners, and I/O connectors. - Configure a local Python development environment using modern virtual environments and SDK dependencies. - Apply transformations to extract, clean, and format data using Apache Beam's built-in functions. - Implement Python type hints to ensure pipeline safety, code readability, and robust data schemas. - Deploy batch processing pipelines to Dataflow on the cloud for fully managed, autoscaling execution. - Test and debug your pipeline code locally before moving it to production environments. You will start by mastering foundational terms and local pipeline execution, gradually moving toward advanced transformation logic. Finally, you will explore cloud integration, learning how to configure permissions and deploy your code to run on managed cloud infrastructure. This course is designed for aspiring data engineers, software developers, and data analysts who have a basic understanding of Python and want to learn cloud-native data processing. No prior experience with Apache Beam or cloud data pipelines is required. Start reading today to build your first scalable data pipeline from scratch.