Foundations of Serverless Data Processing with Dataflow

Modern data engineering requires processing massive datasets without the overhead of managing complex server infrastructure. Serverless data processing solves this challenge by decoupling compute from storage, allowing you to focus purely on pipeline logic. This course introduces you to the core principles of serverless data engineering using Apache Beam and Dataflow. You will transition from understanding basic data processing terminology to writing, testing, and running your own serverless pipelines. What you'll learn: - Understand the foundational concepts of serverless data processing and how Dataflow executes pipelines. - Write Apache Beam pipelines using modern programming practices, including type hints and structured schemas. - Apply transform logic to manipulate, filter, and aggregate both batch and streaming data. - Leverage the Beam Portability framework to understand how pipelines run across different execution backends. - Practice writing unit tests for your data pipelines to ensure reliability before deployment. You will start with core definitions and the architecture of serverless pipelines before moving into step-by-step written explanations on writing pipeline code, executing it locally, and deploying it to a fully managed cloud environment. This course is designed for aspiring data engineers, software developers, and database administrators who are new to serverless data processing and want a clear, conceptual foundation. No prior experience with Apache Beam or Dataflow is required. Start reading today to build your foundation in modern serverless data engineering.