Building Data Pipelines Using Apache Beam by Nuzhi Meyen (.ePUB)+
File Size: 10 MB
Building Data Pipelines Using Apache Beam: Deliver Unified Batch and Streaming Pipelines for Real-World Production Across Dataflow, Flink, and Spark by Nuzhi Meyen
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Overview: Build Data Pipelines that Survive Scale, Failure, and Change. Building Data Pipelines Using Apache Beam provides a practical, production-focused guide to using Beam’s unified programming model to write processing logic once, and run it across multiple runners, without rewriting core code. The book begins with the fundamentals of distributed data processing and Beam’s core abstractions—PCollections, transforms, and pipeline design. You will then progress into stateful and stateless processing, event-time semantics, windows, triggers, watermarks, state, and timers—building the mental models required to reason about correctness at scale. From there, the book moves into advanced transformations, coders, and optimization techniques to help you improve performance, control costs, and ensure reliability. In the later chapters, you will learn how to deploy pipelines across runners such as Dataflow, Flink, and Spark, monitor and debug production workloads, and apply the best practices drawn from real-world case studies. Thus, by the end of the book, you will be able to design, deploy, and operate robust, portable, production-grade data pipelines with confidence. A key focus of this book is helping readers understand both batch and streaming paradigms using one consistent mental model. It explains how Apache Beam handles event time, windows, triggers, and state, enabling developers to build reliable pipelines for real-time and historical data alike. The book also reinforces the importance of Python and Java as primary development languages for Beam, refreshing core concepts, while applying them directly to data engineering use cases. This book is tailored for Data Engineers, Senior Data Engineers, Analytics Engineers, Data Architects, and Platform Engineers who design, build, or operate batch and streaming data systems. Readers should be comfortable with Python or Java, SQL, and basic distributed system concepts such as parallelism, fault tolerance, event-time processing, and cloud-based data platforms.
Genre: Non-Fiction > Tech & Devices
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