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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.
Nuzhi Meyen is a fintech entrepreneur, data scientist, and AI practitioner, Co-Founder and CEO of Helios P2P. He builds production-grade AI, analytics, and blockchain systems for lending and credit risk. With advanced degrees and strong community contributions, he bridges theory and practice to deliver scalable, real-world financial technology solutions.
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