Streaming Systems

Streaming data is now a cornerstone of modern big data architectures, enabling businesses to effectively manage and analyze the massive, unbounded datasets that define our digital world. This practical guide equips data engineers, data scientists, and developers with the conceptual and platform-agnostic understanding needed to build robust streaming systems. Evolving from Tyler Akidau's seminal blog posts "Streaming 101" and "Streaming 102," the book takes you from fundamental principles to advanced topics like watermarks and exactly-once processing, providing a comprehensive toolkit for real-time data stream processing. Why You Should Read? Master the core principles and concepts behind robust out-of-order data processing, comparing streaming and batch patterns. Gain a deep understanding of how watermarks track progress and completeness within infinite datasets. Learn practical, exactly-once data processing techniques to ensure the correctness and reliability of your data. Explore the foundational concepts of streams and tables, and their critical role in both batch and streaming data processing. About the Author Tyler Akidau is a distinguished expert in distributed systems and data processing, widely recognized for his pioneering work in streaming data. He is the author of the influential "Streaming 101" and "Streaming 102" blog posts, which laid much of the conceptual groundwork for modern stream processing. His expertise at Google, where he contributed significantly to Apache Beam and Google Cloud Dataflow, makes him a leading voice in the field. He co-authored "Streaming Systems" with Slava Chernyak and Reuven Lax, further solidifying its authority.