Streaming data is a big deal in big data these days. As more and more businesses seek to tame the massive unbounded data sets that pervade our world, streaming systems have finally reached a level of maturity sufficient for mainstream adoption. With this practical guide, data engineers, data scientists, and developers will learn how to work with streaming data in a conceptual and platform-agnostic way.

Expanded from Tyler Akidau’s popular blog posts "Streaming 101" and "Streaming 102", this book takes you from an introductory level to a nuanced understanding of the what, where, when, and how of processing real-time data streams. You’ll also dive deep into watermarks and exactly-once processing with co-authors Slava Chernyak and Reuven Lax.

You’ll explore:

How streaming and batch data processing patterns compare

The core principles and concepts behind robust out-of-order data processing

How watermarks track progress and completeness in infinite datasets

How exactly-once data processing techniques ensure correctness

How the concepts of streams and tables form the foundations of both batch and streaming data processing

The practical motivations behind a powerful persistent state mechanism, driven by a real-world example

How time-varying relations provide a link between stream processing and the world of SQL and relational algebra

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