Streaming Systems: The What, Where, When, and How of Large-Scale Data Processing by Tyler Akidau, Slava Chernyak, Reuven Lax

Kindle libarary books downloads Streaming Systems: The What, Where, When, and How of Large-Scale Data Processing by Tyler Akidau, Slava Chernyak, Reuven Lax (English literature) 9781491983874


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  • Streaming Systems: The What, Where, When, and How of Large-Scale Data Processing
  • Tyler Akidau, Slava Chernyak, Reuven Lax
  • Page: 352
  • Format: pdf, ePub, mobi, fb2
  • ISBN: 9781491983874
  • Publisher: O'Reilly Media, Incorporated

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Kindle libarary books downloads Streaming Systems: The What, Where, When, and How of Large-Scale Data Processing by Tyler Akidau, Slava Chernyak, Reuven Lax (English literature) 9781491983874

Overview

Streaming Systems: The What, Where, When, and How of Large-Scale Data Processing by Tyler Akidau, Slava Chernyak, Reuven Lax 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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