Streaming Systems: The What,Where,When,and How of Large-Scale Data Processing

Streaming Systems: The What,Where,When,and How of Large-Scale Data Processing
Streaming Systems: The What,Where,When,and How of Large-Scale Data Processing
by 作者: Tyler Akidau - Slava Chernyak - Reuven Lax
ISBN-10 书号: 1491983876
ISBN-13 书号: 9781491983874
Edition 版本: 1
Publisher Finelybook 出版日期: 2018-08-02
Pages: 352


Book Description
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
Preface Or. What Are You Getting Yourself Into Here?
1. The Beam Model
1. Streaming 101
2. The What,Where,When,and How of Data Processing
3. Watermarks
4. Advanced Windowing
5. Exactly-Once and Side Effects
lⅡ. Streams and Tables
6. Streams and Tables
7. The Practicalities of Persistent State
8. Streaming SQL
9. Streaming Joins
10. The Evolution of Large-Scale Data Processing
Index

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