Streaming Systems : The What, Where, When, and How of Large-Scale Data Processing
Language: English
Published by O'reilly Media Aug 2018, 2018
- Softcover
- New
Seller: AHA-BUCH GmbH, Einbeck, GermanyAHA-BUCH GmbH
AbeBooks seller since August 14, 2006
Condition: New
US$ 94.45
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Add to basketItem description from seller
Neuware - 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.…
Seller Inventory # 9781491983874
- Title
- Streaming Systems : The What, Where, When, and How of Large-Scale Data Processing
- Author
- Tyler Akidau
- Publisher
- O'reilly Media Aug 2018
- Publication year
- 2018
- Condition
- Neu
- Binding
- Taschenbuch
- Language
- English
- ISBN 10
- 1491983876
- ISBN 13
- 9781491983874
- Item weight
- 636 grams
- Dimensions
- 236x179x27 mm
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
"Synopsis" may belong to another edition of this title.
About the Author
Slava Chernyak is a senior software engineer at Google Seattle. Slava spent over five years working on Google’s internal massive-scale streaming data processing systems and has since become involved with designing and building Windmill, Google Cloud Dataflow's next-generation streaming backend, from the ground up. Slava is passionate about making massive-scale stream processing available and useful to a broader audience. When he is not working on streaming systems, Slava is out enjoying the natural beauty of the Pacific Northwest.
Reuven Lax is a senior staff software engineer at Google Seattle, and has spent the past nine years helping to shape Google's data processing and analysis strategy. For much of that time he has focused on Google's low-latency, streaming data processing efforts, first as a long-time member and lead of the MillWheel team, and more recently founding and leading the team responsible for Windmill, the next-generation stream processing engine powering Google Cloud Dataflow. He's very excited to bring Google's data-processing experience to the world at large, and proud to have been a part of publishing both the MillWheel paper in 2013 and the Dataflow Model paper in 2015. When not at work, Reuven enjoys swing dancing, rock climbing, and exploring new parts of the world.
"About the title" may belong to another edition of this title.
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