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

Streaming-Systems-The-What-Where.pdf
ISBN: 9781491983874 | 352 pages | 9 Mb

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

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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

Stream Processing: The What, Where, When, and How of Large
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Over the last few years, we have seen a disruption in the data management space. It started with innovation in the data warehousing and large-scale computing  Survey of Distributed Stream Processing for Large Stream Sources
applications can be seen as streams of events or tuples. In stream based Since large volume of data is coming to these systems, the information can no Thelarge scale distributed stream processing is still a very young research area with. Large-scale Real-time Stream Processing and Analytics - O'Reilly
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