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Description
Streaming SystemsStreaming 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.'box sizing: border box; padding: 0px; margin top:
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.'box-sizing: border-box; padding: 0px; margin-top: 0em; margin-bottom: 1em; margin-left: 1em; font-family: "Amazon Ember", Arial, sans-serif; font-size: small; background-color: rgb(255, 255, 255);'Expanded from Tyler Akidaus 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. Youll also dive deep into watermarks and exactly-once processing with co-authors Slava Chernyak and Reuven Lax.'box-sizing: border-box; padding: 0px; margin-top: 0em; margin-bottom: 1em; margin-left: 1em; font-family: "Amazon Ember", Arial, sans-serif; font-size: small; background-color: rgb(255, 255, 255);'Youll 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 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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