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Big Data: Principles and Best Practices of Scalable Realtime Data Systems

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Notă GoodReads:
en Limba Engleză Paperback – 07 May 2015
Services like social networks, web analytics, and intelligent e-commerce often need to manage data at a scale too big for a traditional database. As scale and demand increase, so does Complexity. Fortunately, scalability and simplicity are not mutually exclusive—rather than using some trendy technology, a different approach is needed. Big data systems use many machines working in parallel to store and process data, which introduces fundamental challenges unfamiliar to most developers.
Big Data shows how to build these systems using an architecture that takes advantage of clustered hardware along with new tools designed specifically to capture and analyze web-scale data. It describes a scalable, easy to understand approach to big data systems that can be built and run by a small team. Following a realistic example, this book guides readers through the theory of big data systems, how to use them in practice, and how to deploy and operate them once they're built.
Purchase of the print book comes with an offer of a free PDF, ePub, and Kindle eBook from Manning. Also available is all code from the book.
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Specificații

ISBN-13: 9781617290343
ISBN-10: 1617290343
Pagini: 328
Dimensiuni: 185 x 231 x 15 mm
Greutate: 0.54 kg
Editura: Manning Publications

Notă biografică

Nathan Marz is currently working on a new startup. Previously, he was the lead engineer at BackType before being acquired by Twitter in 2011. At Twitter, he started the streaming compute team which provides and develops shared infrastructure to support many critical realtime applications throughout the company. Nathan is the creator of Cascalog and Storm, open-source projects which are relied upon by over 50 companies around the world, including Yahoo!, Twitter, Groupon, The Weather Channel, Taobao, and many more companies.
James Warren is an analytics architect at Storm8 with a background in big data processing, machine learning and scientific computing.