Big Data: Principles and best practices of scalable realtime data systems

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YOW! 2012 Nathan Marz - Runaway complexity in Big Data systems... and a plan to stop it #YOW

Click on the cover image above to read some pages of this book! Web-scale applications like social networks, real-time analytics, or e-commerce sites deal with a lot of data, whose volume and velocity exceed the limits of traditional database systems. These applications require architectures built around clusters of machines to store and process data of any size, or speed.

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Fortunately, scale and simplicity are not mutually exclusive. Big Data teaches you to build big data systems using an architecture designed specifically to capture and analyze web-scale data. This book presents the Lambda Architecture, a scalable, easy-to-understand approach that can be built and run by a small team. You'll explore the theory of big data systems and how to implement them in practice.

In addition to discovering a general framework for processing big data, you'll learn specific technologies like Hadoop, Storm, and NoSQL databases.

This book requires no previous exposure to large-scale data analysis or NoSQL tools. This book requires no previous exposure to large-scale data analysis or NoSQL tools.


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Familiarity with traditional databases is helpful. Nathan Marz is the creator of Apache Storm and the originator of the Lambda Architecture for big data systems.

James Warren is an analytics architect with a background in machine learning and scientific computing. Navigazione Toggle Download gratuiti di siti di torrent per audiolibri. DMCA Contattaci.

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Om boka Services like social networks, web analytics, and intelligent e-commerce often need to manage data at a scale too big for a traditional database. Big data systems use many machines working in parallel to store and process data, which introduces fundamental challenges unfamiliar to most developers.

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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.