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Архив [Manning] Big Data: Principles and best practices of scalable realtime data systems

Тема в разделе "Неактивные складчины (архив)", создана пользователем BlackMan, 30 янв 2015.

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  1. BlackMan

    BlackMan Модератор

    Big Data
    Principles and best practices of scalable realtime data systems


    Nathan Marz and James Warren

    MEAP Began: January 2012
    Softbound print: March 2015 (est.) | 425 pages
    ISBN: 9781617290343


    Services like social networks, web analytics, and intelligent e-commerce often need to manage data at a scale too big for a traditional database. Complexity increases with scale and demand, and handling big data is not as simple as just doubling down on your RDBMS or rolling out some trendy new technology. Fortunately, scalability and simplicity are not mutually exclusive—you just need to take a different approach. Big data systems use many machines working in parallel to store and process data, which introduces fundamental challenges unfamiliar to most developers.

    Big Data teaches you 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 implement them in practice, and how to deploy and operate them once they're built.

    Big Data shows you how to build the back-end for a real-time service called SuperWebAnalytics.com—our version of Google Analytics. As you read, you'll discover that many standard RDBMS practices become unwieldy with large-scale data. To handle the complexities of Big Data and distributed systems, you must drastically simplify your approach. This book introduces a general framework for thinking about big data, and then shows how to apply technologies like Hadoop, Thrift, and various NoSQL databases to build simple, robust, and efficient systems to handle it.

    • Introduction to the concepts and technologies of Big Data
    • Work with emerging tools like Hadoop, Cassandra, Thrift, and more
    • Build on the skills you've learned using traditional databases
    • Real-time processing of web-scale data
    This book requires no previous exposure to large-scale data analysis or NoSQL tools. Familiarity with traditional databases is helpful.

    Приглашаю в эту складчину всех, кто достаточно безумен, чтобы мечтать создать The Next Big Thing.


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