T�tulo: Big Data: Principles and best practices of scalable realtime data systems
Editor: Manning
Fecha de publicaci�n: 2015
Idioma: Ingl�s
ISBN 10: 1617290343
ISBN 13: 9781617290343
Encuadernaci�n: Encuadernaci�n de tapa blanda
Estado: Good

Acerca de

Sinopsis

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.

AUDIENCE

This book requires no previous exposure to large-scale data analysis or NoSQL tools. Familiarity with traditional databases is helpful.

ABOUT THE TECHNOLOGY

To tackle the challenges of Big Data, a new breed of technologies has emerged. Many of which have been grouped under the term "NoSQL." In some ways these new technologies can be more complex than traditional databases and in other ways, simpler. Using them effectively requires a fundamentally new set of techniques

Acerca del autor

Nathan Marz is an engineer at Twitter. He was previously Lead Engineer at BackType, a marketing intelligence company that was acquired by Twitter in July of 2011. He is the author of two major open source projects: Storm, a distributed realtime computation system, and Cascalog, a tool for processing data on Hadoop. He is a frequent speaker and writes a blog at nathanmarz.com.

James Warren is an analytics architect at Storm8 with a background in big data processing, machine learning and scientific computing.

"Sobre este t�tulo" puede pertenecer a otra edici�n de este libro.

Descripci�n de la librer�a

Welcome to the Goodwill shop! We are the online media-selling business of Goodwill-Easter Seals Minnesota. The proceeds from your purchase support programs that prepare people for work. For more information about our organization and the services we provide, please use the [Ask Bookseller a Question] link to contact us. FYI WE DO NOT SHIP TO PUERTO RICO!!

Ver la p�gina web de la librer�a

Informaci�n empresarial del vendedor

Goodwill Industries, Inc.
553 Fairview Ave N, Saint Paul, MN, 55104-1708, U.S.A.

Condiciones de venta y env�o

Condiciones de venta

We guarantee the condition of every book as it's described on the Abebooks web site. If you're dissatisfied with your purchase (Incorrect Book/Not as Described/Damaged) or if the order hasn't arrived, you're eligible for a refund within 30 days of the estimated delivery date. If you�ve changed your mind about a book that you�ve ordered, please use the [Ask bookseller a question] link to contact us and we'll respond within 2 business days.

Condiciones de env�o

Books are shipped 1-2 business days following receipt of payment.

Shipping is via USPS Media Mail which takes 4-14 business days for delivery. Buyer pays a standard shipping rate of $3.49 for books and $2.49 for CDs. If a book requires extra shipping, buyer will be notified before sale is finalized.

Shipping costs are based on books weighing 2.2 LB, or 1 KG. If your book order is heavy, oversized, or must be insured we may contact you to let you know extra shipping is required.