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| LEADER |
02165nam a22002417a 4500 |
| 020 |
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|a 9789339221270
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| 082 |
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|b UND
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| 100 |
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|a Zikopoulos, Paul C. et al.
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| 245 |
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|a Understanding big data: Analytic for enterprise class handoop and streaming data
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| 260 |
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|a New Delhi
|b MGH
|c 2012
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| 300 |
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|a 141p.
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|a Big Data represents a new era in data exploration and utilization, and IBM is uniquely positioned to help clients navigate this transformation. This book reveals how IBM is leveraging open source Big Data technology, infused with IBM technologies, to deliver a robust, secure, highly available, enterprise-class Big Data platform.
The three defining characteristics of Big Data--volume, variety, and velocity--are discussed. You'll get a primer on Hadoop and how IBM is hardening it for the enterprise, and learn when to leverage IBM InfoSphere BigInsights (Big Data at rest) and IBM InfoSphere Streams (Big Data in motion) technologies. Industry use cases are also included in this practical guide.
Learn how IBM hardens Hadoop for enterprise-class scalability and reliability
Gain insight into IBM's unique in-motion and at-rest Big Data analytics platform
Learn tips and tricks for Big Data use cases and solutions
Get a quick Hadoop primer
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| 505 |
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|a PART 1: The Big Deal about Big Data
Chapter 1 What is Big Data? Hint: You’re a Part of it Every Day
Chapter 2 Why Is Big Data Important
Chapter 3 Why IBM for Big Data
PART II: Big Data: From the Technology Perspective
Chapter 4 All About Hadoop: The Big Data Lingo Chapter
Chapter 5 InfoSphere BigInsights – Analytics for Big Data at Rest
Chapter 6 IBM InfoSphere Streams – Analytics for Big Data in Motion
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| 650 |
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|a Data mining
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| 650 |
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|a Electronic data processing - Distributed processing
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| 650 |
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|a File organization
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| 700 |
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|a Eaton, Chris
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| 700 |
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|a DeRoos, Dirk
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| 700 |
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|a Deutsch, Thomas
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| 700 |
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|a Lapis, George
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| 942 |
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|c BK
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| 999 |
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|c 51627
|d 51627
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| 952 |
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|e Ad.D2/5736/2013 dtd.24/11/2017
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