• Title: Understanding Big Data: Analytics for Enterprise Class Hadoop and Streaming Data
• Author(s) Paul Zikopoulos, Chris Eaton, Dirk DeRoos, Tom Deutsch, George Lapis
• Publisher: McGraw-Hill Osborne Media (2011); eBook: IBM Corporation (2012)
• Hardcover/Paperback: 176 pages
• eBook: PDF (166 pages, 3.38 MB)
• Language: English
• ISBN-10: 0071790535
• ISBN-13: 978-0071790536
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Book Description

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.

In this book, 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. Deployment and scaling strategies plus industry use cases are also included in this practical guide.

• Understand the characteristics of Big Data
• Learn about data at rest analytics
• Learn about data in motion analytics
• Get a quick Hadoop primer

• Paul C. Zikopoulos (Toronto, Canada) is a Database Specialist with the DB2 Sales Support team at IBM. He has written numerous magazine articles and books about DB2. Most recently, he co-authored the books A DBA's Guide to Databases on Linux (Syngress Media) and DB2 for Dummies (IDG Books).

• Amazon

• Big Data
• Data Science
• Data Analysis and Data Mining
• Algorithms and Data Structures
• Statistics, R Language and SAS Programming
• Probability, Stochastic Process, Queueing Theory, etc.

• Understanding Big Data: Analytics for Enterprise Class Hadoop and Streaming Data
• The Mirror Site (1) - PDF
• The Mirror Site (2) - PDF

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