Analytics for Enterprise Class Hadoop and Streaming Data

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CONTENTS AT A GLANCE PART I Big Data: From the Business Perspective 1 What Is Big Data? Hint: You’re a Part of It Every Day . . . 3 2 Why Is Big Data Important? . . . . . . . . . . . . . . . . . . . . . . . . . 15 3 Why IBM for Big Data? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35 PART II Big Data: From the Technology Perspective 4 All About Hadoop: The Big Data Lingo Chapter . . . . . . . 51 5 InfoSphere BigInsights: Analytics for Big Data At Rest . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81 6 IBM InfoSphere Streams: Analytics for Big Data in Motion . . . . . . . . . . . . . . . . . . . . . . . . . . . . 123 ix
CONTENTS Foreword . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Acknowledgments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . About this Book . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xv xxi xxiii PART I Big Data: From the Business Perspective 1 What Is Big Data? Hint: You’re a Part of It Every Day . . 3 Characteristics of Big Data . . . . . . . . . . . . . . . . . . . . . . 5 Can There Be Enough? The Volume of Data . . . . . . . . 5 Variety Is the Spice of Life . . . . . . . . . . . . . . . . . . . . 7 How Fast Is Fast? The Velocity of Data . . . . . . . . . . . . 8 Data in the Warehouse and Data in Hadoop (It’s Not a Versus Thing) . . . . . . . . . . . . . . . . . . . . . . . 9 Wrapping It Up . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 2 Why Is Big Data Important? . . . . . . . . . . . . . . . . . . . . . 15 When to Consider a Big Data Solution . . . . . . . . . . . . . . 15 Big Data Use Cases: Patterns for Big Data Deployment . . . 17 IT for IT Log Analytics . . . . . . . . . . . . . . . . . . . . . . . 18 The Fraud Detection Pattern . . . . . . . . . . . . . . . . . . . 20 They Said What? The Social Media Pattern . . . . . . . . 24 The Call Center Mantra: “This Call May Be Recorded for Quality Assurance Purposes” . . . . . 26 Risk: Patterns for Modeling and Management . . . . . . . 29 Big Data and the Energy Sector . . . . . . . . . . . . . . . . . 31 3 Why IBM for Big Data? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35 Big Data Has No Big Brother: It’s Ready, but Still Young . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37 What Can Your Big Data Partner Do for You? . . . . . . . . . . . . 39 The IBM $100 Million Big Data Investment . . . . . . . . . . . . 40 A History of Big Data Innovation . . . . . . . . . . . . . . . . . . . . . . . 40 Domain Expertise Matters . . . . . . . . . . . . . . . . . . . . . . . . . . . 49 xi
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