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| Foreword | |
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| Preface | |
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| Acknowledgments | |
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| About This Book | |
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| The Big Deal About Big Data | |
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| What Is Big Data? | |
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| Why Is Big Data Important? | |
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| Now, the "What Is Big Data?" Part | |
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| Brought to You by the Letter V: How We Define Big Data | |
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| What About My Data Warehouse in a Big Data World? | |
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| Wrapping It Up | |
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| Applying Big Data to Business Problems: A Sampling of Use Cases | |
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| When to Consider a Big Data Solution | |
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| Before We Start: Big Data, Jigsaw Puzzles, and Insight | |
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| Big Data Use Cases: Patterns for Big Data Deployment | |
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| You Spent the Money to Instrument It-Now Exploit It! | |
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| IT for IT: Data Center, Machine Data, and Log Analytics | |
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| What, Why, and Who? Social Media: Analytics | |
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| Understanding Customer Sentiment | |
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| Social Media Techniques Make the World Your Oyster | |
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| Customer State: Or, Don't Try to Upsell Me When I Am Mad | |
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| Fraud Detection: "Who Buys an Engagement Ring at 4 a.m.?" | |
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| Liquidity and Risk: Moving from Aggregate to Individual | |
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| Wrapping It Up | |
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| Boost Your Big Data IQ: The IBM Big Data Platform | |
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| The New Era of Analytics | |
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| Key Considerations for the Analytic Enterprise | |
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| The Big Data Platform Manifesto | |
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| IBM's Strategy for Big Data and Analytics | |
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| Sustained Investments in Research and Acquisitions | |
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| Strong Commitment to Open Source Efforts and a Fostering of Ecosystem Development | |
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| Support Multiple Entry Points to Big Data | |
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| A Flexible, Platform-Based Approach to Big Data | |
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| Wrapping It Up | |
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| Analytics for Big Data at Rest | |
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| A Big Data Platform for High-Performance Deep Analytics: IBM PureData Systems | |
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| Netezza's Design Principles | |
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| Appliance Simplicity: Minimize the Human Effort | |
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| Hardware Acceleration: Process Analytics Close to the Data Store | |
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| Balanced, Massively Parallel Architecture: Deliver Linear Scalability | |
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| Modular Design: Support Flexible Configurations and Extreme Scalability | |
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| What's in the Box? The Netezza Appliance Architecture Overview | |
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| A Look Inside the Netezza Appliance | |
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| The Secret Sauce: FPGA-Assisted Analytics | |
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| Query Orchestration in Netezza | |
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| Platform for Advanced Analytics | |
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| Extending the Netezza Analytics Platform with Hadoop | |
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| Customers' Success Stories: The Netezza Experience | |
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| T-Mobile: Delivering Extreme Performance with Simplicity at the Petabyte Scale | |
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| State University of New York: Using Analytics to Help Find a Cure for Multiple Sclerosis | |
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| NYSE Euronext: Reducing Data Latency and Enabling Rapid Ad-Hoc Searches | |
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| IBM's Enterprise Hadoop: InfoSphere Biglnsights | |
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| What the Hadoop! | |
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| Where Elephants Come From: The History of Hadoop | |
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| Components of Hadoop and Related Projects | |
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| Hadoop 2.0 | |
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| What's in the Box: The Components of InfoSphere Biglnsights | |
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| Hadoop Components Included in InfoSphere Biglnsights 2.0 | |
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| The Biglnsights Web Console | |
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| The Biglnsights Development Tools | |
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| Biglnsights Editions: Basic and Advanced | |
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| Deploying Biglnsights | |
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| Ease of Use: A Simple Installation Process | |
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| A Low-Cost Way to Get Started: Running Biglnsights on the Cloud | |
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| Higher-Class Hardware: IBM PowerLinux Solution for Big Data | |
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| Cloudera Support | |
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| Analytics: Exploration, Development, and Deployment | |
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| Advanced Text Analytics Toolkit | |
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| Machine Learning for the Masses: Deep Statistical Analysis on Biglnsights | |
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| Analytic Accelerators: Finding Needles in Haystacks of Needles? | |
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| Apps for the Masses: Easy Deployment and Execution of Custom Applications | |
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| Data Discovery and Visualization: BigSheets | |
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| The Biglnsights Development Environment | |
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| The Biglnsights Application Lifecycle | |
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| Data Integration | |
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| The Anlaytics-Based IBM PureData Systems and DB2 | |
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| JDBC Module | |
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| InfoSphere Streams for Data in Motion | |
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| InfoSphere DataStage | |
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| Operational Excellence | |
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| Securing the Cluster | |
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| Monitoring All Aspects of Your Cluster | |
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| Compression | |
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| Improved Workload Scheduling: Intelligent Scheduler | |
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| Adaptive MapReduce | |
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| A Flexible File System for Hadoop: GPFS-FPO | |
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| Wrapping It Up | |
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| Analytics for Big Data in Motion | |
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| Real-Time Analytical Processing with InfoSphere Streams | |
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| The Basics: InfoSphere Streams | |
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| How InfoSphere Streams Works | |
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| What's a Lowercase "stream"? | |
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| Programming Streams Made Easy | |
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| The Streams Processing Language | |
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| Source and Sink Adapters | |
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| Operators | |
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| Streams Toolkits | |
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| Enterprise Class | |
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| High Availability | |
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| Integration Is the Apex of Enterprise Class Analysis | |
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| Industry Use Cases for InfoSphere Streams | |
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| Telecommunications | |
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| Enforcement, Defense, Surveillance, and Cyber Security | |
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| Financial Services Sector | |
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| Health and Life Sciences | |
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| And the Rest We Can't Fit in This Book | |
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| Wrapping It Up | |
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| Unlocking Big Data | |
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| If Data Is the New Oil-You Need Data Exploration and Discovery | |
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| Indexing Data from Multiple Sources with InfoSphere Data Explorer | |
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| Connector Framework | |
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| The Data Explorer Processing Layer | |
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| User Management Layer | |
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| Beefing Up InfoSphere Biglnsights | |
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| An App with a View: Creating Information Dashboards with InfoSphere Data Explorer Application Builder | |
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| Wrapping It Up: Data Explorer Unlocks Big Data | |
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| Big Data Analytic Accelerators | |
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| Differentiate Yourself with Text Analytics | |
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| What Is Text Analysis? | |
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| The Annotated Query Language to the Rescue! | |
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| Productivity Tools That Make All the Difference | |
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| Wrapping It Up | |
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| The IBM Big Data Analytic Accelerators | |
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| The IBM Accelerator for Machine Data Analytics | |
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| Ingesting Machine Data | |
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| Extract | |
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| Index | |
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| Transform | |
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| Statistical Modeling | |
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| Visualization | |
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| Faceted Search | |
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| The IBM Accelerator for Social Data Analytics | |
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| Feedback Extractors: What Are People Saying? | |
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| Profile Extractors: Who Are These People? | |
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| Workflow: Pulling It All Together | |
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| The IBM Accelerator for Telecommunications Event Data Analytics | |
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| Call Detail Record Enrichment | |
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| Network Quality Monitoring | |
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| Customer Experience Indicators | |
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| Wrapping It Up: Accelerating Your Productivity | |
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| Integration and Governance in a Big Data World | |
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| To Govern or Not to Govern: Governance in a Big Data World | |
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| Why Should Big Data be Governed? | |
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| Competing on Information and Analytics | |
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| The Definition of Information Integration and Governance | |
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| An Information Governance Process | |
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| The IBM Information Integration and Governance Technology Platform | |
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| IBM InfoSphere Business Information Exchange | |
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| IBM InfoSphere Information Server | |
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| Data Quality | |
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| Master Data Management | |
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| Data Lifecycle Management | |
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| Privacy and Security | |
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| Wrapping It Up: Trust Is About Turning Big Data into Trusted Information | |
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| Integrating Big Data in the Enterprise | |
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| Analytic Application Integration | |
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| IBM Cognos Software | |
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| IBM Content Analytics with Enterprise Search | |
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| SPSS | |
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| SAS | |
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| Unica | |
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| Q1 Labs: Security Solutions | |
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| IBM i2 Intelligence Analysis Platform | |
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| Platform Symphony MapReduce | |
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| Component Integration Within the IBM Big Data Platform | |
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| InfoSphere Biglnsights | |
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| InfoSphere Streams | |
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| Data Warehouse Solutions | |
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| The Advanced Text Analytics Toolkit | |
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| InfoSphere Data Explorer | |
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| InfoSphere Information Server | |
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| InfoSphere Master Data Management | |
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| InfoSphere Guardium | |
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| InfoSphere Optim | |
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| WebSphere Front Office | |
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| WebSphere Decision Server: iLog Rule | |
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| Rational | |
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| Data Repository-Level Integration | |
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| Enterprise Platform Plug-ins | |
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| Development Tooling | |
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| Analytics | |
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| Visualization | |
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| Wrapping It Up | |