Harness the Power of Big Data the IBM Big Data Platform

ISBN-10: 0071808175

ISBN-13: 9780071808170

Edition: 2012

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Boost your Big Data IQ! Gain insight into how to govern and consume IBM’s unique in-motion and at-rest Big Data analytic capabilitiesBig Data represents a new era of computing—an inflection point of opportunity where data in any format may be explored and utilized for breakthrough insights—whether that data is in-place, in-motion, or at-rest. IBM is uniquely positioned to help clients navigate this transformation. This book reveals how IBM is infusing open source Big Data technologies with IBM innovation that manifest in a platform capable of "changing the game."The four defining characteristics of Big Data—volume, variety, velocity, and veracity—are discussed. You’ll understand how IBM is fully committed to Hadoop and integrating it into the enterprise. Hear about how organizations are taking inventories of their existing Big Data assets, with search capabilities that help organizations discover what they could already know, and extend their reach into new data territories for unprecedented model accuracy and discovery.In this book you will also learn not just about the technologies that make up the IBM Big Data platform, but when to leverage its purpose-built engines for analytics on data in-motion and data at-rest. And you’ll gain an understanding of how and when to govern Big Data, and how IBM’s industry-leading InfoSphere integration and governance portfolio helps you understand, govern, and effectively utilize Big Data. Industry use cases are also included in this practical guide.
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Book details

List price: $32.95
Copyright year: 2012
Publisher: McGraw-Hill Osborne
Publication date: 10/18/2012
Binding: Paperback
Pages: 280
Size: 6.00" wide x 9.00" long x 0.50" tall
Weight: 0.792
Language: English

Paul C. Zikopoulos, B.A., M.B.A., is the Director of Technical Professionals for IBM Software Group's Information Management division. In this role, he has executive responsibilities for the vitality of its client-facing technical professional community and ensuring they're among the most technically skilled personnel in the marketplace. Paul has written more than 300 magazine articles and 14 books on DB2, including Break Free with DB2: A Tour of Cost Saving Features; Information on Demand: Introduction to DB2 9.5 New Features; DB2 Fundamentals Certification for Dummies; DB2 for Dummies and more. Paul is a DB2 Certified Solutions Expert (BI and DBA).Matt Huras, B.ASc., M.Eng., is an IBM Distinguished Engineer, and a senior architect for the DB2 on the Linux, UNIX, and Windows platform. He focuses on database kernel, which includes the data management, index management, locking, concurrency, and other protocols. Matt's most recent project role was the lead architect of DB2 pureScale--a new feature focusing on delivering new levels of scalability and availability.Paul Awad, B.Sc., is an IBM Project Manager whose main focus is DB2 pureScale. Paul has held several leadership positions at IBM, including roles in support, the Project Office, and the Collaboration Center. Paul is currently the DB2 pureScale Product Manager, and in this role he has influence over the DB2 pureScale product, customer, and market feedback channels for the offering as well as enabling the product to market.Aamer Sachedina, B.Eng., P.Eng., is a Senior Technical Staff Member and IBM Master Inventer at the IBM Toronto Lab. Over the years, he contributed to the design of the DB2 kernel, was the architect of DB2's Automatic Storage, and most recently headed the active-active availability effort as HA architect on the DB2 pureScale project.

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