McGraw-Hill authors represent the leading experts in their fields and are dedicated to improving the lives, careers, and interests of readers worldwide

• IntroductionPart I Opening Conversations About Big Data1 Getting Hype out of the Way: Big Data and BeyondThere’s Gold in “Them There” Hills!Why Is Big Data Important?Brought to You by the Letter V: How We Define Big DataCognitive ComputingWhy Does the Big Data World Need Cognitive Computing?A Big Data and Analytics Platform Manifesto1. Discover, Explore, and Navigate Big Data Sources2. Land, Manage, and Store Huge Volumes of Any Data3. Structured and Controlled Data4. Manage and Analyze Unstructured Data5. Analyze Data in Real Time6. A Rich Library of Analytical Functions and Tools7. Integrate and Govern All Data SourcesCognitive Computing SystemsOf Cloud and Manifestos…Wrapping It Up2 To SQL or Not to SQL: That’s Not the Question, It’s the Era of Polyglot PersistenceCore Value Systems: What Makes a NoSQL Practitioner TickWhat Is NoSQL?Is Hadoop a NoSQL Database?Different Strokes for Different Folks: The NoSQL Classification SystemGive Me a Key, I’ll Give You a Value: The Key/Value StoreThe Grand-Daddy of Them All: The Document StoreColumn Family, Columnar Store, or BigTable Derivatives: What Do We Call You?Don’t Underestimate the Underdog: The Graph StoreFrom ACID to CAPCAP Theorem and a Meatloaf Song: “Two Out of Three Ain’t Bad”Let Me Get This Straight: There Is SQL, NoSQL, and Now NewSQL?Wrapping It Up3 Composing Cloud Applications: Why We Love the Bluemix and the IBM CloudAt Your Service: Explaining Cloud Provisioning ModelsSetting a Foundation for the Cloud: Infrastructure as a ServiceIaaS for Tomorrow…Available Today: IBM SoftLayer Powers the IBM CloudNoisy Neighbors Can Be Bad Neighbors: The Multitenant CloudBuilding the Developer’s Sandbox with Platform as a ServiceIf You Have Only a Couple of Minutes: PaaS and IBM Bluemix in a NutshellDigging Deeper into PaaSBeing Social on the Cloud: How Bluemix Integrates Platforms and ArchitecturesUnderstanding the Hybrid Cloud: Playing Frankenstein Without the HorrorTried and Tested: How Deployable Patterns Simplify PaaSComposing the Fabric of Cloud Services: IBM BluemixParting Words on Platform as a ServiceConsuming Functionality Without the Stress: Software as a ServiceThe Cloud Bazaar: SaaS and the API EconomyDemolishing the Barrier to Entry for Cloud-Ready Analytics: IBM’s dashDBBuild More, Grow More, Know More: dashDB’s Cloud SaaSRefinery as a ServiceWrapping It Up4 The Data Zones Model: A New Approach to Managing DataChallenges with the Traditional ApproachAgilityCostDepth of InsightNext-Generation Information Management ArchitecturesPrepare for Touchdown: The Landing ZoneInto the Unknown: The Exploration ZoneInto the Deep: The Deep Analytic ZoneCurtain Call: The New Staging ZoneYou Have Questions? We Have Answers! The Queryable Archive ZoneIn Big Data We Trust: The Trusted Data ZoneA Zone for Business ReportingFrom Forecast to Nowcast: The Real-Time Processing and Analytics ZoneLadies and Gentlemen, Presenting… “The Data Zones Model”Part II Watson Foundations5 Starting Out with a Solid Base: A Tour of Watson FoundationsOverview of Watson FoundationsA Continuum of Analytics Capabilities: Foundations for Watson6 Landing Your Data in Style with Blue Suit Hadoop: InfoSphere BigInsightsWhere Do Elephants Come From: What Is Hadoop?A Brief History of HadoopComponents of Hadoop and Related ProjectsOpen Source…and Proud of ItMaking Analytics on Hadoop EasyThe Real Deal for SQL on Hadoop: Big SQLMachine Learning for the Masses: Big R and SystemMLThe Advanced Text Analytics ToolkitData Discovery and Visualization: BigSheetsSpatiotemporal AnalyticsFinding Needles in Haystacks of Needles: Indexing and Search in BigInsightsCradle-to-Grave Application Development SupportThe BigInsights Integrated Development EnvironmentThe BigInsights Application LifecycleAn App Store for Hadoop: Easy Deployment and Execution of Custom ApplicationsKeeping the Sandbox Tidy: Sharing and Managing HadoopThe BigInsights Web ConsoleMonitoring the Aspects of Your ClusterSecuring the BigInsights for Hadoop ClusterAdaptive MapReduceA Flexible File System for Hadoop: GPFS-FPOPlaying Nice: Integration with Other Data Center SystemsIBM InfoSphere System z Connector for HadoopIBM PureData System for AnalyticsInfoSphere Streams for Data in MotionInfoSphere Information Server for Data IntegrationMatching at Scale with Big MatchSecuring Hadoop with Guardium and OptimBroad Integration SupportDeployment FlexibilityBigInsights Editions: Free, Low-Cost, and Premium OfferingsA Low-Cost Way to Get Started: Running BigInsights on the CloudHigher-Class Hardware: Power and System z SupportGet Started Quickly!Wrapping It Up7 “In the Moment” Analytics: InfoSphere StreamsIntroducing Streaming Data AnalysisHow InfoSphere Streams WorksA Simple Streams ApplicationRecommended Uses for StreamsHow Is Streams Different from CEP Systems?Stream Processing Modes: Preserve Currency or Preserve Each RecordHigh AvailabilityDynamically Distributed ProcessingInfoSphere Streams Platform ComponentsThe Streams ConsoleAn Integrated Development Environment for Streams: Streams StudioThe Streams Processing LanguageSource and Sink AdaptersAnalytical OperatorsStreams ToolkitsSolution AcceleratorsUse CasesGet Started Quickly!Wrapping It Up8 700 Million Times Faster Than the Blink of an Eye: BLU AccelerationWhat Is BLU Acceleration?What Does a Next Generation Database Service for Analytics Look Like?Seamlessly IntegratedHardware OptimizedConvince Me to Take BLU Acceleration for a Test DrivePedal to the Floor: How Fast Is BLU Acceleration?From Minimized to Minuscule: BLU Acceleration Compression RatiosWhere Will I Use BLU Acceleration?How BLU Acceleration Came to Be: Seven Big IdeasBig Idea #1: KISS It!Big Idea #2: Actionable Compression and Computer-Friendly EncodingBig Idea #3: Multiplying the Power of the CPUBig Idea #4: Parallel Vector ProcessingBig Idea #5: Get Organized…by ColumnBig Idea #6: Dynamic In-Memory ProcessingBig Idea #7: Data SkippingHow Seven Big Ideas Optimize the Hardware StackThe Sum of All Big Ideas: BLU Acceleration in ActionDB2 with BLU Acceleration Shadow Tables: When OLTP + OLAP = 1 DBWhat Lurks in These Shadows Isn’t Anything to Be Scared of: Operational ReportingWrapping It Up9 An Expert Integrated System for Deep AnalyticsBefore We Begin: Bursting into the CloudStarting on the Whiteboard: Netezza’s Design PrinciplesAppliance Simplicity: Minimize the Human EffortProcess Analytics Closer to the Data StoreBalanced + MPP = Linear ScalabilityModular Design: Support Flexible Configurations and Extreme ScalabilityWhat’s in the Box? The Netezza Appliance Architecture OverviewA Look Inside a Netezza BoxHow a Query Runs in NetezzaHow Netezza Is a Platform for AnalyticsWrapping It Up10 Build More, Grow More, Sleep More: IBM CloudantCloudant: “White Glove” Database as a ServiceWhere Did Cloudant Roll in From?Cloudant or Hadoop?Being Flexible: Schemas with JSONCloudant Clustering: Scaling for the CloudAvoiding Mongo-Size Outages: Sleep Soundly with Cloudant ReplicationCloudant Sync Brings Data to a Mobile WorldMake Data, Not War: Cloudant Versioning and Conflict ResolutionUnlocking GIS Data with Cloudant GeospatialCloudant LocalHere on In: For Techies…For Techies: Leveraging the Cloudant Primary IndexExploring Data with Cloudant’s Secondary Index “Views”Performing Ad Hoc Queries with the Cloudant Search IndexParameters That Govern a Logical Cloudant DatabaseRemember! Cloudant Is DBaaSWrapping It UpPart III Calming the Waters: Big Data Governance11 Guiding Principles for Data GovernanceThe IBM Data Governance Council Maturity ModelWrapping It Up12 Security Is NOT an AfterthoughtSecurity Big Data: How It’s DifferentSecuring Big Data in HadoopCulture, Definition, Charter, Foundation, and Data GovernanceWhat Is Sensitive Data?The Masquerade Gala: Masking Sensitive DataDon’t Break the DAM: Monitoring and Controlling Access to DataProtecting Data at RestWrapping It Up13 Big Data Lifecycle ManagementA Foundation for Data Governance: The Information Governance CatalogData on Demand: Data ClickData IntegrationData QualityVeracity as a Service: IBM DataWorksManaging Your Test Data: Optim Test Data ManagementA Retirement Home for Your Data: Optim Data ArchiveWrapping It Up14 Matching at Scale: Big MatchWhat Is Matching Anyway?A Teaser: Where Are You Going to Use Big Match?Matching on HadoopMatching ApproachesBig Match ArchitectureBig Match Algorithm Configuration FilesBig Match ApplicationsHBase TablesProbabilistic Matching EngineHow It WorksExtractSearchApplications for Big MatchEnabling the Landing ZoneEnhanced 360-Degree View of Your CustomersMore Reliable Data ExplorationLarge-Scale Searches for Matching RecordsWrapping It Up

McGraw-Hill authors represent the leading experts in their fields and are dedicated to improving the lives, careers, and interests of readers worldwide

• IntroductionPart I Opening Conversations About Big Data1 Getting Hype out of the Way: Big Data and BeyondThere’s Gold in “Them There” Hills!Why Is Big Data Important?Brought to You by the Letter V: How We Define Big DataCognitive ComputingWhy Does the Big Data World Need Cognitive Computing?A Big Data and Analytics Platform Manifesto1. Discover, Explore, and Navigate Big Data Sources2. Land, Manage, and Store Huge Volumes of Any Data3. Structured and Controlled Data4. Manage and Analyze Unstructured Data5. Analyze Data in Real Time6. A Rich Library of Analytical Functions and Tools7. Integrate and Govern All Data SourcesCognitive Computing SystemsOf Cloud and Manifestos…Wrapping It Up2 To SQL or Not to SQL: That’s Not the Question, It’s the Era of Polyglot PersistenceCore Value Systems: What Makes a NoSQL Practitioner TickWhat Is NoSQL?Is Hadoop a NoSQL Database?Different Strokes for Different Folks: The NoSQL Classification SystemGive Me a Key, I’ll Give You a Value: The Key/Value StoreThe Grand-Daddy of Them All: The Document StoreColumn Family, Columnar Store, or BigTable Derivatives: What Do We Call You?Don’t Underestimate the Underdog: The Graph StoreFrom ACID to CAPCAP Theorem and a Meatloaf Song: “Two Out of Three Ain’t Bad”Let Me Get This Straight: There Is SQL, NoSQL, and Now NewSQL?Wrapping It Up3 Composing Cloud Applications: Why We Love the Bluemix and the IBM CloudAt Your Service: Explaining Cloud Provisioning ModelsSetting a Foundation for the Cloud: Infrastructure as a ServiceIaaS for Tomorrow…Available Today: IBM SoftLayer Powers the IBM CloudNoisy Neighbors Can Be Bad Neighbors: The Multitenant CloudBuilding the Developer’s Sandbox with Platform as a ServiceIf You Have Only a Couple of Minutes: PaaS and IBM Bluemix in a NutshellDigging Deeper into PaaSBeing Social on the Cloud: How Bluemix Integrates Platforms and ArchitecturesUnderstanding the Hybrid Cloud: Playing Frankenstein Without the HorrorTried and Tested: How Deployable Patterns Simplify PaaSComposing the Fabric of Cloud Services: IBM BluemixParting Words on Platform as a ServiceConsuming Functionality Without the Stress: Software as a ServiceThe Cloud Bazaar: SaaS and the API EconomyDemolishing the Barrier to Entry for Cloud-Ready Analytics: IBM’s dashDBBuild More, Grow More, Know More: dashDB’s Cloud SaaSRefinery as a ServiceWrapping It Up4 The Data Zones Model: A New Approach to Managing DataChallenges with the Traditional ApproachAgilityCostDepth of InsightNext-Generation Information Management ArchitecturesPrepare for Touchdown: The Landing ZoneInto the Unknown: The Exploration ZoneInto the Deep: The Deep Analytic ZoneCurtain Call: The New Staging ZoneYou Have Questions? We Have Answers! The Queryable Archive ZoneIn Big Data We Trust: The Trusted Data ZoneA Zone for Business ReportingFrom Forecast to Nowcast: The Real-Time Processing and Analytics ZoneLadies and Gentlemen, Presenting… “The Data Zones Model”Part II Watson Foundations5 Starting Out with a Solid Base: A Tour of Watson FoundationsOverview of Watson FoundationsA Continuum of Analytics Capabilities: Foundations for Watson6 Landing Your Data in Style with Blue Suit Hadoop: InfoSphere BigInsightsWhere Do Elephants Come From: What Is Hadoop?A Brief History of HadoopComponents of Hadoop and Related ProjectsOpen Source…and Proud of ItMaking Analytics on Hadoop EasyThe Real Deal for SQL on Hadoop: Big SQLMachine Learning for the Masses: Big R and SystemMLThe Advanced Text Analytics ToolkitData Discovery and Visualization: BigSheetsSpatiotemporal AnalyticsFinding Needles in Haystacks of Needles: Indexing and Search in BigInsightsCradle-to-Grave Application Development SupportThe BigInsights Integrated Development EnvironmentThe BigInsights Application LifecycleAn App Store for Hadoop: Easy Deployment and Execution of Custom ApplicationsKeeping the Sandbox Tidy: Sharing and Managing HadoopThe BigInsights Web ConsoleMonitoring the Aspects of Your ClusterSecuring the BigInsights for Hadoop ClusterAdaptive MapReduceA Flexible File System for Hadoop: GPFS-FPOPlaying Nice: Integration with Other Data Center SystemsIBM InfoSphere System z Connector for HadoopIBM PureData System for AnalyticsInfoSphere Streams for Data in MotionInfoSphere Information Server for Data IntegrationMatching at Scale with Big MatchSecuring Hadoop with Guardium and OptimBroad Integration SupportDeployment FlexibilityBigInsights Editions: Free, Low-Cost, and Premium OfferingsA Low-Cost Way to Get Started: Running BigInsights on the CloudHigher-Class Hardware: Power and System z SupportGet Started Quickly!Wrapping It Up7 “In the Moment” Analytics: InfoSphere StreamsIntroducing Streaming Data AnalysisHow InfoSphere Streams WorksA Simple Streams ApplicationRecommended Uses for StreamsHow Is Streams Different from CEP Systems?Stream Processing Modes: Preserve Currency or Preserve Each RecordHigh AvailabilityDynamically Distributed ProcessingInfoSphere Streams Platform ComponentsThe Streams ConsoleAn Integrated Development Environment for Streams: Streams StudioThe Streams Processing LanguageSource and Sink AdaptersAnalytical OperatorsStreams ToolkitsSolution AcceleratorsUse CasesGet Started Quickly!Wrapping It Up8 700 Million Times Faster Than the Blink of an Eye: BLU AccelerationWhat Is BLU Acceleration?What Does a Next Generation Database Service for Analytics Look Like?Seamlessly IntegratedHardware OptimizedConvince Me to Take BLU Acceleration for a Test DrivePedal to the Floor: How Fast Is BLU Acceleration?From Minimized to Minuscule: BLU Acceleration Compression RatiosWhere Will I Use BLU Acceleration?How BLU Acceleration Came to Be: Seven Big IdeasBig Idea #1: KISS It!Big Idea #2: Actionable Compression and Computer-Friendly EncodingBig Idea #3: Multiplying the Power of the CPUBig Idea #4: Parallel Vector ProcessingBig Idea #5: Get Organized…by ColumnBig Idea #6: Dynamic In-Memory ProcessingBig Idea #7: Data SkippingHow Seven Big Ideas Optimize the Hardware StackThe Sum of All Big Ideas: BLU Acceleration in ActionDB2 with BLU Acceleration Shadow Tables: When OLTP + OLAP = 1 DBWhat Lurks in These Shadows Isn’t Anything to Be Scared of: Operational ReportingWrapping It Up9 An Expert Integrated System for Deep AnalyticsBefore We Begin: Bursting into the CloudStarting on the Whiteboard: Netezza’s Design PrinciplesAppliance Simplicity: Minimize the Human EffortProcess Analytics Closer to the Data StoreBalanced + MPP = Linear ScalabilityModular Design: Support Flexible Configurations and Extreme ScalabilityWhat’s in the Box? The Netezza Appliance Architecture OverviewA Look Inside a Netezza BoxHow a Query Runs in NetezzaHow Netezza Is a Platform for AnalyticsWrapping It Up10 Build More, Grow More, Sleep More: IBM CloudantCloudant: “White Glove” Database as a ServiceWhere Did Cloudant Roll in From?Cloudant or Hadoop?Being Flexible: Schemas with JSONCloudant Clustering: Scaling for the CloudAvoiding Mongo-Size Outages: Sleep Soundly with Cloudant ReplicationCloudant Sync Brings Data to a Mobile WorldMake Data, Not War: Cloudant Versioning and Conflict ResolutionUnlocking GIS Data with Cloudant GeospatialCloudant LocalHere on In: For Techies…For Techies: Leveraging the Cloudant Primary IndexExploring Data with Cloudant’s Secondary Index “Views”Performing Ad Hoc Queries with the Cloudant Search IndexParameters That Govern a Logical Cloudant DatabaseRemember! Cloudant Is DBaaSWrapping It UpPart III Calming the Waters: Big Data Governance11 Guiding Principles for Data GovernanceThe IBM Data Governance Council Maturity ModelWrapping It Up12 Security Is NOT an AfterthoughtSecurity Big Data: How It’s DifferentSecuring Big Data in HadoopCulture, Definition, Charter, Foundation, and Data GovernanceWhat Is Sensitive Data?The Masquerade Gala: Masking Sensitive DataDon’t Break the DAM: Monitoring and Controlling Access to DataProtecting Data at RestWrapping It Up13 Big Data Lifecycle ManagementA Foundation for Data Governance: The Information Governance CatalogData on Demand: Data ClickData IntegrationData QualityVeracity as a Service: IBM DataWorksManaging Your Test Data: Optim Test Data ManagementA Retirement Home for Your Data: Optim Data ArchiveWrapping It Up14 Matching at Scale: Big MatchWhat Is Matching Anyway?A Teaser: Where Are You Going to Use Big Match?Matching on HadoopMatching ApproachesBig Match ArchitectureBig Match Algorithm Configuration FilesBig Match ApplicationsHBase TablesProbabilistic Matching EngineHow It WorksExtractSearchApplications for Big MatchEnabling the Landing ZoneEnhanced 360-Degree View of Your CustomersMore Reliable Data ExplorationLarge-Scale Searches for Matching RecordsWrapping It Up

McGraw-Hill authors represent the leading experts in their fields and are dedicated to improving the lives, careers, and interests of readers worldwide

• IntroductionPart I Opening Conversations About Big Data1 Getting Hype out of the Way: Big Data and BeyondThere’s Gold in “Them There” Hills!Why Is Big Data Important?Brought to You by the Letter V: How We Define Big DataCognitive ComputingWhy Does the Big Data World Need Cognitive Computing?A Big Data and Analytics Platform Manifesto1. Discover, Explore, and Navigate Big Data Sources2. Land, Manage, and Store Huge Volumes of Any Data3. Structured and Controlled Data4. Manage and Analyze Unstructured Data5. Analyze Data in Real Time6. A Rich Library of Analytical Functions and Tools7. Integrate and Govern All Data SourcesCognitive Computing SystemsOf Cloud and Manifestos…Wrapping It Up2 To SQL or Not to SQL: That’s Not the Question, It’s the Era of Polyglot PersistenceCore Value Systems: What Makes a NoSQL Practitioner TickWhat Is NoSQL?Is Hadoop a NoSQL Database?Different Strokes for Different Folks: The NoSQL Classification SystemGive Me a Key, I’ll Give You a Value: The Key/Value StoreThe Grand-Daddy of Them All: The Document StoreColumn Family, Columnar Store, or BigTable Derivatives: What Do We Call You?Don’t Underestimate the Underdog: The Graph StoreFrom ACID to CAPCAP Theorem and a Meatloaf Song: “Two Out of Three Ain’t Bad”Let Me Get This Straight: There Is SQL, NoSQL, and Now NewSQL?Wrapping It Up3 Composing Cloud Applications: Why We Love the Bluemix and the IBM CloudAt Your Service: Explaining Cloud Provisioning ModelsSetting a Foundation for the Cloud: Infrastructure as a ServiceIaaS for Tomorrow…Available Today: IBM SoftLayer Powers the IBM CloudNoisy Neighbors Can Be Bad Neighbors: The Multitenant CloudBuilding the Developer’s Sandbox with Platform as a ServiceIf You Have Only a Couple of Minutes: PaaS and IBM Bluemix in a NutshellDigging Deeper into PaaSBeing Social on the Cloud: How Bluemix Integrates Platforms and ArchitecturesUnderstanding the Hybrid Cloud: Playing Frankenstein Without the HorrorTried and Tested: How Deployable Patterns Simplify PaaSComposing the Fabric of Cloud Services: IBM BluemixParting Words on Platform as a ServiceConsuming Functionality Without the Stress: Software as a ServiceThe Cloud Bazaar: SaaS and the API EconomyDemolishing the Barrier to Entry for Cloud-Ready Analytics: IBM’s dashDBBuild More, Grow More, Know More: dashDB’s Cloud SaaSRefinery as a ServiceWrapping It Up4 The Data Zones Model: A New Approach to Managing DataChallenges with the Traditional ApproachAgilityCostDepth of InsightNext-Generation Information Management ArchitecturesPrepare for Touchdown: The Landing ZoneInto the Unknown: The Exploration ZoneInto the Deep: The Deep Analytic ZoneCurtain Call: The New Staging ZoneYou Have Questions? We Have Answers! The Queryable Archive ZoneIn Big Data We Trust: The Trusted Data ZoneA Zone for Business ReportingFrom Forecast to Nowcast: The Real-Time Processing and Analytics ZoneLadies and Gentlemen, Presenting… “The Data Zones Model”Part II Watson Foundations5 Starting Out with a Solid Base: A Tour of Watson FoundationsOverview of Watson FoundationsA Continuum of Analytics Capabilities: Foundations for Watson6 Landing Your Data in Style with Blue Suit Hadoop: InfoSphere BigInsightsWhere Do Elephants Come From: What Is Hadoop?A Brief History of HadoopComponents of Hadoop and Related ProjectsOpen Source…and Proud of ItMaking Analytics on Hadoop EasyThe Real Deal for SQL on Hadoop: Big SQLMachine Learning for the Masses: Big R and SystemMLThe Advanced Text Analytics ToolkitData Discovery and Visualization: BigSheetsSpatiotemporal AnalyticsFinding Needles in Haystacks of Needles: Indexing and Search in BigInsightsCradle-to-Grave Application Development SupportThe BigInsights Integrated Development EnvironmentThe BigInsights Application LifecycleAn App Store for Hadoop: Easy Deployment and Execution of Custom ApplicationsKeeping the Sandbox Tidy: Sharing and Managing HadoopThe BigInsights Web ConsoleMonitoring the Aspects of Your ClusterSecuring the BigInsights for Hadoop ClusterAdaptive MapReduceA Flexible File System for Hadoop: GPFS-FPOPlaying Nice: Integration with Other Data Center SystemsIBM InfoSphere System z Connector for HadoopIBM PureData System for AnalyticsInfoSphere Streams for Data in MotionInfoSphere Information Server for Data IntegrationMatching at Scale with Big MatchSecuring Hadoop with Guardium and OptimBroad Integration SupportDeployment FlexibilityBigInsights Editions: Free, Low-Cost, and Premium OfferingsA Low-Cost Way to Get Started: Running BigInsights on the CloudHigher-Class Hardware: Power and System z SupportGet Started Quickly!Wrapping It Up7 “In the Moment” Analytics: InfoSphere StreamsIntroducing Streaming Data AnalysisHow InfoSphere Streams WorksA Simple Streams ApplicationRecommended Uses for StreamsHow Is Streams Different from CEP Systems?Stream Processing Modes: Preserve Currency or Preserve Each RecordHigh AvailabilityDynamically Distributed ProcessingInfoSphere Streams Platform ComponentsThe Streams ConsoleAn Integrated Development Environment for Streams: Streams StudioThe Streams Processing LanguageSource and Sink AdaptersAnalytical OperatorsStreams ToolkitsSolution AcceleratorsUse CasesGet Started Quickly!Wrapping It Up8 700 Million Times Faster Than the Blink of an Eye: BLU AccelerationWhat Is BLU Acceleration?What Does a Next Generation Database Service for Analytics Look Like?Seamlessly IntegratedHardware OptimizedConvince Me to Take BLU Acceleration for a Test DrivePedal to the Floor: How Fast Is BLU Acceleration?From Minimized to Minuscule: BLU Acceleration Compression RatiosWhere Will I Use BLU Acceleration?How BLU Acceleration Came to Be: Seven Big IdeasBig Idea #1: KISS It!Big Idea #2: Actionable Compression and Computer-Friendly EncodingBig Idea #3: Multiplying the Power of the CPUBig Idea #4: Parallel Vector ProcessingBig Idea #5: Get Organized…by ColumnBig Idea #6: Dynamic In-Memory ProcessingBig Idea #7: Data SkippingHow Seven Big Ideas Optimize the Hardware StackThe Sum of All Big Ideas: BLU Acceleration in ActionDB2 with BLU Acceleration Shadow Tables: When OLTP + OLAP = 1 DBWhat Lurks in These Shadows Isn’t Anything to Be Scared of: Operational ReportingWrapping It Up9 An Expert Integrated System for Deep AnalyticsBefore We Begin: Bursting into the CloudStarting on the Whiteboard: Netezza’s Design PrinciplesAppliance Simplicity: Minimize the Human EffortProcess Analytics Closer to the Data StoreBalanced + MPP = Linear ScalabilityModular Design: Support Flexible Configurations and Extreme ScalabilityWhat’s in the Box? The Netezza Appliance Architecture OverviewA Look Inside a Netezza BoxHow a Query Runs in NetezzaHow Netezza Is a Platform for AnalyticsWrapping It Up10 Build More, Grow More, Sleep More: IBM CloudantCloudant: “White Glove” Database as a ServiceWhere Did Cloudant Roll in From?Cloudant or Hadoop?Being Flexible: Schemas with JSONCloudant Clustering: Scaling for the CloudAvoiding Mongo-Size Outages: Sleep Soundly with Cloudant ReplicationCloudant Sync Brings Data to a Mobile WorldMake Data, Not War: Cloudant Versioning and Conflict ResolutionUnlocking GIS Data with Cloudant GeospatialCloudant LocalHere on In: For Techies…For Techies: Leveraging the Cloudant Primary IndexExploring Data with Cloudant’s Secondary Index “Views”Performing Ad Hoc Queries with the Cloudant Search IndexParameters That Govern a Logical Cloudant DatabaseRemember! Cloudant Is DBaaSWrapping It UpPart III Calming the Waters: Big Data Governance11 Guiding Principles for Data GovernanceThe IBM Data Governance Council Maturity ModelWrapping It Up12 Security Is NOT an AfterthoughtSecurity Big Data: How It’s DifferentSecuring Big Data in HadoopCulture, Definition, Charter, Foundation, and Data GovernanceWhat Is Sensitive Data?The Masquerade Gala: Masking Sensitive DataDon’t Break the DAM: Monitoring and Controlling Access to DataProtecting Data at RestWrapping It Up13 Big Data Lifecycle ManagementA Foundation for Data Governance: The Information Governance CatalogData on Demand: Data ClickData IntegrationData QualityVeracity as a Service: IBM DataWorksManaging Your Test Data: Optim Test Data ManagementA Retirement Home for Your Data: Optim Data ArchiveWrapping It Up14 Matching at Scale: Big MatchWhat Is Matching Anyway?A Teaser: Where Are You Going to Use Big Match?Matching on HadoopMatching ApproachesBig Match ArchitectureBig Match Algorithm Configuration FilesBig Match ApplicationsHBase TablesProbabilistic Matching EngineHow It WorksExtractSearchApplications for Big MatchEnabling the Landing ZoneEnhanced 360-Degree View of Your CustomersMore Reliable Data ExplorationLarge-Scale Searches for Matching RecordsWrapping It Up