{"product_id":"big-data-for-dummies-isbn-9781118504222","title":"Big Data For Dummies","description":"\u003cp\u003e\u003cb\u003eFind the right big data solution for your business or organization\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eBig data management is one of the major challenges facing business, industry, and not-for-profit organizations. Data sets such as customer transactions for a mega-retailer, weather patterns monitored by meteorologists, or social network activity can quickly outpace the capacity of traditional data management tools. If you need to develop or manage big data solutions, you'll appreciate how these four experts define, explain, and guide you through this new and often confusing concept. You'll learn what it is, why it matters, and how to choose and implement solutions that work.\u003c\/p\u003e \u003cul\u003e \u003cli\u003eEffectively managing big data is an issue of growing importance to businesses, not-for-profit organizations, government, and IT professionals\u003c\/li\u003e \u003cli\u003eAuthors are experts in information management, big data, and a variety of solutions\u003c\/li\u003e \u003cli\u003eExplains big data in detail and discusses how to select and implement a solution, security concerns to consider, data storage and presentation issues, analytics, and much more\u003c\/li\u003e \u003cli\u003eProvides essential information in a no-nonsense, easy-to-understand style that is empowering\u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003e\u003ci\u003eBig Data For Dummies\u003c\/i\u003e cuts through the confusion and helps you take charge of big data solutions for your organization.\u003c\/p\u003e \u003cp\u003eIntroduction 1\u003c\/p\u003e \u003cp\u003eAbout This Book 2\u003c\/p\u003e \u003cp\u003eFoolish Assumptions 2\u003c\/p\u003e \u003cp\u003eHow This Book Is Organized 3\u003c\/p\u003e \u003cp\u003ePart I: Getting Started with Big Data 3\u003c\/p\u003e \u003cp\u003ePart II: Technology Foundations for Big Data 3\u003c\/p\u003e \u003cp\u003ePart III: Big Data Management 3\u003c\/p\u003e \u003cp\u003ePart IV: Analytics and Big Data 4\u003c\/p\u003e \u003cp\u003ePart V: Big Data Implementation 4\u003c\/p\u003e \u003cp\u003ePart VI: Big Data Solutions in the Real World 4\u003c\/p\u003e \u003cp\u003ePart VII: The Part of Tens 4\u003c\/p\u003e \u003cp\u003eGlossary 4\u003c\/p\u003e \u003cp\u003eIcons Used in This Book 5\u003c\/p\u003e \u003cp\u003eWhere to Go from Here 5\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart I: Getting Started with Big Data 7\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 1: Grasping the Fundamentals of Big Data 9\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eThe Evolution of Data Management 10\u003c\/p\u003e \u003cp\u003eUnderstanding the Waves of Managing Data 11\u003c\/p\u003e \u003cp\u003eWave 1: Creating manageable data structures 11\u003c\/p\u003e \u003cp\u003eWave 2: Web and content management 13\u003c\/p\u003e \u003cp\u003eWave 3: Managing big data 14\u003c\/p\u003e \u003cp\u003eDefining Big Data 15\u003c\/p\u003e \u003cp\u003eBuilding a Successful Big Data Management Architecture 16\u003c\/p\u003e \u003cp\u003eBeginning with capture, organize, integrate, analyze, and act 16\u003c\/p\u003e \u003cp\u003eSetting the architectural foundation 17\u003c\/p\u003e \u003cp\u003ePerformance matters 20\u003c\/p\u003e \u003cp\u003eTraditional and advanced analytics 22\u003c\/p\u003e \u003cp\u003eThe Big Data Journey 23\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 2: Examining Big Data Types 25\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eDefining Structured Data 26\u003c\/p\u003e \u003cp\u003eExploring sources of big structured data 26\u003c\/p\u003e \u003cp\u003eUnderstanding the role of relational databases in big data 27\u003c\/p\u003e \u003cp\u003eDefining Unstructured Data 29\u003c\/p\u003e \u003cp\u003eExploring sources of unstructured data 29\u003c\/p\u003e \u003cp\u003eUnderstanding the role of a CMS in big data management 31\u003c\/p\u003e \u003cp\u003eLooking at Real-Time and Non-Real-Time Requirements 32\u003c\/p\u003e \u003cp\u003ePutting Big Data Together 33\u003c\/p\u003e \u003cp\u003eManaging different data types 33\u003c\/p\u003e \u003cp\u003eIntegrating data types into a big data environment 34\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 3: Old Meets New: Distributed Computing 37\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eA Brief History of Distributed Computing 37\u003c\/p\u003e \u003cp\u003eGiving thanks to DARPA 38\u003c\/p\u003e \u003cp\u003eThe value of a consistent model 39\u003c\/p\u003e \u003cp\u003eUnderstanding the Basics of Distributed Computing 40\u003c\/p\u003e \u003cp\u003eWhy we need distributed computing for big data 40\u003c\/p\u003e \u003cp\u003eThe changing economics of computing 40\u003c\/p\u003e \u003cp\u003eThe problem with latency 41\u003c\/p\u003e \u003cp\u003eDemand meets solutions 41\u003c\/p\u003e \u003cp\u003eGetting Performance Right 42\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart II: Technology Foundations for Big Data 45\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 4: Digging into Big Data Technology Components 47\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eExploring the Big Data Stack 48\u003c\/p\u003e \u003cp\u003eLayer 0: Redundant Physical Infrastructure 49\u003c\/p\u003e \u003cp\u003ePhysical redundant networks 51\u003c\/p\u003e \u003cp\u003eManaging hardware: Storage and servers 51\u003c\/p\u003e \u003cp\u003eInfrastructure operations 51\u003c\/p\u003e \u003cp\u003eLayer 1: Security Infrastructure 52\u003c\/p\u003e \u003cp\u003eInterfaces and Feeds to and from Applications and the Internet 53\u003c\/p\u003e \u003cp\u003eLayer 2: Operational Databases 54\u003c\/p\u003e \u003cp\u003eLayer 3: Organizing Data Services and Tools 56\u003c\/p\u003e \u003cp\u003eLayer 4: Analytical Data Warehouses 56\u003c\/p\u003e \u003cp\u003eBig Data Analytics 58\u003c\/p\u003e \u003cp\u003eBig Data Applications 58\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 5: Virtualization and How It Supports Distributed Computing 61\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eUnderstanding the Basics of Virtualization 61\u003c\/p\u003e \u003cp\u003eThe importance of virtualization to big data 63\u003c\/p\u003e \u003cp\u003eServer virtualization 64\u003c\/p\u003e \u003cp\u003eApplication virtualization 65\u003c\/p\u003e \u003cp\u003eNetwork virtualization 66\u003c\/p\u003e \u003cp\u003eProcessor and memory virtualization 66\u003c\/p\u003e \u003cp\u003eData and storage virtualization 67\u003c\/p\u003e \u003cp\u003eManaging Virtualization with the Hypervisor 68\u003c\/p\u003e \u003cp\u003eAbstraction and Virtualization 69\u003c\/p\u003e \u003cp\u003eImplementing Virtualization to Work with Big Data 69\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 6: Examining the Cloud and Big Data 71\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eDefining the Cloud in the Context of Big Data 71\u003c\/p\u003e \u003cp\u003eUnderstanding Cloud Deployment and Delivery Models 72\u003c\/p\u003e \u003cp\u003eCloud deployment models 73\u003c\/p\u003e \u003cp\u003eCloud delivery models 74\u003c\/p\u003e \u003cp\u003eThe Cloud as an Imperative for Big Data 75\u003c\/p\u003e \u003cp\u003eMaking Use of the Cloud for Big Data 77\u003c\/p\u003e \u003cp\u003eProviders in the Big Data Cloud Market 78\u003c\/p\u003e \u003cp\u003eAmazon’s Public Elastic Compute Cloud 78\u003c\/p\u003e \u003cp\u003eGoogle big data services 79\u003c\/p\u003e \u003cp\u003eMicrosoft Azure 80\u003c\/p\u003e \u003cp\u003eOpenStack 80\u003c\/p\u003e \u003cp\u003eWhere to be careful when using cloud services 81\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart III: Big Data Management 83\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 7: Operational Databases 85\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eRDBMSs Are Important in a Big Data Environment 87\u003c\/p\u003e \u003cp\u003ePostgreSQL relational database 87\u003c\/p\u003e \u003cp\u003eNonrelational Databases 88\u003c\/p\u003e \u003cp\u003eKey-Value Pair Databases 89\u003c\/p\u003e \u003cp\u003eRiak key-value database 90\u003c\/p\u003e \u003cp\u003eDocument Databases 91\u003c\/p\u003e \u003cp\u003eMongoDB 92\u003c\/p\u003e \u003cp\u003eCouchDB 93\u003c\/p\u003e \u003cp\u003eColumnar Databases 94\u003c\/p\u003e \u003cp\u003eHBase columnar database 94\u003c\/p\u003e \u003cp\u003eGraph Databases 95\u003c\/p\u003e \u003cp\u003eNeo4J graph database 96\u003c\/p\u003e \u003cp\u003eSpatial Databases 97\u003c\/p\u003e \u003cp\u003ePostGIS\/OpenGEO Suite 98\u003c\/p\u003e \u003cp\u003ePolyglot Persistence 99\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 8: MapReduce Fundamentals 101\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eTracing the Origins of MapReduce 101\u003c\/p\u003e \u003cp\u003eUnderstanding the map Function 103\u003c\/p\u003e \u003cp\u003eAdding the reduce Function 104\u003c\/p\u003e \u003cp\u003ePutting map and reduce Together 105\u003c\/p\u003e \u003cp\u003eOptimizing MapReduce Tasks 108\u003c\/p\u003e \u003cp\u003eHardware\/network topology 108\u003c\/p\u003e \u003cp\u003eSynchronization 108\u003c\/p\u003e \u003cp\u003eFile system 108\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 9: Exploring the World of Hadoop 111\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eExplaining Hadoop 111\u003c\/p\u003e \u003cp\u003eUnderstanding the Hadoop Distributed File System (HDFS) 112\u003c\/p\u003e \u003cp\u003eNameNodes 113\u003c\/p\u003e \u003cp\u003eData nodes 114\u003c\/p\u003e \u003cp\u003eUnder the covers of HDFS 115\u003c\/p\u003e \u003cp\u003eHadoop MapReduce 116\u003c\/p\u003e \u003cp\u003eGetting the data ready 117\u003c\/p\u003e \u003cp\u003eLet the mapping begin 118\u003c\/p\u003e \u003cp\u003eReduce and combine 118\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 10: The Hadoop Foundation and Ecosystem 121\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eBuilding a Big Data Foundation with the Hadoop Ecosystem 121\u003c\/p\u003e \u003cp\u003eManaging Resources and Applications with Hadoop YARN 122\u003c\/p\u003e \u003cp\u003eStoring Big Data with HBase 123\u003c\/p\u003e \u003cp\u003eMining Big Data with Hive 124\u003c\/p\u003e \u003cp\u003eInteracting with the Hadoop Ecosystem 125\u003c\/p\u003e \u003cp\u003ePig and Pig Latin 125\u003c\/p\u003e \u003cp\u003eSqoop 126\u003c\/p\u003e \u003cp\u003eZookeeper 127\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 11: Appliances and Big Data Warehouses 129\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eIntegrating Big Data with the Traditional Data Warehouse 129\u003c\/p\u003e \u003cp\u003eOptimizing the data warehouse 130\u003c\/p\u003e \u003cp\u003eDifferentiating big data structures from data warehouse data 130\u003c\/p\u003e \u003cp\u003eExamining a hybrid process case study 131\u003c\/p\u003e \u003cp\u003eBig Data Analysis and the Data Warehouse 133\u003c\/p\u003e \u003cp\u003eThe integration lynchpin 134\u003c\/p\u003e \u003cp\u003eRethinking extraction, transformation, and loading 134\u003c\/p\u003e \u003cp\u003eChanging the Role of the Data Warehouse 135\u003c\/p\u003e \u003cp\u003eChanging Deployment Models in the Big Data Era 136\u003c\/p\u003e \u003cp\u003eThe appliance model 136\u003c\/p\u003e \u003cp\u003eThe cloud model 137\u003c\/p\u003e \u003cp\u003eExamining the Future of Data Warehouses 137\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart IV: Analytics and Big Data 139\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 12: Defining Big Data Analytics 141\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eUsing Big Data to Get Results 142\u003c\/p\u003e \u003cp\u003eBasic analytics 142\u003c\/p\u003e \u003cp\u003eAdvanced analytics 143\u003c\/p\u003e \u003cp\u003eOperationalized analytics 146\u003c\/p\u003e \u003cp\u003eMonetizing analytics 146\u003c\/p\u003e \u003cp\u003eModifying Business Intelligence Products to Handle Big Data 147\u003c\/p\u003e \u003cp\u003eData 147\u003c\/p\u003e \u003cp\u003eAnalytical algorithms 148\u003c\/p\u003e \u003cp\u003eInfrastructure support 148\u003c\/p\u003e \u003cp\u003eStudying Big Data Analytics Examples 149\u003c\/p\u003e \u003cp\u003eOrbitz 149\u003c\/p\u003e \u003cp\u003eNokia 150\u003c\/p\u003e \u003cp\u003eNASA 150\u003c\/p\u003e \u003cp\u003eBig Data Analytics Solutions 151\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 13: Understanding Text Analytics and Big Data 153\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eExploring Unstructured Data 154\u003c\/p\u003e \u003cp\u003eUnderstanding Text Analytics 155\u003c\/p\u003e \u003cp\u003eThe difference between text analytics and search 156\u003c\/p\u003e \u003cp\u003eAnalysis and Extraction Techniques 157\u003c\/p\u003e \u003cp\u003eUnderstanding the extracted information 159\u003c\/p\u003e \u003cp\u003eTaxonomies 160\u003c\/p\u003e \u003cp\u003ePutting Your Results Together with Structured Data 160\u003c\/p\u003e \u003cp\u003ePutting Big Data to Use 161\u003c\/p\u003e \u003cp\u003eVoice of the customer 161\u003c\/p\u003e \u003cp\u003eSocial media analytics 162\u003c\/p\u003e \u003cp\u003eText Analytics Tools for Big Data 164\u003c\/p\u003e \u003cp\u003eAttensity 164\u003c\/p\u003e \u003cp\u003eClarabridge 165\u003c\/p\u003e \u003cp\u003eIBM 165\u003c\/p\u003e \u003cp\u003eOpenText 165\u003c\/p\u003e \u003cp\u003eSAS 166\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 14: Customized Approaches for Analysis of Big Data 167\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eBuilding New Models and Approaches to Support Big Data 168\u003c\/p\u003e \u003cp\u003eCharacteristics of big data analysis 168\u003c\/p\u003e \u003cp\u003eUnderstanding Different Approaches to Big Data Analysis 170\u003c\/p\u003e \u003cp\u003eCustom applications for big data analysis 171\u003c\/p\u003e \u003cp\u003eSemi-custom applications for big data analysis 173\u003c\/p\u003e \u003cp\u003eCharacteristics of a Big Data Analysis Framework 174\u003c\/p\u003e \u003cp\u003eBig to Small: A Big Data Paradox 177\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart V: Big Data Implementation 179\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 15: Integrating Data Sources 181\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eIdentifying the Data You Need 181\u003c\/p\u003e \u003cp\u003eExploratory stage 182\u003c\/p\u003e \u003cp\u003eCodifying stage 184\u003c\/p\u003e \u003cp\u003eIntegration and incorporation stage 184\u003c\/p\u003e \u003cp\u003eUnderstanding the Fundamentals of Big Data Integration 186\u003c\/p\u003e \u003cp\u003eDefining Traditional ETL 187\u003c\/p\u003e \u003cp\u003eData transformation 188\u003c\/p\u003e \u003cp\u003eUnderstanding ELT — Extract, Load, and Transform 189\u003c\/p\u003e \u003cp\u003ePrioritizing Big Data Quality 189\u003c\/p\u003e \u003cp\u003eUsing Hadoop as ETL 191\u003c\/p\u003e \u003cp\u003eBest Practices for Data Integration in a Big Data World 191\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 16: Dealing with Real-Time Data Streams and Complex Event Processing 193\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eExplaining Streaming Data and Complex Event Processing 194\u003c\/p\u003e \u003cp\u003eUsing Streaming Data 194\u003c\/p\u003e \u003cp\u003eData streaming 195\u003c\/p\u003e \u003cp\u003eThe need for metadata in streams 196\u003c\/p\u003e \u003cp\u003eUsing Complex Event Processing 198\u003c\/p\u003e \u003cp\u003eDifferentiating CEP from Streams 199\u003c\/p\u003e \u003cp\u003eUnderstanding the Impact of Streaming Data and CEP on Business 200\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 17: Operationalizing Big Data 201\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eMaking Big Data a Part of Your Operational Process 201\u003c\/p\u003e \u003cp\u003eIntegrating big data 202\u003c\/p\u003e \u003cp\u003eIncorporating big data into the diagnosis of diseases 203\u003c\/p\u003e \u003cp\u003eUnderstanding Big Data Workflows 205\u003c\/p\u003e \u003cp\u003eWorkload in context to the business problem 206\u003c\/p\u003e \u003cp\u003eEnsuring the Validity, Veracity, and Volatility of Big Data 207\u003c\/p\u003e \u003cp\u003eData validity 207\u003c\/p\u003e \u003cp\u003eData volatility 208\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 18: Applying Big Data within Your Organization 211\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eFiguring the Economics of Big Data 212\u003c\/p\u003e \u003cp\u003eIdentification of data types and sources 212\u003c\/p\u003e \u003cp\u003eBusiness process modifications or new process creation 215\u003c\/p\u003e \u003cp\u003eThe technology impact of big data workflows 215\u003c\/p\u003e \u003cp\u003eFinding the talent to support big data projects 216\u003c\/p\u003e \u003cp\u003eCalculating the return on investment (ROI) from big data investments 216\u003c\/p\u003e \u003cp\u003eEnterprise Data Management and Big Data 217\u003c\/p\u003e \u003cp\u003eDefining Enterprise Data Management 217\u003c\/p\u003e \u003cp\u003eCreating a Big Data Implementation Road Map 218\u003c\/p\u003e \u003cp\u003eUnderstanding business urgency 218\u003c\/p\u003e \u003cp\u003eProjecting the right amount of capacity 219\u003c\/p\u003e \u003cp\u003eSelecting the right software development methodology 219\u003c\/p\u003e \u003cp\u003eBalancing budgets and skill sets 219\u003c\/p\u003e \u003cp\u003eDetermining your appetite for risk 220\u003c\/p\u003e \u003cp\u003eStarting Your Big Data Road Map 220\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 19: Security and Governance for Big Data Environments 225\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eSecurity in Context with Big Data 225\u003c\/p\u003e \u003cp\u003eAssessing the risk for the business 226\u003c\/p\u003e \u003cp\u003eRisks lurking inside big data 226\u003c\/p\u003e \u003cp\u003eUnderstanding Data Protection Options 227\u003c\/p\u003e \u003cp\u003eThe Data Governance Challenge 228\u003c\/p\u003e \u003cp\u003eAuditing your big data process 230\u003c\/p\u003e \u003cp\u003eIdentifying the key stakeholders 231\u003c\/p\u003e \u003cp\u003ePutting the Right Organizational Structure in Place 231\u003c\/p\u003e \u003cp\u003ePreparing for stewardship and management of risk 232\u003c\/p\u003e \u003cp\u003eSetting the right governance and quality policies 232\u003c\/p\u003e \u003cp\u003eDeveloping a Well-Governed and Secure Big Data Environment 233\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart VI: Big Data Solutions in the Real World 235\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 20: The Importance of Big Data to Business 237\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eBig Data as a Business Planning Tool 238\u003c\/p\u003e \u003cp\u003eStage 1: Planning with data 238\u003c\/p\u003e \u003cp\u003eStage 2: Doing the analysis 239\u003c\/p\u003e \u003cp\u003eStage 3: Checking the results 239\u003c\/p\u003e \u003cp\u003eStage 4: Acting on the plan 240\u003c\/p\u003e \u003cp\u003eAdding New Dimensions to the Planning Cycle 240\u003c\/p\u003e \u003cp\u003eStage 5: Monitoring in real time 240\u003c\/p\u003e \u003cp\u003eStage 6: Adjusting the impact 241\u003c\/p\u003e \u003cp\u003eStage 7: Enabling experimentation 241\u003c\/p\u003e \u003cp\u003eKeeping Data Analytics in Perspective 241\u003c\/p\u003e \u003cp\u003eGetting Started with the Right Foundation 242\u003c\/p\u003e \u003cp\u003eGetting your big data strategy started 242\u003c\/p\u003e \u003cp\u003ePlanning for Big Data 243\u003c\/p\u003e \u003cp\u003eTransforming Business Processes with Big Data 244\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 21: Analyzing Data in Motion: A Real-World View 245\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eUnderstanding Companies’ Needs for Data in Motion 246\u003c\/p\u003e \u003cp\u003eThe value of streaming data 247\u003c\/p\u003e \u003cp\u003eStreaming Data with an Environmental Impact 247\u003c\/p\u003e \u003cp\u003eUsing sensors to provide real-time information about rivers and oceans 248\u003c\/p\u003e \u003cp\u003eThe benefits of real-time data 249\u003c\/p\u003e \u003cp\u003eStreaming Data with a Public Policy Impact 249\u003c\/p\u003e \u003cp\u003eStreaming Data in the Healthcare Industry 251\u003c\/p\u003e \u003cp\u003eCapturing the data stream 251\u003c\/p\u003e \u003cp\u003eStreaming Data in the Energy Industry 252\u003c\/p\u003e \u003cp\u003eUsing streaming data to increase energy efficiency 252\u003c\/p\u003e \u003cp\u003eUsing streaming data to advance the production of alternative sources of energy 252\u003c\/p\u003e \u003cp\u003eConnecting Streaming Data to Historical and Other Real-Time Data Sources 253\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 22: Improving Business Processes with Big Data Analytics: A Real-World View 255\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eUnderstanding Companies’ Needs for Big Data Analytics 256\u003c\/p\u003e \u003cp\u003eImproving the Customer Experience with Text Analytics 256\u003c\/p\u003e \u003cp\u003eThe business value to the big data analytics implementation 257\u003c\/p\u003e \u003cp\u003eUsing Big Data Analytics to Determine Next Best Action 257\u003c\/p\u003e \u003cp\u003ePreventing Fraud with Big Data Analytics 260\u003c\/p\u003e \u003cp\u003eThe Business Benefit of Integrating New Sources of Data 262\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart VII: The Part of Tens 263\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 23: Ten Big Data Best Practices 265\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eUnderstand Your Goals 265\u003c\/p\u003e \u003cp\u003eEstablish a Road Map 266\u003c\/p\u003e \u003cp\u003eDiscover Your Data 266\u003c\/p\u003e \u003cp\u003eFigure Out What Data You Don’t Have 267\u003c\/p\u003e \u003cp\u003eUnderstand the Technology Options 267\u003c\/p\u003e \u003cp\u003ePlan for Security in Context with Big Data 268\u003c\/p\u003e \u003cp\u003ePlan a Data Governance Strategy 268\u003c\/p\u003e \u003cp\u003ePlan for Data Stewardship 268\u003c\/p\u003e \u003cp\u003eContinually Test Your Assumptions 269\u003c\/p\u003e \u003cp\u003eStudy Best Practices and Leverage Patterns 269\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 24: Ten Great Big Data Resources 271\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eHurwitz \u0026amp; Associates 271\u003c\/p\u003e \u003cp\u003eStandards Organizations 271\u003c\/p\u003e \u003cp\u003eThe Open Data Foundation 272\u003c\/p\u003e \u003cp\u003eThe Cloud Security Alliance 272\u003c\/p\u003e \u003cp\u003eNational Institute of Standards and Technology 272\u003c\/p\u003e \u003cp\u003eApache Software Foundation 273\u003c\/p\u003e \u003cp\u003eOasis 273\u003c\/p\u003e \u003cp\u003eVendor Sites 273\u003c\/p\u003e \u003cp\u003eOnline Collaborative Sites 274\u003c\/p\u003e \u003cp\u003eBig Data Conferences 274\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 25: Ten Big Data Do’s and Don’ts 275\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eDo Involve All Business Units in Your Big Data Strategy 275\u003c\/p\u003e \u003cp\u003eDo Evaluate All Delivery Models for Big Data 276\u003c\/p\u003e \u003cp\u003eDo Think about Your Traditional Data Sources as Part of Your Big Data Strategy 276\u003c\/p\u003e \u003cp\u003eDo Plan for Consistent Metadata 276\u003c\/p\u003e \u003cp\u003eDo Distribute Your Data 277\u003c\/p\u003e \u003cp\u003eDon’t Rely on a Single Approach to Big Data Analytics 277\u003c\/p\u003e \u003cp\u003eDon’t Go Big Before You Are Ready 277\u003c\/p\u003e \u003cp\u003eDon’t Overlook the Need to Integrate Data 277\u003c\/p\u003e \u003cp\u003eDon’t Forget to Manage Data Securely 278\u003c\/p\u003e \u003cp\u003eDon’t Overlook the Need to Manage the Performance of Your Data 278\u003c\/p\u003e \u003cp\u003eGlossary 279\u003c\/p\u003e \u003cp\u003eIndex 295 \u003c\/p\u003e \u003cp\u003e\u003cb\u003eJudith Hurwitz\u003c\/b\u003e is an expert in cloud computing, information management, and business strategy.\u003c\/p\u003e \u003cp\u003e\u003cb\u003eAlan Nugent\u003c\/b\u003e has extensive experience in cloud-based big data solutions.\u003c\/p\u003e \u003cp\u003e\u003cb\u003eDr. Fern Halper\u003c\/b\u003e specializes in big data and analytics.\u003c\/p\u003e \u003cp\u003e\u003cb\u003eMarcia Kaufman\u003c\/b\u003e specializes in cloud infrastructure, information management, and analytics.\u003c\/p\u003e  \u003cp\u003eLearn to:\u003c\/p\u003e \u003cul\u003e \u003cli\u003eLeverage big data tools and architectures\u003c\/li\u003e \u003cli\u003eExplore how big data can transform your business\u003c\/li\u003e \u003cli\u003eIntegrate structured and unstructured data into your big data environment\u003c\/li\u003e \u003cli\u003eUse predictive analytics to make better decisions\u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003eHere's the guide that can keep big data from becoming a big headache!\u003c\/p\u003e \u003cp\u003eBig data can be a complex concept. \u003ci\u003eFor Dummies\u003c\/i\u003e to the rescue! Here's a plain-English explanation of what big data is (and isn't), the technology and database options supporting it, analytics that help you get meaning from your data, how to manage it, and what it can do for your company. Business executive or IT person, here's what you need to know.\u003c\/p\u003e \u003cul\u003e \u003cli\u003eWhat it is — get your mind around big data from both a technical and business perspective\u003c\/li\u003e \u003cli\u003eOrganize it — meet the big data stack and learn about different architectural levels, operational databases, organizing databases, and analytical data warehouses\u003c\/li\u003e \u003cli\u003eBig data computing model — explore distributed computing as well as the power of virtualization and the cloud\u003c\/li\u003e \u003cli\u003eHadoop and MapReduce — learn the importance of Hadoop and MapReduce for big data analysis\u003c\/li\u003e \u003cli\u003eGet analytical — identify analytics tools for big data and evaluate the various new models that are evolving\u003c\/li\u003e \u003cli\u003eReady? Implement — discover how to implement your big data solution with an eye to operationalizing and protecting your data\u003c\/li\u003e \u003cli\u003eWhat it means — see the importance of big data to your organization and how it's used to solve problems\u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003eOpen the book and find:\u003c\/p\u003e \u003cul\u003e \u003cli\u003eA definition of big data\u003c\/li\u003e \u003cli\u003eProfiles of various available technologies\u003c\/li\u003e \u003cli\u003eThe role of the cloud\u003c\/li\u003e \u003cli\u003eHow MapReduce aids big data management\u003c\/li\u003e \u003cli\u003eWhy Hadoop is so important\u003c\/li\u003e \u003cli\u003eSome specific uses for text analytics\u003c\/li\u003e \u003cli\u003eHow to approach big data security and privacy\u003c\/li\u003e \u003cli\u003eTen best practices for managing big data\u003c\/li\u003e \u003c\/ul\u003e","brand":"For Dummies","offers":[{"title":"Default Title","offer_id":47988810383589,"sku":"NP9781118504222","price":34.99,"currency_code":"USD","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1842\/7735\/files\/9781118504222.jpg?v=1761781678","url":"https:\/\/k12savings.com\/es\/products\/big-data-for-dummies-isbn-9781118504222","provider":"K12savings","version":"1.0","type":"link"}