Verfügbarkeit: Big data beyond the hype | Technische Universität München

Big data beyond the hype: a guide to conversations for today's data center

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Bibliographische Detailangaben
Beteilige Person:	Zikopoulos, Paul (VerfasserIn)
Format:	Buch
Sprache:	Englisch
Veröffentlicht:	New York [u.a.] Mc Graw Hill Education 2015
Schlagwörter:	Big Data
Links:	http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=027729120&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA
Umfang:	XXXIV, 356 S. Ill., graph. Darst.
ISBN:	9780071844659 0071844651

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Datensatz im Suchindex

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adam_text	CONTENTS AT A GLANCE PARTI Opening Conversations About Big Data 1 Getting Hype out or the Way: Big Data and Beyond .... 3 2 To SQL or Not to SQL: That s Not the Question, It s the Era of Polyglot Persistence ................. 31 3 Composing Cloud Applications: Why We Love the Bluemix and the IBM Cloud ...... 59 4 The Data Zones Model: A New Approach to Managing Data ................ 97 PART II Watson Foundations 5 Starting Out with a Solid Base: A Tour of Watson Foundations .................... 123 6 Landing Your Data in Style with Blue Suit Hadoop: InfoSphere Biglnsights ........................... 131 7 In the Moment Analytics: InfoSphere Streams ....... 179 8 700 Million Times Faster Than the Blink of an Eye: BLU Acceleration ................................ 209 9 An Expert Integrated System for Deep Analytics ....... 249 10 Build More, Grow More, Sleep More: IBM Cloudant .... 269 ix PART III Calming the Waters: Big Data Governance 11 Guiding Principles ror Data Governance .............. 303 12 Security Is NOT an Afterthought .................... 309 13 Big Data Lifecycle Management ..................... 329 14 Matching at Scale: Big Match ........................ 343 CONTENTS Foreword .............................................. xix Acknowledgments ..................................... xxi Introduction ........................................... xxiii PART I Opening Conversations About Big Data Getting Hype out oř the Way: Big Data and Beyond .... 3 There s Gold in Them There Hills! .................. 3 Why Is Big Data Important? ......................... 5 Brought to You by the Letter V: How We Define Big Data ....................... 8 Cognitive Computing .............................. 12 Why Does the Big Data World Need Cognitive Computing? .................... 15 A Big Data and Analytics Plattorm Manifesto ........... 17 1. Discover, Explore, and Navigate Big Data Sources .... 18 2. Land, Manage, and Store Huge Volumes of Any Data ...................... 20 3. Structured and Controlled Data .................... 21 4. Manage and Analyze Unstructured Data ............ 22 5. Analyze Data in Real Time ........................ 24 6. A Rich Library of Analytical Functions and Tools ..... 24 7. Integrate and Govern All Data Sources .............. 26 Cognitive Computing Systems 27 Of Cloud and Manifestos ............................. 27 Wrapping It Up ....................................... 28 To SQL or Not to SQL: That s Not the Question, It s the Era of Polyglot Persistence ................... 31 Core Value Systems: What Makes a NoSQL Practitioner Tick ............................ 33 What Is NoSQL? ...................................... 36 Is HadiHłp a NoSQL Database? ....................... 38 Xl xii Contents Different Strokes for Different Folks: The NoSQL Classification System ...................... 39 Give Me a Key, I ll Give You a Value: The Key/Value Store .............................. 39 The Grand-Daddy of Them All: The Document Store ----- 40 Column Family, Columnar Store, or BigTable Derivatives: What Do We Call You? ......... 46 Don t Underestimate the Underdog: The Graph Store ... 47 From ACID to CAP ................................... 54 CAP Theorem and a Meatloaf Song: Two Out oř Three Ain t Bad ...................... 55 Let Me Get This Straight: There Is SQL, NoSQL, and Now NewSQL? ............ 57 Wrapping It Up ....................................... 58 Composing Cloud Applications: Why We Love the Bluemix and the IBM Cloud .............. 59 At Your Service: Explaining Cloud Provisioning Models ........................... 61 Sotting a Foundation for the Cloud: Infrastructure as a Service ............................ 64 IaaS for Tomorrow...Available Today: IBM SortLayer Powers the IBM Cloud ............... 67 Noisy Neighbors Can Be Bad Neighbors: The Multitcnant Cloud ............................ 69 Building the Developer s Sandbox with Platform as a Service ............................ 71 If You Have Only a Couple of Minutes: PaaS and IBM Bluemix in a Nutshell ................. 71 Digging Deeper into PaaS ........................... 74 Being Social on the Cloud: How Bluemix Integrates Platforms and Architectures ............. 76 Understanding the Hybrid Cloud: Playing Frankenstein Without the Horror ............ 77 Tried and Tested: How Déployable Patterns Simplify PaaS . . . .......................... 79 Composing the Fabric of Cloud Services: IBM Bluemix ..... 82 Parting Words on Platform as a Service ................ 85 Consuming Functionality Without the Stress: Software as a Senice ................... g5 The Cloud Bazaar: SaaS and the API Economy ......... 87 Demolishing the Barrier to Entry for Cloud-Ready Analytics: IBM s dashDB ................ 89 Contents xiii Build More, Grow More, Know More: dashDB s Cloud SaaS .............................. 92 Refinery as a Service ................................ 93 Wrapping It Up ....................................... 94 4 The Data Zones Model: A New Approach to Managing Data ................ 97 Challenges with the Traditional Approach ................ 100 Agility ............................................ 100 Cost .............................................. 101 Depth of Insight .................................... 102 Next-Generation Information Management Architectures .... 103 Prepare for Touchdown: The Landing Zone ............ 104 Into the Unknown: The Exploration Zone ............. 105 Into the Deep: The Deep Analytic Zone ............... 108 Curtain Call: The New Staging Zone .................. 109 You Have Questions? We Have Answers! The Queryable Archive Zone ....................... 112 In Big Data We Trust: The Trusted Data Zone .......... 115 A Zone for Business Reporting ....................... 115 From Forecast to Nowcast: The Real-Time Processing and Analytics Zone ........ 116 Ladies and Gentlemen, Presenting... The Data Zones Model ............................. 1 IS PART II Watson Foundations 5 Starting Out with a Solid Base: A Tour of Watson Foundations .................... 123 Overview or Watson Foundations ....................... 124 A Continuum of Analytics Capabilities: Foundations for Watson ............................ 126 β Landing Your Data in Style with Blue Suit Hadoop: InfoSphere Biglnsights ........................... 131 Where Do Elephants Come From: What Is Hadoop? ....... 133 A Brief History or Hadoop ........................... 135 Components of Hadoop and Related Projects 136 Open Source, and Proud of It 137 Making Analytics on Hadoop Fasy 141 xiv Contents The Real Deal for SQL on Hadoop: Big SQL ............ 142 Machine Learning for the Masses: Big R and SystemML .............................. 147 The Advanced Text Analytics Toolkit ................. 149 Data Discovery and Visualization: BigSheets ........... 152 Spatiotemporal Analytics ............................ 154 Finding Needles in Haystacks of Needles: Indexing and Search in Biglnsights .................. 155 Cradle-to-Grave Application Development Support ................................ 155 The Biglnsights Integrated Development Environment ......................... 156 The Biglnsights Application Lifecycle ................. 157 An App Store for Hadoop: Easy Deployment and Execution of Custom Applications .................. 159 Keeping the Sandbox Tidy: Sharing and Managing Hadoop ....................... 160 The Biglnsights Web Console ........................ 160 Monitoring the Aspects of Your Cluster ............... 161 Securing the Biglnsights for Hadoop Cluster ........... 162 Adaptive MapReduce ............................... 163 A Flexible File System for Hadoop: GPFS-FPO ......... 164 Playing Nice: Integration with Other Data Center Systems ........................... 167 IBM InfoSphere System z Connector for Hadoop ....... 168 IBM PureData System for Analytics ................... 168 InfoSphere Streams for Data in Motion ................ 169 InfoSphere Information Server for Data Integration ............................... 170 Matching at Scale with Big Match .................... 170 Securing Hadoop with Guardium and Optim .......... 171 Broad Integration Support ........................... 171 Deployment Flexibility ........................ 172 Biglnsights Editions: Free, Low-Cost, and Premium Offerings ................... 172 A Low-Cost Way to Get Started: Running Biglnsights on the Cloud .................. 174 Higher-Class Hardware: Power and System z Support ............. 176 Contents xv Get Started Quickly! ................................... 177 Wrapping It Up ....................................... 177 7 In the Moment Analytics: InfoSphere Streams ....... 179 Introducing Streaming Data Analysis .................... 179 How InfoSphere Streams Works ........................ 181 A Simple Streams Application ....................... 182 Recommended Uses for Streams ..................... 184 How Is Streams Different from CEP Systems? .......... 184 Stream Processing Modes: Preserve Currency or Preserve Each Record ........................... 185 High Availability ................................... 185 Dynamically Distributed Processing .................. 187 InfoSphere Streams Platform Components ............... 188 The Streams Console ............................... 188 An Integrated Development Environment for Streams: Streams Studio ........................ 191 The Streams Processing Language .................... 145 Source and Sink Adapters ........................... 198 Analytical Operators ................................ 194 Streams Toolkits ................................... 202 Solution Accelera tors ............................... 205 Use Cases ............................................ 205 Get Started Quickly! ................................... 207 Wrapping It Up ....................................... 207 8 700 Million Times Faster Than the Blink or an Eye: BLU Acceleration ................................ 209 What Is BLU Acceleration7 ............................. 214 What D<h»s a Next Generation Databast1 Service for Analytics Look Like? .................... 214 Seamlessly Integrated ............................... 217 Hardware Optimized ............................... 217 Convince Me to Take BLU Acceleration for a Test Drive ...................................... 21S Pedal Uy the Floor: How Fa>t Is BLU Acceleration1 ...... 219 From Minimized to Minuscule: BLU Acceleration Compression K*itu>s 220 Where Will I Use BLU Acceleration? ..................... 222 xvi Contents How BLU Acceleration Carne to Be: Seven Big Ideas ..................................... Big Idea #1: KISS It. ................................. 225 Big Idea #2. Actionable Compression and Computer-Friendly Encoding ................... 226 Big Idea #3: Multiplying the Power of the CPU ......... 229 Big Idea #4: Parallel Vector Processing ................ 231 Big Idea #5: Get Organized...by Column .............. 232 Big Idea #6: Dynamic In-Memory Processing ........... 234 Big Idea #7: Data Skipping ........................... 236 How Seven Big Ideas Optimize the Hardware Stack .... 237 The Sum of All Big Ideas: BLU Acceleration in Action ... 238 DB2 with BLU Acceleration Shadow Tables: When OLTP + OLAP = 1 DB .......................... 241 What Lurks in These Shadows Isn t Anything to Be Scared of: Operational Reporting ............... 241 Wrapping It Up ....................................... 246 9 An Expert Integrated System for Deep Analytics ....... 249 Before We Begin: Bursting into the Cloud ................ 252 Starting on the Whiteboard: Netezza s Design Principles . . . 253 Appliance Simplicity: Minimize the Human Effort ...... 254 Process Analytics Closer to the Data Store ............. 254 Balanced + MPP = Linear Scalability .................. 255 Modular Design: Support Flexible Configurations and Extreme Scalability .............. 255 What s in the Box? The Netezza Appliance Architecture Overview ............................... 256 A Look Inside a Netezza Box ......................... 257 How a Query Runs in Netezza ....................... 261 How Netezza Is a Platform for Analytics .............. 264 Wrapping It Up ....................................... 266 10 Build More, Grow More, Sleep More: IBM Cloudant ____ 269 Cloudant: White Glove Database as a Service ........... 271 Where Did Cloudant Roll in From? ................... 276 Cloudant or Hadoop? ....................... 278 Being Flexible: Schemas with JSON ................ 279 Cloudant Clustering: Scaling for the Cloud ............ 279 Contents xvii Avoiding Mongo-Size Outages: Sleep Soundly with Cloudant Replication ............................ 281 Cloudant Sync Brings Data to a Mobile World .......... 283 Make Data, Not War: Cloudant Versioning and Conflict Resolution .............................. 284 Unlocking GIS Data with Cloudant Geospatial ............ 287 Cloudant Local ....................................... 289 Here on In: For Techies ................................. 290 For Techies: Leveraging the Cloudant Primary Index .... 290 Exploring Data with Cloudant s Secondary Index Views .......................... 292 Performing Ad Hoc Queries with the Cloudant Search Index ......................... 293 Parameters That Govern a Logical Cloudant Database .... 294 Remember! Cloudant Is DBaaS ....................... 298 Wrapping It Up ....................................... 299 PART III Calming the Waters: Big Data Governance 11 Guiding Principles for Data Governance .............. 303 The IBM Data Governance Council Maturity Model ....... 304 Wrapping It Up ....................................... 308 12 Security Is NOT an Afterthought .................... 304 Security Big Data: How It s Different .................... 3I0 Securing Big Data in Hadoop ........................... 313 Culture, Definition, Charter, Foundation, and Data Governance ............................. 314 What Is Sensitive Data? ............................. 31 Я The Masquerade Gala: Masking Sensitive Data ......... 316 Don t Break the DAM: Monitoring and Controlling Access to Data ..................... 323 Protecting Data at Rest .............................. 325 Wrapping It Up ....................................... 32h 13 Big Data Lifecycle Management ..................... 324 A Foundation for Data Governance: The Information Governance Catalog 330 Data on Demand: Data Click ........................... 333 xviii Contents Data Integration ...................................... 334 Data Quality ......................................... 337 Veracity as a Service: IBM Data Works .................... 338 Managing Your Test Data: Optim Test Data Management ... 341 A Retirement Home for Your Data: Optim Data Archive .... 341 Wrapping It Up ....................................... 342 14 Matching at Scale: Big Match ........................ 343 What Is Matching Anyway? ............................ 344 A Teaser: Where Are You Going to Use Big Match? ...... 345 Matching on Hadoop ............................... 346 Matching Approaches .............................. 347 Big Match Architecture ................................ 348 Big Match Algorithm Configuration Files .............. 349 Big Match Applications ............................. 350 HBase Tables ...................................... 350 Probabilistic Matching Engine ....................... 350 How It Works ........................................ 351 Extract ............................................ 352 Search ............................................ 352 Applications for Big Match ............................. 353 Enabling the Landing Zone .......................... 353 Enhanced 360- Degree View of Your Customers ......... 353 More Reliable Data Exploration ...................... 354 Large-Sea le Searches for Matching Records ............ 355 Wrapping It Up ....................................... 356
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title	Big data beyond the hype a guide to conversations for today's data center
title_auth	Big data beyond the hype a guide to conversations for today's data center
title_exact_search	Big data beyond the hype a guide to conversations for today's data center
title_full	Big data beyond the hype a guide to conversations for today's data center Paul Zikopoulos ...
title_fullStr	Big data beyond the hype a guide to conversations for today's data center Paul Zikopoulos ...
title_full_unstemmed	Big data beyond the hype a guide to conversations for today's data center Paul Zikopoulos ...
title_short	Big data beyond the hype
title_sort	big data beyond the hype a guide to conversations for today s data center
title_sub	a guide to conversations for today's data center
topic	Big Data (DE-588)4802620-7 gnd
topic_facet	Big Data
url	http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=027729120&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA
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