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Handbook of Research on Cloud Infrastructures for Big Data Analytics

Book Description

Clouds are being positioned as the next-generation consolidated, centralized, yet federated IT infrastructure for hosting all kinds of IT platforms and for deploying, maintaining, and managing a wider variety of personal, as well as professional applications and services. Handbook of Research on Cloud Infrastructures for Big Data Analytics focuses exclusively on the topic of cloud-sponsored big data analytics for creating flexible and futuristic organizations. This book helps researchers and practitioners, as well as business entrepreneurs, to make informed decisions and consider appropriate action to simplify and streamline the arduous journey towards smarter enterprises.

Table of Contents

  1. Cover
  2. Title Page
  3. Copyright Page
  4. Book Series
  5. Editorial Advisory Board and List of Reviewers
    1. Editorial Advisory Board
  6. Foreword
  7. Preface
  8. Chapter 1: The IT Readiness for the Digital Universe
    1. ABSTRACT
    2. INTRODUCTION
    3. ENVISIONING THE DIGITAL UNIVERSE
    4. BIG DATA IN 2020
    5. BIG DATA ANALYTICS: THE IT INFRASTRUCTURE CHARACTERISTICS
    6. CONCLUSION
    7. ACKNOWLEDGMENT
    8. REFERENCES
  9. Chapter 2: Big Data Computing and the Reference Architecture
    1. ABSTRACT
    2. INTRODUCTION
    3. BIG DATA ANALYTICS
    4. BIG DATA TECHNOLOGY (ECKERSON, 2012)
    5. BIG DATA ANALYSIS IN CLOUD: STORAGE, NETWORK AND SERVER CHALLENGES (SCARPATI, 2012)
    6. A WIDE RANGE OF DATA ANALYTICS
    7. THE BIG DATA REFERENCE ARCHITECTURE (BDRA)
    8. CHALLENGES AND OPPORTUNITIES WITH BIG DATA ANALYTICS AGRAWAL, BARBARA, BERNSTEIN, BERTINO, DAVIDSON, DAYAL, ET AL., 2012)
    9. CONCLUSION
    10. REFERENCES
    11. KEY TERMS AND DEFINITIONS
  10. Chapter 3: Big Data Analytics Demystified
    1. ABSTRACT
    2. INTRODUCTION
    3. THE UNWRAPPING OF BIG DATA COMPUTING
    4. BIG DATA CHARACTERISTICS
    5. WHY BIG DATA COMPUTING?
    6. BIG DATA CONCERNS AND CHALLENGES
    7. INTRODUCING BIG DATA ANALYTICS
    8. BIG DATA ANALYTICS FRAMEWORKS AND INFRASTRUCTURE
    9. BIG DATA ANALYTICS USE CASES
    10. TRADITIONAL DW ANALYTICS VS. BIG DATA ANALYTICS
    11. MACHINE DATA ANALYTICS BY SPLUNK
    12. BIG DATA MIDDLEWARE SOLUTIONS
    13. A DETAILED LOOK ON DATA INTEGRATION
    14. BIG DATA VISUALIZATION
    15. SUMMARY
    16. REFERENCES
    17. ADDITIONAL READING
    18. KEY TERMS AND DEFINITIONS
  11. Chapter 4: The Compute Infrastructures for Big Data Analytics
    1. ABSTRACT
    2. INTRODUCTION
    3. THE BIG DATA COMPUTING DISTINCTIONS
    4. BIG DATA ANALYTICS: THE ARCHITECTURAL SOLUTIONS
    5. BIG DATA ANALYTICS: THE PROMINENT TECHNIQUES AND APPROACHES
    6. BIG DATA ANALYTICS: THE INFRASTRUCTURAL CHALLENGES
    7. THE RENAISSANCE OF CLOUD INFRASTRUCTURES
    8. THE GENERIC AND SPECIFIC CLOUD INFRASTRUCTURES
    9. TAKING BIG DATA ANALYTICS TO CLOUDS (KRISHNA & VARMA, 2012)
    10. CONCLUSION
    11. REFERENCES
  12. Chapter 5: Storage Infrastructure for Big Data and Cloud
    1. ABSTRACT
    2. INTRODUCTION
    3. CHAPTER ORGANIZATION
    4. INTRODUCTION TO CLOUD STORAGE
    5. STORAGE VIRTUALIZATION
    6. REFERENCES
    7. ADDITIONAL READING
    8. KEY TERMS AND DEFINITIONS
  13. Chapter 6: Big Data Architecture
    1. ABSTRACT
    2. INTRODUCTION
    3. TECHNICAL AND TECHNOLOGICAL ADVANCEMENTS
    4. PROGRAMMING ENVIRONMENT FOR BIG DATA
    5. APACHE HADOOP
    6. HADOOP DISTRIBUTED FILE SYSTEM
    7. YARN (MAP REDUCE 2)
    8. NEO4J
    9. GraphChi
    10. TRINITY
    11. CONCLUSION
    12. REFERENCES
    13. ADDITIONAL READING
    14. KEY TERMS AND DEFINITIONS
  14. Chapter 7: The Network Infrastructures for Big Data Analytics
    1. ABSTRACT
    2. INTRODUCTION
    3. ABOUT CLOUD INFRASTRUCTURES
    4. THE EMERGENCE OF CLOUD DATA CENTERS
    5. DESCRIBING CLOUD NETWORKING
    6. EXPLAINING NETWORK VIRTUALIZATION
    7. BIG DATA ANALYTICS: THE NETWORK INFRASTRUCTURE REQUIREMENTS AND CHALLENGES
    8. THE NETWORK APPROACHES FOR BIG DATA ANALYTICS
    9. SOFTWARE-DEFINED CLOUD NETWORKING
    10. CONCLUSION
    11. REFERENCES
  15. Chapter 8: NoSQL Databases
    1. ABSTRACT
    2. INTRODUCTION
    3. NoSQL FEATURES
    4. INDUSTRY PRACTICES IN NoSQL DATABASE
    5. NoSQL CAP ANALYSIS
    6. TWO OUT OF THREE DILEMMAS
    7. NoSQL AND BIG DATA
    8. NoSQL DATABASE CHALLENGES
    9. AT THE FOREFRONT
    10. CONCLUSION
    11. REFERENCES
    12. ADDITIONAL READING
    13. KEY TERMS AND DEFINITIONS
  16. Chapter 9: Cloud Database Systems
    1. ABSTRACT
    2. INTRODUCTION
    3. CLOUD DATABASE
    4. COMPONENTS AND ARCHITECTURE
    5. DATA MODELS FOR CLOUD DATABASES
    6. SQL DATABASES
    7. NOSQL DATABASES
    8. HYBRID SQL-NOSQL DATABASES
    9. FUTURE RESEARCH DIRECTIONS
    10. CONCLUSION
    11. REFERENCES
    12. ADDITIONAL READING
    13. KEY TERMS AND DEFINITIONS
  17. Chapter 10: Driving Big Data with Hadoop Technologies
    1. ABSTRACT
    2. 1. INTRODUCTION
    3. 2. WHAT IS BIG DATA?
    4. 3. HADOOP
    5. 4. HBASE
    6. 5. HIVE
    7. 6. SQOOP
    8. 7. PIG
    9. 8. LIST OF HADOOP TECHNOLOGIES
    10. 9. CONCLUSION
    11. 10. FUTURE RESEARCH DIRECTIONS
    12. REFERENCES
    13. ADDITIONAL READING
    14. KEY TERMS AND DEFINITIONS
  18. Chapter 11: Integrating Heterogeneous Data for Big Data Analysis
    1. ABSTRACT
    2. INTRODUCTION
    3. BACKGROUND: BUSINESS ANALYTICS WITH THEIR CHALLENGES
    4. Solution: Data Virtualisation
    5. FUTURE TRENDS
    6. CONCLUSION
    7. REFERENCES
    8. ADDITIONAL READING
    9. KEY TERMS AND DEFINITIONS
  19. Chapter 12: An Overview on the Virtualization Technology
    1. ABSTRACT
    2. INTRODUCTION
    3. FULL VIRTUALIZATION
    4. PARAVIRTUALIZATION
    5. Xen Limitations
    6. KVM Virtualization
    7. VMware
    8. Hyper-V
    9. CONCLUSION
    10. REFERENCES
    11. KEY TERMS AND DEFINITIONS
  20. Chapter 13: Data Visualization
    1. ABSTRACT
    2. INTRODUCTION
    3. HISTORY OF DATA VISUALIZATION
    4. BENEFITS OF VISUALIZATION
    5. CONCLUSION
    6. REFERENCES
  21. Chapter 14: Significance of In-Memory Computing for Real-Time Big Data Analytics
    1. ABSTRACT
    2. INTRODUCTION
    3. REAL TIME ANALYTICS AND IN-MEMORY COMPUTING
    4. IN-MEMORY DATABASE
    5. TYPES OF IN-MEMORY SYSTEMS
    6. POPULAR IMDS
    7. FEATURES OF IMDS STUDIED
    8. LIMITATIONS OF IN-MEMORY DATABASES
    9. FUTURE OF IN-MEMORY COMPUTING
    10. RAMCloud
    11. CONCLUSION
    12. REFERENCES
    13. KEY TERMS AND DEFINITIONS
  22. Chapter 15: Big Data Predictive and Prescriptive Analytics
    1. ABSTRACT
    2. INTRODUCTION
    3. PREDICTIVE ANALYTICS
    4. PRESCRIPTIVE ANALYTICS
    5. BIG DATA ANALYTICS
    6. IN-DATABASE ANALYTICS
    7. ANALYTICES AS A SERVICE (AaaS)
    8. CONCLUSION
    9. REFERENCES
    10. KEY TERMS AND DEFINITIONS
  23. Chapter 16: A Survey of Big Data Analytics Systems
    1. ABSTRACT
    2. INTRODUCTION
    3. CATEGORIES OF ANALYTICAL PROCESSING SYSTEMS
    4. TRANSACTIONAL RELATIONAL DATABASE MANAGEMENT SYSTEMS
    5. HADOOP DISTRIBUTIONS
    6. NOSQL DATABASES
    7. ANALYTIC PLATFORMS
    8. BIG DATA PLATFORM
    9. SURVEY ON BIG DATA PLATFORMS
    10. IBM BIG DATA PLATFORM
    11. IBM NETEZZA ANALYTICS: HIGH PERFORMANCE ANALYTIC PLATFORM
    12. GREENPLUM UNIFIED ANALYTICS PLATFORM: THE ANSWER TO AGILE (EMC PERSPECTIVE PURSUING THE AGILE ENTERPRISE, 2012; & A WHITE PAPER FROM EMC: BIG DATA AS A SERVICE, 2013)
    13. HP VERTICA PLATFORM WITH ‘R’
    14. SAs PLATFORMS FOR HIGH PERFORMANCE
    15. SAP HANA–IN-MEMORY COMPUTING PLATFORM
    16. 1010 DATA OFFERS BIG DATA AS A SERVICE: CLOUD-BASED BIG DATA ANALYTICS PLATFORM
    17. INTELLICUS: POWER TO UNDERSTAND YOUR BUSINESS (BUSINESS INSIGHTS PLATFORM)
    18. INFORMATICA: A PLATFORM POWERED BY POWERED VBM
    19. TERADATA ASTER’S BIG ANALYTICS APPLIANCE
    20. ORACLE BIG DATA APPLIANCE
    21. KOGNITIO OFFERS THREE APPLIANCE SPEEDS AND VIRTUAL CUBES
    22. MICROSOFT APPLIANCE SCALES OUT SQL SERVER WITH PDW
    23. SAND TECHNOLOGY – COLUMNAR SYSTEMS: A WORLD’S HIGHEST PERFORMING ENTERPRISE ANALYTIC DATABASE PLATFORM
    24. INFOBRIGHT CUTS DBA LABOR AND QUERY TIMES
    25. PARACCEL COMBINES COLUMN-STORE, MPP AND IN-DATABASE ANALYTICS
    26. ALPINE DATA LAB: PREDICTIVE ANALYTICS BUILT FOR BIG DATA
    27. CONNECTING TO ORACLE
    28. SAS AND IBM ARE UNSHAKEABLE LEADERS, WHILE NEWCOMER SAP PERFORMS WELL
    29. ALTERYX DESKTOP-TO-CLOUD SOLUTIONS: THE NEW APPROACH TO STRATEGIC ANALYTICS SOLUTION
    30. TOOLS/PRODUCTS
    31. GREENPLUM IS NOW PIVOTAL-A NEW PLATFORM FOR THE NEW ERA
    32. IBM WEAVES BROCADE INTO BIG DATA FABRIC
    33. IMPETUS ECOSYSTEM
    34. GREENPLUM CHORUS: PRODUCTIVITY ENGINE
    35. ACTIAN DATARUSH: ANALYTICS ENGINE FOR PARALLEL DATA PROCESSING (ANALYTICS ENGINE FOR PARALLEL DATA PROCESSING: ACTIAN DATARUSH, 2013)
    36. SPLUNK ENGINE
    37. BIG DATA ANALYTICS IN NETWORK
    38. MAVERICK FABRIC
    39. JUNIPER NETWORKS
    40. BIG DATA ANALYTICS CAN BOOST NETWORK SECURITY
    41. WAN OPTIMIZATION FOR BIG DATA AND BIG DATA ANALYTICS
    42. VENDORS WHO ARE LESS KNOWN BUT DOES A GREAT JOB IN BIG DATA ANALYTICS
    43. CONCLUSION
    44. REFERENCES
    45. KEY TERMS AND DEFINITIONS
  24. Chapter 17: Middleware for Preserving Privacy in Big Data
    1. ABSTRACT
    2. INTRODUCTION
    3. TOP TEN BIG DATA SECURITY AND PRIVACY CHALLENGES
    4. PRINCIPLES THAT GOVERN PRIVACY
    5. BIG DATA: PRIVACY SOLUTIONS
    6. PRIVACY PRESERVING MIDDLEWARE ARCHITECTURE FOR BIG DATA ANALYTICS
    7. BEST PRACTICES
    8. SUMMARY AND CONCLUSION
    9. REFERENCES
    10. ADDITIONAL READING
    11. KEY TERMS AND DEFINITIONS
  25. Chapter 18: Accessing Big Data in the Cloud Using Mobile Devices
    1. ABSTRACT
    2. INTRODUCTION
    3. GENERATION AND ACQUISITION
    4. STORAGE AND PROCESSING
    5. ACCESSING BIG DATA THROUGH MOBILE DEVICES
    6. USING A CLOUDLET AS AN ACCELERATOR
    7. SUMMARY
    8. ACKNOWLEDGMENT
    9. REFERENCES
    10. KEY TERMS AND DEFINITIONS
  26. Chapter 19: Medical Data Analytics in the Cloud Using Homomorphic Encryption
    1. Abstract
    2. INTRODUCTION
    3. BACKGROUND INFORMATION
    4. THE DESIGN OF A CLOUD-BASED MEDICAL APPLICATION
    5. PERFORMANCE EVALUATION
    6. CONCLUSION AND FUTURE WORK
    7. ACKNOWLEDGMENT
    8. REFERENCES
    9. KEY TERMS AND DEFINITIONS
  27. Chapter 20: Bioinformatics Clouds for High-Throughput Technologies
    1. ABSTRACT
    2. INTRODUCTION
    3. CURIOSITIES
    4. CHALLENGES AND SOLUTIONS
    5. CLOUD TYPES
    6. FUTURE DIRECTIONS
    7. REFERENCES
    8. ADDITIONAL READING
    9. KEY TERMS AND DEFINITIONS
  28. Chapter 21: Green Cloud Computing
    1. ABSTRACT
    2. INTRODUCTION
    3. ENERGY EFFICIENCY
    4. GREEN COMPUTING
    5. POWER-AWARE COMPUTING
    6. BEST PRACTICE
    7. CAN CLOUD COMPUTING IMPROVE ENERGY EFFICIENCY?
    8. DESIGN OF CLOUD COMPUTING
    9. TOTAL POWER CONSUMPTION
    10. SIMULATION
    11. RESULT
    12. CONCLUSION
    13. REFERENCES
    14. KEY TERMS AND DEFINITIONS
    15. ENDNOTES
  29. Compilation of References
  30. About the Contributors