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Big Data Imperatives: Enterprise 'Big Data' Warehouse, 'BI' Implementations and Analytics

Book Description

Big Data Imperatives, focuses on resolving the key questions on everyone's mind: Which data matters? Do you have enough data volume to justify the usage? How you want to process this amount of data? How long do you really need to keep it active for your analysis, marketing, and BI applications?

Big data is emerging from the realm of one-off projects to mainstream business adoption; however, the real value of big data is not in the overwhelming size of it, but more in its effective use.

This book addresses the following big data characteristics:

  • Very large, distributed aggregations of loosely structured data - often incomplete and inaccessible

  • Petabytes/Exabytes of data

  • Millions/billions of people providing/contributing to the context behind the data

  • Flat schema's with few complex interrelationships

  • Involves time-stamped events

  • Made up of incomplete data

  • Includes connections between data elements that must be probabilistically inferred

Big Data Imperatives explains 'what big data can do'. It can batch process millions and billions of records both unstructured and structured much faster and cheaper. Big data analytics provide a platform to merge all analysis which enables data analysis to be more accurate, well-rounded, reliable and focused on a specific business capability.

Big Data Imperatives describes the complementary nature of traditional data warehouses and big-data analytics platforms and how they feed each other. This book aims to bring the big data and analytics realms together with a greater focus on architectures that leverage the scale and power of big data and the ability to integrate and apply analytics principles to data which earlier was not accessible.

This book can also be used as a handbook for practitioners; helping them on methodology,technical architecture, analytics techniques and best practices. At the same time, this book intends to hold the interest of those new to big data and analytics by giving them a deep insight into the realm of big data.

What you'll learn

  • Understanding the technology, implementation of big data platforms and their usage for analytics

  • Big data architectures

  • Big data design patterns

  • Implementation best practices

Who this book is for

This book is designed for IT professionals, data warehousing, business intelligence professionals, data analysis professionals, architects, developers and business users.

Table of Contents

  1. Title Page
  2. Contents at a Glance
  3. Contents
  4. Preface
  5. About the Authors
  6. About the Technical Reviewer
  7. Acknowledgments
  8. Introduction
  9. CHAPTER 1: “Big Data” in the Enterprise
    1. Search at Scale
    2. Multimedia Content
    3. Sentiment Analysis
    4. Enriching and Contextualizing Data
    5. Data Discovery or Exploratory Analytics
    6. Operational Analytics or Embedded Analytics
    7. Realizing Opportunities from Big Data
    8. Taming the “Big Data”
    9. End Points
    10. References
  10. CHAPTER 2: The New Information Management Paradigm
    1. What Is Enterprise Information Management?
    2. New Approach to Enterprise Information Management for Big Data
    3. Implications of Big Data to Enterprise IT?
    4. End Points
    5. References
  11. CHAPTER 3: Big Data Implications for Industry
    1. The Opportunity
    2. Big Data Use Cases by Industry Vertical
    3. End Points
    4. References
  12. CHAPTER 4: Emerging Database Landscape
    1. The Database Evolution
    2. The Scale-Out Architecture
    3. Database Workloads
    4. Database Technologies for Managing the Workloads
    5. Columnar Databases
    6. Requirements for the Next Generation Data Warehouses
    7. Polyglot Persistence: The Next Generation Database Architecture
    8. End Points
    9. References
  13. CHAPTER 5: Application Architectures for Big Data and Analytics
    1. Big Data Warehouse and Analytics
    2. Big Data Warehouse System Requirements and Hybrid Architectures
    3. Enterprise Data Platform Ecosystem – BDW and EDW
    4. How does Traditional Data Warehouse processes map to tools in Hadoop Environment?
    5. How Hadoop Works
    6. The Hadoop Suitability Test
    7. Additional Considerations for Big Data Warehouse (BDW)
    8. Big Data and Master Data Management (MDM)
    9. Data Quality Implications for Big Data
    10. Putting it all Together – A Conceptual BDW Architecture
    11. End Points
    12. References
  14. CHAPTER 6: Data Modeling Approaches for Big Data and Analytics Solutions
    1. Understanding Data Integration Patterns
    2. Big Data Workload Design Approaches
    3. Map-Reduce Patterns, Algorithms, and Use Cases
    4. NoSQL Data Modeling Techniques
    5. End Points
    6. References
  15. CHAPTER 7: Big Data Analytics Methodology
    1. Challenges in Big Data Analysis
    2. Big Data Analytics Methodology
    3. Analyze and Evaluate Business Use Case
    4. Develop Business Hypotheses
    5. End Points
    6. References
  16. CHAPTER 8: Extracting Value From Big Data: In-Memory Solutions, Real Time Analytics, And Recommendation Systems
    1. Building a Recommendation System
    2. End Points
    3. References
  17. CHAPTER 9: Data Scientist
    1. The New Skill: Data Scientist
    2. The Big Data Workflow
    3. Design Principles for Contextualizing Big Data
    4. A Day in the Life of a Data Scientist
    5. End Points
    6. Test-1: “Resonant Story Telling” Test:
    7. Test-2: The “String of Pearls” Test:
    8. Test-3: “Needle Movement” Test:
    9. Test-4: “Sniff The Domain Out” Test:
    10. Test-5: “Actionability” Test:
    11. Test-6: “Use Case Curation” Test:
    12. Test-7: The “North Pole” Test:
    13. Test-8: The “What do You See” Test:
    14. References
  18. Index