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Big Data Analytics

Book Description

Big Data Analytics will assist managers in providing an overview of the drivers for introducing big data technology into the organization and for understanding the types of business problems best suited to big data analytics solutions, understanding the value drivers and benefits, strategic planning, developing a pilot, and eventually planning to integrate back into production within the enterprise.



  • Guides the reader in assessing the opportunities and value proposition
  • Overview of big data hardware and software architectures
  • Presents a variety of technologies and how they fit into the big data ecosystem

Table of Contents

  1. Cover image
  2. Title page
  3. Table of Contents
  4. Copyright
  5. Foreword
  6. Preface
    1. Introduction
    2. The Challenge of Adopting New Technology
    3. What This Book Is
    4. Why You Should Be Reading This Book
    5. Our Approach to Knowledge Transfer
    6. Contact Me
  7. Acknowledgments
  8. Chapter 1. Market and Business Drivers for Big Data Analytics
    1. 1.1 Separating the Big Data Reality from Hype
    2. 1.2 Understanding the Business Drivers
    3. 1.3 Lowering the Barrier to Entry
    4. 1.4 Considerations
    5. 1.5 Thought Exercises
  9. Chapter 2. Business Problems Suited to Big Data Analytics
    1. 2.1 Validating (Against) the Hype: Organizational Fitness
    2. 2.2 The Promotion of the Value of Big Data
    3. 2.3 Big Data Use Cases
    4. 2.4 Characteristics of Big Data Applications
    5. 2.5 Perception and Quantification of Value
    6. 2.6 Forward Thinking About Value
    7. 2.7 Thought Exercises
  10. Chapter 3. Achieving Organizational Alignment for Big Data Analytics
    1. 3.1 Two Key Questions
    2. 3.2 The Historical Perspective to Reporting and Analytics
    3. 3.3 The Culture Clash Challenge
    4. 3.4 Considering Aspects of Adopting Big Data Technology
    5. 3.5 Involving the Right Decision Makers
    6. 3.6 Roles of Organizational Alignment
    7. 3.7 Thought Exercises
  11. Chapter 4. Developing a Strategy for Integrating Big Data Analytics into the Enterprise
    1. 4.1 Deciding What, How, and When Big Data Technologies Are Right for You
    2. 4.2 The Strategic Plan for Technology Adoption
    3. 4.3 Standardize Practices for Soliciting Business User Expectations
    4. 4.4 Acceptability for Adoption: Clarify Go/No-Go Criteria
    5. 4.5 Prepare the Data Environment for Massive Scalability
    6. 4.6 Promote Data Reuse
    7. 4.7 Institute Proper Levels of Oversight and Governance
    8. 4.8 Provide a Governed Process for Mainstreaming Technology
    9. 4.9 Considerations for Enterprise Integration
    10. 4.10 Thought Exercises
  12. Chapter 5. Data Governance for Big Data Analytics: Considerations for Data Policies and Processes
    1. 5.1 The Evolution of Data Governance
    2. 5.2 Big Data and Data Governance
    3. 5.3 The Difference with Big Datasets
    4. 5.4 Big Data Oversight: Five Key Concepts
    5. 5.5 Considerations
    6. 5.6 Thought Exercises
  13. Chapter 6. Introduction to High-Performance Appliances for Big Data Management
    1. 6.1 Use Cases
    2. 6.2 Storage Considerations: Infrastructure Bedrock for the Data Lifecycle
    3. 6.3 Big Data Appliances: Hardware and Software Tuned for Analytics
    4. 6.4 Architectural Choices
    5. 6.5 Considering Performance Characteristics
    6. 6.6 Row- Versus Column-Oriented Data Layouts and Application Performance
    7. 6.7 Considering Platform Alternatives
    8. 6.8 Thought Exercises
  14. Chapter 7. Big Data Tools and Techniques
    1. 7.1 Understanding Big Data Storage
    2. 7.2 A General Overview of High-Performance Architecture
    3. 7.3 HDFS
    4. 7.4 MapReduce and YARN
    5. 7.5 Expanding the Big Data Application Ecosystem
    6. 7.6 Zookeeper
    7. 7.7 HBase
    8. 7.8 Hive
    9. 7.9 Pig
    10. 7.10 Mahout
    11. 7.11 Considerations
    12. 7.12 Thought Exercises
  15. Chapter 8. Developing Big Data Applications
    1. 8.1 Parallelism
    2. 8.2 The Myth of Simple Scalability
    3. 8.3 The Application Development Framework
    4. 8.4 The MapReduce Programming Model
    5. 8.5 A Simple Example
    6. 8.6 More on Map Reduce
    7. 8.7 Other Big Data Development Frameworks
    8. 8.8 The Execution Model
    9. 8.9 Thought Exercises
  16. Chapter 9. NoSQL Data Management for Big Data
    1. 9.1 What is NoSQL?
    2. 9.2 “Schema-less Models”: Increasing Flexibility for Data Manipulation
    3. 9.3 Key–Value Stores
    4. 9.4 Document Stores
    5. 9.5 Tabular Stores
    6. 9.6 Object Data Stores
    7. 9.7 Graph Databases
    8. 9.8 Considerations
    9. 9.9 Thought Exercises
  17. Chapter 10. Using Graph Analytics for Big Data
    1. 10.1 What Is Graph Analytics?
    2. 10.2 The Simplicity of the Graph Model
    3. 10.3 Representation as Triples
    4. 10.4 Graphs and Network Organization
    5. 10.5 Choosing Graph Analytics
    6. 10.6 Graph Analytics Use Cases
    7. 10.7 Graph Analytics Algorithms and Solution Approaches
    8. 10.8 Technical Complexity of Analyzing Graphs
    9. 10.9 Features of a Graph Analytics Platform
    10. 10.10 Considerations: Dedicated Appliances for Graph Analytics
    11. 10.11 Thought Exercises
  18. Chapter 11. Developing the Big Data Roadmap
    1. 11.1 Introduction
    2. 11.2 Brainstorm: Assess the Need and Value of Big Data
    3. 11.3 Organizational Buy-In
    4. 11.4 Build the Team
    5. 11.5 Scoping and Piloting a Proof of Concept
    6. 11.6 Technology Evaluation and Preliminary Selection
    7. 11.7 Application Development, Testing, Implementation Process
    8. 11.8 Platform and Project Scoping
    9. 11.9 Big Data Analytics Integration Plan
    10. 11.10 Management and Maintenance
    11. 11.11 Assessment
    12. 11.12 Summary and Considerations
    13. 11.13 Thought Exercises