Front Cover -- Big Data Analytics: From Strategic Planning to Enterprise Integration with Tools, Techniques, NoSQL, and Graph -- Copyright Page -- Contents -- Foreword -- Preface -- Introduction -- The Challenge of Adopting New Technology -- What This Book Is -- Why You Should Be Reading This Book -- Our Approach to Knowledge Transfer -- Contact Me -- Acknowledgments -- 1 Market and Business Drivers for Big Data Analytics -- 1.1 Separating the Big Data Reality from Hype -- 1.2 Understanding the Business Drivers -- 1.3 Lowering the Barrier to Entry -- 1.4 Considerations -- 1.5 Thought Exercises -- 2 Business Problems Suited to Big Data Analytics -- 2.1 Validating (Against) the Hype: Organizational Fitness -- 2.2 The Promotion of the Value of Big Data -- 2.3 Big Data Use Cases -- 2.4 Characteristics of Big Data Applications -- 2.5 Perception and Quantification of Value -- 2.6 Forward Thinking About Value -- 2.7 Thought Exercises -- 3 Achieving Organizational Alignment for Big Data Analytics -- 3.1 Two Key Questions -- 3.2 The Historical Perspective to Reporting and Analytics -- 3.3 The Culture Clash Challenge -- 3.4 Considering Aspects of Adopting Big Data Technology -- 3.5 Involving the Right Decision Makers -- 3.6 Roles of Organizational Alignment -- 3.7 Thought Exercises -- 4 Developing a Strategy for Integrating Big Data Analytics into the Enterprise -- 4.1 Deciding What, How, and When Big Data Technologies Are Right for You -- 4.2 The Strategic Plan for Technology Adoption -- 4.3 Standardize Practices for Soliciting Business User Expectations -- 4.4 Acceptability for Adoption: Clarify Go/No-Go Criteria -- 4.5 Prepare the Data Environment for Massive Scalability -- 4.6 Promote Data Reuse -- 4.7 Institute Proper Levels of Oversight and Governance -- 4.8 Provide a Governed Process for Mainstreaming Technology.

4.9 Considerations for Enterprise Integration -- 4.10 Thought Exercises -- 5 Data Governance for Big Data Analytics: Considerations for Data Policies and Processes -- 5.1 The Evolution of Data Governance -- 5.2 Big Data and Data Governance -- 5.3 The Difference with Big Datasets -- 5.4 Big Data Oversight: Five Key Concepts -- 5.4.1 Managing Consumer Data Expectations -- 5.4.2 Identifying the Critical Dimensions of Data Quality -- 5.4.3 Consistency of Metadata and Reference Data for Entity Extraction -- 5.4.4 Repurposing and Reinterpretation -- 5.4.5 Data Enrichment and Enhancement -- 5.5 Considerations -- 5.6 Thought Exercises -- 6 Introduction to High-Performance Appliances for Big Data Management -- 6.1 Use Cases -- 6.2 Storage Considerations: Infrastructure Bedrock for the Data Lifecycle -- 6.3 Big Data Appliances: Hardware and Software Tuned for Analytics -- 6.4 Architectural Choices -- 6.5 Considering Performance Characteristics -- 6.6 Row- Versus Column-Oriented Data Layouts and Application Performance -- 6.7 Considering Platform Alternatives -- 6.8 Thought Exercises -- 7 Big Data Tools and Techniques -- 7.1 Understanding Big Data Storage -- 7.2 A General Overview of High-Performance Architecture -- 7.3 HDFS -- 7.4 Mapreduce and Yarn -- 7.5 Expanding the Big Data Application Ecosystem -- 7.6 Zookeeper -- 7.7 HBase -- 7.8 Hive -- 7.9 Pig -- 7.10 Mahout -- 7.11 Considerations -- 7.12 Thought Exercises -- 8 Developing Big Data Applications -- 8.1 Parallelism -- 8.2 The Myth of Simple Scalability -- 8.3 The Application Development Framework -- 8.4 The Mapreduce Programming Model -- 8.5 A Simple Example -- 8.6 More on Map Reduce -- 8.7 Other Big Data Development Frameworks -- 8.8 The Execution Model -- 8.9 Thought Exercises -- 9 NoSQL Data Management for Big Data -- 9.1 What is NoSQL?.

9.2 "Schema-less Models": Increasing Flexibility for Data Manipulation -- 9.3 Key-Value Stores -- 9.4 Document Stores -- 9.5 Tabular Stores -- 9.6 Object Data Stores -- 9.7 Graph Databases -- 9.8 Considerations -- 9.9 Thought Exercises -- 10 Using Graph Analytics for Big Data -- 10.1 What Is Graph Analytics? -- 10.2 The Simplicity of the Graph Model -- 10.3 Representation as Triples -- 10.4 Graphs and Network Organization -- 10.5 Choosing Graph Analytics -- 10.6 Graph Analytics Use Cases -- 10.7 Graph Analytics Algorithms and Solution Approaches -- 10.8 Technical Complexity of Analyzing Graphs -- 10.9 Features of a Graph Analytics Platform -- 10.10 Considerations: Dedicated Appliances for Graph Analytics -- 10.11 Thought Exercises -- 11 Developing the Big Data Roadmap -- 11.1 Introduction -- 11.2 Brainstorm: Assess the Need and Value of Big Data -- 11.3 Organizational Buy-In -- 11.4 Build the Team -- 11.5 Scoping and Piloting a Proof of Concept -- 11.6 Technology Evaluation and Preliminary Selection -- 11.7 Application Development, Testing, Implementation Process -- 11.8 Platform and Project Scoping -- 11.9 Big Data Analytics Integration Plan -- 11.10 Management and Maintenance -- 11.11 Assessment -- 11.12 Summary and Considerations -- 11.13 Thought Exercises.