Big Data Analytics From Strategie Planning to Enterprise Integration with Tools, Techniques, NoSQL, and Graph

Transcription

1 Big Data Analytics From Strategie Planning to Enterprise Integration with Tools, Techniques, NoSQL, and Graph David Loshin ELSEVIER AMSTERDAM BOSTON HEIDELBERG LONDON NEW YORK OXFORD PARIS SAN DIEGO SAN FRANCISCO SINGAPORE SYDNEY TOKYO Morgan Kaufmann is an imprint of Elsevier M<

2 Foreword Preface Acknowledgments ix xiii xxi Chapter 1 Market and Business Drivers for Big Data Analytics Separating the Big Data Reality from Hype Understanding the Business Drivers Lowering the Barrier to Entry Considerations Thought Exercises 9 Chapter 2 Business Problems Suited to Big Data Analytics Validating (Against) the Hype: Organizational Fitness The Promotion of the Value of Big Data Big Data Use Cases Characteristics of Big Data Applications Perception and Quantification of Value Forward Thinking About Value Thought Exercises 19 Chapter 3 Achieving Organizational Alignment for Big Data Analytics Two Key Questions The Historical Perspective to Reporting and Analytics The Culture Clash Challenge Considering Aspects of Adopting Big Data Technology Involving the Right Decision Makers Roles of Organizational Alignment Thought Exercises 28

3 vi Contents Chapter 4 Developing a Strategy for Integrating Big Data Analytics into the Enterprise Deciding What, How, and When Big Data Technologies Are Right for You The Strategic Plan for Technology Adoption Standardize Practices for Soliciting Business User Expectations Acceptability for Adoption: Clarify Go/No-Go Criteria Prepare the Data Environment for Massive Scalability Promote Data Reuse Institute Proper Levels of Oversight and Governance Provide a Governed Process for Mainstreaming Technology Considerations for Enterprise Integration Thought Exercises 37 Chapter 5 Data Governance for Big Data Analytics: Considerations for Data Policies and Processes The Evolution of Data Governance Big Data and Data Governance The Difference with Big Datasets Big Data Oversight: Five Key Concepts Considerations Thought Exercises 48 Chapter 6 Introduction to High-Performance Appliances for Big Data Management Use Cases Storage Considerations: Infrastructure Bedrock for the Data Lifecycle Big Data Appliances: Hardware and Software Tuned for Analytics Architectural Choices Considering Performance Characteristics Row- Versus Column-Oriented Data Layouts and Application Performance 55

4 Contents vii 6.7 Considering Platform Alternatives Thought Exercises 59 Chapter 7 Big Data Tools and Techniques Understanding Big Data Storage A General Overview of High-Performance Architecture HDFS MapReduce and YARN Expanding the Big Data Application Ecosystem Zookeeper HBase Hive Pig Mahout Considerations Thought Exercises 71 Chapter 8 Developing Big Data Applications Parallelism The Myth of Simple Scalability The Application Development Framework The MapReduce Programming Model A Simple Example More on Map Reduce Other Big Data Development Frameworks The Execution Model Thought Exercises 81 Chapter 9 NoSQL Data Management for Big Data What is NoSQL? "Schema-less Models": Increasing Flexibility for Data Manipulation Key-Value Stores Document Stores 86

5 viii Contents 9.5 Tabular Stores Object Data Stores Graph Databases Considerations Thought Exercises 89 Chapter 10 Using Graph Analytics for Big Data What Is Graph Analytics? The Simplicity of the Graph Model Representation as Triples Graphs and Network Organization Choosing Graph Analytics Graph Analytics Use Cases Graph Analytics Algorithms and Solution Approaches Technical Complexity of Analyzing Graphs Features of a Graph Analytics Platform Considerations: Dedicated Appliances for Graph Analytics Thought Exercises 103 Chapter 11 Developing the Big Data Roadmap Introduction Brainstorm: Assess the Need and Value of Big Data Organizational Buy-In Build the Team Scoping and Piloting a Proof of Concept Ill 11.6 Technology Evaluation and Preliminary Selection Application Development, Testing, Implementation Process Platform and Project Scoping Big Data Analytics Integration Plan Management and Maintenance Assessment Summary and Considerations Thought Exercises 120