Big Data Analytics : From Strategic Planning to Enterprise Integration with Tools, Techniques, NoSQL, and Graph.

Format:

Book

Author/Creator:

Loshin, David.

Language:

English

Subjects (All):

Strategic planning.

Physical Description:

1 online resource (143 pages)

Edition:

1st ed.

Place of Publication:

San Diego : Elsevier Science & Technology, 2013.

Contents:

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.

Notes:

Description based on publisher supplied metadata and other sources.

Other Format:

Print version: Loshin, David Big Data Analytics

ISBN:

9780124186644

OCLC:

857276853