Big Data Analytics - 1st Edition - ISBN: 9780124173194, 9780124186644

Big Data Analytics

1st Edition

From Strategic Planning to Enterprise Integration with Tools, Techniques, NoSQL, and Graph

Authors: David Loshin
Paperback ISBN: 9780124173194
eBook ISBN: 9780124186644
Imprint: Morgan Kaufmann
Published Date: 30th August 2013
Page Count: 142
Tax/VAT will be calculated at check-out
Compatible Not compatible
VitalSource PC, Mac, iPhone & iPad Amazon Kindle eReader
ePub & PDF Apple & PC desktop. Mobile devices (Apple & Android) Amazon Kindle eReader
Mobi Amazon Kindle eReader Anything else

Institutional Access


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.

Key Features

  • 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


Line-of-business managers who want to solve their problems with big data analytics.

Table of Contents




The Challenge of Adopting New Technology

What This Book Is

Why You Should Be Reading This Book

Our Approach to Knowledge Transfer

Contact Me


Chapter 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

Chapter 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

Chapter 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

Chapter 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

Chapter 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

5.5 Considerations

5.6 Thought Exercises

Chapter 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

Chapter 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

Chapter 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

Chapter 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

Chapter 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

Chapter 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


No. of pages:
© Morgan Kaufmann 2013
Morgan Kaufmann
eBook ISBN:
Paperback ISBN:

About the Author

David Loshin

David Loshin is President of Knowledge Integrity, Inc., a company specializing in data management consulting. The author of numerous books on performance computing and data management, including “Master Data Management" (2008) and “Business Intelligence – The Savvy Manager’s Guide" (2003), and creator of courses and tutorials on all facets of data management best practices, David is often looked to for thought leadership in the information management industry.

Affiliations and Expertise

President, Knowledge Integrity Incorporated, Silver Spring, MD, USA


The teachings in this book go beyond technologies, skills and processes. Each chapter’s "thought exercises" challenge you to consider technology, business and management concepts in the context of your organization. These questions will help you evaluate next steps for making the technologies valuable to you.

-Michael Goldberg, editor in chief, Data Informed (