Big Data Analytics
From Strategic Planning to Enterprise Integration with Tools, Techniques, NoSQL, and Graph
- 1st Edition - August 30, 2013
- Latest edition
- Author: David Loshin
- Language: English
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 busine… Read more
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
Line-of-business managers who want to solve their problems with big data analytics.
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
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
"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 (www.data-informed.com)
- Edition: 1
- Latest edition
- Published: August 30, 2013
- Language: English
DL
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, USARead Big Data Analytics on ScienceDirect