Big Data Fundamentals: Concepts, Drivers & Techniques, 1st edition
Published by Pearson (January 5, 2016) © 2016
- Thomas Erl
- Wajid Khattak
- Paul Buhler
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Big Data Science Fundamentals offers a comprehensive, easy-to-understand, and up-to-date understanding of Big Data for all business professionals and technologists. Leading enterprise technology author Thomas Erl introduces key Big Data concepts, theory, terminology, technologies, key analysis/analytics techniques, and more - all logically organized, presented in plain English, and supported by easy-to-understand diagrams and case study examples.
- Presents vendor-neutral coverage of concepts, theory, terminology, technologies, key analysis/analytics techniques, and more
- Illuminates fundamental and advanced principles with hundreds of images, diagrams, and real case studies
- Clarifies the linkages between Big Data and existing enterprise technologies, analytics capabilities, and business intelligence systems
- Clear, consistent, logically organized, and up-to-date
- The newest title in The Prentice Hall Service Technology Series from Thomas Erl
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Reader Services    xviii
PART I: THE FUNDAMENTALS OF BIG DATA
Chapter 1: Understanding Big Data    3
Concepts and Terminology    5
Datasets    5
Data Analysis    6
Data Analytics    6
Descriptive Analytics    8
Diagnostic Analytics    9
Predictive Analytics    10
Prescriptive Analytics    11
Business Intelligence (BI)Â Â Â Â 12
Key Performance Indicators (KPI)Â Â Â Â 12
Big Data Characteristics    13
Volume    14
Velocity    14
Variety    15
Veracity    16
Value    16
Different Types of Data    17
Structured Data    18
Unstructured Data    19
Semi-structured Data    19
Metadata    20
Case Study Background    20
History    20
Technical Infrastructure and Automation Environment    21
Business Goals and Obstacles    22
Case Study Example    24
Identifying Data Characteristics    26
Volume    26
Velocity    26
Variety    26
Veracity    26
Value    27
Identifying Types of Data    27
Chapter 2: Business Motivations and Drivers for Big Data Adoption    29
Marketplace Dynamics    30
Business Architecture    33
Business Process Management    36
Information and Communications Technology    37
Data Analytics and Data Science    37
Digitization    38
Affordable Technology and Commodity Hardware    38
Social Media    39
Hyper-Connected Communities and Devices    40
Cloud Computing    40
Internet of Everything (IoE)Â Â Â Â 42
Case Study Example    43
Chapter 3: Big Data Adoption and Planning Considerations    47
Organization Prerequisites    49
Data Procurement    49
Privacy    49
Security    50
Provenance    51
Limited Realtime Support    52
Distinct Performance Challenges    53
Distinct Governance Requirements    53
Distinct Methodology    53
Clouds    54
Big Data Analytics Lifecycle    55
Business Case Evaluation    56
Data Identification    57
Data Acquisition and Filtering    58
Data Extraction    60
Data Validation and Cleansing    62
Data Aggregation and Representation    64
Data Analysis    66
Data Visualization    68
Utilization of Analysis Results    69
Case Study Example    71
Big Data Analytics Lifecycle    73
Business Case Evaluation    73
Data Identification    74
Data Acquisition and Filtering    74
Data Extraction    74
Data Validation and Cleansing    75
Data Aggregation and Representation    75
Data Analysis    75
Data Visualization    76
Utilization of Analysis Results    76
Chapter 4: Enterprise Technologies and Big Data Business Intelligence    77
Online Transaction Processing (OLTP)Â Â Â Â 78
Online Analytical Processing (OLAP)Â Â Â Â 79
Extract Transform Load (ETL)Â Â Â Â 79
Data Warehouses    80
Data Marts    81
Traditional BIÂ Â Â Â 82
Ad-hoc Reports    82
Dashboards    82
Big Data BIÂ Â Â Â 84
Traditional Data Visualization    84
Data Visualization for Big Data    85
Case Study Example    86
Enterprise Technology    86
Big Data Business Intelligence    87
PART II: STORING AND ANALYZING BIG DATA
Chapter 5: Big Data Storage Concepts    91
Clusters    93
File Systems and Distributed File Systems    93
NoSQLÂ Â Â Â 94
Sharding    95
Replication    97
Master-Slave    98
Peer-to-Peer    100
Sharding and Replication    103
Combining Sharding and Master-Slave Replication    104
Combining Sharding and Peer-to-Peer Replication    105
CAP Theorem    106
ACIDÂ Â Â Â 108
BASEÂ Â Â Â 113
Case Study Example    117
Chapter 6: Big Data Processing Concepts    119
Parallel Data Processing    120
Distributed Data Processing    121
Hadoop    122
Processing Workloads    122
Batch    123
Transactional    123
Cluster    124
Processing in Batch Mode    125
Batch Processing with MapReduce    125
Map and Reduce Tasks    126
Map    127
Combine    127
Partition    129
Shuffle and Sort    130
Reduce    131
A Simple MapReduce Example    133
Understanding MapReduce Algorithms    134
Processing in Realtime Mode    137
Speed Consistency Volume (SCV)Â Â Â Â 137
Event Stream Processing    140
Complex Event Processing    141
Realtime Big Data Processing and SCVÂ Â Â Â 141
Realtime Big Data Processing and MapReduce    142
Case Study Example    143
Processing Workloads    143
Processing in Batch Mode    143
Processing in Realtime    144
Chapter 7: Big Data Storage Technology    145
On-Disk Storage Devices    147
Distributed File Systems    147
RDBMS Databases    149
NoSQL Databases    152
Characteristics    152
Rationale    153
Types    154
Key-Value    156
Document    157
Column-Family    159
Graph    160
NewSQL Databases    163
In-Memory Storage Devices    163
In-Memory Data Grids    166
Read-through    170
Write-through    170
Write-behind    172
Refresh-ahead    172
In-Memory Databases    175
Case Study Example    179
Chapter 8: Big Data Analysis Techniques    181
Quantitative Analysis    183
Qualitative Analysis    184
Data Mining    184
Statistical Analysis    184
A/B Testing    185
Correlation    186
Regression    188
Machine Learning    190
Classification (Supervised Machine Learning)Â Â Â Â 190
Clustering (Unsupervised Machine Learning)Â Â Â Â 191
Outlier Detection    192
Filtering    193
Semantic Analysis    195
Natural Language Processing    195
Text Analytics    196
Sentiment Analysis    197
Visual Analysis    198
Heat Maps    198
Time Series Plots    200
Network Graphs    201
Spatial Data Mapping    202
Case Study Example    204
Correlation    204
Regression    204
Time Series Plot    205
Clustering    205
Classification    205
Appendix A: Case Study Conclusion    207
About the Authors    211
Thomas Erl    211
Wajid Khattak    211
Paul Buhler    212
Index    213
Wajid Khattak is a Big Data researcher and trainer at Arcitura Education Inc. His areas of interest include Big Data engineering and architecture, data science, machine learning, analytics and SOA. He has extensive .NET software development experience in the domains of business intelligence reporting solutions and GIS.
Wajid completed his MSc in Software Engineering and Security with distinction from Birmingham City University in 2008. Prior to that, in 2003, he earned his BSc (Hons) degree in Software Engineering from Birmingham City University with first-class recognition. He holds MCAD & MCTS (Microsoft), SOA Architect, Big Data Scientist, Big Data Engineer and Big Data Consultant (Arcitura) certifications.
Dr. Paul Buhler is a seasoned professional who has worked in commercial, government and academic environments. He is a respected researcher, practitioner and educator of service-oriented computing concepts, technologies and implementation methodologies. His work in XaaS naturally extends to cloud, Big Data and IoE areas. Dr. Buhler’s more recent work has been focused on closing the gap between business strategy and process execution by leveraging responsive design principles and goal-based execution.
As Chief Scientist at Modus21, Dr. Buhler is responsible for aligning corporate strategy with emerging trends in business architecture and process execution frameworks. He also holds an Affiliate Professorship at the College of Charleston, where he teaches both graduate and undergraduate computer science courses. Dr. Buhler earned his Ph.D. in Computer Engineering at the University of South Carolina. He also holds an MS degree in Computer Science from Johns Hopkins University and a BS in Computer Science from The Citadel.
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