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Big Data Fundamentals: Concepts, Drivers & Techniques

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Big Data Fundamentals: Concepts, Drivers & Techniques

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  • 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


  • Copyright 2016
  • Dimensions: 7" x 9-1/8"
  • Pages: 240
  • Edition: 1st
  • Book
  • ISBN-10: 0-13-429107-7
  • ISBN-13: 978-0-13-429107-9

“This text should be required reading for everyone in contemporary business.”
--Peter Woodhull, CEO, Modus21

“The one book that clearly describes and links Big Data concepts to business utility.”
--Dr. Christopher Starr, PhD

“Simply, this is the best Big Data book on the market!”
--Sam Rostam, Cascadian IT Group

“...one of the most contemporary approaches I’ve seen to Big Data fundamentals...”
--Joshua M. Davis, PhD

The Definitive Plain-English Guide to Big Data for Business and Technology Professionals

Big Data Fundamentals provides a pragmatic, no-nonsense introduction to Big Data. Best-selling IT author Thomas Erl and his team clearly explain key Big Data concepts, theory and terminology, as well as fundamental technologies and techniques. All coverage is supported with case study examples and numerous simple diagrams.

The authors begin by explaining how Big Data can propel an organization forward by solving a spectrum of previously intractable business problems. Next, they demystify key analysis techniques and technologies and show how a Big Data solution environment can be built and integrated to offer competitive advantages.

  • Discovering Big Data’s fundamental concepts and what makes it different from previous forms of data analysis and data science
  • Understanding the business motivations and drivers behind Big Data adoption, from operational improvements through innovation
  • Planning strategic, business-driven Big Data initiatives
  • Addressing considerations such as data management, governance, and security
  • Recognizing the 5 “V” characteristics of datasets in Big Data environments: volume, velocity, variety, veracity, and value
  • Clarifying Big Data’s relationships with OLTP, OLAP, ETL, data warehouses, and data marts
  • Working with Big Data in structured, unstructured, semi-structured, and metadata formats
  • Increasing value by integrating Big Data resources with corporate performance monitoring
  • Understanding how Big Data leverages distributed and parallel processing
  • Using NoSQL and other technologies to meet Big Data’s distinct data processing requirements
  • Leveraging statistical approaches of quantitative and qualitative analysis
  • Applying computational analysis methods, including machine learning


Author's Site

Please visit the author's site at http://servicetechbooks.com/

Sample Content

Online Sample Chapter

Big Data Adoption and Planning Considerations

Sample Pages

Download the sample pages (includes Chapter 3 and Index)

Table of Contents

Acknowledgments     xvii
Reader Services     xviii
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)     12
Key Performance Indicators (KPI)     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)     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)     78
Online Analytical Processing (OLAP)     79
Extract Transform Load (ETL)     79
Data Warehouses     80
Data Marts     81
Traditional BI     82
Ad-hoc Reports     82
Dashboards     82
Big Data BI     84
Traditional Data Visualization     84
Data Visualization for Big Data     85
Case Study Example     86
Enterprise Technology     86
Big Data Business Intelligence     87
Chapter 5: Big Data Storage Concepts     91

Clusters     93
File Systems and Distributed File Systems     93
NoSQL     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
ACID     108
BASE     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)     137
Event Stream Processing     140
Complex Event Processing     141
Realtime Big Data Processing and SCV     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)     190
Clustering (Unsupervised Machine Learning)     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


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