Big Data Fundamentals: Concepts, Drivers & Techniques

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

Inhaltsverzeichnis:

Acknowledgments     xvii
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)     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