Big Data Science Fundamentals: Concepts, Drivers, and Techniques : 9780134291079

Big Data Science Fundamentals: Concepts, Drivers, and Techniques

 
Edition
 
1
ISBN
 
9780134291079
ISBN 10
 
0134291077
Published
 
05/01/2016
Published by
 
Pearson Higher Ed USA
Pages
 
240
Format
 
Available on demand
 
Title type
Book
$54.99
 
 
Title type
 
$37.99
 
 
Description

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.

Erl provides a uniquely valuable methodology for Big Data analysis, and introduces the underlying analysis techniques and enabling technological constructs that constitute a Big Data solution environment. He presents vendor-neutral guidance on implementing Big Data for competitive advantage; and for successfully integrating Big Data with existing enterprise systems. Coverage includes:

  • Big Data's fundamental concepts and key business/technology drivers
  • "5 V" characteristics of data in Big Data environments: volume, velocity, variety, veracity, and value
  • Types of Big Data: structured, unstructured, semi-structured, and meta-data
  • Big Data's relationships with OLTP, OLAP, ETL, data warehouses, and data marts
  • Fundamental types of analysis, analytics, and machine learning
  • Requirements and tools for visualizing big data
  • Adoption and planning: business cases, privacy, security, provenance, performance, governance, and more
  • Big Data technologies, including clusters, NoSQL, distributed and parallel data processing, Hadoop, cloud computing, and storage
  • Big Data analysis and analytics across the full lifecycle
  • And much more
Table of contents
Part I. Big Data Fundamentals
1. Understanding Big Data Terminology and Concepts
2. Big Data Business and Technology Drivers
3. Characteristics of Data in Big Data Environments
4. Types of Data in Big Data Environments
5. Traditional Enterprise Technologies Related to Big Data
6. Fundamental Analysis, Analytics and Machine Learning Types
7. Business Intelligence and Data Visualization
8. Adoption and Planning Considerations

Part II. Big Data Technology
9. Concepts
10. Mechanisms

Part III. Big Data Analysis and Analytics
11. Big Data Analysis Lifecycle
12. Fundamental Big Data Analysis and Analytics Techniques
Features & benefits
  • 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
Author biography
Thomas Erl is a top-selling IT author, founder of Arcitura Education and series editor of the Prentice Hall Service Technology Series from Thomas Erl. With more than 200,000 copies in print worldwide, his books have become international bestsellers and have been formally endorsed by senior members of major IT organizations, such as IBM, Microsoft, Oracle, Intel, Accenture, IEEE, HL7, MITRE, SAP, CISCO, HP and many others. As CEO of Arcitura Education Inc., Thomas has led the development of curricula for the internationally recognized Big Data Science Certified Professional (BDSCP), Cloud Certified Professional (CCP) and SOA Certified Professional (SOACP) accreditation programs, which have established a series of formal, vendor-neutral industry certifications obtained by thousands of IT professionals around the world. Thomas has toured more than 20 countries as a speaker and instructor. More than 100 articles and interviews by Thomas have been published in numerous publications, including The Wall Street Journal and CIO Magazine.

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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