Big Data Fundamentals(PrenticeHall,2016)

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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 fundame
Big Data Fundamentals Concepts, Drivers Techniques Thomas erl Wajid Khattak, and paul buhler 88 PRENTICE HALL ServiceTech (O) PRE BOSTON· COLUMBUS· INDIANAPOLIS· NEW YORK· SAN FRANCISCO AMSTERDAM· CAPE TOWN· DUBAL· LONDON· MADRID·MLAN· MUNICH PARIS· MONTREAL· TORONTO· DELHI· MEXICO CITY· SAO PAULO SIDNEY· HONG KONG· SEOUL· SINGAPORE· TAIPEI· TOKYO Many of the designations used by manufacturers and sellers to distinguish their products are claimed as trademarks. Where those designations appear in this book, and the publisher was aware of a trademark claim, the designations have been printed with initial capital letters or in all capitals The authors and publisher have taken care in the preparation of this book, but make no expressed or implied warranty of any kind and assume no responsibility for errors or omissions. No liability is assumed for incidental or consequential damages in connection with or arising out of the use of the information or programs contained herein For information about buying this title in bulk quantities, or for special sales opportunities (which may include electronic versions; custom cover designs; and content particular to your business, training goals, marketing focus, or branding interests), please contact our corporate sales department at corpsales(@pearsoned com or(800)382-3419 Forgovernmentsalesinquiriespleasecontactgovernmentsales(@pearsoned.com For questions about sales outside the u. s, please contact international@pearsoned com Visit us on the Web: informit. com/ph Library of congress control Number: 2015953680 Copyright@ 2016 Arcitura Education Inc All rights reserved. Printed in the United States of America. This publication is protected by copyright, and permission must be obtained from the publisher prior to any prohibited reproduction, storage in a retrieval system, or transmission in any form or by any means electronic, mechanical, photocopying, recording, or likewise. For information regarding permissions, request forms and the appropriate contacts within the Pearson Education GlobalRights&PermissionsDepartmentpleasevisitwww.pearsoned.com/permissions/ ISBN-13:978-0-13-429107-9 ISBN-10:0-13-429107-7 Text printed in the United States on recycled paper at rR Donnelley in Crawfordsville, ana First printing December 2015 Editor-in-Chief Mark taub Senior Acquisitions Editor Trina macDonald Managing Editor Kristy hart Senior Project Editor Betsy gratner Copyeditors Natalie gitt Alexandra Kropova Senior indexer Cheryl lenser Proofreaders Alexandra Kropova Debbie williams Publishing Coordinator Olivia basegio Cover Designer Thomas erl Compositor Bumpy Design Graphics Jasper paladino Photos Thomas erl Educational Content Development Arcitura education inc To my family and friends Thomas erl i dedicate this book to my daughters hadia and areesha my wife Natasha, and my parents wajid Khattak I thank my wife and family for their patience and for putting up with my busyness over the years I appreciate all the students and colleagues i have had the privilege of teaching and learning from John 3: 16. 2 Peter 1: 5-8 -Paul buhler phd Contents at a glance PARTI: THE FUNDAMENTALS OF BIG DATA CHAPTER 1: Understanding Big Data CHAPTER 2: Business Motivations and Drivers for Big Data Adoption CHAPTER 3: Big Data Adoption and Planning Considerations CHAPTER 4: Enterprise Technologies and big data Business Intelligence PART: STORING AND ANALYZING BIG DATA C HAPTER :5: Big Data Storage Concepts CHAPTER 6: Big Data Processing Concepts CHAPTER7, Big Data Storage Technology CHAPTER 8 Big Data Analysis Techniques APPENDIX A: Case Study Conclusion about the authors Index Contents Acknowledgments Reader services PAT IA THE FUNDAMENTALS OF BIG DATA CHAPTER 1: Understanding Big Data Concepts and Terminology Datasets Data analysis Data Analytics Descriptive analytics Diagnostic Analytics Predictive Analytics Prescriptive analytics Business intelligence(bi Key performance Indicators(KPD Big data Characteristics Volume Velocity Variety Veracity Value Different Types of Data Structured data Unstructured data Semi-structured data Metadata Case Study background History Technical infrastructure and automation environment Business goals and obstacles Case Study example Identifying data Characteristics Volume Velocity variety Veracity Value Identifying Types of Data CHAPTER 2: Business Motivations and Drivers for Big Data Adoption Marketplace dynamics Business architecture Business Process Management Information and Communications Technology Data Analytics and Data Science Digitization Affordable Technology and commodity hardware Social media Hyper-Connected Communities and Devices Cloud Computing Internet of Everything (loE) Case Study example CHAPTER 3: Big Data Adoption and Planning Considerations Organization prerequisites Data procurement Privacy Security provenance Limited Realtime support Distinct Performance Challenges Distinct Governance requirements Distinct Methodology Clouds Big Data Analytics Lifecycle Business case evaluation Data Identification Data Acquisition and filtering Data extraction Data Validation and Cleansing Data Aggregation and Representation Data Analysis Data visualization Utilization of Analysis Results Case Study example Big Data Analytics Lifecycle Business case evaluation Data ldentification Data Acquisition and Filtering Data extraction Data Validation and Cleansing Data Aggregation and Representation Data Analysis Data visualization Utilization of Analysis Results CHAPTER 4: Enterprise Technologies and Big Data Business Intelligence Online Transaction Processing (OLtp Online Analytical Processing OLAP Extract Transform Load EtL) Data warehouses Data marts raditional bl Ad-hoc Reports Dashboards Big data bl raditional data visualization Data Visualization for Big Data

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