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E-raamat: Big Data: A Tutorial-Based Approach [Taylor & Francis e-raamat]

(DBTech Consultants Inc., Sunnyvale, USA)
  • Formaat: 202 pages, 3 Tables, black and white; 43 Illustrations, black and white
  • Ilmumisaeg: 25-Feb-2019
  • Kirjastus: Chapman & Hall/CRC
  • ISBN-13: 9780429060939
Teised raamatud teemal:
  • Taylor & Francis e-raamat
  • Hind: 82,16 €*
  • * hind, mis tagab piiramatu üheaegsete kasutajate arvuga ligipääsu piiramatuks ajaks
  • Tavahind: 117,37 €
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Big Data: A Tutorial-Based ApproachSuurem pilt
  • Formaat: 202 pages, 3 Tables, black and white; 43 Illustrations, black and white
  • Ilmumisaeg: 25-Feb-2019
  • Kirjastus: Chapman & Hall/CRC
  • ISBN-13: 9780429060939
Teised raamatud teemal:
Taylor & Francis e-raamatudRohkem infot Taylor & Francis e-raamatute kohta
Raamatu kodulehekülg: https://www.taylorfrancis.com/books/9780429060939
Big Data: A Tutorial-Based Approach explores the tools and techniques used to bring about the marriage of structured and unstructured data. It focuses on Hadoop Distributed Storage and MapReduce Processing by implementing (i) Tools and Techniques of Hadoop Eco System, (ii) Hadoop Distributed File System Infrastructure, and (iii) efficient MapReduce processing. The book includes Use Cases and Tutorials to provide an integrated approach that answers the What, How, and Why of Big Data.

Features











Identifies the primary drivers of Big Data





Walks readers through the theory, methods and technology of Big Data





Explains how to handle the 4 Vs of Big Data in order to extract value for better business decision making





Shows how and why data connectors are critical and necessary for Agile text analytics





Includes in-depth tutorials to perform necessary set-ups, installation, configuration and execution of important tasks





Explains the command line as well as GUI interface to a powerful data exchange tool between Hadoop and legacy r-dbms databases
List of Tutorials
xiii
List of Figures/Illustrations
xv
Foreword xvii
Preface xix
Acknowledgements xxiii
Author xxv
Chapter 1 Introduction to Big Data
1(8)
Overview
1(1)
Rapid Growth of Big Data
1(2)
Big Data Definition
3(1)
Big Data Projects
4(1)
Business Value of Big Data
5(4)
Chapter 2 Big Data Implementation
9(24)
Overview
9(1)
High-Level Tasks to Implement Informatica Bdm, Cloudera Hive, and Tableau
10(1)
Big Data Triggers Digital Transformation of the Production Model
11(2)
Big Data Challenges and Associated Use Cases
13(1)
Hadoop Infrastructure: Overview
14(1)
Hadoop Infrastructure: Defined
15(5)
Hyperconverged Hadoop Infrastructure
15(1)
Compute Hardware Components
16(1)
Network Hardware Components
17(2)
Storage Hardware Architecture and Components
19(1)
Hadoop Eco System
20(2)
Hadoop: Jvm Framework
22(1)
Hadoop Distributed File Processing
22(4)
Mapreduce Software
26(1)
Mapreduce Software Installation
27(1)
Mapreduce Processing
28(5)
Chapter 3 Big Data Use Cases
33(6)
Overview
33(1)
Big Data Use Case: Health
33(2)
Big Data Use Case: Manufacturing
35(1)
Big Data Use Case: Insurance
36(3)
Chapter 4 Big Data Migration
39(10)
Overview
39(2)
Challenges In Migrating Oracle Data Using Sqoop
41(1)
Where Is Sqoop Used?
41(1)
Sqoop Commands
42(1)
Hive Arguments Used By Sqoop
43(1)
Apache Sqoop Architecture
44(1)
Apache Sqoop Command Line Interface
45(4)
Chapter 5 Big Data Ingestion, Integration, and Management
49(10)
Overview
49(1)
Informatica: Mature and Comprehensive Big Data Solution
50(2)
Informatica Data Integration
52(7)
Chapter 6 Big Data Repository
59(16)
Overview
59(2)
Data Repository Layer
61(1)
Hive Big Data Warehouse
62(1)
Slowly Changing Dimension In Hive
63(2)
Hive Metadata: Definitions
65(7)
Integrated Use Of Data Integration, Data Management, and Data Visualization Tools
72(3)
Chapter 7 Big Data Visualization
75(28)
Overview
75(8)
Variable Types
83(4)
Numbers
83(2)
Strings
85(1)
Factors
86(1)
Success Factors For Tableau
87(1)
Tableau: Step Forward In Data Analytics
88(5)
Tableau Connectors For Data Sources
93(1)
Tableau Data Engine Tuning
93(7)
Tableau Tuning Features
100(3)
Fast Interactive Query Engine
100(1)
Strategically Utilize Live Connections Versus Extracts
100(1)
Curate Data From The Data Lake
100(1)
Optimize Data Extracts
101(1)
Customize Tableau Connection Performance
102(1)
Chapter 8 Structured and Un-Structured Data Analytics
103(12)
Overview
103(1)
Text Analytics As Means To Extract Value From Un-Structured Data
104(1)
Major Players In Text Analytics
105(2)
Decision Maker
105(1)
Domain Expert
106(1)
Linguist
106(1)
Data Scientists
106(1)
Conclusion
107(1)
From Data To Action
107(7)
Conclusion
114(1)
Chapter 9 Data Virtualization
115(22)
Overview
115(22)
Conclusion: Flexibility and Agility
123(1)
Pre-Installation Steps To Set Up Denodo Development Environment
124(12)
Conclusion
136(1)
Chapter 10 Cloud Computing
137(16)
Overview
137(2)
A Quick Glance At Cloud Computing
139(2)
Software As A Service (Saas)
139(1)
Platform As A Service (Paas)
139(1)
Infrastructure as a Service (Iaas)
140(1)
Cloud Computing Versus Hadoop Processing
141(1)
Cloud Service Most Suited For Big Data
142(1)
Infrastructure As A Service (Iaas)
142(1)
Advantages Of Iaas
143(1)
Conclusion
143(2)
Self-Assessment Quiz
145(8)
Answers To The Self-Assessment Quiz 153(10)
References 163(4)
Index 167
Nasir Raheem is an accomplished, innovative, and results-driven project manager, architect and business analyst with over 20 years of wide-ranging experience encompassing I.T Infra-structure design, planning and implementation of highly integrated systems that included Big Data (HIVE) Database Administration, Business Re-engineering, Asset & Data management (ServiceNow), Data Integration, Data Modeling, Disaster Recovery and ERP Database /Application cloning projects. He is an experienced manager of IT projects related to multi-billion dollar corporate mergers, migration, server upgrades, database upgrades, data conversion, cloning and integration of supply chain management ERP and CRM application modules at Wells Fargo Bank, WebTV (now Microsoft) and Hitachi Global Storage Technologies (now Western Digital). He is also a published author and instructor of an online course approved by Harvard University Innovation Lab, March towards Big Data.
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