Big Data A Tutorial-Based Approach

203 Pages·2019·4.228 MB·English

by  Nasir Raheem

Big Data
A Tutorial-Based Approach

Big Data
A Tutorial-Based Approach
Nasir Raheem
CRC Press
Taylor & Francis Group
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Library of Congress Cataloging-in-Publication Data
Names: Raheem, Nasir, author.
Title: Big data : a tutorial-based approach / Nasir Raheem.
Description: First edition. | Boca Raton, FL : Taylor & Francis
Group, [2019] | Includes bibliographical references and index.
Identifiers: LCCN 2018060975| ISBN 9780367183455 (hardback :
acid-free paper) | ISBN 9780429060939 (ebook)
Subjects: LCSH: Big data--Programmed instruction.
Classification: LCC QA76.9.B45 R34 2019 | DDC 005.7--dc23
LC record available at https://lccn.loc.gov/2018060975
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Dedicated to my wife, Saba, for
her support and endurance.

Contents
List of Tutorials, xiii
List of Figures/Illustrations, xv
Foreword, xvii
Preface, xix
Acknowledgements, xxiii
Author, xxv
Chapter 1 ◾ I ntroduction to Big Data 1
OVERVIEW 1
RAPID GROWTH OF BIG DATA 1
BIG DATA DEFINITION 3
BIG DATA PROJECTS 4
BUSINESS VALUE OF BIG DATA 5
Chapter 2 ◾ B ig Data Implementation 9
OVERVIEW 9
HIGH-LEVEL TASKS TO IMPLEMENT INFORMATICA
BDM, CLOUDERA HIVE, AND TABLEAU 10
BIG DATA TRIGGERS DIGITAL TRANSFORMATION
OF THE PRODUCTION MODEL 11
vii
viii ◾ Contents
BIG DATA CHALLENGES AND ASSOCIATED USE
CASES 13
HADOOP INFRASTRUCTURE: OVERVIEW 14
HADOOP INFRASTRUCTURE: DEFINED 15
Hyperconverged Hadoop Infrastructure 15
Compute Hardware Components 16
Network Hardware Components 17
Storage Hardware Architecture and Components 19
HADOOP ECO SYSTEM 20
HADOOP: JVM FRAMEWORK 22
HADOOP DISTRIBUTED FILE PROCESSING 22
MapReduCe SOFTWARE 26
MapReduCe SOFTWARE INSTALLATION 27
MapReduCe PROCESSING 28
Chapter 3 ◾ B ig Data Use Cases 33
OVERVIEW 33
BIG DATA USE CASE: HEALTH 33
BIG DATA USE CASE: MANUFACTURING 35
BIG DATA USE CASE: INSURANCE 36
Chapter 4 ◾ B ig Data Migration 39
OVERVIEW 39
CHALLENGES IN MIGRATING ORACLE DATA
USING SQOOP 41
WHERE IS SQOOP USED? 41
SQOOP COMMANDS 42
HIVE ARGUMENTS USED BY SQOOP 43
APACHE SQOOP ARCHITECTURE 44
APACHE SQOOP COMMAND LINE INTERFACE 45
Contents   ◾   ix
Chapter 5 ◾ B ig Data Ingestion, Integration, and
Management 49
OVERVIEW 49
INFORMATICA: MATURE AND COMPREHENSIVE
BIG DATA SOLUTION 50
INFORMATICA DATA INTEGRATION 52
Chapter 6 ◾ B ig Data Repository 59
OVERVIEW 59
DATA REPOSITORY LAYER 61
HIVE BIG DATA WAREHOUSE 62
SLOWLY CHANGING DIMENSION IN HIVE 63
HIVE METADATA: DEFINITIONS 65
INTEGRATED USE OF DATA INTEGRATION, DATA
MANAGEMENT, AND DATA VISUALIZATION TOOLS 72
Chapter 7 ◾ B ig Data Visualization 75
OVERVIEW 75
VARIABLE TYPES 83
Numbers 83
Strings 85
Factors 86
SUCCESS FACTORS FOR TABLEAU 87
TABLEAU: STEP FORWARD IN DATA ANALYTICS 88
TABLEAU CONNECTORS FOR DATA SOURCES 93
TABLEAU DATA ENGINE TUNING 93
TABLEAU TUNING FEATURES 100
Fast Interactive Query Engine 100
Strategically Utilize Live Connections versus Extracts 100
Curate Data from the Data Lake 100