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Big data : a tutorial-based approach / Nasir Raheem.

By: Raheem, Nasir [author.]Material type: TextTextSeries: Publisher: Boca Raton : CRC Press, [2019]Copyright date: ©2019Edition: First editionDescription: 1 online resource : illustrationsContent type: text Media type: computer Carrier type: online resourceISBN: 9780429060939; 0429060939; 9780429590511; 0429590512; 9780429588570; 0429588577; 9780429592454; 0429592450Subject(s): Big data -- Programmed instruction | COMPUTERS / Databases / General | COMPUTERS / General | COMPUTERS / Data Processing / General | COMPUTERS / Database Management / GeneralDDC classification: 005.7 LOC classification: QA76.9.B45Online resources: Taylor & Francis | OCLC metadata license agreement
Contents:
Cover; Half Title; Title Page; Copyright Page; Dedication; Contents; List of Tutorials; List of Figures/Illustrations; Foreword; Preface; Acknowledgements; Author; Chapter 1: Introduction to Big Data; OVERVIEW; RAPID GROWTH OF BIG DATA; BIG DATA DEFINITION; BIG DATA PROJECTS; BUSINESS VALUE OF BIG DATA; Chapter 2: Big Data Implementation; OVERVIEW; HIGH-LEVEL TASKS TO IMPLEMENT INFORMATICA BDM, CLOUDERA HIVE, AND TABLEAU; BIG DATA TRIGGERS DIGITAL TRANSFORMATION OF THE PRODUCTION MODEL; BIG DATA CHALLENGES AND ASSOCIATED USE CASES; HADOOP INFRASTRUCTURE: OVERVIEW
HADOOP INFRASTRUCTURE: DEFINEDHyperconverged Hadoop Infrastructure; Compute Hardware Components; Network Hardware Components; Storage Hardware Architecture and Components; HADOOP ECO SYSTEM; HADOOP: JVM FRAMEWORK; HADOOP DISTRIBUTED FILE PROCESSING; MAPREDUCE SOFTWARE; MAPREDUCE SOFTWARE INSTALLATION; MAPREDUCE PROCESSING; Chapter 3: Big Data Use Cases; OVERVIEW; BIG DATA USE CASE: HEALTH; BIG DATA USE CASE: MANUFACTURING; BIG DATA USE CASE: INSURANCE; Chapter 4: Big Data Migration; OVERVIEW; CHALLENGES IN MIGRATING ORACLE DATA USING SQOOP; WHERE IS SQOOP USED?; SQOOP COMMANDS
HIVE ARGUMENTS USED BY SQOOPAPACHE SQOOP ARCHITECTURE; APACHE SQOOP COMMAND LINE INTERFACE; Chapter 5: Big Data Ingestion, Integration, and Management; OVERVIEW; INFORMATICA: MATURE AND COMPREHENSIVE BIG DATA SOLUTION; INFORMATICA DATA INTEGRATION; Chapter 6: Big Data Repository; OVERVIEW; DATA REPOSITORY LAYER; HIVE BIG DATA WAREHOUSE; SLOWLY CHANGING DIMENSION IN HIVE; HIVE METADATA: DEFINITIONS; INTEGRATED USE OF DATA INTEGRATION, DATA MANAGEMENT, AND DATA VISUALIZATION TOOLS; Chapter 7: Big Data Visualization; OVERVIEW; VARIABLE TYPES; Numbers; Strings; Factors
SUCCESS FACTORS FOR TABLEAUTABLEAU: STEP FORWARD IN DATA ANALYTICS; TABLEAU CONNECTORS FOR DATA SOURCES; TABLEAU DATA ENGINE TUNING; TABLEAU TUNING FEATURES; Fast Interactive Query Engine; Strategically Utilize Live Connections versus Extracts; Curate Data from the Data Lake; Optimize Data Extracts; Customize Tableau Connection Performance; Chapter 8: Structured and Un-Structured Data Analytics; OVERVIEW; TEXT ANALYTICS AS MEANS TO EXTRACT VALUE FROM UN-STRUCTURED DATA; MAJOR PLAYERS IN TEXT ANALYTICS; Decision Maker; Domain Expert; Linguist; Data Scientists; Conclusion; FROM DATA TO ACTION
CONCLUSIONChapter 9: Data Virtualization; OVERVIEW; Conclusion: Flexibility and Agility; Pre-Installation Steps to Set Up Denodo Development Environment; CONCLUSION; Chapter 10: Cloud Computing; OVERVIEW; A QUICK GLANCE AT CLOUD COMPUTING; Software as a Service (SaaS); Platform as a Service (PaaS); Infrastructure as a Service (IaaS); CLOUD COMPUTING VERSUS HADOOP PROCESSING; CLOUD SERVICE MOST SUITED FOR BIG DATA; Infrastructure as a Service (IaaS); Advantages of IaaS; CONCLUSION; SELF-ASSESSMENT QUIZ; ANSWERS TO THE SELF-ASSESSMENT QUIZ; REFERENCES; INDEX
Summary: "This book explores the tools and techniques 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"-- Provided by publisher.
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"This book explores the tools and techniques 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"-- Provided by publisher.

Cover; Half Title; Title Page; Copyright Page; Dedication; Contents; List of Tutorials; List of Figures/Illustrations; Foreword; Preface; Acknowledgements; Author; Chapter 1: Introduction to Big Data; OVERVIEW; RAPID GROWTH OF BIG DATA; BIG DATA DEFINITION; BIG DATA PROJECTS; BUSINESS VALUE OF BIG DATA; Chapter 2: Big Data Implementation; OVERVIEW; HIGH-LEVEL TASKS TO IMPLEMENT INFORMATICA BDM, CLOUDERA HIVE, AND TABLEAU; BIG DATA TRIGGERS DIGITAL TRANSFORMATION OF THE PRODUCTION MODEL; BIG DATA CHALLENGES AND ASSOCIATED USE CASES; HADOOP INFRASTRUCTURE: OVERVIEW

HADOOP INFRASTRUCTURE: DEFINEDHyperconverged Hadoop Infrastructure; Compute Hardware Components; Network Hardware Components; Storage Hardware Architecture and Components; HADOOP ECO SYSTEM; HADOOP: JVM FRAMEWORK; HADOOP DISTRIBUTED FILE PROCESSING; MAPREDUCE SOFTWARE; MAPREDUCE SOFTWARE INSTALLATION; MAPREDUCE PROCESSING; Chapter 3: Big Data Use Cases; OVERVIEW; BIG DATA USE CASE: HEALTH; BIG DATA USE CASE: MANUFACTURING; BIG DATA USE CASE: INSURANCE; Chapter 4: Big Data Migration; OVERVIEW; CHALLENGES IN MIGRATING ORACLE DATA USING SQOOP; WHERE IS SQOOP USED?; SQOOP COMMANDS

HIVE ARGUMENTS USED BY SQOOPAPACHE SQOOP ARCHITECTURE; APACHE SQOOP COMMAND LINE INTERFACE; Chapter 5: Big Data Ingestion, Integration, and Management; OVERVIEW; INFORMATICA: MATURE AND COMPREHENSIVE BIG DATA SOLUTION; INFORMATICA DATA INTEGRATION; Chapter 6: Big Data Repository; OVERVIEW; DATA REPOSITORY LAYER; HIVE BIG DATA WAREHOUSE; SLOWLY CHANGING DIMENSION IN HIVE; HIVE METADATA: DEFINITIONS; INTEGRATED USE OF DATA INTEGRATION, DATA MANAGEMENT, AND DATA VISUALIZATION TOOLS; Chapter 7: Big Data Visualization; OVERVIEW; VARIABLE TYPES; Numbers; Strings; Factors

SUCCESS FACTORS FOR TABLEAUTABLEAU: STEP FORWARD IN DATA ANALYTICS; TABLEAU CONNECTORS FOR DATA SOURCES; TABLEAU DATA ENGINE TUNING; TABLEAU TUNING FEATURES; Fast Interactive Query Engine; Strategically Utilize Live Connections versus Extracts; Curate Data from the Data Lake; Optimize Data Extracts; Customize Tableau Connection Performance; Chapter 8: Structured and Un-Structured Data Analytics; OVERVIEW; TEXT ANALYTICS AS MEANS TO EXTRACT VALUE FROM UN-STRUCTURED DATA; MAJOR PLAYERS IN TEXT ANALYTICS; Decision Maker; Domain Expert; Linguist; Data Scientists; Conclusion; FROM DATA TO ACTION

CONCLUSIONChapter 9: Data Virtualization; OVERVIEW; Conclusion: Flexibility and Agility; Pre-Installation Steps to Set Up Denodo Development Environment; CONCLUSION; Chapter 10: Cloud Computing; OVERVIEW; A QUICK GLANCE AT CLOUD COMPUTING; Software as a Service (SaaS); Platform as a Service (PaaS); Infrastructure as a Service (IaaS); CLOUD COMPUTING VERSUS HADOOP PROCESSING; CLOUD SERVICE MOST SUITED FOR BIG DATA; Infrastructure as a Service (IaaS); Advantages of IaaS; CONCLUSION; SELF-ASSESSMENT QUIZ; ANSWERS TO THE SELF-ASSESSMENT QUIZ; REFERENCES; INDEX

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