bigcovers/noimage.jpg
Big Data Analytics
S Chandramouli et al
Price
1250.00
ISBN
9789393330468
Language
English
Pages
1088
Format
Paperback
Dimensions
216 x 280 mm
Year of Publishing
2024
Territorial Rights
World
Imprint
Universities Press
Big Data Analytics is intended for use as a textbook for third- and fourth-year students of B.E., B.Tech., B.Sc., BCA, MCA, and M.Tech. courses in IT, Software, and Computer Science Engineering. The book has been written to help students who enter the software industry to gain a broad understanding of Big Data and the nuances of handling it to extract useful information. Spread across 21 chapters, it elucidates the concept of Big Data and walks the reader through popular frameworks such as Hadoop, MongoDB, Pig and Hive that are used for processing Big Data. The book will also benefit professionals, at all levels, who seek transition to the software field.

S Chandramouli is Associate Director, Cognizant Technology Solutions.
Asha A George is PPM and Strategy Consultant, Verbat Technologies.
CR Rene Robin is Professor (CSE) and Dean (Innovation), Sri Sairam Engineering College, Chennai.
D Doreen Hephzibah Miriam is Founder and Director, Computational Intelligence Research Foundation.
J Jasmine Christina Magdalene is Assistant Professor, PG Department of Computer Applications, Bishop Heber College, Tiruchirappalli.

Preface
Acknowledgements
About the Author

Chapter 1 Introduction to Data Analytics

1.1                Introduction                                                                                      

1.2                What Is Data?                                                                                   

1.1.1     Data Relationships                                                                 

1.1.2     Data Models                                                                           

1.3                Types of Data                                                                                   

1.4                Nature of Data                                                                                  

1.5                Data Visualization                                                                            

1.6                Data Analysis Methods                                                                    

1.6.1     Correlation                                                                              

1.6.2     Regression                                                                              

1.6.3     Forecasting                                                                             

1.6.4     Clustering                                                                               

1.6.5     Classification                                                                          

1.7                Web Data                                                                                          

1.7.1     Evolution of Analytic Scalability                                           

1.7.2     Reporting vs. Analysis                                                           

Summary | Multiple Choice Questions | Short-answer Questions | Essay-type Questions

Chapter 2 Data Analytics Life-cycle 

2.1                Introduction                                                                                      

2.2                Business Drivers for Analytics                                                        

2.2.1     Increasing Profitability and Growth                                       

2.2.2     Strengthening Customer Experience and Intimacy               

2.2.3     Driving Digital Transformation and Innovation                    

2.2.4     Managing Regulatory and Compliance Risks                       

2.2.5     Increasing Operational Efficiency                                         

2.3                Typical Analytical Architecture                                                       

2.3.1     Data Analytical Architecture                                                 

2.3.2     Challenges of Conventional Systems

2.4                Analytic Processes and Tools                                                                                                          

2.4.1     Types of Analytics

2.4.2     Modern Data Analytic Tools

2.5                Data Analytic Life-cycle

2.5.1     Need of Data Analytic Life-cycle

2.5.2     Phases of Data Analytic Life-cycle

2.6                Key Roles for Successful Analytic Projects

2.7                Modern-day Intelligence

2.7.1     Business Intelligence vs. Data Science

2.7.2     Intelligent Data Analysis

Summary | Multiple Choice Questions | Short-answer Questions | Essay-type Questions

Chapter 3 Fundamentals of Big Data 

3.1                Introduction to Big Data                                                                                                          

3.2                Big Data Concepts and Terminology

3.2.1     Big Data Processing Activities

3.2.2     Common Terminologies

3.3                Fundamentals of Big Data Types                                                    

3.4                Big Data Analytics                                                                           

3.4.1     Text Analytics                                                                        

3.4.2     Audio Analytics                                                                   

3.4.3     Video Content Analytics                                                      

3.4.4     Social Media Analytics                                                           

3.4.5     Predictive Analytics                                                                

3.5                Distributed File System in Big Data                                               

3.6                Big Data Characteristics                                                                

3.6.1     The 5 V â€™s of Big Data                                                           

3.6.2     Challenges of Processing Big Data                                       

3.7                Drivers for Big Data                                                                       

Summary | Multiple Choice Questions | Short-answer Questions | Essay-type Questions

Chapter 4 Big Data Analytics Technology 

4.1                Introduction to Big Data Analytics                                                

4.2                Big Data Analysis Framework                                                          

4.3                Approaches for Big Data Analysis                                                    

4.4                Understanding Text Analytics and Big Data                                    

4.4.1     Text Mining Process                                                             

4.4.2     Applications of Text Analytics

4.5                Predictive Analysis of Big Data                                                        

4.5.1     Predictive Analytics Models                                                   

4.5.2     Predictive Analytics Algorithms                                             

4.6                Procedural vs. Functional Programming Models for Big Data       

4.7                Big Data Integration Process                                                          

4.8                Big Data Technology Landscape                                                    

4.8.1     Big Data Architecture                                                          

4.8.2     Big Data Storage                                                                  

4.9                Big Data Key Roles                                                                        

Summary | Multiple Choice Questions | Short-answer Questions | Essay-type Questions

Chapter 5 Fundamentals of Hadoop 

5.1                Introduction                                                                                    

5.2                Problems with Traditional Large-scale Systems                               

5.3                Five V â€™s of Big Data                                                                        

5.4                What Is Hadoop?                                                                            

5.5                History of Hadoop                                                                          

5.6                Why Hadoop?                                                                                 

5.7                Different Flavors of Hadoop                                                           

5.8                Different Modes of Hadoop                                                           

5.8.1     Standalone Mode                                                                  

5.8.2     Pseudo-distributed Mode (Single-node Cluster)                  

5.8.3     Fully Distributed Mode                                                        

5.9                Core Components of Hadoop                                                        

5.10           Hadoop Ecosystem                                                                         

5.11           Data Ingestion Layer                                                                      

5.12           ETL and ELT                                                                                  

5.13           Ingestion Tools in Hadoop Ecosystem                                           

5.14           Data Storage Layer                                                                         

5.14.1     Data Storage Tools                                                             

5.15           Processing Layer                                                                              

5.16           Analysis Layer                                                                                 

5.17           Management and Coordination                                                     

5.18           Anatomy of a Hadoop Cluster: HDFS Architecture                      

5.19           Data Locality in Hadoop                                                                

5.20           Configuration files in Hadoop                                                        

5.21           Limitations of Hadoop                                                                   

5.22           Distributed Cache in Apache Hadoop

Summary | Multiple Choice Questions | Short-answer Questions | Essay-type Questions

Chapter 6 Hadoop Distributed File System 

6.1                Introduction                                                                                    

6.2                Virtualization                                                                                 

6.3                Downloading VMware                                                                   

6.4                Installing VMware                                                                          

6.5                VirtualBox                                                                                      

6.5.1     VirtualBox Installation Steps                                                 

6.6                HDP Sandbox Download and Installation                                     

6.7                Ambari Administration                                                                  

6.8                HDFS Command Line Interface                                                    

6.8.1     JPS Command                                                                      

6.8.2     List of Files                                                                            

6.8.3     File Management                                                                 

6.8.4     Upload and Download Files                                                 

6.8.5     Ownership and Validation

Summary | Multiple Choice Questions | Short-answer Questions | Essay-type Questions

Chapter 7 MapReduce

7.11           Hadoop Reducer                                                                               

7.12            Hadoop Key-Value Pair                                                                   

7.13            Input Format in MapReduce                                                            

7.14            InputSplit in MapReduce                                                                 

7.15            Hadoop Record Reader                                                                    

7.16            MapReduce Partitioner                                                                    

7.16.1     MapReduce Combiner                                                         

7.17            Shuffling and Sorting in MapReduce                                              

7.17.1     Hadoop Output Format                                                        

7.18            Input Split vs. HDFS Block in MapReduce                                    

7.19            MapOnly Job in MapReduce                                                           

7.20            Hadoop Speculative Execution                                                        

7.21            Hadoop Counters                                                                              

7.22            Hadoop Optimization                                                                       

7.23            MapReduce Performance Tuning: Best Practices                           

7.23.1     System Level Best Practices                                                

7.23.2     Application Level Best Practices                                         

7.24            YARN                                                                                              

Summary | Multiple Choice Questions | Short-answer Questions | Essay-type Questions

Chapter 8 Hadoop Ingestion

8.1                Introduction                                                                                      

8.2                Data Ingestion Types                                                                       

8.2.1     Real-time Data Ingestion (RTDI)                                          

8.2.2     Batch-based Data Ingestion (BBDI)                                      

8.2.3     Lambda Architecture Data Ingestion (LADI)                        

8.3                Benefits of Data Ingestion                                                               

8.3.1     Data Ingestion Tools Selection                                              

8.4                Introduction to Sqoop                                                                      

8.5                Features of Sqoop                                                                            

8.6                Basic SQL Commands and Connecting from Cloudera                  

8.7                Basic Sqoop Commands from Cloudera Command Prompt           

8.8                Sqoop Importing                                                                              

8.9                Sqoop Incremental Import                                                               

8.10           Sqoop Export                                                                                    

8.11           Advantages of Sqoop                                                                       

8.12           Disadvantages of Sqoop

10.8            HBase Coprocessor                                                                          

10.9            Setting HBase Environment                                                             

10.10       Creating HBase Tables                                                                    

10.11       Listing all Tables                                                                              

10.12       Adding Data to a Table                                                                    

10.13       Getting a Row of Data                                                                     

10.14       Scanning a Table                                                                              

10.15       Counting the Number of Rows in a Table                                       

10.16       Altering a Table                                                                               

10.17       Deleting a Table Row, Column                                                       

10.18       Disabling and Enabling a Table                                                       

10.19       Truncating and Dropping a Table                                                    

10.20       Determining if Table Exists                                                             

10.21       Creating a Hive External Table Stored by HBase                           

10.21.1     Defining an External Table over HBase Tables                 

10.21.2    Mapping Specific HBase Columns and Column Families     

10.21.3     Working Hive with HBase (Integration)                            

10.22       Advanced Indexing in HBase                                                          

10.23       HIndex                                                                                              

10.23.1     Writing Data with Index                                                    

10.23.2     Reading Data with Index                                                    

10.23.3     HIndex Features                                                                 

10.24       HBase Admin API                                                                           

10.25       HBAse Client API                                                                            

10.25.1     Put Method                                                                         

10.25.2     Get Method                                                                         

10.26       Using HBase in Hadoop Applications                                             

10.27       HBase Advanced Usage                                                                   

10.27.1     Filters                                                                                  

10.27.2     The Filter Hierarchy                                                           

10.27.3     Comparison Operators                                                        

10.27.4     Comparators                                                                       

10.27.5     Comparison Filters                                                             

10.28       Dedicated Filters                                                                              

10.29       Decorating Filters

Summary | Multiple Choice Questions | Short-answer Questions | Essay-type Questions

Chapter 11 Hadoop Streaming 

11.1           Introduction                                                                                    

11.2           Real-time Analytics                                                                        

11.2.1     Choosing the Proper Tool for Real-time Analytics            

11.2.2     Apache Spark Streaming                                                    

11.2.3     Apache Samza                                                                      

11.2.4     What Would a Perfect Solution Entail?                              

11.2.5     Challenges to Be Solved                                                     

11.3           Thread Pooling                                                                               

11.4           Stream Computing                                                                         

11.5           The Future of Data Streaming                                                        

11.6            Stream Computing’s Advantages in the Big Data world                

11.7            How Streaming Works                                                                   

11.8            Real-time Streams vs. Batch Processing                                         

11.9            Hadoop Streaming                                                                          

11.9.1     Hadoop Streaming Characteristics                                     

11.9.2     Specifying Other Plugins for Jobs                                      

Summary | Multiple Choice Questions | Short-answer Questions | Essay-type Questions

Chapter 12 Pig Latin 

12.1           Introduction                                                                                    

12.2           Basic Features of Apache Pig

Summary | Multiple Choice Questions | Short-answer Questions | Essay-type Questions

Chapter 13 Fundamentals of Spark

13.6            Design Principles of Apache Spark                                                 

13.7            Advantages of Spark                                                                        

13.8            Disadvantages of Apache Spark                                                      

13.9            Installation of Apache Spark on Windows                                      

13.10       Apache Spark Physical Architecture                                               

13.11       Apache Spark Layered Architecture                                                

13.11.1     Resilient Distributed Dataset                                              

13.11.2     Directed Acyclic Graph (DAG)                                         

13.12       Ways to Create RDD in Spark                                                         

13.13       Paired RDD                                                                                      

13.14       Features of Spark RDD                                                                    

13.15       Persistence and Caching Mechanisms in Apache Spark                  

13.16       Operations of Apache Spark RDD                                                   

13.16.1     Transformations                                                                  

13.16.2     Actions                                                                                

13.17       Limitations of Apache Spark RDD and Ways to Overcome It        

13.18       Directed Acyclic Graph (DAG)                                                       

13.19       DAG in Apache Spark                                                                     

13.19.1     Need for DAG in Apache Spark                                        

13.19.2     Working Principle of DAG in Spark                                  

13.20       Applications of Apache Spark                                                         

13.20.1     Streaming Data                                                                   

13.21       Spark in Real-world                                                                         

13.22       Use Cases of Spark                                                                          

13.23       Spark vs. Hadoop                                                                             

13.24       Sample Program                                                                               

Summary | Multiple Choice Questions | Short-answer Questions | Essay-type Questions

Chapter 14 Introduction to NoSQL Database Concepts 

14.1           Introduction                                                                                      

14.2           Relational Databases

14.3           NoSQL Definition                                                                         

14.4            Types of NoSQL Databases                                                            

14.4.1     Column Family Databases                                                  

14.4.2     Key-Value Pair Database                                                      

14.4.3     Document Store                                                                 

14.4.4     Graph Database                                                                  

14.5            Examples of NoSQL Databases                                                     

14.6            Advantages of NoSQL Databases                                                     

14.7            NoSQL Usage                                                                                

14.8            SQL vs. NoSQL                                                                             

14.9            New SQL                                                                                        

14.10       ACID                                                                                              

14.10.1     Atomicity                                                                         

14.10.2     Consistency                                                                      

14.10.3     Isolation                                                                            

14.10.4     Durability                                                                         

14.11       BASE                                                                                              

14.12       Two-phase Commit                                                                        

14.12.1     Commit–request Phase                                                     

14.13       Schema                                                                                           

14.13.1     Sharding and Share Nothing Architecture                       

14.13.2     Partitioning Horizontal and Vertical Data                        

14.13.3     Four Basic Strategies for Shard Structure                         

14.14       Brewer’s CAP Theorem                                                                 

14.15       Cassandra â€“ Definition and Features                                              

14.15.1     Definition                                                                         

14.15.2     Features                                                                            

14.15.3     Key Structures in Cassandra                                               

14.15.4     Cassandra Advantages and Use Cases                                 

14.16       MongoDB                                                                                       

14.16.1     Architecture of MongoDB                                                

14.16.2     MongoDB Advantages and Use Cases                             

14.17       HBase                                                                                             

14.17.1     HBase Architecture                                                          

14.18       Comparing Cassandra, MongoDB, and HBase                              

Summary | Multiple Choice Questions | Short-answer Questions | Essay-type Questions

Chapter 15 Cassandra Data Model 

15.1           Introduction                                                                                    

15.2           Use Cases of Cassandra                                                                    

15.3            Cassandra Installation in Windows Environment                           

15.3.1     Installing Python 2.7.x Edition                                          

15.3.2     Installing Apache Cassandra                                                 

15.4            Cassandra Basic CQL                                                                     

15.5            How to Create, Alter, Drop and Use Keyspace in Cassandra         

15.5.1     Create Keyspace                                                                 

15.5.2     Simple Strategy                                                                  

15.5.3     Network Topology Strategy                                               

15.6            Column Families                                                                            

15.6.1     Types of Columns                                                              

15.7            Cassandra Table                                                                             

15.7.1     Inserting and Displaying Data from the Table                    

15.7.2     Updating the Table Data                                                    

15.8            Data Types in Cassandra                                                                   

15.8.1     Collection Data Type in Cassandra                                    

15.9            Cassandra BATCH                                                                         

15.10       Difference Between Cassandra and RDBMS                                 

15.11       Denormalization                                                                            

15.12       Design Patterns                                                                              

15.12.1     Coexistence Patterns                                                        

15.13       RDBMS Migration Patterns                                                          

15.14       CAP Patterns                                                                                  

15.15       Temporal Patterns                                                                          

Summary | Multiple Choice Questions | Short-answer Questions | Essay-type Questions

Chapter 16 Cassandra Architecture 

16.1           Introduction                                                                                    

16.1.1     Cassandra Architecture                                                      

16.1.2     Features of Cassandra                                                           

16.2           Cassandra’s Peer-to-Peer Approach                                               

16.3            Gossip and Failure Detection                                                         

16.4            SS Tables and Commit Log                                                            

16.4.1     Partition and Token                                                            

16.4.2     Compression Offset Map                                                   

16.4.3     Cassandra Commit Log

16.5           Cassandra Memtable                                                                      

16.5.1     Memtable Allocation Types                                               

16.5.2     Slab Allocator                                                                       

16.5.3     Memtable Flush                                                                  

16.5.4     Row Cache                                                                         

16.5.5     Cassandra Memtable Metrics                                             

16.6            Hashing to the Rescue                                                                   

16.7            Compaction in Cassandra                                                               

16.8            Tombstones in Cassandra                                                               

16.9            Hinted Handoff                                                                              

16.10       Anti-entropy and Read Repair                                                       

16.10.1     Anti-entropy                                                                     

16.10.2     Read repair                                                                       

16.11       Bloom Filters in Cassandra                                                             

16.11.1     Bloom Filter                                                                     

16.11.2     Changing Bloom Filter                                                    

16.12       Load Balancing in Cassandra                                                            

16.13       Cassandra Read Process                                                                 

16.13.1     Example of Cassandra Read Process                                

16.14       Cassandra Write Process                                                                

16.15       Staged Event-Driven Architecture (SEDA)                                    

16.16       Cassandra Migration                                                                      

16.16.1     Migration Approaches                                                      

16.16.2     Partition Key Cache                                                          

16.16.3     Partition Summary                                                           

16.16.4     Partition Index                                                                  

16.16.5     Cache Migration Pattern                                                  

16.16.6     Estimating a Migration                                                     

16.17       Streaming                                                                                       

16.17.1     Streaming Based on Netty                                                

16.17.2     Zero-copy Streaming                                                        

16.17.3     Parallelizing of Streaming of Keyspaces                              

Summary | Multiple Choice Questions | Short-answer Questions | Essay-type Questions

Chapter 17 MongoDB 

17.1           Introduction                                                                                    

17.2           History of MongoDB

17.3           MongoDB Environment Setup                                                      

17.3.1     Install MongoDB on Windows                                          

17.3.2     Starting the MongoDB Server                                           

17.4            MongoDB Schema Design                                                             

17.5            Key Features of MongoDB                                                             

17.6            RDBMS vs. MongoDB                                                                  

17.7            MongoDB Query Language (MQL)                                              

17.8            MongoDB Database, Collection and Documents                          

17.9            MongoDB Server                                                                           

17.10       MongoDB Client Through the JavaScript’s Shell                          

17.11       CRUD Operation in MongoDB                                                     

17.11.1     Creating Database in MongoDB (C of CRUD)               

17.11.2     Creating Collection in MongoDB                                    

17.11.3     Listing Down the Databases Available in MongoDB       

17.11.4     Inserting Records into Collection (Table)                        

17.11.5     Showcasing the Current Database Used                           

17.11.6     Showcasing the Tables (Collections) in the

Current Database                                                              

17.11.7     Reading Collections in MongoDB (R of CRUD)           

17.11.8     Updating documents in MongoDB (U of CRUD)          

17.11.9     Delete Operation in MongoDB (D of CRUD)                

17.11.10     Dropping (Deleting) a Particular Database                     

17.12       Pretty () Method                                                                       

17.13       AND in MongoDB                                                                          

17.14       OR in MongoDB                                                                            

17.15       Using AND and OR Together                                                          

17.16       NOR in MongoDB                                                                          

17.17       NOT in MongoDB                                                                          

17.18       Creating and Querying Through Indexes                                       

17.18.1     The createIndex () method                                      

17.18.2     MongoDB’s dropIndex () Method                              

17.18.3     The dropIndexes () Method                                    

17.18.4     The getIndexes () Method                                      

17.19       Mongo Compass                                                                            

17.19.1     MongoDB Connection                                                    

17.19.2     Creating Database in Compass                                         

17.19.3     Adding Documents in Compass

17.19.4      MongoDB View                                                               

17.19.5      Filters in Compass                                                            

17.19.6      Sorting in Compass                                                          

17.19.7      Limit Option in Compass                                                 

17.19.8      Skip Option in Compass                                                  

17.19.9      Project Option in Compass                                              

17.19.10     Dropping a Database in Compass                                   

17.19.11     Dropping a Collection in Compass                                

17.19.12     Importing Documents in Compass                                 

17.19.13     Aggregations Option in Compass                                   

17.19.14     Schema Option in Compass                                           

17.19.15     Update MongoDB Compass with the Latest Version    

Summary | Multiple Choice Questions | Short-answer Questions | Essay-type Questions

Chapter 18 Big Data Visualizations 

18.1           Introduction                                                                                    

18.2           History of Data Visualization                                                         

18.3            Big Data Visualization                                                                     

18.4            Importance of Big Data Visualization                                            

18.5            How Does Data Visualization Work?                                            

18.6            Types of Data Visualization                                                              

18.7            Challenges of Big Data Visualization                                               

18.8            Introduction to Tableau                                                                  

18.8.1     Features of Tableau                                                            

18.8.2     Tableau Product Suite                                                        

18.8.3     Installation of Tableau                                                        

18.8.4     Tableau for Big Data Visualization                                       

18.9            Python for Data Visualization                                                        

18.9.1     Installation of Python                                                         

18.9.2     Visualization of Data Using Python                                   

18.9.3     Matplotlib                                                                          

Summary | Multiple Choice Questions | Short-answer Questions | Essay-type Questions

Chapter 19 Business Implementation of Big Data 

19.1           Introduction                                                                                    

19.2           Big Data in Business                                                                       

19.2.1     Big Data in Marketing                                                       

19.2.2     Big Data in Banking Sector

19.2.3     Big Data in Healthcare Sector

19.2.4     Big Data in Education Sector

19.3            Security in Big Data                                                                         

19.3.1     User Access Control                                                             

19.4            Big Data on Cloud                                                                           

19.5            Best Practices in Big Data Implementation                                     

19.6            Latest Trends in Big Data                                                                

19.6.1     Big Data Analytics Will Incorporate Artificial Intelligence

19.6.2     The Use of Blockchain for Data Security Will Increase      

19.6.3     The Internet of Things (IoT) Will Drive Streaming

Analytics Adoption                                                              

19.6.4     The Rise of DataOps                                                            

19.6.5     Data-as-a-Service (DaaS)                                                     

19.6.6     Data Mesh                                                                            

19.6.7     Synthetic Data                                                                      

19.6.8     Empowerment of Self-service Analytics                             

19.6.9     Data Democratization                                                          

Summary | Multiple Choice Questions | Short-answer Questions | Essay-type Questions

Chapter 20 Limitations of Hadoop and Solutions to Overcome Them 

20.1           Introduction                                                                                      

20.2           Problem with Small Files                                                                 

20.3            Vulnerability                                                                                    

20.4            Long Processing Time                                                                     

20.5            Not Easy to Use                                                                               

20.6            Supports Only Batch Processing                                                      

20.7            No Delta Iteration                                                                            

20.8            Security Issues                                                                                  

Summary | Multiple Choice Questions | Short-answer Questions | Essay-type Questions

Chapter 21 Big Data Case Studies 

21.1           Applications of Big Data in the Retail Industry                              

21.1.1     Customer Segmentation                                                       

21.1.2     Inventory Management                                                        

21.1.3     Price Optimization                                                               

21.1.4     Fraud Detection                                                                    

21.1.5     Supply Chain Optimization                                                  

21.1.6     Predictive Analytics

21.2            Applications of Big Data in the Logistics Industry                       

21.2.1     Route Optimization                                                              

21.2.2     Supply Chain Visibility                                                        

21.2.3     Risk Management                                                                

21.2.4     Fleet Management                                                                

21.2.5     Warehouse Optimization                                                      

21.2.6     Pricing Optimization                                                            

21.2.7     Quality Control                                                                    

21.2.8     Environmental Sustainability                                               

21.3            Applications of Big Data in the Manufacturing Industry                

21.3.1     Predictive Maintenance                                                        

21.3.2     Quality Control                                                                    

21.3.3     Supply Chain Optimization                                                  

21.3.4     Production Optimization                                                      

21.3.5     Energy Efficiency                                                                

21.3.6     Product Development                                                           

21.3.7     Risk Management                                                                

21.3.8     Warranty Analytics                                                              

21.3.9     Customer Analytics                                                              

21.4            Applications of Big Data in the Travel Industry                              

21.4.1     Customer Service                                                                 

21.4.2     Predictive Maintenance                                                        

21.4.3     Weather Forecasting                                                            

21.4.4     Customer Sentiment Analysis                                              

21.4.5     Destination Management                                                     

21.4.6     Operational Efficiency                                                         

21.4.7     Revenue Management                                                          

Summary

Appendix A: Model Questions                                                                

Appendix B: Capstone Projects                                                              

Appendix C: Model Syllabi                                                                     

Index

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