Previously issued in print: 2020.

<P>Preface <br />Author Bios <br />Acknowledgements <br />List of Figures <br />List of Tables </p><p><br /><strong>Introduction to Big Data Systems </strong><br />1.1 INTRODUCTION: REVIEW OF BIG DATA SYSTEMS<br />1.2 UNDERSTANDING BIG DATA <br />1.3 TYPE OF DATA: TRANSACTIONAL OR ANALYTICAL<br />1.4 REQUIREMENTS AND CHALLENGES OF BIG DATA <br />1.5 CONCLUDING REMARKS <br />1.6 FURTHER READING <br />1.7 EXERCISE QUESTIONS </p><p><strong>Architecture and Organization of Big Data Systems </strong><br />2.1 ARCHITECTURE FOR BIG DATA SYSTEMS <br />2.2 ORGANIZATION OF BIG DATA SYSTEMS: CLUSTERS<br />2.3 CLASSIFICATION OF CLUSTERS: DISTRIBUTED MEMORY VS. SHARED MEMORY<br />2.4 CONCLUDING REMARKS <br />2.5 FURTHER READING <br />2.6 EXERCISE QUESTIONS </p><p><strong>Cloud Computing for Big Data </strong><br />3.1 CLOUD COMPUTING <br />3.2 VIRTUALIZATION <br />3.3 PROCESSOR VIRTUALIZATION <br />3.4 CONTAINERIZATION <br />3.5 VIRTUALIZATION OR CONTAINERIZATION <br />3.6 FOG COMPUTING <br />3.7 EXAMPLES <br />3.8 CONCLUDING REMARKS <br />3.9 FURTHER READING <br />3.10 EXERCISE QUESTIONS <br /><br /><strong>HADOOP: An Efficient Platform for Storing and Processing Big Data</strong> <br />4.1 REQUIREMENTS FOR PROCESSING AND STORING BIG DATA <br />4.2 HADOOP -- THE BIG PICTURE <br />4.3 HADOOP DISTRIBUTED FILE SYSTEM <br />4.4 MAPREDUCE <br />4.5 HBASE <br />4.6 CONCLUDING REMARKS <br />4.7 FURTHER READING <br />4.8 EXERCISE QUESTIONS </p><p><strong>Enhancements in Hadoop </strong><br />5.1 ISSUES WITH HADOOP <br />5.2 YARN <br />5.3 PIG <br />5.4 HIVE <br />5.5 DREMEL <br />5.6 IMPALA <br />5.7 DRILL <br />5.8 DATA TRANSFER <br />5.9 AMBARI <br />5.10 CONCLUDING REMARKS <br />5.11 FURTHER READING <br />5.12 EXERCISE QUESTIONS </p><p><strong>Spark </strong><br />6.1 LIMITATIONS OF MAPREDUCE <br />6.2 INTRODUCTION TO SPARK <br />6.3 SPARK CONCEPTS <br />6.4 SPARK SQL <br />6.5 SPARK MLLIB <br />6.6 STREAM BASED SYSTEM <br />6.7 SPARK STREAMING <br />6.8 CONCLUDING REMARKS <br />6.9 FURTHER READING <br />6.10 EXERCISE QUESTIONS </p><p><strong>NoSQL Systems </strong><br />7.1 INTRODUCTION <br />7.2 HANDLING BIG DATA SYSTEMS -- PARALLEL RDBMS <br />7.3 EMERGENCE OF NOSQL SYSTEMS <br />7.4 KEY-VALUE DATABASE <br />7.5 DOCUMENT-ORIENTED DATABASE <br />7.6 COLUMN-ORIENTED DATABASE <br />7.7 GRAPH DATABASE <br />7.8 CONCLUDING REMARKS <br />7.9 FURTHER READING <br />7.10 EXERCISE QUESTIONS </p><p><strong>NewSQL Systems </strong><br />8.1 INTRODUCTION<br />8.2 TYPES OF NEWSQL SYSTEMS <br />8.3 FEATURES <br />8.4 NEWSQL SYSTEMS: CASE STUDIES <br />8.5 CONCLUDING REMARKS <br />8.6 FURTHER READING<br />8.7 EXERCISE QUESTIONS </p><p><br /><strong>Networking for Big Data</strong> <br />9.1 NETWORK ARCHITECTURE FOR BIG DATA SYSTEMS<br />9.2 CHALLENGES AND REQUIREMENTS <br />9.3 NETWORK PROGRAMMABILITY AND SOFTWARE DEFINED NETWORKING <br />9.4 LOW LATENCY AND HIGH SPEED DATA TRANSFER<br />9.5 AVOIDING TCP INCAST -- ACHIEVING LOW LATENCY<br />AND HIGH THROUGHPUT <br />9.6 FAULT TOLERANCE<br />9.7 CONCLUDING REMARKS <br />9.8 FURTHER READING <br />9.9 EXERCISE QUESTIONS </p><p><strong>Security for Big Data </strong><br />10.1 INTRODUCTION <br />10.2 SECURITY REQUIREMENTS <br />10.3 SECURITY: ATTACK TYPES AND MECHANISMS <br />10.4 ATTACK DETECTION AND PREVENTION <br />10.5 CONCLUDING REMARKS <br />10.6 FURTHER READING <br />10.7 EXERCISE QUESTIONS </p><p><strong>Privacy for Big Data</strong> <br />11.1 INTRODUCTION <br />11.2 UNDERSTANDING BIG DATA AND PRIVACY <br />11.3 PRIVACY VIOLATIONS AND THEIR IMPACT <br />11.4 TYPES OF PRIVACY VIOLATIONS <br />11.5 PRIVACY PROTECTION SOLUTIONS AND THEIR LIMITATIONS <br />11.6 CONCLUDING REMARKS <br />11.7 FURTHER READING <br />11.8 EXERCISE QUESTIONS <br /><br /><strong>High Performance Computing for Big Data </strong><br />12.1 INTRODUCTION <br />12.2 SCALABILITY: NEED FOR HPC <br />12.3 GRAPHIC PROCESSING UNIT <br />12.4 TENSOR PROCESSING UNIT <br />12.5 HIGH SPEED INTERCONNECTS <br />12.6 MESSAGE PASSING INTERFACE <br />12.7 OPENMP <br />12.8 OTHER FRAMEWORKS <br />12.9 CONCLUDING REMARKS <br />12.10 FURTHER READING <br />12.11 EXERCISE QUESTIONS </p><p><strong>Deep Learning with Big Data </strong><br />13.1 INTRODUCTION <br />13.2 FUNDAMENTALS <br />13.3 NEURAL NETWORK <br />13.4 TYPES OF DEEP NEURAL NETWORK <br />13.5 BIG DATA APPLICATIONS USING DEEP LEARNING<br />13.6 CONCLUDING REMARKS <br />13.7 FURTHER READING <br />13.8 EXERCISE QUESTIONS </p><p><strong>Big Data Case Studies </strong><br />14.1 GOOGLE EARTH ENGINE <br />14.2 FACEBOOK MESSAGES APPLICATION <br />14.3 HADOOP FOR REAL-TIME ANALYTICS <br />14.4 BIG DATA PROCESSING AT UBER <br />14.5 BIG DATA PROCESSING AT LINKEDIN <br />14.6 DISTRIBUTED GRAPH PROCESSING AT GOOGLE <br />14.7 FUTURE TRENDS <br />14.8 CONCLUDING REMARKS <br />14.9 FURTHER READING <br />14.10 EXERCISE QUESTIONS </p><p>Bibliography <br />Index </p>

Big Data Systems encompass massive challenges related to data diversity, storage mechanisms, and requirements of massive computational power. Further, capabilities of big data systems also vary with respect to type of problems. For instance, distributed memory systems are not recommended for iterative algorithms. Similarly, variations in big data systems also exist related to consistency and fault tolerance. The purpose of this book is to provide a detailed explanation of big data systems. The book covers various topics including Networking, Security, Privacy, Storage, Computation, Cloud Computing, NoSQL and NewSQL systems, High Performance Computing, and Deep Learning. An illustrative and practical approach has been adopted in which theoretical topics have been aided by well-explained programming and illustrative examples. Key Features: Introduces concepts and evolution of Big Data technology. Illustrates examples for thorough understanding. Contains programming examples for hands on development. Explains a variety of topics including NoSQL Systems, NewSQL systems, Security, Privacy, Networking, Cloud, High Performance Computing, and Deep Learning. Exemplifies widely used big data technologies such as Hadoop and Spark. Includes discussion on case studies and open issues. Provides end of chapter questions for enhanced learning.

OCLC-licensed vendor bibliographic record.