1st Edition

Big Data Systems
A 360-degree Approach




ISBN 9781498752701
Published July 5, 2021 by Chapman and Hall/CRC
340 Pages 120 B/W Illustrations

USD $99.95

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Book Description

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.

Table of Contents

Preface 
Author Bios 
Acknowledgements 
List of Figures 
List of Tables 


Introduction to Big Data Systems 
1.1 INTRODUCTION: REVIEW OF BIG DATA SYSTEMS
1.2 UNDERSTANDING BIG DATA 
1.3 TYPE OF DATA: TRANSACTIONAL OR ANALYTICAL
1.4 REQUIREMENTS AND CHALLENGES OF BIG DATA 
1.5 CONCLUDING REMARKS 
1.6 FURTHER READING 
1.7 EXERCISE QUESTIONS 

Architecture and Organization of Big Data Systems 
2.1 ARCHITECTURE FOR BIG DATA SYSTEMS 
2.2 ORGANIZATION OF BIG DATA SYSTEMS: CLUSTERS
2.3 CLASSIFICATION OF CLUSTERS: DISTRIBUTED MEMORY VS. SHARED MEMORY
2.4 CONCLUDING REMARKS 
2.5 FURTHER READING 
2.6 EXERCISE QUESTIONS 

Cloud Computing for Big Data 
3.1 CLOUD COMPUTING 
3.2 VIRTUALIZATION 
3.3 PROCESSOR VIRTUALIZATION 
3.4 CONTAINERIZATION 
3.5 VIRTUALIZATION OR CONTAINERIZATION 
3.6 FOG COMPUTING 
3.7 EXAMPLES 
3.8 CONCLUDING REMARKS 
3.9 FURTHER READING 
3.10 EXERCISE QUESTIONS 

HADOOP: An Efficient Platform for Storing and Processing Big Data 
4.1 REQUIREMENTS FOR PROCESSING AND STORING BIG DATA 
4.2 HADOOP - THE BIG PICTURE 
4.3 HADOOP DISTRIBUTED FILE SYSTEM 
4.4 MAPREDUCE 
4.5 HBASE 
4.6 CONCLUDING REMARKS 
4.7 FURTHER READING 
4.8 EXERCISE QUESTIONS 

Enhancements in Hadoop 
5.1 ISSUES WITH HADOOP 
5.2 YARN 
5.3 PIG 
5.4 HIVE 
5.5 DREMEL 
5.6 IMPALA 
5.7 DRILL 
5.8 DATA TRANSFER 
5.9 AMBARI 
5.10 CONCLUDING REMARKS 
5.11 FURTHER READING 
5.12 EXERCISE QUESTIONS 

Spark 
6.1 LIMITATIONS OF MAPREDUCE 
6.2 INTRODUCTION TO SPARK 
6.3 SPARK CONCEPTS 
6.4 SPARK SQL 
6.5 SPARK MLLIB 
6.6 STREAM BASED SYSTEM 
6.7 SPARK STREAMING 
6.8 CONCLUDING REMARKS 
6.9 FURTHER READING 
6.10 EXERCISE QUESTIONS 

NoSQL Systems 
7.1 INTRODUCTION 
7.2 HANDLING BIG DATA SYSTEMS - PARALLEL RDBMS 
7.3 EMERGENCE OF NOSQL SYSTEMS 
7.4 KEY-VALUE DATABASE 
7.5 DOCUMENT-ORIENTED DATABASE 
7.6 COLUMN-ORIENTED DATABASE 
7.7 GRAPH DATABASE 
7.8 CONCLUDING REMARKS 
7.9 FURTHER READING 
7.10 EXERCISE QUESTIONS 

NewSQL Systems 
8.1 INTRODUCTION
8.2 TYPES OF NEWSQL SYSTEMS 
8.3 FEATURES 
8.4 NEWSQL SYSTEMS: CASE STUDIES 
8.5 CONCLUDING REMARKS 
8.6 FURTHER READING
8.7 EXERCISE QUESTIONS 


Networking for Big Data 
9.1 NETWORK ARCHITECTURE FOR BIG DATA SYSTEMS
9.2 CHALLENGES AND REQUIREMENTS 
9.3 NETWORK PROGRAMMABILITY AND SOFTWARE DEFINED NETWORKING 
9.4 LOW LATENCY AND HIGH SPEED DATA TRANSFER
9.5 AVOIDING TCP INCAST - ACHIEVING LOW LATENCY
AND HIGH THROUGHPUT 
9.6 FAULT TOLERANCE
9.7 CONCLUDING REMARKS 
9.8 FURTHER READING 
9.9 EXERCISE QUESTIONS 

Security for Big Data 
10.1 INTRODUCTION 
10.2 SECURITY REQUIREMENTS 
10.3 SECURITY: ATTACK TYPES AND MECHANISMS 
10.4 ATTACK DETECTION AND PREVENTION 
10.5 CONCLUDING REMARKS 
10.6 FURTHER READING 
10.7 EXERCISE QUESTIONS 

Privacy for Big Data 
11.1 INTRODUCTION 
11.2 UNDERSTANDING BIG DATA AND PRIVACY 
11.3 PRIVACY VIOLATIONS AND THEIR IMPACT 
11.4 TYPES OF PRIVACY VIOLATIONS 
11.5 PRIVACY PROTECTION SOLUTIONS AND THEIR LIMITATIONS 
11.6 CONCLUDING REMARKS 
11.7 FURTHER READING 
11.8 EXERCISE QUESTIONS 

High Performance Computing for Big Data
12.1 INTRODUCTION 
12.2 SCALABILITY: NEED FOR HPC 
12.3 GRAPHIC PROCESSING UNIT 
12.4 TENSOR PROCESSING UNIT 
12.5 HIGH SPEED INTERCONNECTS 
12.6 MESSAGE PASSING INTERFACE 
12.7 OPENMP 
12.8 OTHER FRAMEWORKS 
12.9 CONCLUDING REMARKS 
12.10 FURTHER READING 
12.11 EXERCISE QUESTIONS 

Deep Learning with Big Data 
13.1 INTRODUCTION 
13.2 FUNDAMENTALS 
13.3 NEURAL NETWORK 
13.4 TYPES OF DEEP NEURAL NETWORK 
13.5 BIG DATA APPLICATIONS USING DEEP LEARNING
13.6 CONCLUDING REMARKS 
13.7 FURTHER READING 
13.8 EXERCISE QUESTIONS 

Big Data Case Studies 
14.1 GOOGLE EARTH ENGINE 
14.2 FACEBOOK MESSAGES APPLICATION 
14.3 HADOOP FOR REAL-TIME ANALYTICS 
14.4 BIG DATA PROCESSING AT UBER 
14.5 BIG DATA PROCESSING AT LINKEDIN 
14.6 DISTRIBUTED GRAPH PROCESSING AT GOOGLE 
14.7 FUTURE TRENDS 
14.8 CONCLUDING REMARKS 
14.9 FURTHER READING 
14.10 EXERCISE QUESTIONS 

Bibliography 
Index 

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Author(s)

Biography

Jawwad A. Shamsi completed B.E. (Electrical Engineering) from NED University of Enginnering and Technology, Karachi in 1998. He completed his MS in Computer and Information Sciences from University of Michigan-Dearborn, MI, USA in 2002. In 2009, he completed his PhD. from Wayne State University, MI, USA. He has also worked as a Programmar Analyst in USA from 2000 to 2002. In 2009, he joined FAST- National Univesity of Computer and Emerging Sciences (NUCES), Karachi. He has served as the head of computer science department from 2012 to 2017. Currently, he is serving as a Professor of Computer Science and Director of the Karachi Campus. He also leads a research group - syslab (http://syslab.khi.nu.edu.pk). His research is focused on developing systems which can meet the growing needs of scalability, security, high performance, robustness, and agility. His research has been funded by different International and National agencies including NVIDIA and Higher Education Commission, Pakistan.

Muhammad Ali Khojaye has more than decade of industrial experience ranging from the cloud-native side of things to distributed systems design, CI/CD, and infrastructure. His current technical interests revolve around big data, cloud, containers, and large scale systems design. He currently lives in the Glasgow suburbs with his wife and son. When he's not at work, Ali enjoys cycling, travelling, and spending time with family and friends.