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Big Data Systems: A 360-degree Approach [Kõva köide]

(National University of Computer and Emerging Sciences, Karachi, Sindh, Pakistan), (Sopra Steria, Glasgow, United Kingdom)
  • Formaat: Hardback, 340 pages, kõrgus x laius: 254x178 mm, kaal: 820 g, 20 Tables, black and white; 120 Line drawings, black and white; 120 Illustrations, black and white
  • Sari: Chapman & Hall/CRC Big Data Series
  • Ilmumisaeg: 05-Jul-2021
  • Kirjastus: Chapman & Hall/CRC
  • ISBN-10: 1498752705
  • ISBN-13: 9781498752701
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Big Data Systems: A 360-degree ApproachSuurem pilt
  • Formaat: Hardback, 340 pages, kõrgus x laius: 254x178 mm, kaal: 820 g, 20 Tables, black and white; 120 Line drawings, black and white; 120 Illustrations, black and white
  • Sari: Chapman & Hall/CRC Big Data Series
  • Ilmumisaeg: 05-Jul-2021
  • Kirjastus: Chapman & Hall/CRC
  • ISBN-10: 1498752705
  • ISBN-13: 9781498752701
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Püsilink: https://www.kriso.ee/db/9781498752701.html
Märksõnad:
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.
Preface xiii
Author Bios xv
Acknowledgments xvii
List of Examples xix
List of Figures xxiii
List of Tables xxvii
Section I Introduction
Chapter 1 Introduction To Big Data Systems
3(8)
1.1 Introduction: Review Of Big Data Systems
3(1)
1.2 Understanding Big Data
4(1)
1.3 Type Of Data: Transactional Or Analytical
5(3)
1.4 Requirements And Challenges Of Big Data
8(1)
1.5 Concluding Remarks
9(1)
1.6 Further Reading
9(1)
1.7 Exercise Questions
9(2)
Chapter 2 Architecture And Organization Of Big Data Systems
11(18)
2.1 Architecture For Big Data Systems
11(2)
2.2 Organization Of Big Data Systems: Clusters
13(5)
2.3 Classification Of Clusters: Distributed Memory vs. Shared Memory
18(7)
2.4 Concluding Remarks
25(1)
2.5 Further Reading
25(1)
2.6 Exercise Questions
26(3)
Chapter 3 Cloud Computing For Big Data
29(36)
3.1 Cloud Computing
30(9)
3.2 Virtualization
39(2)
3.3 Processor Virtualization
41(4)
3.4 Containerization
45(2)
3.5 Virtualization Or Containerization
47(1)
3.6 Cluster Management
48(4)
3.7 Fog Computing
52(1)
3.8 Examples
53(5)
3.9 Concluding Remarks
58(1)
3.10 Further Reading
59(1)
3.11 Exercise Questions
59(6)
Section II Storage And Processing For Big Data
Chapter 4 Hadoop: An Efficient Platform For Storing And Processing Big Data
65(28)
4.1 Requirements For Processing And Storing Big Data
66(1)
4.2 Hadoop - The Big Picture
66(1)
4.3 Hadoop Distributed File System
67(5)
4.4 Mapreduce
72(15)
4.5 HBase
87(3)
4.6 Concluding Remarks
90(1)
4.7 Further Reading
90(1)
4.8 Exercise Questions
90(3)
Chapter 5 Enhancements In Hadoop
93(24)
5.1 Issues With Hadoop
93(1)
5.2 Yarn
94(4)
5.3 Pig
98(2)
5.4 Hive
100(3)
5.5 Dremel
103(1)
5.6 Impala
104(1)
5.7 Drill
105(1)
5.8 Data Transfer
106(5)
5.9 Ambari
111(2)
5.10 Concluding Remarks
113(1)
5.11 Further Reading
114(1)
5.12 Exercise Questions
114(3)
Chapter 6 Spark
117(26)
6.1 Limitations Of Mapreduce
118(1)
6.2 Introduction To Spark
119(1)
6.3 Spark Concepts
120(6)
6.4 Spark SQL
126(1)
6.5 Spark MLlib
127(5)
6.6 Stream-Based System
132(1)
6.7 Spark Streaming
133(5)
6.8 GraphX
138(2)
6.9 Concluding Remarks
140(1)
6.10 Further Reading
140(1)
6.11 Exercise Questions
140(3)
Chapter 7 NoSQL Systems
143(28)
7.1 Introduction
144(1)
7.2 Handling Big Data Systems - Parallel RDBMS
144(4)
7.3 Emergence Of NoSQL Systems
148(2)
7.4 Key-Value Database
150(5)
7.5 Document-Oriented Database
155(5)
7.6 Column-Oriented Database
160(4)
7.7 Graph Database
164(4)
7.8 Concluding Remarks
168(1)
7.9 Further Reading
168(1)
7.10 Exercise Questions
169(2)
Chapter 8 NewSQL Systems
171(12)
8.1 Introduction
171(1)
8.2 Types Of Newsql Systems
171(1)
8.3 Features
172(2)
8.4 NewSQL Systems: Case Studies
174(5)
8.5 Concluding Remarks
179(1)
8.6 Further Reading
179(1)
8.7 Exercise Questions
179(4)
Section III Networking, Security, And Privacy For Big Data
Chapter 9 Networking For Big Data
183(20)
9.1 Network Architecture For Big Data Systems
183(3)
9.2 Challenges And Requirements
186(1)
9.3 Network Programmability And Software-Defined Net-Working
187(5)
9.4 Low-Latency And High-Speed Data Transfer
192(5)
9.5 Avoiding TCP Incast - Achieving Low-Latency And High-Throughput
197(1)
9.6 Fault Tolerance
198(1)
9.7 Concluding Remarks
199(1)
9.8 Further Reading
200(1)
9.9 Exercise Questions
200(3)
Chapter 10 Security For Big Data
203(16)
10.1 Introduction
203(1)
10.2 Security Requirements
204(1)
10.3 Security: Attack Types And Mechanisms
205(3)
10.4 Attack Detection And Prevention
208(8)
10.5 Concluding Remarks
216(1)
10.6 Further Reading
216(1)
10.7 Exercise Questions
216(3)
Chapter 11 Privacy For Big Data
219(14)
11.1 Introduction
219(1)
11.2 Understanding Big Data And Privacy
220(1)
11.3 Privacy Violations And Their Impact
220(1)
11.4 Types Of Privacy Violations
221(3)
11.5 Privacy Protection Solutions And Their Limitations
224(5)
11.6 Concluding Remarks
229(1)
11.7 Further Reading
229(1)
11.8 Exercise Questions
229(4)
Section IV Computation For Big Data
Chapter 12 High-Performance Computing For Big Data
233(20)
12.1 Introduction
233(1)
12.2 Scalability: Need For HPC
234(1)
12.3 Graphic Processing Unit
235(4)
12.4 Tensor Processing Unit
239(2)
12.5 High Speed Interconnects
241(2)
12.6 Message Passing Interface
243(4)
12.7 OpenMP
247(2)
12.8 Other Frameworks
249(1)
12.9 Concluding Remarks
249(1)
12.10 Further Reading
249(1)
12.11 Exercise Questions
250(3)
Chapter 13 Deep Learning With Big Data
253(20)
13.1 Introduction
253(1)
13.2 Fundamentals
254(3)
13.3 Neural Network
257(1)
13.4 Types Of Deep Neural Network
258(6)
13.5 Big Data Applications Using Deep Learning
264(4)
13.6 Concluding Remarks
268(1)
13.7 Further Reading
268(1)
13.8 Exercise Questions
268(5)
Section V Case Studies And Future Trends
Chapter 14 Big Data: Case Studies And Future Trends
273(10)
14.1 Google Earth Engine
273(1)
14.2 Facebook Messages Application
274(2)
14.3 Hadoop For Real-Time Analytics
276(1)
14.4 Big Data Processing At Uber
277(1)
14.5 Big Data Processing At Linkedin
278(2)
14.6 Distributed Graph Processing At Google
280(1)
14.7 Future Trends
280(1)
14.8 Concluding Remarks
281(1)
14.9 Further Reading
281(1)
14.10 Exercise Questions
281(2)
Bibliography 283(26)
Index 309
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.