Shamsi / Khojaye - Big Data Systems

Big Data Systems

A 360-degree Approach

Medium: Buch

ISBN: 978-1-4987-5270-1

Verlag: Chapman and Hall/CRC

Erscheinungstermin: 05.07.2021

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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.

Produkteigenschaften

Artikelnummer: 9781498752701
Medium: Buch
ISBN: 978-1-4987-5270-1
Verlag: Chapman and Hall/CRC
Erscheinungstermin: 05.07.2021
Sprache(n): Englisch
Auflage: 1. Auflage 2021
Serie: Chapman & Hall/CRC Big Data Series
Produktform: Gebunden, HC gerader Rücken kaschiert
Gewicht: 839 g
Seiten: 340
Format (B x H x T): 183 x 260 x 23 mm
Ausgabetyp: Kein, Unbekannt

Themen

Autoren

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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