Cover
Half Title
Title Page
Copyright Page
Dedication
Contents
Preface
Author Bios
Acknowledgments
List of Examples
List of Figures
List of Tables
Section I: Introduction
Chapter 1: 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
Chapter 2: 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
Chapter 3: 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. CLUSTER MANAGEMENT
3.7. FOG COMPUTING
3.8. EXAMPLES
3.9. CONCLUDING REMARKS
3.10. FURTHER READING
3.11. EXERCISE QUESTIONS
Section II: Storage and Processing for Big Data
Chapter 4: HADOOP: An Efficient Platform for Storing and Processing Big Data
4.1. REQUIREMENTS FOR PROCESSING AND STORING BIG DATA
4.2. HADOOP โ 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
Chapter 5: 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
Chapter 6: 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. GRAPHX
6.9. CONCLUDING REMARKS
6.10. FURTHER READING
6.11. EXERCISE QUESTIONS
Chapter 7: NoSQL Systems
7.1. INTRODUCTION
7.2. HANDLING BIG DATA SYSTEMS โ 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
Chapter 8: 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
Section III: Networking, Security, and Privacy for Big Data
Chapter 9: 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 HIGHSPEED DATA TRANSFER
9.5. AVOIDING TCP INCAST โ ACHIEVING LOWLATENCY AND HIGH-THROUGHPUT
9.6. FAULT TOLERANCE
9.7. CONCLUDING REMARKS
9.8. FURTHER READING
9.9. EXERCISE QUESTIONS
Chapter 10: 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
Chapter 11: 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
Section IV: Computation for Big Data
Chapter 12: 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
Chapter 13: 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
Section V: Case Studies and Future Trends
Chapter 14: Big Data: Case Studies and Future Trends
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