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  • Produktbild: Big Data Systems
  • Produktbild: Big Data Systems
- 11%

Big Data Systems A 360-degree Approach

Aus der Reihe Chapman & Hall/CRC Big Data Series
11% sparen

59,99 € UVP 68,00 €

inkl. gesetzl. MwSt., Versandkostenfrei

Beschreibung

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

24.07.2023

Abbildungen

120 SW-Abb., 120 SW-Zeichn., 20 Tabellen

Verlag

Taylor & Francis

Seitenzahl

342

Maße (L/B/H)

25,4/17,8/1,8 cm

Gewicht

645 g

Sprache

Englisch

ISBN

978-0-367-75523-2

Beschreibung

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

24.07.2023

Abbildungen

120 SW-Abb., 120 SW-Zeichn., 20 Tabellen

Verlag

Taylor & Francis

Seitenzahl

342

Maße (L/B/H)

25,4/17,8/1,8 cm

Gewicht

645 g

Sprache

Englisch

ISBN

978-0-367-75523-2

Herstelleradresse

Libri GmbH
Europaallee 1
36244 Bad Hersfeld
DE

Email: gpsr@libri.de

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Die Leseprobe wird geladen.
  • Produktbild: Big Data Systems
  • Produktbild: Big Data Systems

  • 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