This book primarily aims to provide an in-depth understanding of recent advances in big data computing technologies, methodologies, and applications along with introductory details of big data computing models such as Apache Hadoop, MapReduce, Hive, Pig, Mahout in-memory storage systems, NoSQL databases, and big data streaming services such as Apache Spark, Kafka, and so forth. It also covers developments in big data computing applications such as machine learning, deep learning, graph processing, and many others.

Features:

• Provides comprehensive analysis of advanced aspects of big data challenges and enabling technologies.
• Explains computing models using real-world examples and dataset-based experiments.
• Includes case studies, quality diagrams, and demonstrations in each chapter.
• Describes modifications and optimization of existing technologies along with the novel big data computing models.
• Explores references to machine learning, deep learning, and graph processing.

This book is aimed at graduate students and researchers in high-performance computing, data mining, knowledge discovery, and distributed computing.

This book primarily aims to provide an in-depth understanding of recent advances in big data computing technologies, methodologies, and applications along with introductory details of big data computing models such as Apache Hadoop, MapReduce, Hive, Pig, Mahout in-memory storage systems, NoSQL databases, and big data streaming services such as Apache Spark, Kafka, and so forth. It also covers developments in big data computing applications such as machine learning, deep learning, graph processing, and many others.

Features:

• Provides comprehensive analysis of advanced aspects of big data challenges and enabling technologies.
• Explains computing models using real-world examples and dataset-based experiments.
• Includes case studies, quality diagrams, and demonstrations in each chapter.
• Describes modifications and optimization of existing technologies along with the novel big data computing models.
• Explores references to machine learning, deep learning, and graph processing.

This book is aimed at graduate students and researchers in high-performance computing, data mining, knowledge discovery, and distributed computing.

This book primarily aims to provide an in-depth understanding of recent advances in big data computing technologies, methodologies, and applications along with introductory details of big data computing models such as Apache Hadoop, MapReduce, Hive, Pig, Mahout in-memory storage systems, NoSQL databases, and big data streaming services such as Apache Spark, Kafka, and so forth. It also covers developments in big data computing applications such as machine learning, deep learning, graph processing, and many others.

Features:

• Provides comprehensive analysis of advanced aspects of big data challenges and enabling technologies.
• Explains computing models using real-world examples and dataset-based experiments.
• Includes case studies, quality diagrams, and demonstrations in each chapter.
• Describes modifications and optimization of existing technologies along with the novel big data computing models.
• Explores references to machine learning, deep learning, and graph processing.

This book is aimed at graduate students and researchers in high-performance computing, data mining, knowledge discovery, and distributed computing.