Big Data analytics involves the intricate process of analyzing large datasets to discover insights such as correlations, hidden patterns, trends, and user or customer preferences, enabling organizations to make better-informed decisions. These techniques and technologies have become widespread across science, engineering, business, and management, thanks to the growth of data-driven approaches and advances in data engineering, including parallel and distributed analytics frameworks, algorithm parallelization, and GPGPU programming. Despite these advances, challenges remain in achieving real-time big data processing and analytics.
The first volume of this comprehensive two-part handbook introduces a variety of methodologies for Big Data analytics, covering topics such as database management, processing frameworks and architectures, data lakes, query optimization, real-time data processing, data stream analytics, fog and edge computing, and the integration of artificial intelligence with Big Data.
The second volume focuses on a broad spectrum of applications, including secure data storage, privacy-preserving techniques, Software Defined Networks (SDN), Internet of Things (IoT), behavior analytics, traffic prediction, gender-based classification in e-commerce, recommender systems, Big Data regression with Apache Spark, visual sentiment analysis, wavelet neural networks using GPUs, stock market prediction, and financial reporting.
This two-volume set serves as a valuable resource for researchers, engineers, developers, educators, and advanced students working in the field of Big Data analytics.
