Big Data 2.0 Processing Systems: A Systems Overview

Big Data 2.0 Processing Systems: A Systems Overview is a focused, readable survey of the modern big data landscape after the rise of Hadoop and MapReduce. Sherif Sakr traces how the field expanded into a wider ecosystem of engines and frameworks built for different kinds of data and workloads, from structured data and graph processing to streaming systems and machine/deep learning.

Big Data beyond the original Hadoop model

The strength of this volume is its wide-angle view. Rather than centering on one platform, it explains why the first generation of big data tools reached practical limits and how newer systems emerged to answer those limits. That makes the book especially useful for readers trying to understand not just one technology, but the overall direction of the field.

A structured survey of major system families

• general-purpose big data processing systems
• large-scale processing systems for structured data
• large-scale graph processing systems
• large-scale stream processing systems
• machine learning and deep learning frameworks
• conclusions and future research directions

This structure gives the book a natural flow: background first, then a tour through the major system categories, and finally a look at open problems and what still needs attention.

Why readers use it

The book works well as a survey text and as a practical orientation guide. Students can use it to build a map of the subject area. Researchers can use it to identify topics and compare approaches. Professionals and practitioners will find it especially helpful as a compact reference to the state of the art in big data processing systems.

Edition details

This is the second edition, published by Springer, and presented in the digital edition used by Digital Delights as an ebook-ready technical monograph.