Real-Time Big Data Analytics: Emerging Architecture

The rapidly accelerating universe of new advanced technologies has dramatically reduced data processing cycle time. Thus, making it possible to explore and experiment with data in ways that would not have been practical or even possible a few years ago. Despite the availability of new tools and techniques for dealing with massive amounts of data at incredible and phenomenal speeds, however, the real promise of advanced data analytics lies beyond the realm of pure technology. In real-time big data analytics, big data is processed as it arrives. As a result, real-time big data makes it possible for a business user to get consumable insights without exceeding the time given for decision-making or an analytical system to prompt an action or a notification.

This book, Real-time big data analytics, attempts to give a glimpse of the things to come. A range of solutions appears that will help a scalable hardware solution based on the emerging technology (such as nonvolatile memory devices) and architecture (such as in-memory computing) with the correspondingly well-tuned data analytics algorithm (such as machine learning).

This book discusses different frameworks such as Hadoop, Spark, Storm, and NoSQL for data analytics. It also provides a comparative analysis of the performance of each framework. This book will be a useful resource for students, scholars, and others interested to learn big data and real-time analytics.