Expounding the Edge/Fog Computing Infrastructures for Data Science

Expounding the Edge/Fog Computing Infrastructures for Data Science

Pethuru Raj (Reliance Jio Infocomm. Ltd., India) and Pushpa J. (Jain University, India)
DOI: 10.4018/978-1-5225-5972-6.ch001

Abstract

Data is the new fuel for any system to deliver smart and sophisticated services. Data is being touted as the strategic asset for any organization to plan ahead and provide next-generation capabilities with all the clarity and confidence. Whether data is internally sourced or aggregated from different and distributed source, it is essential for all kinds of data to be continuously and consciously collected, transmitted, cleansed, and hosted on storage systems. There are several types of analytical methods and machines to do deeper and decisive analytics on those curated and consolidated data to extract actionable insights in real-time. Precise and concise analytics guarantee perfect decision-making and action. We need competent and highly integrated analytics platform for speeding up, simplifying and streamlining data analytics, which is becoming a hard nut to crack due to the multi-structured and massive quantities of data. On the infrastructure front, we need highly optimized compute, storage and network infrastructure for achieving data analytics with ease. Another noteworthy point is that there are batch, real-time, and interactive processing of data. Most of the personal and professional applications need real-time insights in order to produce real-time applications. That is, real-time capture, processing, and decision-making are being insisted and hence the edge or fog computing concept has become very popular. This chapter is exclusively designed in order to tell all on how to accomplish real-time analytics on fog devices data.
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Briefing Fog / Edge Computing

Traditional networks, which feed data from devices or transactions to a central storage hub (data warehouses and data marts) can’t keep up with the data volume and velocity created by IoT devices. Nor can the data warehouse model meet the low latency response times that users demand. The Hadoop platform in the cloud was supposed to be an answer. But sending the data to the cloud for analysis also poses a risk of data bottlenecks as well as security concerns. New business models, however, need data analytics in a minute or less. The problem of data congestion will only get worse as IoT applications and devices continue to proliferate.

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