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Practical Real-time Data Processing and Analytics

Practical Real-time Data Processing and Analytics

By : Shilpi Saxena, Saurabh Gupta, Selva raj Ramasamy, Prateek Bhati
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Practical Real-time Data Processing and Analytics

Practical Real-time Data Processing and Analytics

1 (1)
By: Shilpi Saxena, Saurabh Gupta, Selva raj Ramasamy, Prateek Bhati

Overview of this book

With the rise of Big Data, there is an increasing need to process large amounts of data continuously, with a shorter turnaround time. Real-time data processing involves continuous input, processing and output of data, with the condition that the time required for processing is as short as possible. This book covers the majority of the existing and evolving open source technology stack for real-time processing and analytics. You will get to know about all the real-time solution aspects, from the source to the presentation to persistence. Through this practical book, you’ll be equipped with a clear understanding of how to solve challenges on your own. We’ll cover topics such as how to set up components, basic executions, integrations, advanced use cases, alerts, and monitoring. You’ll be exposed to the popular tools used in real-time processing today such as Apache Spark, Apache Flink, and Storm. Finally, you will put your knowledge to practical use by implementing all of the techniques in the form of a practical, real-world use case. By the end of this book, you will have a solid understanding of all the aspects of real-time data processing and analytics, and will know how to deploy the solutions in production environments in the best possible manner.
Table of Contents (14 chapters)
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Summary

In this chapter we have discussed various aspects of the big data technology landscape and big data as an infrastructure and computation candidate. We walked the reader through various considerations and caveats to be taken into account while designing and deciding upon the big data infrastructural space. We had our uses introduced to reality of real–time analytics, the NRT architecture, and also touched upon the vast variety of use cases which can possibly be addressed by harnessing the power of IOT and NRT. Towards the end of the chapter, we briefly touched upon the concept of edge computing and cloud infrastructure for IOT.

In the next chapter, we will have the readers moving a little deeper into the real–time analytical application, architecture, and concepts. We will touch upon the basic building blocks of an NRT application, the technology stack required and the challenges encountered while developing it.

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