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Real-Time Analytics: Techniques to Analyze



Real-Time Analytics: Techniques to Analyze

Author: Byron Ellis

Publisher: Wiley

Genres:

Publish Date: July 21, 2014

ISBN-10: 1118837916

Pages: 432

File Type: Epub, Mobi

Language: English

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Book Preface

It seems like the world moves at a faster pace every day. People and places become more connected, and people and organizations try to react at an ever-increasing pace. Reaching the limits of a human’s ability to respond, tools are built to process the vast amounts of data available to decision makers, analyze it, present it, and, in some cases, respond to events as they happen.

The collection and processing of this data has a number of application areas, some of which are discussed in the next section. These applications, which are discussed later in this chapter, require an infrastructure and method of analysis specific to streaming data. Fortunately, like batch processing before it, the state of the art of streaming infrastructure is focused on using commodity hardware and software to build its systems rather than the specialized systems required for real-time analysis prior to the Internet era. This, combined with flexible cloud-based environment, puts the implementation of a real-time system within the reach of nearly any organization. These commodity systems allow organizations to analyze their data in real time and scale that infrastructure to meet future needs as the organization grows and changes over time.

The goal of this book is to allow a fairly broad range of potential users and implementers in an organization to gain comfort with the complete stack of applications. When real-time projects reach a certain point, they should be agile and adaptable systems that can be easily modified, which requires that the users have a fair understanding of the stack as a whole in addition to their own areas of focus. “Real time” applies as much to the development of new analyses as it does to the data itself. Any number of well-meaning projects have failed because they took so long to implement that the people who requested the project have either moved on to other things or simply forgotten why they wanted the data in the first place. By making the projects agile and incremental, this can be avoided as much as possible.


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