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Learning Elastic Stack 6.0

Learning Elastic Stack 6.0

By : Pranav Shukla, Sharath Kumar
4.5 (10)
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Learning Elastic Stack 6.0

Learning Elastic Stack 6.0

4.5 (10)
By: Pranav Shukla, Sharath Kumar

Overview of this book

The Elastic Stack is a powerful combination of tools for distributed search, analytics, logging, and visualization of data from medium to massive data sets. The newly released Elastic Stack 6.0 brings new features and capabilities that empower users to find unique, actionable insights through these techniques. This book will give you a fundamental understanding of what the stack is all about, and how to use it efficiently to build powerful real-time data processing applications. After a quick overview of the newly introduced features in Elastic Stack 6.0, you’ll learn how to set up the stack by installing the tools, and see their basic configurations. Then it shows you how to use Elasticsearch for distributed searching and analytics, along with Logstash for logging, and Kibana for data visualization. It also demonstrates the creation of custom plugins using Kibana and Beats. You’ll find out about Elastic X-Pack, a useful extension for effective security and monitoring. We also provide useful tips on how to use the Elastic Cloud and deploy the Elastic Stack in production environments. On completing this book, you’ll have a solid foundational knowledge of the basic Elastic Stack functionalities. You’ll also have a good understanding of the role of each component in the stack to solve different data processing problems.
Table of Contents (12 chapters)
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Summary

In this chapter, we built a sensor data analytics application that has a wide variety of applications, as it is related to the emerging IoT field. We understood the problem domain and the data model, including metadata related to sensors. We wanted to build an analytics application using only Elastic Stack components, without using any other tools and programming languages, to get a powerful tool that can handle large volumes of data.

We started at the very core by designing the data model for Elasticsearch. Then we designed a data pipeline that is secured and can accept data over the internet using HTTP. We enriched the incoming data using the metadata that we had in a relational database and stored in Elasticsearch. We sent some test data over HTTP just like real sensors send over the internet. We built some meaningful visualizations that will give answers to some typical...

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