Today, understanding data, whatever the nature of the data is, keeps getting harder. They are couple of reasons for that such as the volume, the variety of data, the pace at which the data is created and the complexity to correlate data from different sources.
It’s hard for anyone to cope with this constant increasing challenge, that’s why more and more applications are built to facilitate data management, at every level: ingesting data, processing data, storing data, and ultimately visualizing the data to understand it.
All those levels put together are the fundamental layers to build a data-driven architecture that needs to scale with a growing demand and expectation from users.
There is tons of software and applications out there that could answer those challenges, but rarely will you find a stack that could fulfill all the requirements altogether and across many types of use cases.
The Elastic Stack is one of them: it gives the user a way to access their data in an agile and scalable way. Kibana is part of the Elastic Stack and provide a visualization layer on top of data indexed in Elasticsearch, the storage layer.
In Learning Kibana 5.0, we’ll go through the holistic visualization experience that Kibana offers to address very different use cases, such as creating dashboards using accidents data, or building statistics on top system data, or even detecting anomalies in data.
Rather than listing and going through Kibana features one by one, this book adopts a pragmatic approach where you will learn based on concrete examples and hands-on.
Chapter 1, Introduction to Data-Driven Architecture, describes the fundamental layers that compose a data-driven architecture, and how the Elastic Stack can be used to build it.
Chapter 2, Installing and Setting up Kibana 5.0, covers the installation of Elasticsearch and Kibana, and a walkthrough in Kibana 5.0 anatomy.
Chapter 3, Business Analytics with Kibana 5.0, tackles the first use case of this book, namely business analytics, with the help of Paris accidentology data.
Chapter 4, Logging Analytics with Kibana 5.0, covers a technical logging use case on top of Apache logs data.
Chapter 5, Metric Analytics with Metricbeat and Kibana 5.0, walks the reader through the brand new feature of metrics analytics in Kibana 5.0 with the help of system data from Metricbeat.
Chapter 6, Graph Exploration in Kibana, explains the concept of graphs in the Elastic Stack and introduces forensic graph analysis on top of Stack Overflow data.
Chapter 7, Customizing Kibana 5.0 Timelion, shows how to extend the capabilities of Timelion and build an extension to fetch data from Google Analytics.
Chapter 8, Anomaly Detection in Kibana 5.0, covers the Elastic Stack machine learning features and how to use Kibana to visualize anomalies on top of system data.
Chapter 9, Creating a Custom Plugin for Kibana 5.0, explains how to create a plugin to visualize the Elasticsearch cluster topology.
In this book, you will need to download and install the Elastic Stack, specifically, Elasticsearch, Kibana, Metricbeat, Logstash, and the X-Pack. All the software is available from the following page: http://www.elastic.co/downloads.
The Elastic Stack can be run on a various environment on commodity machines; here is the support matrix: https://www.elastic.co/support/matrix.
This book is for developers, operation teams, business analytics, and data architects who want to learn how to deploy a data-driven architecture using the Elastic Stack 5.0, and more specifically, how to enable visualization on top of the data indexed in Elasticsearch with Kibana 5.0.
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{
"type": "fs",
"settings": {
"location":
"/Users/bahaaldine/Dropbox/Packt/sources/chapter3/
basic_logstash_repository",
"compress": true
}
}Any command-line input or output is written as follows:
GET _cat/indices/basic*
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