Book Image

Learning Kibana 5.0

By : Bahaaldine Azarmi
Book Image

Learning Kibana 5.0

By: Bahaaldine Azarmi

Overview of this book

Kibana is an open source data visualization platform that allows you to interact with your data through stunning, powerful graphics. Its simple, browser-based interface enables you to quickly create and share dynamic dashboards that display changes to Elasticsearch queries in real time. In this book, you’ll learn how to use the Elastic stack on top of a data architecture to visualize data in real time. All data architectures have different requirements and expectations when it comes to visualizing the data, whether it’s logging analytics, metrics, business analytics, graph analytics, or scaling them as per your business requirements. This book will help you master Elastic visualization tools and adapt them to the requirements of your project. You will start by learning how to use the basic visualization features of Kibana 5. Then you will be shown how to implement a pure metric analytics architecture and visualize it using Timelion, a very recent and trendy feature of the Elastic stack. You will learn how to correlate data using the brand-new Graph visualization and build relationships between documents. Finally, you will be familiarized with the setup of a Kibana development environment so that you can build a custom Kibana plugin. By the end of this book you will have all the information needed to take your Elastic stack skills to a new level of data visualization.
Table of Contents (17 chapters)
Learning Kibana 5.0
About the Author
About the Reviewers
Customer Feedback

Introducing the basics of Elastic Graph

Elastic Graph was created to reveal significant relations between data, so that we can see how the variables in question interact. It forms recommendations based on these relations. Data is highly connected, either implicitly or explicitly. These connections can be represented as a graph. Graph based data analysis provides unique insights based on the use case:

  • In a search use case, using a graph, the search experience could be enhanced if the user gets related content based on the query they submitted. This is typically what we could see on an e-commerce website; for example, when purchasing a phone, you could get related accessories. But in the context of Elasticsearch, based on the click stream on a website, the user could get real-time, relevant, and significant suggestions based on his purchase behavior.

  • In the security analytics use case, suspicious connections could be proactively detected based on the logged data. If we have all the access logs...