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

Chapter 8. Anomaly Detection in Kibana 5.0

In September 2016, Elastic announced the acquisition of Prelert, now called Machine Learning, a behavioral analytics company. Prelert combines an anomaly detection engine, Elasticsearch for storing the analysis, and Kibana for visualizing the analysis.

The anomaly detection engine brings unsupervised machine learning capabilities to the Elastic Stack so that Prelert is able to learn from the data as it ingests them, and can highlight events that deviate from expectations.

In this chapter we'll explore the following:

  • Applying the use case of Prelert to find a solution in anomaly detection

  • Using Prelert and Kibana for operational analytics

  • Leveraging Timelion, X-Pack alerting, and reporting features to visualize and be apprised of anomalies


As a disclaimer, the version of Prelert used in this chapter is an exclusive preview of what Prelert will look like in the upcoming GA version. It's not a public version. At the time of writing this chapter, the current...