Kibana is an open source data exploration and visualization platform. It is part of Elastic Stack, where we have Elasticsearch, Logstash, and Beats, along with Kibana. Using Kibana, we can explore data visually and can analyze it in real time. Kibana enables us to implement APM for application performance monitoring and Timelion enables us to play with time-series data. Then we have Dev Tools, by means of which we can run Elasticsearch queries direct from the Kibana interface. We have ML, by means of which we can predict future trends or ascertain anomalies in the data. Kibana provides us with Reporting, through which we can export CSV or PDF reports, Monitoring, to get insights into the complete Elastic Stack, and Watcher, to alert you in the event of any issue with the data.
Kibana, along with other Elastic Stack components, provides us with full-stack monitoring capability. Using Beats, we can get system metrics, log data, packet data, and so on. Logstash enables us to retrieve data from any other possible sources, including DBMS, CSV, or any other third-party tool, and then, using APM, we can fetch application data to monitor application performance. In this way, using Kibana, we can have an end-to-end monitoring system where a single dashboard can show all key performance indicators.
This book is there to help you understand the core concepts and the practical implementations, by means of which you can start using Kibana for a variety of use cases. It covers how to ingest data from different sources, using Beats or Logstash, into Elasticsearch, and then how to explore, analyze, and visualize it in Kibana. It covers how to play with time-series data to create complex graphs using Timelion and show them on your dashboard along with other visualizations, and then how to embed your dashboard or visualization on a web page. You will also learn about APM to monitor your application by installing and configuring the APM server and APM agents. We have also covered different X-Pack features, such as user and role management under security, alerting, monitoring, and ML. This book will also explain how to create ML jobs to find anomalies in your data.
