Chapter 1, Revising the ELK Stack, this chapter will explain details of ELK stack which is now known as Elastic Stack. Although they've all been built to work exceptionally well together, each one is a separate project that is driven by the open-source vendor Elastic. Through this chapter reader will get complete idea of these three software and will able to figure out that how we can combine these to achieve different use cases.
Chapter 2, Setting Up and Customizing the Kibana Dashboard, In this chapter we will know how to customize Kibana visualization by adding title, resizing panels, change colors and opacity, modify the legends etc. This will also explain how we can embed the dashboard on our existing application, By tweaking these features we can create more meaningful and impact full dashboards.Â
Chapter 3, Â Exploring Your Data, Here we will come to know the Discover tab functionalities like Search Bar, Time Filter, Field Selector, Data Histogram and Log View. Discover option provide us the way to search and select required fields from our dataset. It provides us the complete picture of Elastic search data which is loaded into Kibana.
Chapter 4, Visualizing the Data, The Kibana Visualize page is where we can create, modify, and view our own custom visualizations. There are different types of visualizations, ranging from Vertical bar and Pie charts to Tile maps and Data tables. Different type of visualization can be created using Kibana Visualize option. Visualizations can also be shared with other users who have access to the Kibana instance.In this chapter reader will learn to create various types of data visualizations like Vertical bar,Pie charts, Tile maps,Data tables and tag clouds etc.
Chapter 5, Dashboarding to Showcase Key Performance Indicators, With a dashboard, we can combine multiple visualizations onto a single page. Here we can filter them by providing a search query or by selecting filters by clicking elements in the visualization. Dashboards are useful when we want to get an overview of logs, and make correlations among various visualizations and logs. We can also export the csv data from data tables of Kibana.
Chapter 6, Handling Time Series Data with Timelion , In this chapter we will learn about Timelion which is a time series visualization plugin for Kibana which enables us to combine independent data sources within the same visualization. As with normal visualizations in Kibana, we can visualize Timelion expressions from the Visualize tab. It provides us various features such as function chaining, analyzing trends, data formatting, and performing basic calculations.
Chapter 7, Interact with Your Data Using Dev Tools , in this chapter we will learn about Dev Tools which contains development tools that we can use to interact with data in Kibana. Console plugin of Kibana Dev Tools provides a UI to interact with the REST API of Elasticsearch. Console has two main areas: the editor, where we can compose requests to Elasticsearch, and the response pane, which displays the responses to the request.
Chapter 8, Tweaking Your Configuration with Kibana Management, in this chapter we will cover Kibana Management interface is used to perform runtime configuration of Kibana, initial setup and ongoing configuration of index patterns, advanced settings that tweak the behaviors of Kibana itself, and various "objects" that we can save throughout Kibana such as searches, visualizations, and dashboards.
Chapter 9, Understanding X-Pack Features , in this chapter we will come to know how to setup X-Pack and use different features like security, alerting, monitoring, reporting and machine learning. In default setup of ELK we do not have these features and for using X-Pack we need to purchase the license. X-Pack provide us the feature to secure the ELK stack will user role and permission.
Chapter 10, Machine Learning with Kibana , in this chapter we will learn about Machine learning which is the science of getting computers to act without being explicitly programmed. For applying machine learning on our dataset we need to use any programming language like R or Python but Kibana provides us a tab with X-Pack for creating machine learning jobs and managing them. We can apply machine learning in any time based dataset and can get the output in Kibana UI. We can detect anomalies, find root cause of any problem, easily forecast the future trends and find many answers from our data using machine learning.
Chapter 11, Create Super Cool Dashboard from a Web Application , in this chapter we will cover how we can create a super cool dashboard from an existing web application through practical example. Here I will drive through application data flow from database to Kibana and then from Kibana visualization to Dashboard. The dashboard can independently be used or we can embed it in our web application.
Chapter 12, Different Use Cases of Kibana, in this chapter we will cover different important use cases of Kibana like handling time series data where we will cover conditional formatting and tracking trends etc. After that we will cover how to work with visual builder to handle the time series data and then will cover GeoIP for Elastic Search and how we can plot data on maps.
Chapter 13, Create Monitoring Dashboard Using Beats, in this chapter we will learn about Beats which works as a data shippers. This chapter will explain to create a quick monitoring dashboard using Beats. We will come to know about different type of beats like Metricbeat, Packetbeat, Filebeat, and so on. Here I will cover each steps from Beats configuration to dashboard creation.In this chapter reader would be able to create quick monitoring dashboard using Beats.
 Chapter 14, Best Practices, in this chapter we will cover different best practices which we need to ensure while working with Elastic Stack. By following these best practices we can get optimum performance from our Elastic stack setup.
