Tutorial Series: Kibana Analytics
In this tutorial series, we will take you from setting up an Elasticsearch cluster with the Twitter River plugin to advanced analysis of Tweet data using Kibana’s friendly UI.
Almost 6,000 items are Tweeted per second, corresponding to about 500 million Tweets per day. Each Tweet contains indexable data. Aside form the obvious text content and the hashtag classification, you can get the Tweet creation time stamp, the location, the user profile information, and more.
In this tutorial, we will use Kibana’s friendly UI to analyze this data using these fields (individually or collectively) and then to visualize the results.
<p”>In Part 1 of this series, Using Kibana and Elasticsearch to Examine Twitter Trends, we learned several things, including:</p”>
- <p”>Setting up an Elasticsearch cluster on qbox.io</p”>
- <p”>Installation of the Twitter river plugin to stream the data of our topic of interest, represented by a hashtag (in our case, #Marvel), to your Qbox Elasticsearch instance from Twitter.</p”>
- <p”>Familiarizing ourselves with the anatomy and structure of our data.</p”>
<p”>At this point, we are ready to learn the Kibana landscape. In this post, we will learn how to a</p”>ccess Kibana from the cluster, a<li”>ccess the Twitter data index we created in the previous post while familiarizing ourselves with the Kibana dashboard, and a</li”>nalyze the trending hashtags of our indexed Tweets using differrent forms of visualization such as pie charts, histograms, and tables
We set up for this post, Part 3 in our Kibana tutorial blog series, by working through the basics of the setup and usage our Kibana analytics dashboard in Using Kibana and Elasticsearch to Examine Twitter Trends(Part 1) and in Tutorial: Simple Analysis using Kibana (Part 2).
<p”>Here, we’ll take a look at some of the advanced analytics features in Kibana, including </p”>
- Maps analytics
- <li”>Hits analytics </li”>
- <li”>List analytics</li”>
We come now to the 4th article in our Kibana tutorial blog series. We covered three advanced analytics features (maps analytics, hits analytics, and list anaytics) in Advanced Kibana Analytics (Part 3 of Series). In this article we’ll look look at three more, including:
- Histogram analytics
- Trends analytics
- Stats analytics