Book Image

Mastering Kibana 6.x

Book Image

Mastering Kibana 6.x

Overview of this book

Kibana is one of the popular tools among data enthusiasts for slicing and dicing large datasets and uncovering Business Intelligence (BI) with the help of its rich and powerful visualizations. To begin with, Mastering Kibana 6.x quickly introduces you to the features of Kibana 6.x, before teaching you how to create smart dashboards in no time. You will explore metric analytics and graph exploration, followed by understanding how to quickly customize Kibana dashboards. In addition to this, you will learn advanced analytics such as maps, hits, and list analytics. All this will help you enhance your skills in running and comparing multiple queries and filters, influencing your data visualization skills at scale. With Kibana’s Timelion feature, you can analyze time series data with histograms and stats analytics. By the end of this book, you will have created a speedy machine learning job using X-Pack capabilities.
Table of Contents (21 chapters)
Title Page
Copyright and Credits
Packt Upsell
Contributors
Preface
Index

Bar charts


Bar charts are a type of visualization which are used to compare the number, frequency, or other measures for different categories of data. Bar charts are the most common type of visualization, and are easy to create and easy to interpret as well. They are used to present categorical data in the form of rectangular bars with heights/lengths proportional to the given values.

There are two categories of aggregation that we need to configure for creating bar charts: metric aggregation and bucket aggregation.

Metric aggregation

Metric aggregation computes metrics on the basis of the way the values of the documents are aggregated. The following are the types of metric aggregations:

  • Average: This is used to calculate the average of a numeric field.
  • Max: This is used to calculate the maximum value of a numeric field.
  • Median: This is used to calculate the median value of a numeric field.
  • Min: This is used to calculate the minimum value of a numeric field.
  • Percentile ranks: This returns the percentile...