Book Image

Kibana 8.x – A Quick Start Guide to Data Analysis

By : Krishna Shah
Book Image

Kibana 8.x – A Quick Start Guide to Data Analysis

By: Krishna Shah

Overview of this book

Unleash the full potential of Kibana—an indispensable tool for data analysts to seamlessly explore vast datasets, uncover key insights, identify trends and anomalies, and share results. This book guides you through its user-friendly interface, interactive visualizations, and robust features, including real-time data monitoring and advanced analytics, showing you how Kibana revolutionizes your approach to navigating and analyzing complex datasets. Starting with the foundational steps of installing, configuring, and running Kibana, this book progresses systematically to explain the search and data visualization capabilities for data stored in the Elasticsearch cluster. You’ll then delve into the practical details of creating data views and optimizing spaces to better organize the analysis environment. As you advance, you'll get to grips with using the discover interface and learn how to build different types of extensive visualizations using Lens. By the end of this book, you’ll have a complete understanding of how Kibana works, helping you leverage its capabilities to build an analytics and visualization solution from scratch for your data-driven use case.
Table of Contents (17 chapters)
Free Chapter
Part 1: Exploring Kibana
Part 2: Visualizations in Kibana
Part 3: Analytics on a Dashboard
Part 4: Querying on Kibana and Advanced Concepts

Analyzing data with entity-centric analysis

The feature of Elastic’s machine learning entity-centric analytics allows you to analyze your data by utilizing algorithms for classification, outlier detection, and regression. It also enables you to generate new indices that include the results alongside your original data.

If you possess a license that includes machine learning features, you can create jobs for entity-centric analytics and view the outcomes on the Data Frame Analytics page in Kibana. The key features that help with this type of analysis are transforms and DataFrame analytics.

Let’s understand both.


Transforms are specific implementations that are used to convert typical time series data into entity-centric data so that we can categorize the data into specific entities. We can do this by creating new indices with summarized data in them. Transforms work by helping us leverage their continuous mode functionality, where we can not only...