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


In this chapter, we understood how TSVB is a very powerful and useful tool that lets us create dynamic and versatile visualizations based on near-real-time data. We studied the Metric visualization, which helps us plot metric aggregations in the form of a metric on the visualization and has the dynamic effect of updating color conditionals automatically.

Top N and Gauge, on the other hand, focus on the implementation of the bucket aggregation. Markdown is a very unique type of visualization that helps us add text or information on any particular use case-specific dashboard. Lastly, the Table visualization helps us to visualize the data in tabular format along with the aggregation implementation against individual fields.

In the next chapter, we shall open up new doors to learn how machine learning helps us take this data analysis to an advanced level.