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

You're reading from   Kibana 8.x – A Quick Start Guide to Data Analysis Learn about data exploration, visualization, and dashboard building with Kibana

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Product type Paperback
Published in Feb 2024
Publisher Packt
ISBN-13 9781803232164
Length 198 pages
Edition 1st Edition
Tools
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Author (1):
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Krishna Shah Krishna Shah
Author Profile Icon Krishna Shah
Krishna Shah
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Toc

Table of Contents (17) Chapters Close

Preface 1. Part 1: Exploring Kibana
2. Chapter 1: Introduction to Kibana FREE CHAPTER 3. Chapter 2: Creating Data Views and Introducing Spaces 4. Chapter 3: Discovering the Data through Discover 5. Part 2: Visualizations in Kibana
6. Chapter 4: How About We Visualize? 7. Chapter 5: Powering Visualizations with Near Real-Time Updates 8. Part 3: Analytics on a Dashboard
9. Chapter 6: Data Analysis with Machine Learning 10. Chapter 7: Graph Visualization 11. Chapter 8: Finally, the Dashboard 12. Part 4: Querying on Kibana and Advanced Concepts
13. Chapter 9: ES|QL and Advanced Kibana Concepts 14. Chapter 10: Query DSL and Management through Kibana 15. Index 16. Other Books You May Enjoy

Summary

In this chapter, we explored and deep-dived into how different types of visualizations can be created and saved to a library or added to a dashboard. We explored a Lens visualization as one sure solution to many problems, with the help of two types of aggregations: metric and bucket aggregations, which work for us in the backend to retrieve the data. We also studied how geospatial fields that have geographical coordinates mapped to them in the data can be used to create a Maps visualization to pictorially display data on a world map. Also, we saw that Canvas, on the other hand, helps us create every type of view that could be part of a completely custom-defined requirement for a use case.

In the upcoming chapter, we will see how we utilize these visualizations to create a view called a dashboard that will help us connect a lot of dots in establishing important relationships within our data in the cluster.

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