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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

Product type Paperback

Published in Feb 2024

Publisher Packt

ISBN-13 9781803232164

Length 198 pages

Edition 1st Edition

Tools

Kibana

Concepts

Data Analysis

Author (1):

Krishna Shah

View More author details

Table of Contents (17) Chapters

Preface

1. Part 1: Exploring Kibana FREE CHAPTER

2. Chapter 1: Introduction to Kibana

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

Why subscribe?

16. Other Books You May Enjoy

Technical requirements

Kibana requires specific hardware specifications when installed on a server, which includes support for 64-bit operating systems. The installation process offers multiple package formats, such as tar, deb, rpm, and Docker. Kibana can be installed, configured, and started from an archive on Linux, macOS, or Windows.

The hardware requirements for Kibana may vary depending on the specific use-case requirements. However, it is generally recommended to allocate 1 GB to 2 GB of RAM and 2 CPUs for use cases involving PDF, CSV, and PNG reporting in Kibana.

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Authors (1)

Shah

Shah

Krishna Shah is a data architect from Melbourne, Australia with 9+ years of experience, and she knows how to make data work. She's been an official trainer for Elasticsearch and Kibana, crafting the courses that empower people to unlock the secrets of data. Prior to that, she worked for a start-up in India as the data engineer behind building and maintaining data engineering pipelines, then transforming that raw information into stunning visuals and insights using Kibana and other data engineering technologies. Today, she's an advocate, a mentor, and a bridge-builder, inviting everyone to find their own rhythm in the data's dance. Whether you're a novice or seasoned analyst, brace yourself for her infectious enthusiasm and knack for making the driest of datasets sing!

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