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

Troubleshooting a graph

If you face a simple error of the results not appearing, then the first step is to be mindful of how the Graph API requests are designed to filter out irrelevant result documents by implementing various techniques. Let’s look into a few common issues and some strategies to tackle them.

Performance-related issues

To enhance the performance of the graph visualization, the Graph API incorporates a background frequency check for the terms it discovers during exploration. By default, each unique term requires a lookup in the index, which incurs a disk seek and can be costly. However, if noise filtering is not necessary, you can disable this check by setting the Use significance parameter to false. This eliminates the expensive frequency checks but also means that no quality filtering will be performed on the terms.

If your data contains noise and you need to filter based on significance, there are several strategies you can employ to reduce the number...