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
1
Part 1: Exploring Kibana
5
Part 2: Visualizations in Kibana
8
Part 3: Analytics on a Dashboard
12
Part 4: Querying on Kibana and Advanced Concepts

Creating a graph

The Graph API offers an alternative method for retrieving and summarizing data about the documents and keywords in your Elasticsearch index. Essentially, a graph represents a network of interconnected elements. In our context, this refers to a network of related keywords within the index.

Figure 7.1 – Vertices and edges in a graph in Kibana

Figure 7.1 – Vertices and edges in a graph in Kibana

The keywords that you wish to include in the graph are referred to as vertices. Each connection between two vertices represents a relationship. This relationship summarizes the documents that contain both of the terms associated with the vertices. The terms that have been indexed serve as the graph vertices. By utilizing Elasticsearch aggregations, the connections are generated dynamically. The API utilizes Elasticsearch relevance scoring to identify the most significant connections. This means that the same data structures and relevance-ranking tools used for text searches in Elasticsearch are...