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

Understanding anomaly detection in time series data

Anomaly detection is the process of identifying the points in data that don’t fit the normal data behavioral patterns. To make this effective, we can automate the whole process. The important point to note here is that this process will be more efficient when the size of the data has increased. The Elastic Stack supports several data analysis use cases that use supervised and unsupervised machine learning, as follows:

  • Anomaly detection
  • Outlier detection
  • Fraud detection
  • Forecasting
  • Language detection

Our main intention behind putting various techniques to use is to bring out the insights from the most normal-looking data. When we look into anomaly detection, we identify patterns and unusual behavior in the near real-time current and historical data. An unusual data point can be seen in the form of a high spike or very low data behavior, as shown here:

Figure 6.1 – A spike (unusual data behavior) in a sample anomaly detection job in the machine learning app, Kibana

Figure 6.1 –...