Sign In Start Free Trial
Account

Add to playlist

Create a Playlist

Modal Close icon
You need to login to use this feature.
  • Elastic Stack 8.x Cookbook
  • Toc
  • feedback
Elastic Stack 8.x Cookbook

Elastic Stack 8.x Cookbook

By : Huage Chen, Yazid Akadiri
5 (3)
close
Elastic Stack 8.x Cookbook

Elastic Stack 8.x Cookbook

5 (3)
By: Huage Chen, Yazid Akadiri

Overview of this book

Learn how to make the most of the Elastic Stack (ELK Stack) products—including Elasticsearch, Kibana, Elastic Agent, and Logstash—to take data reliably and securely from any source, in any format, and then search, analyze, and visualize it in real-time. This cookbook takes a practical approach to unlocking the full potential of Elastic Stack through detailed recipes step by step. Starting with installing and ingesting data using Elastic Agent and Beats, this book guides you through data transformation and enrichment with various Elastic components and explores the latest advancements in search applications, including semantic search and Generative AI. You'll then visualize and explore your data and create dashboards using Kibana. As you progress, you'll advance your skills with machine learning for data science, get to grips with natural language processing, and discover the power of vector search. The book covers Elastic Observability use cases for log, infrastructure, and synthetics monitoring, along with essential strategies for securing the Elastic Stack. Finally, you'll gain expertise in Elastic Stack operations to effectively monitor and manage your system.
Table of Contents (16 chapters)
close

Finding deviations in your data with outlier detection

In data analysis, uncovering meaningful insights often includes identifying patterns, trends, deviations, and anomalies. One useful technique is outlier detection—it involves detecting data points that significantly deviate from the majority of the dataset. This helps us identify elements that differentiate normal data points from anomalous ones. Unlike anomaly detection for time series data, as we learned in the previous chapter, we are not concerned with the temporal evolution of the dataset. Instead, we focus on data clusters, evaluating their density and distance using multivariate analysis.

In the first four recipes of this chapter, we will continue to use the Rennes traffic data, which has become familiar to us from previous chapters.

Getting ready

Before delving into the supervised learning intricacies in the context of the Elastic Stack, let’s review the ML methodology from end to end, as shown in...

bookmark search playlist download font-size

Change the font size

margin-width

Change margin width

day-mode

Change background colour

Close icon Search
Country selected

Close icon Your notes and bookmarks

Delete Bookmark

Modal Close icon
Are you sure you want to delete it?
Cancel
Yes, Delete