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Elastic Stack 8.x Cookbook

Elastic Stack 8.x Cookbook

By : Huage Chen, Yazid Akadiri
5 (3)
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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)
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Detecting anomalies in your data with unsupervised machine learning jobs

In this recipe, we’ll introduce you to the concept of anomaly detection and guide you through creating an unsupervised ML job to uncover unusual patterns in your dataset.

But first, what exactly is anomaly detection? Elasticsearch’s machine learning anomaly detection feature is a dynamic tool capable of automatically learning the typical behavior of your time series data and pinpointing anomalies as they occur. This feature is equipped to perform sophisticated analysis, enhance root cause investigation, and minimize the occurrence of false positives, ultimately providing automated, real-time anomaly detection for time series data. These techniques are part of the unsupervised machine learning category.

In this recipe, we’ll create a machine learning configuration known as a job to detect abnormal patterns in our traffic dataset by focusing on data points such as travel time, average...

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