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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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Advanced Data Analysis and Processing

In the previous chapter, we explored how you can perform anomaly detection using an unsupervised learning method for timestamped data within the Elastic Stack. In this chapter, we will shift our focus to additional aspects of the Elastic Stack’s Machine Learning (ML) capabilities, such as data frame analytics, as displayed in Figure 8.1. Data frame analytics includes unsupervised learning for outlier detection, along with supervised learning methods that employ trained models for both classification and regression predictions:

Figure 8.1 – ML in the Elastic Stack

Figure 8.1 – ML in the Elastic Stack

Elasticsearch’s supervised learning capabilities provide a robust framework, enabling you to train ML models with labeled training data. Once these models are trained, they can be deployed to predict outcomes or infer patterns in new datasets. This proves particularly useful when dealing with a significant amount of data and when seeking...

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