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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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Alerting and Anomaly Detection

Alerting and anomaly detection tools are crucial for proactive decision-making across various domains, from IT operations to business intelligence. In this chapter, we will explore the strategies, techniques, and tools needed to set up effective alerts and detect anomalies in your data. This chapter aims to equip you with the knowledge and skills necessary for the early detection of anomalies, enabling timely responses in your data-driven environment.

In this chapter, we’re going to cover the following main topics:

  • Creating alerts in Kibana
  • Monitoring alert rules
  • Investigating data with log rate analysis
  • Investigating data with log pattern analysis
  • Investigating data with change point detection
  • Detecting anomalies in your data with unsupervised machine learning jobs
  • Creating anomaly detection jobs from a Lens visualization

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