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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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Setting up a data stream manually

Data streams are a new feature introduced in Elasticsearch 7.9 and solidified in version 8. They allow for more efficient management and reduced overhead of time series data. As you learned in the previous recipes of this chapter, both Elastic Agent and Beats create timestamped data automatically in data streams. What about custom data sources where we cannot easily install Elastic Agent or Beats as data shippers? By following the steps in this recipe’s How to do it… section, you’ll be able to create a data stream and start ingesting and analyzing time-based data manually in Elasticsearch.

Dataset

In this and the following recipe, we will use a free dataset provided by European public sector datasets (https://data.europa.eu/) – specifically, a real-time traffic status dataset for the city of Rennes, one of the biggest cities in France (https://data.europa.eu/data/datasets/5caaf5ee9ce2e75d0c8c381a?locale=en).

Dataset...

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