Book Image

Getting Started with Elastic Stack 8.0

By : Asjad Athick
5 (1)
Book Image

Getting Started with Elastic Stack 8.0

5 (1)
By: Asjad Athick

Overview of this book

The Elastic Stack helps you work with massive volumes of data to power use cases in the search, observability, and security solution areas. This three-part book starts with an introduction to the Elastic Stack with high-level commentary on the solutions the stack can be leveraged for. The second section focuses on each core component, giving you a detailed understanding of the component and the role it plays. You’ll start by working with Elasticsearch to ingest, search, analyze, and store data for your use cases. Next, you’ll look at Logstash, Beats, and Elastic Agent as components that can collect, transform, and load data. Later chapters help you use Kibana as an interface to consume Elastic solutions and interact with data on Elasticsearch. The last section explores the three main use cases offered on top of the Elastic Stack. You’ll start with a full-text search and look at real-world outcomes powered by search capabilities. Furthermore, you’ll learn how the stack can be used to monitor and observe large and complex IT environments. Finally, you’ll understand how to detect, prevent, and respond to security threats across your environment. The book ends by highlighting architecture best practices for successful Elastic Stack deployments. By the end of this book, you’ll be able to implement the Elastic Stack and derive value from it.
Table of Contents (18 chapters)
Section 1: Core Components
Section 2: Working with the Elastic Stack
Section 3: Building Solutions with the Elastic Stack

Preparing data for machine learning jobs

In order for machine learning jobs to analyze document field values when building baselines and identifying anomalies, it is important to ensure the index mappings are accurately defined. Furthermore, it is useful to parse out complex fields (using ETL tools or ingest pipelines) into their own subfields to use in machine learning jobs.

The machine learning application provides useful functionality to visualize the index you're looking to run jobs on, and ensure mappings and values are as expected. The UI lists all fields, data types, and some sample values where appropriate.

Navigate to the machine learning app on Kibana and perform the following steps:

  1. Click on the Data Visualizer tab.
  2. Select the webapp data view you created in the previous section.
  3. Click on Use full webapp data to automatically update the time range filter for the full duration of your dataset.
  4. Inspect the fields in the index and confirm all...