Book Image

Getting Started with Elastic Stack 8.0

By : Asjad Athick
Book Image

Getting Started with Elastic Stack 8.0

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


In this chapter, we looked at applying supervised and unsupervised machine learning techniques on data in Elasticsearch for various use cases.

First, we explored the use of unsupervised learning to look for anomalous behavior in time series data. We used single-metric, multi-metric, and population jobs to analyze a dataset of web application logs to look for potentially malicious activity.

Next, we looked at the use of supervised learning to train a machine learning model for classifying to classify requests to the web application as malicious using features in the request (primarily the HTTP request/response size values).

Finally, we looked at how the inference processor in ingest pipelines can be used to run continuous inference using a trained model for new data.

In the next chapter, we will move our focus to Beats and their role in the data pipeline. We will look at how different types of events can be collected by Beats agents and sent to Elasticsearch or Logstash...