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

Chapter 5: Running Machine Learning Jobs on Elasticsearch

In the previous chapter, we looked at how large volumes of data can be managed and leveraged for analytical insight. We looked at how changes in data can be detected and responded to using rules (also called alerts). This chapter explores the use of machine learning techniques to look for unknowns in data and understand trends that cannot be captured using a rule-based approach.

Machine learning is a dense subject with a wide range of theoretical and practical concepts to cover. In this chapter, we will focus on some of the more important aspects of running machine learning jobs on Elasticsearch. Specifically, we will cover the following:

  • Preparing data for machine learning
  • Running single- and multi-metric anomaly detection jobs on time series data
  • Classifying data using supervised machine learning models
  • Running machine learning inference on incoming data