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

Running classification on data

Unsupervised anomaly detection is useful when looking for abnormal or unexpected behavior in a dataset to guide investigation and analysis. It can unearth silent faults, unexpected usage patterns, resource abuse, or malicious user activity. This is just one class of use cases enabled by machine learning.

It is common to have historical data where, with post analysis, it is rather easy to label or tag this data with a meaningful value. For example, if you have access to service usage data for your subscription-based online application along with a record of canceled subscriptions, you could tag snapshots of the usage activity with a label indicating whether the customer churned.

Consider a different example where an IT team has access to web application logs where, with post analysis, given the request payloads are different to normal requests originating from the application, they can label events that indicate malicious activity, such as password...