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 briefly looked at three core aspects of Elasticsearch.

First, we looked at the internals of an index in Elasticsearch. We explored how settings can be applied to indices and learned how to configure mappings for document fields. We also looked at a range of different data types that are supported and how they can be leveraged for various use cases.

We then looked at how nodes on Elasticsearch host indices and data. We understood the different roles a node plays as part of a cluster, as well as the concept of data tiers, to take advantage of different hardware profiles on nodes, depending on how the data is used.

Lastly, we ingested some sample data and learned how to ask questions about our data using the search API.

In the next chapter, we will dive a little bit deeper into how to derive statistical insights, use ingest pipelines to transform data, create entity-centric indices by pivoting on incoming data, manage time series sources using...