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

Manipulating incoming data with ingest pipelines

Elasticsearch is a "schema on write" data store. Once a document has been indexed into Elasticsearch, the field names and values that have been indexed cannot be changed unless the document is reindexed. Therefore, documents must be parsed, transformed, and cleansed before ingestion.

Runtime fields can be used to compute or evaluate the value of a field at query time. Runtime fields can be used to manipulate and transform field values when searching for data, but they can be costly and time-consuming to run across large volumes of search requests. The intended use of runtime fields is to apply temporary or one-off changes to data, rather than on every search request.

Ingest pipelines on Elasticsearch offer lightweight and convenient data transformation and manipulation functionality for when an ETL tool such as Logstash is not used. As ingest pipelines run on Elasticsearch nodes, they can scale easily as part of the...