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 7: Using Logstash to Extract, Transform, and Load Data

In previous chapters, one of our areas of focus was looking at how data can be indexed and searched on Elasticsearch. We looked at index mappings and the importance of defining correct mappings in downstream use cases such as computing aggregations, running alerting, and using machine learning features.

In this chapter, we look at how ETL tools such as Logstash can be used to extract data from a range of source systems (such as Syslog streams, CSV files, message-streaming platforms, or Beats agents), and transform events to their desired format before loading them into Elasticsearch. Upon completion of this chapter, you will be able to use Logstash to process and ingest a variety of data sources into Elasticsearch.

In this chapter, we will specifically focus on the following:

  • Understanding the internals of Logstash and the anatomy of a Logstash pipeline
  • Exploring common input, filter, and output plugins...