Book Image

Getting Started with Elastic Stack 8.0

By : Asjad Athick
5 (1)
Book Image

Getting Started with Elastic Stack 8.0

5 (1)
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)
1
Section 1: Core Components
4
Section 2: Working with the Elastic Stack
12
Section 3: Building Solutions with the Elastic Stack

Looking at pipelines for real-world data-processing scenarios

This section will explore a number of real-world ETL scenarios and the corresponding Logstash pipelines that can be used to implement them.

The full datasets used and the pipeline configuration files can be found in the code repository for the book.

Loading data from CSV files into Elasticsearch

Comma-Separated Value (CSV) files are a commonly used file format and can be easily generated by a range of source systems and tools. We will explore how a dataset containing taxi trip details from the city of Chicago can be parsed and loaded into Elasticsearch for analysis.

Navigate to Chapter7/processing-csv-files in the code repository and explore the chicago-taxi-data.csv file. The first row contains header information, indicating what information each column contains. The following screenshot is an extract of some of the key fields in the file:

Figure 7.4 – An overview of the CSV file...