The Elastic Stack is made up of four core products:

• Elasticsearch is a full-text search engine and a versatile data store. It can store and allow you to search and compute aggregations on large volumes of data quickly.
• Kibana provides a user interface for Elasticsearch. Users can search for and create visualizations, and then administer Elasticsearch, using this tool. Kibana also offers out-of-the-box solutions (in the form of apps) for use cases such as search, security, and observability.
• Beats can be used to collect and ship data directly from a range of source systems (such as different types of endpoints, network and infrastructure appliances, or cloud-based API sources) into Logstash or Elasticsearch.
• Logstash is an Extract, Transform, and Load (ETL) tool that's used to process and ingest data from various sources (such as log files on servers, Beats agents in your environment, or message queues and streaming platforms) into Elasticsearch.

This diagram shows how the core components of the Elastic Stack work together to ingest, store, and search on data:

Figure 1.1 – Components of the Elastic Stack

Each core component solves a single, common data-related problem. This genericity makes the stack flexible and domain-agnostic, allowing it to be adopted in multiple solution areas. Most users start with a simple logging use case where data is collected, parsed, and stored in Elasticsearch to create dashboards and alerts. Others might create more sophisticated capabilities, such as a workplace search to make information across a range of data sources accessible to your team; leveraging SIEM and machine learning to look for anomalous user/machine behavior and hunt for adversaries on your company network; understanding performance bottlenecks in applications; and monitoring infrastructure logs/metrics to respond to issues on critical systems.

The evolution of the Elastic Stack

Multiple independent projects have evolved over the years to create the present-day version of the Elastic Stack. Knowing how these components evolved indicates some of the functional gaps that existed in the big data space and how the Elastic Stack components come together to solve these challenges. Let's take a look:

1. An open source transactional Object/Search Engine Mapping (OSEM) framework for Java called Compass was released. Compass leveraged Lucene, an open source search engine library for implementing high-performance full-text search and indexing functionality.
2. To address scalability concerns in Compass, it was rewritten as a distributed search engine called Elasticsearch. Elasticsearch implemented RESTful APIs over HTTP using JSON, allowing programming languages other than Java to interact with it. Elasticsearch quickly gained popularity in the open source community.
3. As Elasticsearch was adopted by the community, a modular tool called Logstash was being developed to collect, transform, and send logs to a range of target systems. Elasticsearch was one of the target systems supported by Logstash.
4. Kibana was written to act as a user interface for using the REST APIs on Elasticsearch to search for and visualize data. Elasticsearch, Logstash, and Kibana were commonly referred to as the ELK Stack.
5. Elastic started providing managed Elasticsearch clusters on the cloud. Elastic Cloud Enterprise (ECE) was offered for customers to orchestrate and manage Elasticsearch deployments on-premises or on private cloud infrastructure.
6. An open source tool called Packetbeat was created to collect and ship network packet data to Elasticsearch. This later evolved into the Beats project, a collection of lightweight agents designed to collect and ship several types of data into Elasticsearch.
7. Machine learning capabilities were added to Elasticsearch and Kibana to support anomaly detection use cases on data residing on Elasticsearch.
8. Application Performance Monitoring (APM) capabilities were added to the Elastic Stack. The APM app on Kibana, together with the Logs, Metrics, and Uptime apps, formed the Observability solution.
9. Kibana added security analytics functionality as part of the Security Information and Event Management (SIEM) app.
10. A collection of proprietary features known as X-Pack was made open source under the Elastic licensing model.
11. Endpoint Detection and Response (EDR) capabilities were added to the Elastic Stack. EDR and SIEM capabilities formed the Security solution.
12. Out-of-the-box website, application, and content search functionality was offered as part of the Enterprise Search solution.

The core components of the stack are open source software projects, licensed under a mix of the Apache 2, Elastic License version 2 (ELv2), and Server Side Public License (SSPL) licensing agreements. The LICENSE.txt file in the root of each product's GitHub repository should explain how the code is licensed.

A paid license is not required to learn about and explore the Elastic Stack features covered in this book. A trial license can be activated for full access to all the features for a limited period upon installing the software.

To focus on learning about the features and technical aspects of the product, there will be no notes on licensing implications after this section. Please refer to the Elastic Subscriptions page to understand what kind of license you might need for a production deployment of the technology:

https://www.elastic.co/subscriptions