The emergence of the web, mobiles, social networks, blogs, and photo sharing has created a massive amount of data in recent years. These new data sources create information that cannot be handled using traditional data storage technology, typically relational databases. As an application developer or business intelligence developer, your job is to fulfill the search and analytics needs of the application.

A number of data stores, capable of big data scale, have emerged in the last few years. These include Hadoop ecosystem projects, several NoSQL databases, and search and analytics engines such as Elasticsearch. 

The Elastic Stack is a rich ecosystem of components serving as a full search and analytics stack. The main components of the Elastic Stack are Kibana, Logstash, Beats, X-Pack, and Elasticsearch.

Elasticsearch is at the heart of the Elastic Stack, providing storage, search, and analytical capabilities. Kibana, also referred to as a window into the Elastic Stack, is a user interface for the Elastic Stack with great visualization capabilities. Logstash and Beats help get the data into the Elastic Stack. X-Pack provides powerful features including monitoring, alerting, security, graph, and machine learning to make your system production-ready. Since Elasticsearch is at the heart of the Elastic Stack, we will cover the stack inside-out, starting from the heart and moving on to the surrounding components.

In this chapter, we will cover the following topics:

• What is Elasticsearch, and why use it?
• A brief history of Elasticsearch and Apache Lucene
• Elastic Stack components
• Use cases of Elastic Stack

We will look at what Elasticsearch is and why you should consider it as your data store. Once you know the key strengths of Elasticsearch, we will look at the history of Elasticsearch and its underlying technology, Apache Lucene. We will then look at some use cases of the Elastic Stack, and provide an overview of the Elastic Stack's components.