We are living in an advanced stage of the information age. 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 big data scale data stores have emerged in the last few years. This includes Hadoop ecosystem projects, several NoSQL databases, and search and analytics engines such as Elasticsearch. Hadoop and each NoSQL database have their own strengths and use cases.Â
Elastic Stack is a rich ecosystem of components serving as a full search and analytics stack. The main components of Elastic Stack are Kibana, Logstash, Beats, X-Pack, and Elasticsearch. Elasticsearch is at the heart of Elastic Stack, providing storage, search, and analytics capabilities. Kibana, which is also called a window into Elastic Stack, is a great visualization and user interface for Elastic Stack. Logstash and Beats help in getting the data into Elastic Stack. X-Pack provides powerful features including monitoring, alerting, and security to make your system production ready. Since Elasticsearch is at the heart of 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 componentsÂ
- 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 Elastic Stack, and we will provide an overview of the Elastic Stack components.
