Since you are reading this book, you probably already know what Elasticsearch is. For the sake of completeness, let's define Elasticsearch:

Elasticsearch is at the core of the Elastic Stack, playing the central role of a search and analytics engine. Elasticsearch is built on a radically different technology, Apache Lucene. This fundamentally different technology in Elasticsearch sets it apart from traditional relational databases and other NoSQL solutions. Let's look at the key benefits of using Elasticsearch as your data store:

• Schemaless, document-oriented...

The Elastic Stack components are shown in the following diagram. It is not necessary to include all of them in your solution. Some components are general-purpose and can be used outside the Elastic Stack without using any other components.

Let's look at the purpose of each component and how they fit into the stack:

Elasticsearch is at the heart of the Elastic Stack. It stores all your data and provides search and analytic capabilities in a scalable way. We have already looked at the strengths of Elasticsearch and why you would want to use it. Elasticsearch can be used without using any other components to power your application in terms of search and analytics...

Elastic Stack components have a variety of practical use cases, and new use cases are emerging as more plugins are added to existing components. As mentioned earlier, you may use a subset of the components for your use case. The following list of example use cases is by no means exhaustive, but highlights some of the most common ones:

• Log and security analytics
• Product search
• Metrics analytics
• Web searches and website searches

Let's look at each use case.

The Elasticsearch, Logstash, and Kibana trio was, previously, very popular as a stack. The presence of Elasticsearch, Logstash, and Kibana (also known as ELK) makes the Elastic Stack an excellent stack for aggregating...

Now that we have enough motivation and reasons to learn about Elasticsearch and the Elastic Stack, let's start by downloading and installing the key components. Firstly, we will download and install Elasticsearch and Kibana. We will install the other components as we need them on the course of our journey. We also need Kibana because, apart from visualizations, it also has a UI for developer tools and for interacting with Elasticsearch.

Starting from Elastic Stack 5.x, all Elastic Stack components are now released together; they share the same version and are tested for compatibility with each other. This is also true for Elastic Stack 6.x components.

At the time of writing, the current version of Elastic Stack is 7.0.0. We will use this version for all components.

In this chapter, we started by understanding the motivations of various search and analytics technologies other than relational databases and NoSQL stores. We looked at the strengths of Elasticsearch, which is at the heart of the Elastic Stack. We then looked at the rest of the components of the Elastic Stack and how they fit into the ecosystem. We also looked at real-world use cases of the Elastic Stack. We successfully downloaded and installed Elasticsearch and Kibana to begin the journey of learning about the Elastic Stack.

In the next chapter, we will understand the core concepts of Elasticsearch. We will learn about indexes, types, shards, datatypes, mappings, and other fundamentals. We will also interact with Elasticsearch by using Create, Read, Update, and Delete (CRUD) operations, and learn the basics of searching.