Elastic Stack – Introduction
- Elastic Stack is a rich ecosystem of components serving as full search adn analytics stack. The main components of Elastic Stack are
- Elastic Search
- Elasticsearch is at the heart of the Elastic Stack providing storage, search and analytics capabilities.
- Kibana is referred as user interface for Elastic Stack with great visualization capabilities
- Logstash and Beats help the data into Elastic stack
- X-Pack provides features including alterting, security, graph & machine learning to make Elastic Stack production ready
- Elastic Search intro by official documentation is as follows "Elasticsearch is the distributed search and analytics engine at the heart of the Elastic Stack. Elasticsearch is where the indexing, search, and analysis magic happens. Elasticsearch provides near real-time search and analytics for all types of data. Whether you have structured or unstructured text, numerical data, or geospatial data, Elasticsearch can efficiently store and index it in a way that supports fast searches. You can go far beyond simple data retrieval and aggregate information to discover trends and patterns in your data. And as your data and query volume grows, the distributed nature of Elasticsearch enables your deployment to grow seamlessly right along with it."
- Elastic stack is built on the radically different technology ‘Apache Lucene’
- Key Benefits of Elastic Search
- Schemaless, document-oriented
- Rich client library support and the REST API
- Easy to operate and Easy to Scale
- Near real time
- Exercise: Refer Here to this video to understand JSON and YAML
Schema less and document oriented
- Elasticsearch doesnot impose a strict structure on your data; you can store any json documents.
- These JSON documents are first-class citizents of Elastic search as opposed to rows and columns in a relational database