If you are working on a project with lots of data in your database, this post is worth reading. All we will talk about how to use a bulk of data ( here bulk means really bulk ) to make a useful search engine.

Here I will share my experience of Elastic Search and how to set up and get going with it.

So we will start with WHAT:

Elastic search can be defined as the hub of data that stores data in such a manner that it is always easy to search and fetch the data from it. It is done using indexing and some other concept which we will discuss in this post.

Elasticsearch is the distributed search and analytics engine at the heart of the Elastic Stack. Logstash ( Server-side data processing pipeline ) and Beats ( Open platform for single-purpose data shippers ) facilitate collecting, aggregating, and enriching your data and storing it in Elasticsearch. Kibana enables you to interactively explore, visualize, and share insights into your data and manage and monitor the 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.

While not every problem is a search problem, Elasticsearch offers speed and flexibility to handle data in a wide variety of use cases:

• Add a search box to an app or website
• Store and analyze logs, metrics, and security event data
• Use machine learning to automatically model the behavior of your data in real-time
• Automate business workflows using Elasticsearch as a storage engine
• Manage, integrate, and analyze spatial information using Elasticsearch as a geographic information system (GIS)
• Store and process genetic data using Elasticsearch as a bioinformatics research tool

We’re continually amazed by the novel ways people use search. But whether your use case is similar to one of these, or you’re using Elasticsearch to tackle a new problem, the way you work with your data, documents, and indices in Elasticsearch is the same.

#data-science #data #data analysis