Why Elastic Stack is needed?
As per the survey, Facebook generates 4 Petabytes data every day i.e 40 million GB. The Data, Now it’s a world of data. So We need a system that analyzes our data. There are two terms to understand:
- Analysis – In the analysis part, You’ll get results from the past data or the existing data that you have.
- Analytics – When you want to predict user requirements, You want graphs based visualization for better business clarity and also want to understand Data patterns.
Setting up Elasticsearch, Logstash, and Kibana
At first let’s download the three open-source software from their respective links [elasticsearch], [logstash], and [kibana]. Unzip the files and put all three in the project folder. Firstly, set up Kibana and Elasticsearch on the local system. We run Kibana by the following command in the bin folder of Kibana.
- bin\kibana
Similarly, Elasticsearch is set up like this:
bin\elasticsearch
Now, in the two separate terminals, we can see both of the modules running. In order to check that the services are running open localhost:5621 for Kibana and localhost:9600 for Elasticsearch.
Here, we are ready with set up for elastic stack. Now go to localhost:5621 and open dev tools here in the console. It is the place where you can write Elasticsearch queries. As we will talk more on Elasticsearch this time. Now we’ll see how exactly Elasticsearch Works.
What is Elastic Stack?
Elastic Stack is a group of products that can reliably and securely take data from any source, in any format, then search, analyze, and visualize it in real-time. Elasticsearch is a distributed, RESTful search and analytics engine that can address a huge number of use cases. Also considered as the heart of the Elastic Stack, it centrally stores user data for high-efficiency search, excellent relevancy, and powerful analytics that is highly scalable.