Get Started with ElasticSearch and Kibana on Windows

ElasticSearch is a distributed, restful analytics search engine which is used to provide faster search through your data than the traditional databases including RDBMS and NoSQL databases. It works by regularly creating data indices from the data stored in traditional databases and use Lucene library to search through the data.

Kibana is the visualization client used to explore, visualize and discover the data.

In this post, you would learn about some of the following:

• Setup ElasticSearch and Kibana on Windows
• Load Sample Data and play around

Setup ElasticSearch and Kibana on Windows

• Setup ElasticSearch on Windows: Go to the elasticsearch installation page and download the file elasticsearch-6.2.2.zip. Follow the instructions on the page to start the elasticsearch server. Go to elasticsearch-6.2.2/bin folder and execute the command elasticsearch.bat. This would start elasticsearch server. Confirm the installation by accessing the URL: http://127.0.01:9200. Something like following would get printed:

  {
    "name" : "lp9UsQx",
    "cluster_name" : "elasticsearch",
    "cluster_uuid" : "vrpmq49cQTq4E2fJ9rEEmA",
    "version" : {
      "number" : "6.2.2",
      "build_hash" : "10b1edd",
      "build_date" : "2018-02-16T19:01:30.685723Z",
      "build_snapshot" : false,
      "lucene_version" : "7.2.1",
      "minimum_wire_compatibility_version" : "5.6.0",
      "minimum_index_compatibility_version" : "5.0.0"
    },
    "tagline" : "You Know, for Search"
  }
  

• Setup Kibana: Go to the Kibana installation page, and, download the file, kibana-6.2.2-windows-x86_64.zip. Follow the instructions on the page to setup Kibana. Go to kibana-6.2.2/bin and execute kibana.bat. This would start the Kibana server. Access the URL: http://127.0.0.1:5601 and you would see the page like following:
  

  Figure 1. Kibana Dashboard

   

Load Sample Data and Play Around

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Ajitesh Kumar

I have been recently working in the area of Data analytics including Data Science and Machine Learning / Deep Learning. I am also passionate about different technologies including programming languages such as Java/JEE, Javascript, Python, R, Julia, etc, and technologies such as Blockchain, mobile computing, cloud-native technologies, application security, cloud computing platforms, big data, etc. For latest updates and blogs, follow us on Twitter. I would love to connect with you on Linkedin.
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