Search

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

Get started by loading sample data from the following page:
Follow instructions on the following pages to get a hang of whats happening. However, we may be interested only in defining index patterns and discovering the data from the following pages.

Summary

In this post, you learned about installing Elasticsearch and Kibana on Windows and getting started by loading the data.

Did you find this article useful? Do you have any questions or suggestions about this article in relation to setting up ElasticSearch and Kibana on Windows? Leave a comment and ask your questions and I shall do my best to address your queries.

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. I would love to connect with you on Linkedin. Check out my latest book titled as First Principles Thinking: Building winning products using first principles thinking.

Recent Posts

Building an OpenAI Chatbot with LangChain

Have you ever wondered how to use OpenAI APIs to create custom chatbots? With advancements…

4 hours ago

Building a RAG Application with LangChain: Example Code

The combination of Retrieval-Augmented Generation (RAG) and powerful language models enables the development of sophisticated…

1 day ago

How Indexing Works in LLM-Based RAG Applications

When building a Retrieval-Augmented Generation (RAG) application powered by Large Language Models (LLMs), which combine…

4 days ago

Retrieval Augmented Generation (RAG) & LLM: Examples

Last updated: 25th Jan, 2025 Have you ever wondered how to seamlessly integrate the vast…

5 days ago

What are AI Agents? How do they work?

Artificial Intelligence (AI) agents have started becoming an integral part of our lives. Imagine asking…

3 weeks ago

Agentic AI Design Patterns Examples

In the ever-evolving landscape of agentic AI workflows and applications, understanding and leveraging design patterns…

3 weeks ago