Deploy Elasticsearch & Kibana Environment with Docker

Tamir Suliman
Published in
7 min readMar 5


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ELK stack which includes Elasticsearch, Kibana, and Logstash considered one of the powerful tools for logging, searching and analyzing data. Manual installation of those tools may prove sometimes to be laborious and complicated.However, using docker containerization tool to deploy ELK stack could give a way to streamline the procedure.In this article we are going to talk about how to use docker to run your Elasticsearch stack environment.

if you’re here , you probably already know about Docker, the ELK stack and each of its constituent components. So I won’t waste your time with introductions and justifications in this blog. But I will walk you through the process of deploying these tools using Docker.

Knowing that I’m not perfect, if you have any suggestions on how to improve the article please add them to the comments area below. If you think this article helped you in any shape of form please hit the CLAP button. So let’s get started.

Initial Setup

The first step is to make sure you have Docker installed on your environment to which can be downloaded from the Docker website.

Once installed open your shell environment and run the following command:

Please note ,at the time of writing this article , Docker is at version 20.10.22 and Elasticsearch is at version 8.6.2.

Note: I updated the article for version 8.8.1.

Lets pull the Elasticstack images from elastic docker hub.

# pull the elasticsearch image 

docker pull

# pull kibana image

docker pull kibana:8.8.0

# pull logstash image

docker pull logstash:8.8.0

# pull elasticagent image

docker pull

Since I already pulled all these images earlier I received the following message :

image by the author docker image pull for elastic stack

Create the docker Network

When you install Docker Engine it creates a bridge network automatically for you [1].



Tamir Suliman

Writer, Engineer, Cyber security enthusiast ,PhD. Candidate & 4 Open Source write about my day to day experience in Software Data, and Engineering.