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Beginners Guide how to Install Superset (Opensource BI platform) on EC2 AWS instance

Beginners Guide how to Install Superset (Opensource BI platform) on EC2 AWS instance

Why SuperSet?

Superset is a data exploration platform designed to be visual, intuitive and interactive, the main objective is to slice, dice and visualize data easily. It is open-source BI platform which can be deployed on every virtual server with no usage costs. Some of the main advantages are:

  • it is maintained by Apache foundation and supported by AirBnB
  • Many visualization and ability to edit the code
  • Support geolocations and uses mapbox
  • Able to cache data for dashboards visualizations
  • Admin panel available with very detailed settings
  • Able to access many SQL and NoSQL databases
  • Easy and friendly user interface

According to GitHub repo, Superset is currently being used by Airbnb, Twitter, GfK Data Lab, Yahoo!, Udemy and many others.

We at ShopUp decided to give a try of that great platform and noticed that as products driven by the community, sometimes there is missing documentation. We have met some difficulties in setting up the platform on EC2 instance, therefore decided to share the steps and to contribute. Also there is a video on our channel where we did all steps and if you prefer to watch rather than read that is your place.

Let’s start



Open AWS console

Once you log in find EC2 services and enter into EC2 instance

  • Launch instance
  • Select Ubuntu free-tier eligable – t2.micro
  • Select storage, we choose 8 GB
  • Security group – choose all traffic
  • Assign key to the instance
  • Launch it


Log into EC2 Instance:

You can select option connect from the menu where the instance is something like

"ssh -i "your_certificate.pem" ec2-user@ec2-25-193-24-151.eu-central-1.compute.amazonaws.com"

Get root access

sudo -i



We are starting with seting up the environment like update Python and PIP versions on EC2 (Amazon AMI) and installing other required packages

sudo apt-get update
sudo apt-get install build-essential libssl-dev libffi-dev python3.6-dev python-pip libsasl2-dev libldap2-dev


Create Python virtual environment

It is recommended to install Superset inside a virtualenv. Python 3 already ships virtualenv.

apt-get install python3-venv
pip install virtualenv
python3 -m venv venv
. venv/bin/activate


Superset installation and initialization

Follow these few simple steps to install Superset.:

# update to latest version
pip install --upgrade setuptools pip
# Install superset
pip install apache-superset
# Create an admin user (you will be prompted to set a username, first and last name before setting a password)
export FLASK_APP=superset
flask fab create-admin
# Initialize the database
superset db upgrade
# Load some data to play with
superset load_examples
# Create default roles and permissions
superset init
#Start screen session
#Start the web server on port 8080, use -p to bind to another port
fabmanager run --app superset

Then you need to detach from the screen and use keyward combination – Ctr+A + Ctr + D and you are ready for the next step.

Access the webserver


Copy external IP of EC2 and add port 8080, like “” and paste it on your browser.
Your first report can visualize csv data, so for that reason you need to enable CSV upload.


Enable Upload CSV – Go to Sources -> Databases and edit examples database -> Check Allow CSV upload


We hope that you found what you were looking for.
Now you are able to create your first visualization of any data, it is better to refer to some of templated dashboards and to watch different videos and manuals, also the platform can be personalized with your retailer or ecommerce name and logo.

That is all for now, please let us know what you like and what else you want to hear.



  1. Analyze and predict physical customers’ patterns in Hypermarkets with high precision using the latest advancement in technologies | ShopUp - […] are also considering migrating some of our old visualization dashboard onto the opensource platform SuperSet, which is scalable and…

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