Apache Superset in the Production Environment

Visualizing data helps in building a much deeper understanding of the data and fastens analytics around the data. There are several mature paid products available in the market. Recently, I explored an open-source product name Apache-Superset which I found a very upbeat product in this space. Some prominent features of Superset are:

• A rich set of data visualizations.
• An easy-to-use interface for exploring and visualizing data.
• Create and share dashboards.

After reading about Superset, I wanted to try it, and as Superset is a python programming language based project, we can easily install it using pip, but I decided to set it up as a container based on Docker. You can follow my other post on how simple is to explore Superset using Docker. Apache-Superset GitHub Repo contains code for building and running Superset as a container. Since I want to run Superset in a completely distributed manner and less modification is possible in the code(my opinion), I decided to modify the code so that it could run in multiple different modes. Below is a list of specific changes/enhancements done in the code

• A different version of the Superset image can be built using the same code.
• Superset configuration can be easily edited and mounted into the container, with no need of rebuilding the image.
• Asynchronous query execution through Celery based executor and managing it through Flower UI