In the previous article, we went through the process of building a quick and dirty data pipeline to fill a local database with three sources of information relating to space weather (e.g., sunspots, magnetic fields, plasma density/speed/temperature). Now we have extracted, transformed, and loaded that data into our data warehouse system, we will put that data to use in our final product: an interactive dashboard having a custom layout designed by us with the aid of a relatively new dashboard and data visualization tool — Dash by Plotly.

Before we dive into the details of our space weather dashboard, let’s review quickly what Dash is exactly. Wikipedia describes Dash as an “open-source Python and R framework for building web-based analytic applications.” The Dash user guide gives a more detailed explanation of how Dash actually works:

“Written on top of Flask, Plotly.js, and React.js, Dash is ideal for building data visualization apps with highly custom user interfaces in pure Python. It’s particularly suited for anyone who works with data in Python.

Through a couple of simple patterns, Dash abstracts away all of the technologies and protocols that are required to build an interactive web-based application. Dash is simple enough that you can bind a user interface around your Python code in an afternoon.

Dash apps are rendered in the web browser. You can deploy your apps to servers and then share them through URLs. Since Dash apps are viewed in the web browser, Dash is inherently cross-platform and mobile ready.”

From my (admittedly limited) experience working with Dash, one of its main benefits is the ability to make highly elaborate and customizable web-based analytics interfaces in a straightforward and logical manner, using only Python and associated libraries (no need to learn how to code React.js!).

#python #dashboard #web-development #data-visualization #space