Dash simple deployment with docker

The previous post was already about dash. So why return to the subject? In some ways I got carried away by the possibilities of dash. I therefore included some concepts that are nice, by themselves, however introduce a level of complexity that is not fully necessary to start you first deployment. This post is really aimed at people who found the previous post too demanding, and want a more gentle introduction to deploying dash with docker. 
Additionally there gonna be a stronger focus on infrastructure, in particular how to add ssl encryption and a basic login.

So here’s an image of what we’re going to build: 

Here’s the full-width view : http://dashsimple4.arthought.com

The login credentials are:
user
word

As you can see already, this is a very simple page.

The code can be found in the following github repository: https://github.com/hfwittmann/dash

The dashboard can be found under this link: https://dashsimple4.arthought.com/

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Dash for timeseries

Dash is an amazing dasboarding framework. If you’re looking for an easy-to- setup dashboarding framework that is able to produce amazing plots that wow your audience, chances are that this is your perfect fit. Further it is also friendly to your CPU. The solution I will show here is running simultaneously on the same 5€/month digital ocean instance as the WordPress installation hosting the article you’re reading.

Dash is fully open source, produced by the makers of plotly. So you can host it yourself, at no cost, or alternatively you can buy a service subscription from plotly, the pricelist you find under this link.

In this post we focus on doing things ourselves, so there is no cost, apart from time spent. And, of course, you need a server, running a standard operating system, and some knowledge.

So let’s work on the latter, and try and augment our knowledge.

The code can be found in the following github repository: https://github.com/hfwittmann/dash

The dashboard can be found under this link: https://dashdax.arthought.com/

Continue reading “Dash for timeseries”