Python Cloud Computing #2: Practical Hands-on Using Heroku - Part 1 of 2

 
Heroku Office Entrance

Heroku Office Entrance
Pulled out the image from here.

 

This is yet another article in this series that shows a practical hands-on using another Python Cloud Computing solution there existed in our planet: Heroku. We will going to try to publish our Openshift's Flask Biography application in Heroku, and along the way --it's unavoidable-- we will compare the process with Openshift, which due to Openshift's generosity in offering 1 GB free plan (including database and user uploaded files!), I still think Openshift is the best Python Cloud Computing platform for developers out there. I got to remind you though, I am not talking in term of its performance comparison in handling multitude number of visitors. We are just going to find out how to create, deploy and manage application installed in Heroku. The branch that we are going to use, is the Blueprints branch in our public pythonthusiast/bio Github repository.

Great, lets get started!

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Python Cloud Computing #1: Practical Hands-on Using PythonAnywhere

Pythonanywhere unique online working environment

PythonAnywhere simple and unique online working environment

My first series in this blog was about Python Cloud Computing: developing Flask application and using Openshift to properly manage and deploy it. This is a series that bring another possibility: why don't I complete this blog with practical hands-on using another Python Cloud Computing solution? The internet is filled with this Platform as a Service solution. Some specifically state that they are Python only solution, other are much more general and open in nature. Having practical hands-on on the usage of some of them will add another advantage to this blog. Although, I am not sure that I can dive to all of the existing Cloud Computing/PaaS solution. For the most part, I will be unable to explore at the fullest on cloud solution that require actual billing to use important features such as database access, e.g Google Cloud or Amazon Web Services. In this regard, I really love Openshift way of attracting developers to use their platform: a 1GB storage including user uploaded files and database. I don't think there are other PaaS solution out there that can surpass this free offering.

Well then, lets start at our first stop : PythonAnywhere.

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