Our first step – A python analytics web-service

To start off with what we basically want is a data analytics webservice that allows users to login, upload files, allows them to create processes and then a way of charging them for that processing.

So the service is cost-effective and scalable we began creating an implementation that could be deployed as a Google cloud run service. We chose Google Cloud as there tends to be less network configuration to deal with (compared to AWS) and we have developed with it before.

Although we could use any language, many cloud run and cloud functions tend to be written in python, so we chose that. We first developed a framework that allows multiple sites to be handled by a single cloud run instance passing the requests to a custom handler.

There is more to safe user management than you think. It requires salting and hashing passwords for security, you need pages to send password resets by email and send registration emails. You do not want to allow bots to create accounts or attempt to obtain user emails by brute-force, so that required adding a captcha implementation. We also want to allow users to sign-in using their Google or Facebook accounts.

We are ahead of the wave and use AI tools (github copilot) in our development. We do not do the trendy “vibe-coding” YouTube loves which developers have very deep concerns about, instead we incrementally plan, implement and verify each feature we come up with in order to ensure a clean cohesive structure.

In just over a month we had developed the architecture shown at the top of this post and had a working prototype (but more work needs to be done on the HTML / CSS). We will talk more about that in our next post.