Python

I'm currently building a testing and validation framework using Python for a very old and quirky TM1 model. TM1py provides all the functionality you need to pull data from a cell but I found myself writing wrappers for specific cubes to make this a bit easier.

I started using the [pre-commit](https://pre-commit.com/) framework a while back on my projects but only with a very basic setup on pretty simple codebases. Common uses are applying code formatters like [black](https://github.com/psf/black) or to run static type checking with [mypy](http://mypy-lang.org/).

At a meetup I attended in Manchester earlier this year, I had an interesting conversation with a guy who was the founder of a startup. When I asked about his business model, he looked a little sheepish and explained that it was a SaaS offering that allowed clubs and schools to manage subscriptions and billing.

My earliest memory of analytics is of my sister and I watching my father enter data in Lotus 123 spreadsheets to track our athletic performances. Although an average athlete, I was transfixed by the charts, rudimentary though they were, seeing my progression in cyan and magenta was thrilling! Now we take tools like Strava for granted and also have GPS data to work with! Still, I found a couple of questions Strava didn't answer out of the box so I wanted to play with the API to download my data.

Inspired by the Airflow Summit 2020, I wanted to explore whether an orchestrator like Apache Airflow could be used to help manage tasks and workloads on a TM1 server.

Inspired by an enthralling Ashes series, I wanted to play with whatever cricket data I could find from free and commercial sources. Despite a bit of trawling, I couldn't find a single document detailing what was available.

Annoyed at often forgetting some of this, I thought I'd try to document parts of my workflow here so that I could refer back to it in future.