I read another blogpost recently claiming that Python was going to spell the end of Excel's reign as one of, if not the most, used tool for analytics. Maybe it's largely true in some pockets but I think the real picture is a bit more complicated.

My earliest memory of analytics is my sister and I watching my father creating Lotus 123 spreadsheets to track and even visualise our weekly performances at athletics. Despite being a fairly average athlete, I remember being transfixed as the charts updated each week. Now we live in the Strava era and there were a couple of questions I had that I couldn't answer without using their API to get at my data.

I've seen numerous solutions for handling data integration where data needs to be extracted form TM1. Most end up involving multiple tasks triggered and managed in different tools by different teams. None of the moving parts are particularly complicated but the end to end process can be difficult to debug and it never seems to result in a particularly reusable solution. The introduction of the REST API and development of [TM1py](https://github.com/cubewise-code/tm1py) have increased the options. I thought it might be interesting to see how Airflow might be used to manage pipelines need data from TM1.