I find creative ways to abuse data using the Python data science stack to fight propagandists. I do academic research with DePaul's Divergent Design Lab, and data analytics at Hu-Friedy.
I recently switched from Postgres to Mongo (which I am using for the first time) to store data that I am feeding into a few Python ML analyses. I switched to Mongo because I felt like the data requirements from stakeholders kept evolving, and it was a nightmare to re-organize the tables in Postgres to adapt.
If I wanted more long term storage for others to access I think Postgres would be the way to go because it is less accommodating of one off changes. I might prefer Postgres if I had more time, but I am bouncing between stakeholders, doing the data engineering and the analysis on my own so I need something that just works without thinking about it.
I switched to Mongo because I felt like the data requirements from stakeholders kept evolving, and it was a nightmare to re-organize the tables in Postgres to adapt.
This is the perfect use case! It happened to me as well. When stakeholders have no idea of what they actually want, MongoDB or similar alternatives is a perfect solution. :-D
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I recently switched from Postgres to Mongo (which I am using for the first time) to store data that I am feeding into a few Python ML analyses. I switched to Mongo because I felt like the data requirements from stakeholders kept evolving, and it was a nightmare to re-organize the tables in Postgres to adapt.
If I wanted more long term storage for others to access I think Postgres would be the way to go because it is less accommodating of one off changes. I might prefer Postgres if I had more time, but I am bouncing between stakeholders, doing the data engineering and the analysis on my own so I need something that just works without thinking about it.
This is the perfect use case! It happened to me as well. When stakeholders have no idea of what they actually want, MongoDB or similar alternatives is a perfect solution. :-D