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 found a use case to replace a Postgres database of open data sources I was maintaining for my job. The data was being pulled from a few sources into Mongo, but it took a lot of time to process and create tables and load the data where I wanted it. I switched to mongo, mostly using the PyMongo API. It took me an afternoon to watch some videos get it installed, and rewrite a half dozen pipelines to load the data into MongoDB.
I loved that I was able to learn enough to get what I needed out of it in a few hours, and haven’t needed to go back into it since. I am also able to add new data sources faster than I used to, so I am way more productive.
I found a use case to replace a Postgres database of open data sources I was maintaining for my job. The data was being pulled from a few sources into Mongo, but it took a lot of time to process and create tables and load the data where I wanted it. I switched to mongo, mostly using the PyMongo API. It took me an afternoon to watch some videos get it installed, and rewrite a half dozen pipelines to load the data into MongoDB.
I loved that I was able to learn enough to get what I needed out of it in a few hours, and haven’t needed to go back into it since. I am also able to add new data sources faster than I used to, so I am way more productive.
That's great Peter! Finding a more efficient solution always feels amazing.
The lower barrier to entry is also very nice.