Startups are hard and it isn’t easy being positive in a high-stress environment. As a younger employee I struggled with this, but over time I have built up some mental tools to help me deal with the downs of startup life - because working at a startup is not all ping-pong and catered lunches. In fact, now that I think about it, I have never worked anywhere with a ping-pong table, going to add that to my bucket list.
Joining the Astronomer Team
In the name of simplicity I am using a Extract, Load and Transform (ELT) architecture on a few recent data warehouse build-outs. In my case, this means that one database server will do the transformation and serving of reporting data. Using postgres json tools I am able to dump my extracts immediately into my reporting database and begin the transformations from there.
Budgeting to me represents a plan. A budget is a plan to maximize the value you get out of your money. Of course, for the vast majority of us, our dollars are severely limited. We are in a constant balancing act of maximizing our long-term and short-term happiness.
One of the more common operations I have come across when cleaning up data for analysis is a mapping transformation. This can be useful when you are wanting to clean up known dirty data or just tranforming the data to be easier to read. One other reason and probably the most compelling case for the mapping transformation is the need to convert features to float values for sckit-learn models. That is the example I am going to show in this post.