I have used a number of budgeting tools in the past to take control over my finances, but none have ever checked all my boxes. The latest and perhaps most interesting tool I have discovered is ledger-cli.
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.