This is taken from the numpy tutorial https://github.com/numpy/numpy-tutorials/blob/main/content/tutorial-air-quality-analysis.md.
Is where the analysis steps are defined as Apache Hamilton functions.
Versus doing this analysis in a notebook, the strength of Apache Hamilton here is in forcing concise definitions and language around steps in the analysis -- and then magically the analysis is pretty reusable / very easy to augment. E.g. add some @config.when or split things into python modules to be swapped out, to extend the analysis to new data sets, or new types of analyses.
Here is a simple visualization of the functions and thus the analysis: 
Is where the driver code lives to create the DAG and exercise it.
To exercise it:
python run_analysis.py
You can even run this example in Google Colab: 
The code found here was copied and pasted, and then tweaked to run with Apache Hamilton. If something from the modeling perspective isn't clear, please read https://github.com/numpy/numpy-tutorials/blob/main/content/tutorial-air-quality-analysis.md
