Most enterprise data used by Data Scientists to train machine learning models is tabular data that comes from data warehouses and data lakes. Recent growth in the popularity of the modern data stack, based on lakehouses like Snowflake, Delta Lake, Big Query, and Redshift, have led to growth in the use of SQL-centric tools for data engineers, such as DBT. However, Data Scientists' language of choice is Python. How do we square this circle?