There are many tools, including DBeaver, TablePlus, and MySQL Workbench, that make it easy to define complex empty database schemas; however, in the case of scientific data, defining the ETL pipeline is much more difficult than defining the empty database schema. There are several existing Python packages, including psycopg2, pymysql, and sqlalchemy that expose SQL functionality to a python user, but they do not provide any framework for defining SMART ETL pipelines. DBgen fills this gap in the current tooling. If one were to implement 50 complex, SMART ETL pipelines, each for a different use case, yes, some of the code would specific to each use case, but a large amount of the code would be common to all of the use cases. DBgen is that code that is common code. In other words, DBgen is a Python package that provides a framework for the definition of SMART ETL pipelines, just as pytorch and tensorflow provide a framework for the definition of GPU-accelerated, complex deep learning models.