PyOhio 2026
The Zen of Python reminds us to strive for "beautiful over ugly" and "explicit over implicit" yet when we step into the world of data modeling, that elegance often disappears into long static queries that are difficult to transform and even harder to maintain. dbt (data build tool) Python powered framework is the missing link that simplifies the "T" in ELT (Extract, Transform, Load), bringing software engineering best practices to any data storage system.
In this session, we’ll walk through how I bridged the gap between application development and data modeling, becoming proficient in dbt in just one week. We will explore how to handle complex transformations with ease and maintain a flexible architecture that works across any database. I'll show you Jinja2 templating and you will see how dbt allows you to treat your data repository like an application—complete with version control, testing, and modularity. Whether you are a seasoned Data Engineer or a Software Developer looking for a more structured way to interact with data, you will leave with a blueprint for building cleaner, more reliable models following the guiding principles for Python design.
