In this post, I'll share how I'm using Kedro to manage Machine Learning (ML) pipelines while efficiently storing and executing SQL queries within my Python Kedro project. By integrating Ibis via the Kedro Ibis table dataset, I'm able to keep all my SQL code within the Kedro project and execute it on the database side, specifically using Google BigQuery.