In this tutorial, we implement an advanced data pipeline using Dagster. We set up a custom CSV-based IOManager to persist assets, define partitioned daily data generation, and process synthetic sales data through cleaning, feature engineering, and model training. Along the way, we add a data-quality asset check to validate nulls, ranges, and categorical values, and we ensure that metadata and outputs are stored in a structured way. The focus throughout is on hands-on implementation, showing how to integrate raw data ingestion, transformations, quality checks, and machine learning into a single reproducible workflow. Check out the FULL CODES here. import sys, subprocess,…
Read More