Zaya-M/Ecommerce-ETL-Pipeline-PySpark

This is a tutorial-grade project combining standard, well-established tools (PySpark for distributed processing, Airflow for DAG orchestration, MySQL for persistence) in a straightforward e-commerce use case. Zero stars, forks, and velocity indicate no adoption or community traction. The architecture reflects common patterns taught in data engineering bootcamps—no novel algorithmic contribution, no specialized domain insight, and no technical moat. The specific domain (e-commerce GMV/category trends) is narrow but the implementation uses commodity components. Frontier labs have no incentive to replicate this; they either build their own internal infrastructure or use managed services (Databricks, BigQuery, etc.). The project is easily reproducible by anyone with basic PySpark/Airflow knowledge. The extremely recent age (0 days) and zero engagement metrics confirm this is either a fresh personal experiment or course project. Defensibility is minimal—any team needing this pattern would build their own customized version in hours.