Dagster vs Airflow: choosing the right orchestrator for a small data team

A solo data engineer compares Dagster and Airflow, arguing the core difference is what each tool schedules: Airflow runs tasks, while Dagster manages the data assets those tasks produce. The author runs a production Dagster pipeline with 112 assets, 93 asset checks and 57 schedules, and credits the asset model with tracking partitions and keeping checks beside the data they validate. The piece notes that partition granularity is a deliberate trade-off, citing a case where one unpartitioned asset rebuilt 1,882 dates in about 45 seconds. The author has not run Airflow in production, so the Airflow comparison draws on its documentation. They conclude that for a large team already on Airflow migration rarely pays off, while a small team starting fresh may benefit from Dagster's asset-centric approach.