Workflow orchestration sits at the heart of modern data engineering. Whether you’re running daily ETL jobs, streaming pipelines, or machine learning workflows, you need a scheduler to manage dependencies, retries, and monitoring. For years, Apache Airflow has been the default choice, but newer tools like Dagster and Prefect have emerged, each promising a more modern approach.
The question is: Which scheduler best fits your data team? In this article, we’ll explore the strengths and trade-offs of Airflow, Dagster, and Prefect through real-world lenses. We’ll focus less on abstract features and more on how these tools behave in practice.
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