Apache Airflow is the most-deployed workflow orchestrator with over 7 million monthly PyPI downloads, but its age is showing -- Dagster's asset-centric model and first-class testing make it the stronger choice for greenfield data pipelines.
The authoritative source on how Airflow models dependencies, scheduling, and task execution. Read this before any tutorial.
Dagster's asset-centric model is a conceptual upgrade over Airflow's task-centric model. The getting-started tutorial is one of the best in the data tooling space.
The definitive guide to writing production-quality Airflow DAGs. Covers idempotency, dynamic task mapping, and the common pitfalls that cause silent failures.
Prefect is the third major orchestrator worth knowing. Its hybrid execution model (cloud orchestration, local workers) fits teams that cannot run their own Airflow cluster.
Orchestration turns a pipeline that runs once into one that runs reliably for years -- handling scheduling, dependency management, retries, alerting, and observability. Apache Airflow is the market leader by adoption: the apache-airflow package exceeds 7 million PyPI downloads per month, according to PyPI Stats. That install base reflects history -- the scheduler struggles at scale, the UI is dated, and writing tests for DAGs is painful.
Dagster was designed from the ground up to fix Airflow's problems, and its traction reflects it: Dagster has accumulated over 16,000 GitHub stars, making it the leading modern alternative. Its asset-centric model -- defining data artifacts with lineage and freshness policies instead of task graphs -- aligns better with how data teams reason about pipelines. For greenfield projects, evaluate Dagster before defaulting to Airflow.
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