Pipeline Orchestration: Airflow, Prefect, and Dagster for Data Workflows
Orchestrate data pipelines with Airflow DAGs, Prefect flows, or Dagster jobs. Compare scheduling, retry logic, observability, and local development.
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Tags: etl, orchestration, data-engineering
Pipeline Orchestration: Airflow, Prefect, and Dagster for Data Workflows Orchestration is the layer that decides when pipelines run, in what order, with what dependencies, and what to do when something fails. Without it, you have a collection of scripts that someone runs manually — and when the ingestion job runs before the transformation job and produces garbage, nobody knows why. This guide covers the three leading orchestrators — Airflow, Prefect, and Dagster — with an honest assessment of each. Apache Airflow Airflow is the incumbent — built at Airbnb in 2014 and open-sourced shortly after. It defines workflows as DAGs (Directed Acyclic Graphs) in Python files. Every node in the DAG is a task; edges define dependencies. A Basic Airflow DAG What Makes Airflow Good Massive ecosystem —…
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