Comparisons that start from Apache Airflow, the Apache-2.0 workflow scheduler, against newer Python orchestrators, now that Airflow 2 has reached end of life.
Task-centric Python DAGs against asset-centric orchestration: migration effort for existing Airflow 2 estates now past end of life, which features stay in open-source Dagster versus Dagster+, and which fits a data platform team.
DAG-based scheduling against Python-native flows: Airflow 3 with its metadata database and executors versus Prefect's self-hosted server, where RBAC, SSO and audit logs stay in Prefect Cloud. Verdict by team skills, event-driven needs and what an Airflow 2 upgrade would cost anyway.
The three orchestrators data teams shortlist: task DAGs, software-defined assets and Python flows compared on licensing, which features sit in Dagster+ or Prefect Cloud, self-hosted components and migration effort from Airflow 2.
Which of Apache Airflow's official installation methods fits a team: the official Helm chart on Kubernetes, the production Docker images in your own orchestration, or a PyPI install on hosts you manage. Covers what the Airflow docs say each suits, what you remain responsible for, and why the Docker Compose quick start is not a production option.
Which Apache Airflow executor to run tasks on: CeleryExecutor with a queue and long-running shared workers, KubernetesExecutor with one pod per task, or both through Airflow's multi-executor configuration. Covers the trade-offs the Airflow docs state, startup latency against noisy neighbors, and the hybrid executors removed in Airflow 3.
Python DAGs on a scheduler against scripts and flows in many languages on a PostgreSQL job queue, for teams replacing Airflow 2 after its end of life. Covers authoring and local testing, infrastructure (scheduler, DAG processor, API server and metadata database against Windmill's API servers and workers), SSO and audit features in Windmill Enterprise, licensing and migration effort.
Python DAGs against declarative YAML flows with event triggers: Airflow 3 versus Kestra 2.0. Covers who can author pipelines, how each scales, what the Kestra open-source edition lacks (SSO, RBAC, audit logs, HA backends) compared with Enterprise, and the cost of moving existing DAGs.
For teams on Kubernetes choosing between Airflow's Python DAGs, scheduler and metadata database and Argo Workflows, a CNCF graduated engine where every step is a container defined as a Kubernetes custom resource. Compares scheduling and backfills, Python authoring (Hera), workflow history and archiving, and who can operate each.
Airflow against Mage for data pipelines: Airflow's scheduler-centred platform versus Mage's notebook-style block editor, and the catch that Mage positions its open-source edition as a development environment while Kubernetes executors and workspaces sit in paid Mage Pro. Covers dbt support, databases needed and production operation.
Buying Airflow as a service from Astronomer or running it yourself: Astro's published per-hour deployment, worker and cluster rates, the Private Cloud option for your own environment including air-gapped sites, and the scheduler, database, executor and upgrade work you keep when self-hosting.
Amazon Managed Workflows for Apache Airflow against running Airflow on your own servers or Kubernetes: MWAA's published environment, worker, scheduler and database storage rates in US East, the serverless option, and what self-hosting adds in effort but returns in version control, executors and data location.
Cloud Composer, Google's managed Airflow on GKE, against self-hosted Airflow: which Airflow major versions each Composer generation supports, the GKE cluster, managed database and Cloud Storage bucket every environment brings, and when running Airflow yourself on Kubernetes is the cheaper or more controllable choice.
Tell us what you're working on, whether it's a deployment, an audit, a security test or a cyber range. You'll speak with an engineer who can help you scope it.