Modern Data Orchestration
Build, run, and observe data pipelines-as-code with Astro, the cloud-native data orchestration platform powered by Apache Airflow™.Get Started
Focus on your pipelines, not managing Apache Airflow
Accelerate the development of data pipelines with tools that allow your entire data team to focus on code that impacts your business.
Run With Confidence
Increase data availability with a reliable and efficient production runtime environment optimized for the cloud.
See the Whole Picture
Make sense of your data universe with real-time visibility and actionable insights across environments.
Unified Data Flows
Bring order to your distributed data ecosystem with a modern orchestration platform.
Choose Your Deployment
deployed in your cloud
Keep orchestration close to your data with a single-tenant data plane in your cloud, no DevOps required. With a common control plane for data pipelines across clouds, you’ll sleep easy knowing your environment is managed by the core developers behind Apache Airflow.Learn More
Self managed, deployed in your private cloud
Launch, manage, and secure Airflow environments with an enterprise-ready software platform built for the most demanding settings.Learn More
Work Locally and Control Astro from Your Terminal
Write, test, and run DAGs in a lightweight local development environment.
astro dev init > Airflow project Initialized! ├── Dockerfile # Base Airflow image ├── README.md ├── airflow_settings.yaml # For local connections ├── dags │ ├── example-dag-advanced.py │ └── example-dag-basic.py ├── include # Scripts, helpers, etc. ├── packages.txt # OS packages ├── plugins └── requirements.txt # Python packages
astro dev start > Creating containers for Airflow Scheduler, Webserver, and Database... > Airflow is starting up... > Your local instance of Airflow is now running on localhost: 8080 🚀
astro dev logs > Printing logs from Airflow Scheduler, Webserver, and Workers.... ================================================================================ DAG File Processing Stats File Path PID Runtime # DAGs # Errors Last Runtime Last Run ----------------------------------------------- ----- --------- -------- ---------- -------------- ------------------- /usr/local/airflow/dags/example-dag-advanced.py 1 0 0.48s 2022-02-17T16: 32: 14 /usr/local/airflow/dags/example-dag-basic.py 1 0 1.22s 2022-02-17T16: 32: 15 ================================================================================
Create, manage, and deploy to production from the comfort of your terminal or CI/CD processes.
astro deployment create my-airflow NAME Namespace DEPLOYMENT ID TAG AIRFLOW VERSION my-airflow my-namespace my-uuid 4.0.8 2.2.2 Successfully created deployment. Deployment can be accessed at the following URLs Airflow Dashboard: https: //my-org.astronomer.run/my-airflow/home Deployment Dashboard: https: //cloud.astronomer.io/my-workspace/deployments/my-airflow
astro deploy my-airflow > Deploying: updated DAGs to my-airflow > Building image... > Pushing image to Astronomer... > Deploy Succeeded! Your DAGs are now running at https: //my-org.astronomer.run/my-airflow/home
astro deployment list > Listing Deployments available to modify... * Prod Deployment Stage Deployment Dev Deployment