All your data pipelines, one dynamic platform.
Greg Neiheisel, Astronomer CTO, gives a preview of how this works with Apache Airflow.
Define workflows in code.
Author all standard or custom data pipelines in Python for maintenance, versioning, portability, extensibility and collaboration.
Connect anything.
Build scheduled, dependency-based data pipelines to centralize and route data from any source to any destination.
Automate processes.
Fuel data warehousing, ETL, analytics, experimentation, targeting, sessionization, data infrastructure maintenance and more.
Built on open source, tailored to you.
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One-click deployment and monitoring.
Run workflows as directed acyclic graphs (DAGS).
Create new sources and destinations.
Schedule tasks on an array of workers.
Set Service Level Agreements (SLAs) for every DAG.
Process large batches of data by micro-batching.
Modify any DAG, anytime.
Access in-depth walkthroughs, docs and support.
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Built for security, ready to scale.
Via Astronomer's CLI, access an Apache Airflow instance and workers running in our secure, fully-managed and monitored environment.
