Astronomer

Astronomer is a platform designed for data engineers and teams to automate their Apache Airflow pipelines, making it easier to build, run, and observe data, ML, and AI workflows at scale. The platform is powered by Apache Airflow and augmented with AI, providing features like native data observability, local and remote development, and architecture and performance optimization. With Astronomer, data engineers can raise the ceiling of data engineering, moving from data exploration to migration without needing to babysit their workflows. The platform offers a range of tools and features, including Otto, an agent built for Apache Airflow that writes DAGs, investigates failures, and plans upgrades. Astronomer also provides a hardened runtime, an agent-based executor, and an optimization layer for managing health and resources across deployments. The Astro Engine, a key component of Astronomer, handles higher concurrency, recovers from failures automatically, and stays efficient across a wide range of workloads without constant tuning. Data engineers can manage Airflow infrastructure the same way they manage DAGs, with config living in Git and changes going through PR review. They can also monitor health, data quality, and SLAs in real time, with task-level lineage connecting failures to downstream impact and AI-powered root cause analysis getting them from alert to fix in minutes. Astronomer offers flexible, usage-based pricing for teams of all sizes, with a free tier available for developers and small teams. The platform is suitable for data engineers and teams looking to automate their Apache Airflow pipelines and improve their data engineering workflows. With Astronomer, teams can focus on building and testing pipelines without the overhead of managing Airflow infrastructure. The platform provides a range of benefits, including improved efficiency, reduced overhead, and enhanced data observability. Data engineers can use Astronomer to automate their workflows, from data exploration to migration, and to observe and manage their pipelines at scale.