Workflow Orchestration

Upgrade your legacy workflow scheduler to Apache Airflow—open source, born-in-the-cloud pipeline-as-code technology for maximum flexibility and scalability. Astronomer provides an unrivaled breadth of integration connectors, developer resources, and deployment options.

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Faster, easier, more flexible workflow software

Automate scheduled and event-driven workflows integrating a range of data sources, event sources and applications into a set of logical flows expressed as code. Sophisticated algorithmic control enables many execution modes and models as well as failure recovery, automated retries and replaying historical workflows.

Apache Airflow—run your data pipelines and tasks automatically

Apache Airflow runs your workflows as code, offering data engineers the same benefits as for app development teams: automation, tools, versioning and CI/CD techniques. With a massive ecosystem and user base, Airflow has the widest range of supported providers and integrations originating in the community and other vendors.

Apache Airflow
  1. End-to-end

    Express all your pipelines using the flexibility of Python and SQL. Data engineers can quickly assemble pipelines using the library of providers, sample modules and template pipelines in the Astronomer Registry. Building pipelines-as-code means workflows can be treated like every other software artefact for development, testing, updates, CI/CD pipelines, versioning and DevOps practices.

  2. Advanced pipeline orchestration

    Use Airflow to ensure that a pipeline executes at the specified time or event, with respect to other dependent workflows, and that the tasks are allocated the required infrastructure to run. Airflow also manages pipeline and task failures, automating retries or custom recovery and rollback code as needed.

  3. Rapid
    data innovation

    Combine multiple workflows to quickly solve new business problems reusing internal and external workflows from the Astronomer Registry.

I love how my data science team has become self-sufficient and effective. Airflow made it very easy for them to get the data they need and manage it in a way that allows them to do their job quickly and efficiently.

Mark Gergess

VP of Data & Analytics at Herman Miller

After 6-7 months with Apache Airflow, we’ve built more than ninety DAGs. The tool made the experience so much easier.

Gautam Doulani

Data Engineering Lead at CRED

An open source project, such as Apache Airflow, works great in the production environment, even for the sensitive use cases of the banking industry.

Alaeddine Maaoui

Product Owner at Societe Generale

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