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    Apache Airflow

    Apache Airflow

    Apache Airflow is an open-source platform designed to help organizations build, schedule, orchestrate, and monitor data pipelines, ETL processes, and other complex workflows. It offers basic CRON-like scheduling capabilities, allowing users to set workflows to run at specified intervals based on time, day of week or month. Airflow uses Directed Acyclic Graphs (DAGs), where tasks are represented as nodes, and the dependencies between them are clearly defined. It enables users to programmatically author workflows using Python.

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    Apache Airflow

    Benefits and Use Cases

    • Automate complex data workflows, pipelines, and task dependencies for greater control and visibility.
    • Manage and monitor Airflow jobs with enterprise workloads in a single automation platform.
    • View DAG execution status and task-level progress directly within the Automic interface.
    • Access execution logs and trigger retries from Automic to reduce manual intervention and downtime.

    Example Use Case: Data Pipeline Orchestration and Monitoring

    Schedule, execute, and monitor critical data pipelines across cloud and on-prem environments.

    Automic Features

    • Schedule execution of Apache Airflow DAGs
    • Monitor to completion Airflow DAGs
    • Retrieve DAG logs for problem diagnosis
    • Alert on failure of DAG processing
    • Create dependencies between DAG executions
    • Incorporate DAG processing in existing enterprise automation workflows including dependency enforcement
    Additional Information