Broadcom Automic → Apache Airflow

Broadcom Automic (formerly UC4 / Automic Workload Automation) organizes work into objects — jobs, job plans, schedules, and workflows. Its proprietary scripting language (AE scripting) and object-oriented model require careful translation to Airflow DAGs and operators. BatchFoundry handles the full object inventory, AE script analysis, and DAG generation — including cross-system connectors and file transfer jobs.

Concept Mapping

The Hard Parts We Handle

• AE proprietary script language → Python / Bash translation
• Object hierarchy extraction from Automic repository
• VARA variable resolution at runtime vs. design time
• Cross-client object dependencies
• File transfer job re-platforming
• Automic agent mapping to Airflow worker pools/queues

Automic Workflow to DAG

/* Automic Workflow (simplified) */
WORKFLOW: DAILY_ETL
  PREDECESSOR: <none>
  JOB: EXTRACT_JOB
    SCRIPT: :SET &FILE# = "/data/input.csv"
            :RUN /opt/etl/extract.sh &FILE#
  JOB: LOAD_JOB
    PREDECESSOR: EXTRACT_JOB
    SCRIPT: :RUN /opt/etl/load.sh

# Apache Airflow DAG
from airflow.decorators import dag
from airflow.operators.bash import BashOperator
from pendulum import datetime

@dag(schedule="@daily", start_date=datetime(2025,1,1), catchup=False)
def daily_etl():
    extract = BashOperator(
        task_id="extract_job",
        bash_command="/opt/etl/extract.sh /data/input.csv",
    )
    load = BashOperator(task_id="load_job", bash_command="/opt/etl/load.sh")
    extract >> load

daily_etl()

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