Tidal Workload Automation → Apache Airflow

Tidal Workload Automation (formerly Cisco Tidal) is a distributed enterprise scheduler with a rich UI-driven job definition model. Its job groups, dependency conditions, and business calendars map naturally to Airflow DAGs. BatchFoundry extracts Tidal job definitions via the Tidal API or database export and generates idiomatic Airflow DAGs with full dependency and schedule fidelity.

Concept Mapping

The Hard Parts We Handle

• Tidal calendar and holiday schedule translation
• Job group hierarchy → nested TaskGroups
• Cross-job-stream dependencies → ExternalTaskSensor
• Agent and connection mapping
• Variable inheritance within job groups
• Tidal API extraction for large job catalogs

Tidal job stream to DAG

/* Tidal job stream (simplified) */
JobStream: DAILY_ETL
  Schedule: MON-FRI 01:00
  Job: EXTRACT
    Agent: UNIX-AGENT-01
    Command: /opt/etl/extract.sh
  Job: LOAD
    Agent: UNIX-AGENT-01
    Command: /opt/etl/load.sh
    Predecessor: EXTRACT (Complete)

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

@dag(schedule="0 1 * * 1-5", start_date=datetime(2025,1,1), catchup=False)
def daily_etl():
    extract = BashOperator(task_id="extract", bash_command="/opt/etl/extract.sh")
    load = BashOperator(task_id="load", bash_command="/opt/etl/load.sh")
    extract >> load

daily_etl()

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