fta-cli --server hostname --port 21 --username user --password pass \ --put /local/file.txt --target /remote/destination/
# Poll for completion while True: status = requests.get(f"API_BASE/transfer/transfer_id", headers=headers) if status.json()["state"] == "COMPLETED": break time.sleep(5) return True run_transfer("/data/sales.csv", "/incoming/sales.csv") run_transfer("/data/inventory.xml", "/incoming/inventory.xml") print("All workloads completed") 3. Advanced Workload Patterns Pattern 1: Parallel Transfers (Multi-Threaded) Use xargs or Python ThreadPoolExecutor to send multiple files simultaneously. filecatalyst workload automation
For native workload automation features (dependency management, SLA tracking, visual pipelines), you would typically wrap FileCatalyst commands into a dedicated workload automation platform like , using FileCatalyst as the file movement plugin. def main(): files_to_send = ["/data/file1
def main(): files_to_send = ["/data/file1.bin", "/data/file2.bin"] for f in files_to_send: # Pre-processing: compute hash with open(f, "rb") as fp: original_hash = hashlib.sha256(fp.read()).hexdigest() def main(): files_to_send = ["/data/file1.bin"
from airflow import DAG from airflow.operators.bash import BashOperator from datetime import datetime default_args = 'retries': 3 with DAG('fc_transfer_dag', start_date=datetime(2024,1,1), schedule='0 2 * * *', default_args=default_args) as dag: transfer = BashOperator( task_id='send_to_fc', bash_command='fta-cli --server fc.prod.com --put /daily/report.csv --target /archive/' ) Enable detailed logs: