Adapt existent dags

orchestrate/dags/daily_loan_run.py

@@ -1,8 +1,7 @@
 import datetime
 
 from airflow.decorators import dag, task_group
-from airflow.providers.airbyte.operators.airbyte import \
-    AirbyteTriggerSyncOperator
+from airflow.providers.airbyte.operators.airbyte import AirbyteTriggerSyncOperator
 from fivetran_provider.operators.fivetran import FivetranOperator
 from fivetran_provider.sensors.fivetran import FivetranSensor
 from operators.datacoves.bash import DatacovesBashOperator
@@ -56,7 +55,6 @@ def extract_and_load_fivetran():
     tg_extract_and_load_fivetran = extract_and_load_fivetran()
     extract_and_load_dlt = DatacovesBashOperator(
         task_id="extract_and_load_dlt",
-        activate_venv=True,
         tooltip="dlt Extract and Load",
         bash_command="python load/dlt/csv_to_snowflake/load_csv_data.py",
     )

orchestrate/dags/datacoves_sample_dag.py

@@ -1,14 +1,15 @@
 """## Datacoves Bash Operator DAG
 This DAG is a sample using the Datacoves Airflow Operators"""
 
-from pendulum import datetime
 from airflow import DAG
 from airflow.decorators import dag, task
-from operators.datacoves.dbt import DatacovesDbtOperator
 from operators.datacoves.bash import DatacovesBashOperator
+from operators.datacoves.dbt import DatacovesDbtOperator
+from pendulum import datetime
 
 # Only here for reference, this is automatically activated by Datacoves Operator
-DATACOVES_VIRTIAL_ENV = '/opt/datacoves/virtualenvs/main/bin/activate'
+DATACOVES_VIRTIAL_ENV = "/opt/datacoves/virtualenvs/main/bin/activate"
+
 
 @dag(
     default_args={
@@ -17,39 +18,37 @@
         "email": "[email protected]",
         "email_on_failure": True,
     },
-
     catchup=False,
-    tags = ["version_6"],
-    description = "Datacoves Sample dag",
-
+    tags=["version_6"],
+    description="Datacoves Sample dag",
     # This is a regular CRON schedule. Helpful resources
     # https://cron-ai.vercel.app/
     # https://crontab.guru/
-    schedule_interval = "0 0 1 */12 *"
+    schedule_interval="0 0 1 */12 *",
 )
 def datacoves_sample_dag():
 
     # Calling dbt commands
     dbt_task = DatacovesDbtOperator(
-        task_id = "run_dbt_task",
-        bash_command = "dbt debug",
-        doc_md = """\
+        task_id="run_dbt_task",
+        bash_command="dbt debug",
+        doc_md="""\
             #### Task Documentation
             This task leveraged the DatacovesDbtOperator
-        """
+        """,
     )
 
     # This is calling an external Python file after activating the venv
     # use this instead of the Python Operator
     python_task = DatacovesBashOperator(
-        task_id = "run_python_script",
-        activate_venv=True,
-        bash_command = "python orchestrate/python_scripts/sample_script.py"
+        task_id="run_python_script",
+        bash_command="python orchestrate/python_scripts/sample_script.py",
     )
 
     # Define task dependencies
     python_task.set_upstream([dbt_task])
 
+
 # Invoke Dag
 dag = datacoves_sample_dag()
 dag.doc_md = __doc__

orchestrate/dags_yml_definitions/daily_loan_run.yml

@@ -26,7 +26,6 @@ nodes:
   extract_and_load_dlt:
     type: task
     operator: operators.datacoves.bash.DatacovesBashOperator
-    activate_venv: true
 
     tooltip: "dlt Extract and Load"
     bash_command: "python load/dlt/csv_to_snowflake/load_csv_data.py"