For your ETL scenario with ingestion followed by dbt transformation, you have several options, each with its own advantages. Here are the recommended approaches, from simplest to most sophisticated: 1. Single Flow with Multiple Tasks

python
from prefect import flow, task

@task
def ingest_data():
    # Your ingestion logic here
    pass

@task
def run_dbt():
    # Your dbt transformation logic here
    pass

@flow
def etl_pipeline():
    data = ingest_data()
    run_dbt()

2. Parent-Child Flow Pattern

python
from prefect import flow

@flow
def ingest_flow():
    # Your ingestion logic here
    pass

@flow
def dbt_flow():
    # Your dbt transformation logic here
    pass

@flow
def parent_flow():
    ingest_result = ingest_flow()
    dbt_flow()

3. Separate Deployments with

run_deployment

python
from prefect import flow
from prefect.deployments import run_deployment

@flow
def orchestrator():
    # Run ingest deployment
    ingest_result = run_deployment(
        name="ingest/default",
        timeout=300,
        wait=True  # Important: Wait for completion before proceeding
    )
    
    # Only run dbt if ingestion succeeded
    if ingest_result.state.is_completed():
        dbt_result = run_deployment(
            name="dbt-transform/default",
            timeout=600,
            wait=True
        )

Recommendations: 1. For Simple Workflows: Use the single flow with multiple tasks approach. It's the most straightforward and provides built-in dependency management. 2. For Reusable Components: Use the parent-child flow pattern when you want to: - Reuse the individual flows independently - Have separate logging for each major step - Maintain cleaner separation of concerns 3. For Complex Orchestration: Use

run_deployment

when you need: - Dynamic deployment selection - Different scheduling patterns for each component - Ability to trigger the components independently - Different infrastructure/work pools for each component Additional Considerations: - If using dbt, consider using the prefect-dbt integration package for better dbt integration - For production environments, ensure proper error handling and notifications - Consider using

wait=True

with

run_deployment

to ensure sequential execution - Use flow parameters to pass data between flows if needed The choice largely depends on: - How often you need to run each component independently - Whether the components need different infrastructure/resources - How tightly coupled the steps are - Your monitoring and observability requirements For your specific case, since you're running on a hosted Prefect server, I'd recommend starting with the parent-child flow pattern as it provides a good balance of separation and simplicity while maintaining clear dependency relationships.