It is common to need to switch out the ways you run your pipelines as you progress through your work. When you start out, a local orchestrator is all you need: you run your pipelines on small slices of your data and you build out your pipeline incrementally. At a certain point, it might make sense to scale up the load, or to run it on something closer to what your final ‘production’ stack will resemble. Switching your orchestrator is a matter of a single CLI command. Let’s take a look at how you would do this. We can start off with a very simple pipeline, one that loads a DataFrame and returns it: 
import random

import pandas as pd

from zenml.pipelines import pipeline
from zenml.steps import step

@step
def get_dataframe() -> pd.DataFrame:
    """Returns a DataFrame filled with random numbers."""
    return pd.DataFrame(
        {
            "a": [random.random() for _ in range(100)],
            "b": [random.random() for _ in range(100)],
        }
    )

@pipeline
def test_pipeline(get_dataframe) -> None:
    """Pipeline to get a simple DataFrame."""
    get_dataframe()

test_pipeline(get_dataframe=get_dataframe()).run()
When we start out, we’ll be using the default stack which includes a default orchestrator. You can see how this is all configured with the following CLI command: 
zenml stack describe default
Now that we see that it’s working, we might already know that our stack will require a certain orchestrator, the Airflow orchestrator for example. Running our pipeline on Airflow is a simple matter of installing the integration, creating the new stack, then switching our stack: 
zenml integration install airflow
zenml orchestrator register airflow_orchestrator --flavor=airflow
zenml stack register airflow_stack -a default -o airflow_orchestrator --set
zenml stack up
Once our Airflow stack is up and running, we can run our pipeline again with python run.py and we can rerun on the new orchestrator. Switching out all the other pieces of your ZenML infrastructure is just as easy. Simple CLI commands gives you the flexibility to switch between different components, and the underlying code doesn’t need to change at all! To read a more detailed guide about how Orchestrators function in ZenML, click here.