[DOC] Update airflow migration

docs/getting_started_with_workflow_development/index.md

@@ -1,4 +1,5 @@
 (getting_started_workflow_development)=
+
 # Getting started with workflow development
 
 Machine learning engineers, data engineers, and data analysts often represent the processes that consume, transform, and output data with directed acyclic graphs (DAGs). In this section, you will learn how to create a Flyte project to contain the workflow code that implements your DAG, as well as the configuration files needed to package the code to run on a local or remote Flyte cluster.

docs/user_guide/development_lifecycle/index.md

@@ -20,4 +20,5 @@ running_workflows
 running_launch_plans
 inspecting_executions
 debugging_executions
+migrating_from_airflow_to_flyte
 ```

docs/user_guide/development_lifecycle/migrating_from_airflow_to_flyte.md

@@ -0,0 +1,89 @@
+(migrating_from_airflow_to_flyte)=
+
+# Migrating from Airflow to Flyte
+
+:::{important}
+Many Airflow operators and sensors have been tested on Flyte, but some may not work as expected.
+If you encounter any issues, please file an [issue](https://github.com/flyteorg/flyte/issues) or reach out to the Flyte community on [Slack](https://slack.flyte.org/).
+:::
+
+Flyte can compile Airflow tasks into Flyte tasks without changing code, which allows you 
+to migrate your Airflow DAGs to Flyte with minimal effort.
+
+In addition to migration capabilities, Flyte users can seamlessly integrate Airflow tasks into their workflows, leveraging the ecosystem of Airflow operators and sensors.
+By combining the robust Airflow ecosystem with Flyte's capabilities such as scalability, versioning, and reproducibility, users can run more complex data and machine learning workflows with ease.
+For more information, see the [Airflow agent documentation](https://docs.flyte.org/en/latest/flytesnacks/examples/airflow_agent/index.html).
+
+# For Current Flyte Users
+Even if you're already utilizing Flyte and have no intentions of migrating from Airflow,
+you can still incorporate Airflow tasks into your Flyte workflows. For instance, Airflow offers support
+for Google Cloud [Dataproc Operators](https://airflow.apache.org/docs/apache-airflow-providers-google/stable/operators/cloud/dataproc.html),
+facilitating the execution of Spark jobs on Google Cloud Dataproc clusters. Therefore, rather than developing a custom plugin in Flyte, you can seamlessly integrate
+Airflow's Dataproc operators into your Flyte workflows to execute Spark jobs
+
+## Prerequisites
+
+- Install `flytekitplugins-airflow` in your Python environment.
+- Enable an {ref}`Airflow agent<deployment-agent-setup-airflow>` in your Flyte cluster.
+
+## Steps
+
+### 1. Define your Airflow tasks in a Flyte workflow
+
+Flytekit compiles Airflow tasks into Flyte tasks, so you can use
+any Airflow sensor or operator in a Flyte workflow.
+
+
+```python
+from flytekit import task, workflow
+from airflow.sensors.filesystem import FileSensor
+
+@task
+def say_hello() -> str:
+    return "Hello, World!"
+
+@workflow
+def airflow_wf():
+    flyte_task = say_hello()
+    airflow_task = FileSensor(task_id="sensor", filepath="/")
+    airflow_task >> flyte_task
+
+if __name__ == "__main__":
+    print(f"Running airflow_wf() {airflow_wf()}")
+```
+
+### 2. Test your workflow locally
+
+:::{note}
+Before running your workflow locally, you must configure the [Airflow connection](https://airflow.apache.org/docs/apache-airflow/stable/howto/connection.html) by setting the `AIRFLOW_CONN_{CONN_ID}` environment variable.
+For example,
+```bash
+export AIRFLOW_CONN_MY_PROD_DATABASE='my-conn-type://login:password@host:port/schema?param1=val1&param2=val2'
+```
+:::
+
+Although Airflow doesn't support local execution, you can run your workflow that contains Airflow tasks locally, which is helpful for testing and debugging your tasks before moving to production.
+
+```bash
+AIRFLOW_CONN_FS_DEFAULT="/" pyflyte run workflows.py airflow_wf
+```
+
+:::{warning}
+Some Airflow operators may require certain permissions to execute. For instance, `DataprocCreateClusterOperator` requires the `dataproc.clusters.create` permission. 
+When running Airflow tasks locally, you may need to set the necessary permissions locally for the task to execute successfully.
+:::
+
+### 3. Move your workflow to production
+
+:::{note}
+In production, we recommend storing connections in a [secrets backend](https://airflow.apache.org/docs/apache-airflow/stable/security/secrets/secrets-backend/index.html).
+Make sure the agent pod has the right permission (IAM role) to access the secret from the external secrets backend.
+:::
+
+After you have tested your workflow locally, you can execute it on a Flyte cluster using the `--remote` flag.
+In this case, Flyte creates a pod in the Kubernetes cluster to run the `say_hello` task, and then runs
+your Airflow `BashOperator` task on the Airflow agent.
+
+```bash
+pyflyte run --remote workflows.py airflow_wf
+```