**Description**

A guide that describes how to use all the operators for Azure (https://github.com/apache/airflow/tree/master/airflow/providers/databricks/operators) would be useful.

Other guides are available:
https://airflow.readthedocs.io/en/latest/howto/operator/index.html

Source code for those guides are at:
https://github.com/apache/airflow/tree/master/docs/howto/operator

Are you wondering how to start contributing to this project? Start by reading our [contributor guide](https://github.com/apache/airflow/blob/master/CONTRIBUTING.rst)

Best regards,
Kaxil﻿

airflow/providers/databricks/example_dags/example_databricks.py
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"""
This is an example DAG which uses the DatabricksSubmitRunOperator.
In this example, we create two tasks which execute sequentially.
The first task is to run a notebook at the workspace path "/test"
and the second task is to run a JAR uploaded to DBFS. Both,
tasks use new clusters.
Because we have set a downstream dependency on the notebook task,
the spark jar task will NOT run until the notebook task completes
successfully.
The definition of a successful run is if the run has a result_state of "SUCCESS".
For more information about the state of a run refer to
https://docs.databricks.com/api/latest/jobs.html#runstate
"""
from airflow import DAG
from airflow.providers.databricks.operators.databricks import DatabricksSubmitRunOperator
from airflow.utils.dates import days_ago
default_args = {
    'owner': 'airflow',
    'email': ['airflow@example.com'],
    'depends_on_past': False,
}
with DAG(