BRIEF OVERVIEW

The Databricks Operator for Apache Airflow is a tool that allows you to easily integrate and manage your Databricks workloads within the Airflow workflow management system. It provides operators and hooks for interacting with Databricks clusters, jobs, notebooks, and other resources.

FAQs

Q: What is Apache Airflow?

A: Apache Airflow is an open-source platform used to programmatically author, schedule, and monitor workflows. It allows you to define complex data pipelines as directed acyclic graphs (DAGs) and execute them in a reliable manner.

Q: What are some use cases for using the Databricks Operator with Apache Airflow?

A: Some common use cases include:

• Scheduling notebook runs on Databricks clusters.
• Running Spark jobs on Databricks clusters.
• Cleaning up temporary resources after job completion.

Q: How do I install the Databricks Operator for Apache Airflow?

A: You can install the operator using pip by running the following command:

$ pip install apache-airflow-providers-databricks

Note:You need to have Apache-AirFlow installed before installing this package.

Q: How do I configure the connection between my airflow instance and databrick workspace ?

A: You can configure the connection by following these steps:

1. Go to your Airflow web interface.
2. Navigate to Admin > Connections
3. Click on “Create” button.
4. Fill in the required details like Connection Name, Host, Login and Password.
5. In Extra field you need to provide databricks workspace url as json key value pair.
   For example : {“workspace_url”: “https://your-databricks-workspace-url”} .

Note:

You must have a Databricks Personal Access Token (PAT) for authentication.

Q: How do I use the Databricks Operator in my Airflow DAG?

A: To use the Databricks Operator in your DAG, you need to import it and create an instance of it within your DAG definition. Here’s an example:

# Import the necessary modules
from airflow import DAG
from airflow.providers.databricks.operators.databricks import DatabricksRunNowOperator

# Define your DAG
with DAG(dag_id="my_dag", ...) as dag:

    # Create a task using the Databricks Operator
    run_notebook_task = DatabricksRunNowOperator(
        task_id="run_my_notebook",
        job_id=12345,
        notebook_params={"param1": "value1"}
    )

    # Add other tasks and dependencies as needed

BOTTOM LINE

The Databricks Operator for Apache Airflow provides a convenient way to integrate and manage your Databrick workloads within Airflow workflows. By leveraging this operator, you can easily schedule and monitor Databricks jobs, notebooks, and clusters as part of your data pipeline.