9/19/2023, 5:18:34 AM

While Databricks provides some orchestration capabilities through Databricks Workflows, they have limited functionality and may not integrate seamlessly with the rest of your data stack. On the other hand, Apache Airflow is a tool-agnostic orchestrator that offers several advantages:

• You can use CI/CD to manage your workflow deployment. Airflow DAGs are Python code, and can be integrated with various CI/CD tools and tested.

• If a task fails in your Databricks Workflow, you can re-run it without having to re-run the entire Workflow, saving valuable compute resources.

• You can use task groups within Databricks Workflows, enabling you to collapse and expand parts of larger Databricks Workflows visually.

• Airflow allows for cross-DAG dependencies to trigger Databricks Workflows from other DAGs in your Airflow environment, enabling a data-driven architecture.

• You can use familiar Airflow code as your interface to orchestrate Databricks notebooks as Workflows.

• You can inject parameters into your Databricks Workflow at the Workflow-level. These parameters can be dynamic and retrieved at runtime from other Airflow tasks.

Therefore, if you are looking for a more robust, flexible, and integrative solution for your data workflows, Apache Airflow would be a better choice.

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9/19/2023, 5:18:20 AM

should I use databricks workflows or airflow?