Comparisons of ADF with Apache Airflow

Choosing the right tool for a data transfer job is highly important. Previous articles have introduced Azure Data Factory and Apache Airflow as cloud tools to do large scale and dependable transfers along with a comparison to DEIS workflow. This section enumerates the differences between ADF and Apache Airflow.

Azure offers various services, each with its strength and use cases. ADF is a fully managed serverless data integration service that provides visual designer tools for configuring source, destination and ETL processes. It can handle large scale data pipelines and transformations. Apache Airflow is not a primary citizen of the Azure cloud, and it is an open-source scheduler for workflow management. While it is not a native Azure service, it can be deployed to Azure Kubernetes and Azure Container Instances.

Azure does have a native equivalent of Apache Airflow in the form of Azure Logic Apps which provides workflow automation and app integration. But for more complex scenarios requiring code-based orchestration and dynamic workflows, developers might opt for deploying Airflow on Azure.

Airflow offers more flexibility with its Python based DAGs, allowing for complex logic and dependencies. ADF’s strength lies in its no-code or low code approach and integration with other Azure services.

Apache Airflow is also more suitable for event-driven workflows and streaming data while ADF is suitable for batch processing and regular ETL tasks with an emphasis on visual authoring and monitoring.

Best practices and most efficient use of the resources are continuously updated in the official documentation from both their respective sources.

One of the often-overlooked features is that Apache Airflow can be integrated with Azure Data Factory which allows for the orchestration of complex workflows across various cloud services. This integration leverages the strengths of both platforms to create a robust data pipeline solution. The serverless architecture of the ADF can handle large-scale data workloads, while Airflow’s scheduling capabilities can manage the workflow orchestration.

Airflow’s python-based workflows allow for dynamic pipeline generation which can be tailored to specific needs. Both platforms also offer extensivity connectivity options with various data sources and processing services.

The integration steps can be listed as follows: 1. Create a data factory first with the appropriate storage and compute resources. 2. Configure the airflow environment by ensuring that it has network access to Azure Data Factory. 3. Create custom airflow operators to interact with the ADF APIs for triggering and monitoring pipelines.4. Define workflows to use Airflow DAGs to define the sequence of tasks, including the custom operators to manage ADF activities.5. use the user interface to monitor the workflow execution and manage any necessary interventions.

Setting up an active directory connection to Apache Airflow is a prerequisite.

This can be done with commands such as:

pip install apache-airflow[azure]

airflow connections add azure_ad_conn \

  --conn-type azure_data_explorer \

  --conn-login <client_id> \

  --conn-password <client_secret> \

  --conn-extra '{"tenantId": "<tenant_id>"}'

and validated with:

airflow connections get my_azure_connection

The integration can then be tested with:

from airflow import DAG

from airflow.providers.microsoft.azure.operators.data_factory import AzureDataFactoryRunPipelineOperator

with DAG('azure_data_factory_integration', schedule_interval='@daily', default_args=default_args) as dag:

    run_pipeline = AzureDataFactoryRunPipelineOperator(

        task_id='run_pipeline',

        azure_data_factory_conn_id='azure_data_factory_default',

        pipeline_name='MyPipeline',

        parameters={'param1': 'value1'},

        wait_for_completion=True

    )

This completes the comparision of ADF with Airflow.

Previous articles: IaCResolutionsPart191.docx