The service in Azure Data Factory responsible for orchestrating and managing data workflows is called Azure Data Factory Pipeline. Here are the steps to explain its role:

1. **Creation**: The first step involves creating a pipeline in Azure Data Factory. This is done through the Azure portal, where you specify the tasks that the pipeline should perform.

2. **Orchestration**: Once the pipeline is created, it orchestrates the data movement and transformation activities. It determines what tasks need to be performed, in what order, and manages the resources required for these tasks.

3. **Execution**: The pipeline then executes the tasks. This could involve moving data from one place to another, transforming the data, or running computations on the data.

4. **Monitoring**: After execution, the pipeline provides tools for monitoring the tasks. This includes checking the status of the tasks, viewing any errors, and analyzing the performance of the tasks.

5. **Management**: Lastly, the pipeline manages the overall workflow. This involves handling any failures, retrying tasks if necessary, and ensuring that all tasks are completed successfully.

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The service in Azure Data Factory responsible for orchestrating and managing data workflows is called Azure Data Factory Pipeline. Here are the steps to explain its role:

1. **Creation**: The first step involves creating a pipeline in Azure Data Factory. This is done through the Azure portal, where you specify the tasks that the pipeline should perform.

2. **Orchestration**: Once the pipeline is created, it orchestrates the data movement and transformation activities. It determines what tasks need to be performed, in what order, and manages the resources required for these tasks.

3. **Execution**: The pipeline then executes the tasks. This could involve moving data from one place to another, transforming the data, or running computations on the data.

4. **Monitoring**: After execution, the pipeline provides tools for monitoring the tasks. This includes checking the status of the tasks, viewing any errors, and analyzing the performance of the tasks.

5. **Management**: Lastly, the pipeline manages the overall workflow. This involves handling any failures, retrying tasks if necessary, and ensuring that all tasks are completed successfully.

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The service in Azure Data Factory responsible for orchestrating and managing data workflows is called Azure Data Factory Pipeline. Here are the steps to explain its role:

1. **Creation**: The first step involves creating a pipeline in Azure Data Factory. This is done through the Azure portal, where you specify the tasks that the pipeline should perform.

2. **Orchestration**: Once the pipeline is created, it orchestrates the data movement and transformation activities. It determines what tasks need to be performed, in what order, and manages the resources required for these tasks.

3. **Execution**: The pipeline then executes the tasks. This could involve moving data from one place to another, transforming the data, or running computations on the data.

4. **Monitoring**: After execution, the pipeline provides tools for monitoring the tasks. This includes checking the status of the tasks, viewing any errors, and analyzing the performance of the tasks.

5. **Management**: Lastly, the pipeline manages the overall workflow. This involves handling any failures, retrying tasks if necessary, and ensuring that all tasks are completed successfully.

Which service in Azure Data Factory is responsible for orchestrating and managing data workflows?

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