Export Data component
This article describes a component in Azure Machine Learning designer.
Use this component to save results, intermediate data, and working data from your pipelines into cloud storage destinations.
This component supports exporting your data to the following cloud data services:
- Azure Blob Container
- Azure File Share
- Azure Data Lake Storage Gen1
- Azure Data Lake Storage Gen2
- Azure SQL database
Before exporting your data, you need to first register a datastore in your Azure Machine Learning workspace. For more information, see Access data in Azure storage services.
How to configure Export Data
Add the Export Data component to your pipeline in the designer. You can find this component in the Input and Output category.
Connect Export Data to the component that contains the data you want to export.
Select Export Data to open the Properties pane.
For Datastore, select an existing datastore from the dropdown list. You can also create a new datastore. Check how by visiting Access data in Azure storage services.
Exporting data of a certain data type to a SQL database column specified as another data type is not supported. The target table does not need to exist first.
The checkbox, Regenerate output, decides whether to execute the component to regenerate output at running time.
It's by default unselected, which means if the component has been executed with the same parameters previously, the system will reuse the output from last run to reduce run time.
If it is selected, the system will execute the component again to regenerate output.
Define the path in the datastore where the data is. The path is a relative path.Take
data/testoutputas an example, which means the input data of Export Data will be exported to
data/testoutputin the datastore you set in the Output settings of the component.
The empty paths or URL paths are not allowed.
For File format, select the format in which data should be stored.
Submit the pipeline.
Due to datstore access limitation, if your inference pipeline contains Export Data component, it will be auto-removed when deploy to real-time endpoint.
See the set of components available to Azure Machine Learning.
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