Merge pull request #105253 from likebupt/update-module-articles

update import/export data module

Author: PMEds28

articles/machine-learning/algorithm-module-reference/export-data.md

@@ -9,13 +9,13 @@ ms.topic: reference
 
 author: likebupt
 ms.author: keli19
-ms.date: 10/22/2019
+ms.date: 02/22/2020
 ---
 # Export Data module
 
 This article describes a module in Azure Machine Learning designer (preview).
 
-Use this module to save results, intermediate data, and working data from your pipelines into cloud storage destinations outside Azure Machine Learning. 
+Use this module to save results, intermediate data, and working data from your pipelines into cloud storage destinations. 
 
 This module supports exporting your data to the following cloud data services:
 
@@ -24,7 +24,7 @@ This module supports exporting your data to the following cloud data services:
 - Azure Data Lake
 - Azure Data Lake Gen2
 
-Before exporting your data, you need to first register a datastore in your Azure Machine Learning workspace first. For more information, see [Access data in Azure storage services](../how-to-access-data.md).
+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-access-data.md).
 
 ## How to configure Export Data
 
@@ -36,7 +36,13 @@ Before exporting your data, you need to first register a datastore in your Azure
 
 1. 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](../how-to-access-data.md).
 
-1. Define the path in the datastore to write the data to. 
+1. The checkbox, **Regenerate output**, decides whether to execute the module to regenerate output at running time. 
+
+    It's by default unselected, which means if the module 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 module again to regenerate output.
+
+1. Define the path in the datastore where the data is. The path is a relative path. The empty paths or a URL paths are not allowed.
 
 
 1. For **File format**, select the format in which data should be stored.

articles/machine-learning/algorithm-module-reference/import-data.md

@@ -43,9 +43,7 @@ If your source data changes, you can refresh the dataset and add new data by rer
 
 1. Add the **Import Data** module to your pipeline. You can find this module in the **Data Input and Output** category in the designer.
 
-1. Click **Launch Data Import Wizard** to configure the data source using a wizard.
-
-    The wizard gets the account name and credentials, and help you configure other options. If you are editing an existing configuration, it loads the current values first.
+1. Select the module to open the right pane.
 
 1. Select **Data source**, and choose the data source type. It could be HTTP or datastore.
 
@@ -56,6 +54,11 @@ If your source data changes, you can refresh the dataset and add new data by rer
 
     ![import-data-preview](media/module/import-data.png)
 
+1. The checkbox, **Regenerate output**, decides whether to execute the module to regenerate output at running time. 
+
+    It's by default unselected, which means if the module 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 module again to regenerate output. So select this option when underlying data in storage is updated, it can help to get the latest data.
 
 
 1. Run the pipeline.