Revision

Author: PeterCLu

articles/machine-learning/how-to-designer-import-data.md

@@ -14,83 +14,66 @@ ms.date: 01/16/2020
 
 # Import your data into Azure Machine Learning designer (preview)
 
-You can use your own data in Azure Machine Learning designer to create predictive analytics solutions. You can import data into the designer in one of two ways: 
+In this article, you learn how to import your own data in the designer to create custom solutions. You can import data into the designer two ways: 
 
-* **Azure Machine Learning datasets** - Register [datasets](concept-data.md#datasets) in Azure Machine Learning to help you manage datasets and use advanced features.
+* **Azure Machine Learning datasets** - Register [datasets](concept-data.md#datasets) in Azure Machine Learning to enable advanced features that help you manage your data.
 * **Import Data module** - Use the [Import Data](algorithm-module-reference/import-data.md) module to directly access data from online datasources.
 
 To learn more about the differences between datasets and datastores, see [Data access in Azure Machine Learning](concept-data.md).
 
-## Import data using datasets
+## Use Azure Machine Learning datasets
 
 We recommend that you use [Azure Machine Learning datasets](concept-data.md#datasets) when you import data into the designer. When you register a dataset, you can take full advantage of advanced data features like [versioning and tracking](how-to-version-track-datasets.md) and [data monitoring](how-to-monitor-datasets.md).
 
 
 ### Register a dataset
 
-Register a dataset [programatically with the SDK](how-to-create-register-datasets.md#use-the-sdk) or [visually in Azure Machine Learning studio](how-to-create-register-datasets.md#use-the-ui).
+You can register existing datasets [programatically with the SDK](how-to-create-register-datasets.md#use-the-sdk) or [visually in Azure Machine Learning studio](how-to-create-register-datasets.md#use-the-ui).
 
-You can also register the output for any module as a dataset directly in the designer.
+However, you can also register the output for any designer module as a dataset.
 
 1. Select the module that outputs the data you want to register.
 
 1. In the properties pane, select **Outputs** > **Register dataset**.
 
     ![Screenshot showing how to navigate to the Register Dataset option](media/how-to-designer-import-data/register-dataset-designer.png)
 
-### Use datasets
+### Use a dataset
 
-Any dataset registered to your workspace will appear, you aren't limited to datasets created in the designer.
-
-> [!NOTE]
-> The designer currently only supports processing [tabular datasets](how-to-create-register-datasets.md#dataset-types). For other datasets which need [file datasets](how-to-create-register-datasets.md#dataset-types), use the Azure Machine Learning SDK available for Python or R.
-
-Registered datasets can be found in the module palette, under **Datasets** > **My Datasets**. To use a dataset, drag and drop the dataset onto the pipeline canvas. Then, connect the output port of the dataset to other modules in the palette.
+Your registered datasets can be found in the module palette, under **Datasets** > **My Datasets**. To use a dataset, drag and drop it onto the pipeline canvas. Then, connect the output port of the dataset to other modules in the palette.
 
 ![Screenshot showing location of saved datasets in the designer palette](media/how-to-designer-import-data/use-datasets-designer.png)
 
-## Import data using the Import Data module
-
-You can also use the [Import Data](algorithm-module-reference/import-data.md) module to import data directly from either Azure Machine Learning [datastores](concept-data.md#datastores) or HTTP URLs. We recommend you create a dataset first to take full advantage of advanced data features like versioning and monitoring.
+Any [file datasets](how-to-create-register-datasets.md#dataset-types) registered to your machine learning workspace will appear in the module palette. You aren't limited to only datasets created in the designer.
 
 > [!NOTE]
-> Pipelines converted from the visual interface will default to the **Import Data** module. If you are using a converted visual interface pipeline, we recommend creating a dataset and importing data via the dataset method.
-
-### Create a new datastore
+> The designer currently only supports processing [tabular datasets](how-to-create-register-datasets.md#dataset-types). If you want to use [file datasets](how-to-create-register-datasets.md#dataset-types), use the Azure Machine Learning SDK available for Python and R.
 
-Creating a datastore can be done [programatically with the SDK](how-to-access-data.md#create-and-register-datastores) or [visually in Azure Machine Learning studio](how-to-access-data.md#azure-machine-learning-studio).
-
-You can also create a datastore directly the designer through the **Import Data** module.
-
-1. Drag and drop an **Import Data** module to the pipeline canvas. 
-1. Select the **Import Data** module.
-1. In the properties pane, select **New datastore**
-1. Select the datastore type.
-1. Provide valid authentication.
-
-    > [!NOTE]
-    > You may be asked for different authentication information depending on the type of datasource you are connecting to.
+## Import data using the Import Data module
 
-### Import Data
+We recommend that you use datasets to import data in the designer. However, you can also use the [Import Data](algorithm-module-reference/import-data.md) module. The Import Data module skips registering your datasets and imports data directly from [datastores](concept-data.md#datastores) or HTTP URLs. 
 
-For more information on how to use the Import Data module, see its [algorithm module reference page](algorithm-module-reference/import-data.md).
+For detailed information on how to use the Import Data module, see the [Import Data reference page](algorithm-module-reference/import-data.md).
 
 
-## Supported data sources
+## Supported sources
 
-There are two methods for importing data in the designer, this section lists the supported data sources for datastores and [tabular datasets](how-to-create-register-datasets.md#dataset-types).
+This section lists the supported data sources for the designer. You can bring data into the designer from either a datastore or from [tabular datasets](how-to-create-register-datasets.md#dataset-types).
 
+### Datastore sources
 For a list of supported datastore sources, see [Access data in Azure storage services](how-to-access-data.md#supported-data-storage-service-types).
 
+### Tabular dataset sources
+
 The designer supports tabular datasets created from the following sources:
  * Delimited files
  * JSON files
  * Parquet files
  * SQL queries
 
-## Supported data types
+## Data types
 
-The designer recognizes the following data types:
+The designer internally recognizes the following data types:
 
 * String
 * Integer
@@ -100,12 +83,10 @@ The designer recognizes the following data types:
 
 The designer uses an internal data type to pass data between modules. You can explicitly convert your data into data table format using the [Convert to Dataset](algorithm-module-reference/convert-to-dataset.md) module.
 
-Any module that accepts formats other than data table will convert the data to data table silently before passing it to the next module.
-
-## Data capacities
+## Data constraints
 
-Modules in Azure Machine Learning designer are limited by the size of the compute target. For larger datasets, you should use a larger Azure Machine Learning compute resource. For more information on Azure Machine Learning compute, see [What are compute targets in Azure Machine Learning?](concept-compute-target.md#azure-machine-learning-compute-managed)
+Modules in the designer are limited by the size of the compute target. For larger datasets, you should use a larger Azure Machine Learning compute resource. For more information on Azure Machine Learning compute, see [What are compute targets in Azure Machine Learning?](concept-compute-target.md#azure-machine-learning-compute-managed)
 
 ## Next steps
 
-Learn the basics of the designer with [Tutorial: Predict automobile price with the designer](tutorial-designer-automobile-price-train-score.md)
+Learn the basics of the designer with [Tutorial: Predict automobile price with the designer](tutorial-designer-automobile-price-train-score.md).