proof pass

Author: Jak-MS

articles/data-factory/solution-template-databricks-notebook.md

@@ -15,11 +15,11 @@ ms.date: 03/03/2020
 
 # Transformation with Azure Databricks
 
-In this tutorial, you create an end-to-end pipeline containing **Validation**, **Copy**, and **Notebook** activities in Data Factory.
+In this tutorial, you create an end-to-end pipeline containing the **Validation**, **Copy data**, and **Notebook** activities in Data Factory.
 
 - **Validation** ensures that your source dataset is ready for downstream consumption before you trigger the copy and analytics job.
 
-- **Copy** duplicates the source dataset to the sink storage, which is mounted as DBFS in the Databricks notebook. In this way, the dataset can be directly consumed by Spark.
+- **Copy data** duplicates the source dataset to the sink storage, which is mounted as DBFS in the Databricks notebook. In this way, the dataset can be directly consumed by Spark.
 
 - **Notebook** triggers the Databricks notebook that transforms the dataset. It also adds the dataset to a processed folder or SQL Data Warehouse.
 
@@ -84,7 +84,7 @@ To import a **Transformation** notebook to your Databricks workspace:
 
     ![5](media/solution-template-Databricks-notebook/generate-new-token.png)
 
-   **Save the access token** for later use in creating a Databricks linked service. The access token looks something like 'dapi32db32cbb4w6eee18b7d87e45exxxxxx'.
+   **Save the access token** for later use in creating a Databricks linked service. The access token looks something like `dapi32db32cbb4w6eee18b7d87e45exxxxxx`.
 
 ## How to use this template
 
@@ -102,13 +102,13 @@ To import a **Transformation** notebook to your Databricks workspace:
 
     - **Destination Blob Connection** – to store the copied data.
 
-       In the linked service, select your sink storage blob.
+       In the **New linked service** window, select your sink storage blob.
 
        ![7](media/solution-template-Databricks-notebook/destination-blob-connection.png)
 
     - **Azure Databricks** – to connect to the Databricks cluster.
 
-        Create a Databricks-linked service using the access key you generated previously. You may opt to select an *interactive cluster* if you have one. This example uses the *New job cluster* option.
+        Create a Databricks-linked service using the access key you generated previously. You may opt to select an *interactive cluster* if you have one. This example uses the **New job cluster** option.
 
         ![8](media/solution-template-Databricks-notebook/databricks-connection.png)
 
@@ -118,9 +118,9 @@ To import a **Transformation** notebook to your Databricks workspace:
 
 ## Pipeline introduction and configuration
 
-In the new pipeline, most settings are  configured automatically with default values. Review the configurations of your pipeline and make any necessary changes:
+In the new pipeline, most settings are  configured automatically with default values. Review the configurations of your pipeline and make any necessary changes.
 
-1. In the **Validation** activity **Availability flag**, verify that the source Dataset value is set to the `SourceAvailabilityDataset`  created earlier.
+1. In the **Validation** activity  **Availability flag**, verify that the source **Dataset** value is set to `SourceAvailabilityDataset` that you created earlier.
 
    ![12](media/solution-template-Databricks-notebook/validation-settings.png)
 
@@ -139,7 +139,7 @@ In the new pipeline, most settings are  configured automatically with default va
 
    To check the **Notebook** settings:
 
-    1. Select **Settings** tab. For **Notebook path**, verify that the default path is correct. You may need to browse and choose the correct notebook path.
+    1. Select the **Settings** tab. For **Notebook path**, verify that the default path is correct. You may need to browse and choose the correct notebook path.
 
        ![17](media/solution-template-Databricks-notebook/notebook-settings.png)
 
@@ -175,7 +175,7 @@ In the new pipeline, most settings are  configured automatically with default va
     You can also verify the data file using storage explorer.
 
     > [!NOTE]
-    > For correlating with Data Factory pipeline runs, this example appends the pipeline run ID from data factory to the output folder. This way you can track back the files generated via each run.
+    > For correlating with Data Factory pipeline runs, this example appends the pipeline run ID from data factory to the output folder. This helps keep track of files generated by each run.
     > ![19](media/solution-template-Databricks-notebook/verify-data-files.png)
 
 ## Next steps