Merge pull request #209863 from jess-hu-340/0831-update-screenshot15

Update screenshot in doc ( Blob Storage, SQL Database, Amazon S3 )

Author: v-ccolin

articles/data-factory/connector-amazon-simple-storage-service.md

@@ -8,7 +8,7 @@ ms.service: data-factory
 ms.subservice: data-movement
 ms.topic: conceptual
 ms.custom: synapse
-ms.date: 06/29/2022
+ms.date: 09/01/2022
 ---
 
 # Copy and transform data in Amazon Simple Storage Service using Azure Data Factory or Azure Synapse Analytics
@@ -302,7 +302,7 @@ Format specific settings are located in the documentation for that format. For m
 
 In source transformation, you can read from a container, folder, or individual file in Amazon S3. Use the **Source options** tab to manage how the files are read. 
 
-:::image type="content" source="media/data-flow/sourceOptions1.png" alt-text="Screenshot of Source options.":::
+:::image type="content" source="media/data-flow/source-options-1.png" alt-text="Screenshot of Source options.":::
 
 **Wildcard paths:** Using a wildcard pattern will instruct the service to loop through each matching folder and file in a single source transformation. This is an effective way to process multiple files within a single flow. Add multiple wildcard matching patterns with the plus sign that appears when you hover over your existing wildcard pattern.
 
@@ -324,7 +324,7 @@ Wildcard examples:
 
 First, set a wildcard to include all paths that are the partitioned folders plus the leaf files that you want to read.
 
-:::image type="content" source="media/data-flow/partfile2.png" alt-text="Screenshot of partition source file settings.":::
+:::image type="content" source="media/data-flow/part-file-2.png" alt-text="Screenshot of partition source file settings.":::
 
 Use the **Partition root path** setting to define what the top level of the folder structure is. When you view the contents of your data via a data preview, you'll see that the service will add the resolved partitions found in each of your folder levels.
 

articles/data-factory/connector-azure-blob-storage.md

@@ -8,7 +8,7 @@ ms.service: data-factory
 ms.subservice: data-movement
 ms.topic: conceptual
 ms.custom: synapse
-ms.date: 08/24/2022
+ms.date: 09/01/2022
 ---
 
 # Copy and transform data in Azure Blob Storage by using Azure Data Factory or Azure Synapse Analytics
@@ -629,7 +629,7 @@ Format specific settings are located in the documentation for that format. For m
 
 In source transformation, you can read from a container, folder, or individual file in Azure Blob Storage. Use the **Source options** tab to manage how the files are read. 
 
-:::image type="content" source="media/data-flow/sourceOptions1.png" alt-text="Source options":::
+:::image type="content" source="media/data-flow/source-options-1.png" alt-text="Screenshot of source options tab in mapping data flow source transformation.":::
 
 **Wildcard paths:** Using a wildcard pattern will instruct the service to loop through each matching folder and file in a single source transformation. This is an effective way to process multiple files within a single flow. Add multiple wildcard matching patterns with the plus sign that appears when you hover over your existing wildcard pattern.
 
@@ -651,7 +651,7 @@ Wildcard examples:
 
 First, set a wildcard to include all paths that are the partitioned folders plus the leaf files that you want to read.
 
-:::image type="content" source="media/data-flow/partfile2.png" alt-text="Partition source file settings":::
+:::image type="content" source="media/data-flow/part-file-2.png" alt-text="Screenshot of partition source file settings in mapping data flow source transformation.":::
 
 Use the **Partition root path** setting to define what the top level of the folder structure is. When you view the contents of your data via a data preview, you'll see that the service will add the resolved partitions found in each of your folder levels.
 

articles/data-factory/connector-azure-data-lake-storage.md

@@ -8,7 +8,7 @@ ms.service: data-factory
 ms.subservice: data-movement
 ms.topic: conceptual
 ms.custom: synapse
-ms.date: 08/15/2022
+ms.date: 09/01/2022
 ---
 
 # Copy and transform data in Azure Data Lake Storage Gen2 using Azure Data Factory or Azure Synapse Analytics
@@ -543,7 +543,7 @@ Format specific settings are located in the documentation for that format. For m
 
 In the source transformation, you can read from a container, folder, or individual file in Azure Data Lake Storage Gen2. The **Source options** tab lets you manage how the files get read. 
 
-:::image type="content" source="media/data-flow/sourceOptions1.png" alt-text="Source options":::
+:::image type="content" source="media/data-flow/source-options-1.png" alt-text="Screenshot of source options tab in mapping data flow source transformation.":::
 
 **Wildcard path:** Using a wildcard pattern will instruct ADF to loop through each matching folder and file in a single Source transformation. This is an effective way to process multiple files within a single flow. Add multiple wildcard matching patterns with the + sign that appears when hovering over your existing wildcard pattern.
 
@@ -565,7 +565,7 @@ Wildcard examples:
 
 First, set a wildcard to include all paths that are the partitioned folders plus the leaf files that you wish to read.
 
-:::image type="content" source="media/data-flow/partfile2.png" alt-text="Partition source file settings":::
+:::image type="content" source="media/data-flow/part-file-2.png" alt-text="Screenshot of partition source file settings in mapping data flow source transformation.":::
 
 Use the Partition Root Path setting to define what the top level of the folder structure is. When you view the contents of your data via a data preview, you'll see that ADF will add the resolved partitions found in each of your folder levels.
 

articles/data-factory/connector-azure-data-lake-store.md

@@ -8,7 +8,7 @@ ms.service: data-factory
 ms.subservice: data-movement
 ms.topic: conceptual
 ms.custom: synapse
-ms.date: 07/04/2022
+ms.date: 09/01/2022
 ---
 
 # Copy data to or from Azure Data Lake Storage Gen1 using Azure Data Factory or Azure Synapse Analytics
@@ -439,7 +439,7 @@ Format-specific settings are located in the documentation for that format. For m
 
 In the source transformation, you can read from a container, folder, or individual file in Azure Data Lake Storage Gen1. The **Source options** tab lets you manage how the files get read. 
 
-:::image type="content" source="media/data-flow/sourceOptions1.png" alt-text="Source options":::
+:::image type="content" source="media/data-flow/source-options-1.png" alt-text="Screenshot of source options tab in mapping data flow source transformation.":::
 
 **Wildcard path:** Using a wildcard pattern will instruct the service to loop through each matching folder and file in a single Source transformation. This is an effective way to process multiple files within a single flow. Add multiple wildcard matching patterns with the + sign that appears when hovering over your existing wildcard pattern.
 
@@ -461,7 +461,7 @@ Wildcard examples:
 
 First, set a wildcard to include all paths that are the partitioned folders plus the leaf files that you wish to read.
 
-:::image type="content" source="media/data-flow/partfile2.png" alt-text="Partition source file settings":::
+:::image type="content" source="media/data-flow/part-file-2.png" alt-text="Screenshot of partition source file settings in mapping data flow source transformation.":::
 
 Use the Partition Root Path setting to define what the top level of the folder structure is. When you view the contents of your data via a data preview, you'll see that the service will add the resolved partitions found in each of your folder levels.
 

articles/data-factory/connector-azure-sql-data-warehouse.md

@@ -1015,7 +1015,7 @@ By default, a data flow run will fail on the first error it gets. You can choose
 
 **Report success on error:** If enabled, the data flow will be marked as a success even if error rows are found. 
 
-:::image type="content" source="media/data-flow/sql-error-row-handling.png" alt-text="Screenshot that shows the error row handling" border="false":::
+:::image type="content" source="media/data-flow/sql-error-row-handling.png" alt-text="Diagram that shows the error row handling in mapping data flow sink transformation.":::
 
 ## Lookup activity properties
 

articles/data-factory/connector-azure-sql-database.md

@@ -8,7 +8,7 @@ ms.service: data-factory
 ms.subservice: data-movement
 ms.topic: conceptual
 ms.custom: synapse
-ms.date: 08/10/2022
+ms.date: 09/02/2022
 ---
 
 # Copy and transform data in Azure SQL Database by using Azure Data Factory or Azure Synapse Analytics