status

meganbradley · meganbradley · commit d90833445a9a · 2025-01-10T22:55:50.000-07:00
diff --git a/articles/data-factory/data-flow-external-call.md b/articles/data-factory/data-flow-external-call.md
@@ -16,7 +16,7 @@ ms.date: 01/05/2024
 
 The external call transformation enables data engineers to call out to external REST end points row-by-row in order to add custom or third party results into your data flow streams.
 
-> [!VIDEO https://www.microsoft.com/en-us/videoplayer/embed/RWPXGN]
+> [!VIDEO https://learn-video.azurefd.net/vod/player?id=1496fa22-7187-4339-895a-f1daaa3f89f7]
 
 ## Configuration
 
diff --git a/articles/data-factory/data-flow-filter.md b/articles/data-factory/data-flow-filter.md
@@ -19,7 +19,7 @@ ms.date: 01/05/2024
 
 The Filter transforms allows row filtering based upon a condition. The output stream includes all rows that match the filtering condition. The filter transformation is similar to a WHERE clause in SQL.
 
-> [!VIDEO https://www.microsoft.com/en-us/videoplayer/embed/RE4xnxN]
+> [!VIDEO https://learn-video.azurefd.net/vod/player?id=735b7b28-3280-471d-bd81-c45700908a6e]
 
 ## Configuration
 
diff --git a/articles/data-factory/data-flow-flatten.md b/articles/data-factory/data-flow-flatten.md
@@ -19,7 +19,7 @@ ms.date: 01/05/2024
 
 Use the flatten transformation to take array values inside hierarchical structures such as JSON and unroll them into individual rows. This process is known as denormalization.
 
-> [!VIDEO https://www.microsoft.com/en-us/videoplayer/embed/RWLX9j]
+> [!VIDEO https://learn-video.azurefd.net/vod/player?id=df700f65-858e-4017-9114-bba7b4e6bbd9]
 
 ## Configuration
 
diff --git a/articles/data-factory/data-flow-flowlet.md b/articles/data-factory/data-flow-flowlet.md
@@ -21,7 +21,7 @@ Use the flowlet transformation to run a previously created mapping data flow flo
 > [!NOTE] 
 > The flowlet transformation in Azure Data Factory and Synapse Analytics pipelines is currently in public preview
 
-> [!VIDEO https://www.microsoft.com/en-us/videoplayer/embed/RWQK3m]
+> [!VIDEO https://learn-video.azurefd.net/vod/player?id=18076f34-9a6b-41bb-a2a8-88b2f279307f]
 
 ## Configuration
 
@@ -55,4 +55,3 @@ If the selected flowlet has input columns, you can map columns from the input st
 source1 derive(Test = "test") ~> DerivedColumn1
 DerivedColumn1 output() ~> output1 
 ```    
-
diff --git a/articles/data-factory/data-flow-join.md b/articles/data-factory/data-flow-join.md
@@ -57,7 +57,7 @@ If you would like to explicitly produce a full cartesian product, use the Derive
 
 You can choose to join based on fuzzy join logic instead of exact column value matching by turning on the "Use fuzzy matching" checkbox option.
 
-> [!VIDEO https://www.microsoft.com/en-us/videoplayer/embed/RE4ZeWr]
+> [!VIDEO https://learn-video.azurefd.net/vod/player?id=d7e53d75-099c-44d3-bcc0-95dc8da2d1fb]
 
 * Combine text parts: Use this option to find matches by remove space between words. For example, Data Factory is matched with DataFactory if this option is enabled.
 * Similarity score column: You can optionally choose to store the matching score for each row in a column by entering a new column name here to store that value.
diff --git a/articles/data-factory/data-flow-lookup.md b/articles/data-factory/data-flow-lookup.md
@@ -21,7 +21,7 @@ Use the lookup transformation to reference data from another source in a data fl
 
 A lookup transformation is similar to a left outer join. All rows from the primary stream will exist in the output stream with additional columns from the lookup stream.
 
-> [!VIDEO https://www.microsoft.com/en-us/videoplayer/embed/RE4xsVT]
+> [!VIDEO https://learn-video.azurefd.net/vod/player?id=e4b08344-f63d-4cb8-88ab-df1dc1b6208f]
 
 ## Configuration
 
diff --git a/articles/data-factory/data-flow-parse.md b/articles/data-factory/data-flow-parse.md
@@ -16,7 +16,7 @@ ms.date: 05/15/2024
 
 Use the Parse transformation to parse text columns in your data that are strings in document form. The current supported types of embedded documents that can be parsed are JSON, XML, and delimited text.
 
-> [!VIDEO https://www.microsoft.com/en-us/videoplayer/embed/RWykdO]
+> [!VIDEO https://learn-video.azurefd.net/vod/player?id=96271183-7b44-46e9-9fc7-7a3fca27c8ca]
 
 ## Configuration
 
diff --git a/articles/data-factory/data-flow-pivot.md b/articles/data-factory/data-flow-pivot.md
@@ -18,7 +18,7 @@ ms.date: 05/15/2024
 
 Use the pivot transformation to create multiple columns from the unique row values of a single column. Pivot is an aggregation transformation where you select group by columns and generate pivot columns using [aggregate functions](data-flow-aggregate-functions.md).
 
-> [!VIDEO https://www.microsoft.com/en-us/videoplayer/embed/RE4C4YN]
+> [!VIDEO https://learn-video.azurefd.net/vod/player?id=10a0178b-4ae1-4af7-a092-84d90ff2e284]
 
 ## Configuration
 
diff --git a/articles/data-factory/data-flow-rank.md b/articles/data-factory/data-flow-rank.md
@@ -19,7 +19,7 @@ ms.date: 05/15/2024
 
 Use the rank transformation to generate an ordered ranking based upon sort conditions specified by the user. 
 
-> [!VIDEO https://www.microsoft.com/en-us/videoplayer/embed/RE4GGJo]
+> [!VIDEO https://learn-video.azurefd.net/vod/player?id=c6c8f590-1cba-4cf9-ada3-58e44516804a]
 
 ## Configuration
 
diff --git a/articles/data-factory/data-flow-script.md b/articles/data-factory/data-flow-script.md
@@ -140,7 +140,7 @@ derive1 sink(allowSchemaDrift: true,
 
 Script snippets are shareable code of Data Flow Script that you can use to share across data flows. This video below talks about how to use script snippets and utilizing Data Flow Script to copy and paste portions of the script behind your data flow graphs:
 
-> [!VIDEO https://www.microsoft.com/en-us/videoplayer/embed/RE4tA9b]
+> [!VIDEO https://learn-video.azurefd.net/vod/player?id=a0b4b213-00d1-4cee-a817-51bf52ef50c0]
 
 
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