status

Author: meganbradley

articles/data-factory/concepts-data-flow-performance.md

@@ -18,7 +18,7 @@ Mapping data flows in Azure Data Factory and Synapse pipelines provide a code-fr
 
 Watch the following video to see shows some sample timings transforming data with data flows.
 
-> [!VIDEO https://www.microsoft.com/en-us/videoplayer/embed/RE4rNxM]
+> [!VIDEO https://learn-video.azurefd.net/vod/player?id=0c322fbc-bcd2-4698-b031-4a51b1d9d129]
 
 ## Monitoring data flow performance
 

articles/data-factory/concepts-data-flow-schema-drift.md

@@ -29,7 +29,7 @@ You need to make an architectural decision in your data flow to accept schema dr
 
 This video provides an introduction to some of the complex solutions that you can build easily in Azure Data Factory or Synapse Analytics pipelines with data flow's **schema drift** feature. In this example, we build reusable patterns based on flexible database schemas:
 
-> [!VIDEO https://www.microsoft.com/en-us/videoplayer/embed/RE4tyx7]
+> [!VIDEO https://learn-video.azurefd.net/vod/player?id=941aff82-3f60-45be-853c-088bff9d703e]
 
 ## Schema drift in source
 

articles/data-factory/concepts-data-flow-udf.md

@@ -20,7 +20,7 @@ A user defined function is a customized expression you can define to be able to
 
 Whenever you find yourself building the same logic in an expression across multiple mapping data flows this would be a good opportunity to turn that into a user defined function.
 
-> [!VIDEO https://www.microsoft.com/en-us/videoplayer/embed/RE4Zkek]
+> [!VIDEO https://learn-video.azurefd.net/vod/player?id=6ee2ba96-a6ca-4a57-8545-d03032aa68a2]
 > 
 
 ## Getting started

articles/data-factory/continuous-integration-delivery-hotfix-environment.md

@@ -43,7 +43,7 @@ Use the following steps to deploy a hotfix in your production and test environme
 ## Video tutorial
 See the video below an in-depth video tutorial on how to hot-fix your environments. 
 
-> [!VIDEO https://www.microsoft.com/videoplayer/embed/RE4I7fi]
+> [!VIDEO https://learn-video.azurefd.net/vod/player?id=b0bab151-85b7-4684-a184-ec5e6b972415]
 
 ## Related content
 

articles/data-factory/continuous-integration-delivery.md

@@ -83,7 +83,7 @@ If you're using Git integration with your data factory and have a CI/CD pipeline
 
   To learn how to set up a feature flag, see the below video tutorial:
 
-  >[!VIDEO https://www.microsoft.com/videoplayer/embed/RE4IxdW]
+  >[!VIDEO https://learn-video.azurefd.net/vod/player?id=753e946c-f8e0-4a70-b352-2ed1aa296466]
 
 ## Unsupported features
 

articles/data-factory/control-flow-power-query-activity.md

@@ -16,7 +16,7 @@ The Power Query activity allows you to build and execute Power Query mash-ups to
 
 You can work directly inside of the Power Query mash-up editor to perform interactive data exploration and then save your work. Once complete, you can take your Power Query activity and add it to a pipeline. Azure Data Factory will automatically scale it out and operationalize your data wrangling using Azure Data Factory's data flow Spark environment.
 
-> [!VIDEO https://www.microsoft.com/en-us/videoplayer/embed/RE4MFYn]
+> [!VIDEO https://learn-video.azurefd.net/vod/player?id=a7e9315d-4903-40c1-a759-d6fbd40813de]
 
 ## Create a Power Query activity with UI
 

articles/data-factory/data-flow-alter-row.md

@@ -23,7 +23,7 @@ Use the Alter Row transformation to set insert, delete, update, and upsert polic
 
 Alter Row transformations only operate on database, REST, or Azure Cosmos DB sinks in your data flow. The actions that you assign to rows (insert, update, delete, upsert) doesn't occur during debug sessions. To enact the alter row policies on your database tables, run an Execute Data Flow activity in a pipeline.
 
-> [!VIDEO https://www.microsoft.com/en-us/videoplayer/embed/RE4vJYc]
+> [!VIDEO https://learn-video.azurefd.net/vod/player?id=6e4cb953-1ae0-4a20-9d7f-42bb85c4245b]
 
 > [!NOTE]
 > An Alter Row transformation is not needed for Change Data Capture data flows that use native CDC sources like SQL Server or SAP. In those instances, ADF will automatically detect the row marker so Alter Row policies are unnecessary.

articles/data-factory/data-flow-assert.md

@@ -16,7 +16,7 @@ ms.date: 01/05/2024
 
 The Assert transformation enables you to build custom rules inside your mapping data flows for data quality and data validation. You can build rules that determine whether values meet an expected value domain. Additionally, you can build rules that check for row uniqueness. The Assert transformation helps to determine if each row in your data meets a set of criteria. The Assert transformation also allows you to set custom error messages when data validation rules aren't met.
 
-> [!VIDEO https://www.microsoft.com/en-us/videoplayer/embed/RWRdIu]
+> [!VIDEO https://learn-video.azurefd.net/vod/player?id=d7dab33e-7008-45f8-b982-1451b157900a]
 
 :::image type="content" source="media/data-flow/data-flow-assert-001.png" alt-text="Assert type":::
 

articles/data-factory/data-flow-conditional-split.md

@@ -19,7 +19,7 @@ ms.date: 01/05/2024
 
 The conditional split transformation routes data rows to different streams based on matching conditions. The conditional split transformation is similar to a CASE decision structure in a programming language. The transformation evaluates expressions, and based on the results, directs the data row to the specified stream.
 
-> [!VIDEO https://www.microsoft.com/en-us/videoplayer/embed/RE4wKCX]
+> [!VIDEO https://learn-video.azurefd.net/vod/player?id=232f117d-99a3-4742-8c68-b6dc4d7c6172]
 
 ## Configuration
 

articles/data-factory/data-flow-exists.md

@@ -19,7 +19,7 @@ ms.date: 01/05/2024
 
 The exists transformation is a row filtering transformation that checks whether your data exists in another source or stream. The output stream includes all rows in the left stream that either exist or don't exist in the right stream. The exists transformation is similar to ```SQL WHERE EXISTS``` and ```SQL WHERE NOT EXISTS```.
 
-> [!VIDEO https://www.microsoft.com/en-us/videoplayer/embed/RE4vZKz]
+> [!VIDEO https://learn-video.azurefd.net/vod/player?id=53f2da73-3587-4d51-9f25-9f89e9446a8c]
 
 ## Configuration