Merge pull request #292233 from meganbradley/meganbradley/docutune-autopr-20241220-032428-5790803-ignore-build

Jill Grant · web-flow · commit 170a5669bb41 · 2025-01-09T09:01:06.000-07:00
[BULK] - DocuTune remediation - Red Tiger deprecation  (part 6)
diff --git a/articles/data-factory/concepts-change-data-capture-resource.md b/articles/data-factory/concepts-change-data-capture-resource.md
@@ -23,7 +23,7 @@ The new Change Data Capture resource in ADF allows for full fidelity change data
 
 :::image type="content" source="media/adf-cdc/change-data-capture-resource-1.png" alt-text="Screenshot of new top-level resource in Factory Resources panel.":::
 
-> [!VIDEO https://www.microsoft.com/en-us/videoplayer/embed/RE5geIG]
+> [!VIDEO https://learn-video.azurefd.net/vod/player?id=add55f05-eabf-4185-8db6-aef373d635f1]
 
 > [!NOTE]
 > The Change Data Capture resource in Azure Data Factory is currently in public preview
diff --git a/articles/data-factory/concepts-change-data-capture.md b/articles/data-factory/concepts-change-data-capture.md
@@ -31,7 +31,7 @@ The easiest and quickest way to get started in data factory with CDC is through
 
 The changed data including inserted, updated and deleted rows can be automatically detected and extracted by ADF mapping data flow from the source databases.  No timestamp or ID columns are required to identify the changes since it uses the native change data capture technology in the databases.  By simply chaining a source transform and a sink transform reference to a database dataset in a mapping data flow, you can see the changes happened on the source database to be automatically applied to the target database, so that you can easily synchronize data between two tables.  You can also add any transformations in between for any business logic to process the delta data. When defining your sink data destination, you can set insert, update, upsert, and delete operations in your sink without the need of an Alter Row transformation because ADF is able to automatically detect the row makers.
 
-> [!VIDEO https://www.microsoft.com/en-us/videoplayer/embed/RE5bkg2]
+> [!VIDEO https://learn-video.azurefd.net/vod/player?id=ba3e201c-c9d0-4c1d-806c-b3c8ca601de2]
 
 **Supported connectors**
 -   [SAP CDC](connector-sap-change-data-capture.md)
diff --git a/articles/data-factory/concepts-data-flow-column-pattern.md b/articles/data-factory/concepts-data-flow-column-pattern.md
@@ -20,7 +20,7 @@ Several mapping data flows transformations allow you to reference template colum
 * If incoming source fields change often such as the case of changing columns in text files or NoSQL databases. This scenario is known as [schema drift](concepts-data-flow-schema-drift.md).
 * If you wish to do a common operation on a large group of columns. For example, wanting to cast every column that has 'total' in its column name into a double.
 
-> [!VIDEO https://www.microsoft.com/videoplayer/embed/RE4Iui1]
+> [!VIDEO https://learn-video.azurefd.net/vod/player?id=8848564b-ff1c-4ad5-9ccd-74357f93a348]
 
 ## Column patterns in derived column and aggregate
 
diff --git a/articles/data-factory/concepts-data-flow-debug-mode.md b/articles/data-factory/concepts-data-flow-debug-mode.md
@@ -42,7 +42,7 @@ In most cases, it's a good practice to build your Data Flows in debug mode so th
 > Every debug session that a user starts from their browser UI is a new session with its own Spark cluster. You can use the monitoring view for debug sessions shown in the previous images to view and manage debug sessions. You are charged for every hour that each debug session is executing including the TTL time.
 
 This video clip talks about tips, tricks, and good practices for data flow debug mode.
-> [!VIDEO https://www.microsoft.com/en-us/videoplayer/embed/RE5c8Jx]
+> [!VIDEO https://learn-video.azurefd.net/vod/player?id=8e101169-59fb-4371-aa88-039304f61b53]
 
 ## Cluster status
 
diff --git a/articles/data-factory/concepts-data-flow-flowlet.md b/articles/data-factory/concepts-data-flow-flowlet.md
@@ -18,7 +18,7 @@ ms.date: 10/20/2023
  
  With flowlets you can create logic to do things such as address cleaning or string trimming. You can then map the input and outputs to columns in the calling data flow for a dynamic code reuse experience.
 
-> [!VIDEO https://www.microsoft.com/en-us/videoplayer/embed/RWQK3m]
+> [!VIDEO https://learn-video.azurefd.net/vod/player?id=18076f34-9a6b-41bb-a2a8-88b2f279307f]
 
 ## Getting started
 To create a flowlet, select the new flowlet action from the mapping data flow menu options.
diff --git a/articles/data-factory/concepts-data-flow-monitoring.md b/articles/data-factory/concepts-data-flow-monitoring.md
@@ -16,7 +16,7 @@ ms.date: 10/20/2023
 
 After you have completed building and debugging your data flow, you want to schedule your data flow to execute on a schedule within the context of a pipeline. You can schedule the pipeline using Triggers. For testing and debugging your data flow from a pipeline, you can use the Debug button on the toolbar ribbon or Trigger Now option from the Pipeline Builder to execute a single-run execution to test your data flow within the pipeline context.
 
-> [!VIDEO https://www.microsoft.com/videoplayer/embed/RE4P5pV]
+> [!VIDEO https://learn-video.azurefd.net/vod/player?id=5526e304-14f3-40cf-bd60-2f4bccf1fddb]
 
 When you execute your pipeline, you can monitor the pipeline and all of the activities contained in the pipeline including the Data Flow activity. Select the monitor icon in the left-hand UI panel. You can see a screen similar to the one that follows. The highlighted icons allow you to drill into the activities in the pipeline, including the Data Flow activity.
 
diff --git a/articles/data-factory/concepts-data-flow-performance-sinks.md b/articles/data-factory/concepts-data-flow-performance-sinks.md
@@ -22,7 +22,7 @@ With Azure SQL Database, the default partitioning should work in most cases. The
 
 Here's a video walk-through of how to use data flows with exists, alter row, and sink transformations to achieve this common pattern: 
 
-> [!VIDEO https://www.microsoft.com/en-us/videoplayer/embed/RWMLr5]
+> [!VIDEO https://learn-video.azurefd.net/vod/player?id=45ca63d6-06e0-4d90-bf2d-437220de663b]
 
 ### Impact of error row handling to performance
 
diff --git a/articles/data-factory/concepts-data-flow-performance.md b/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
 
diff --git a/articles/data-factory/concepts-data-flow-schema-drift.md b/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
 
diff --git a/articles/data-factory/concepts-data-flow-udf.md b/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
diff --git a/articles/data-factory/continuous-integration-delivery-hotfix-environment.md b/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
 
diff --git a/articles/data-factory/continuous-integration-delivery.md b/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
 
diff --git a/articles/data-factory/control-flow-power-query-activity.md b/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
 
diff --git a/articles/data-factory/data-flow-alter-row.md b/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.
diff --git a/articles/data-factory/data-flow-assert.md b/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":::
 
diff --git a/articles/data-factory/data-flow-conditional-split.md b/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
 
diff --git a/articles/data-factory/data-flow-exists.md b/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
 
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
 

Original file line number	Diff line number	Diff line change
`@@ -20,7 +20,7 @@ A user defined function is a customized expression you can define to be able to`
`20`	`20`
`21`	`21`	`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.`
`22`	`22`
`23`		`-> [!VIDEO https://www.microsoft.com/en-us/videoplayer/embed/RE4Zkek]`
	`23`	`+> [!VIDEO https://learn-video.azurefd.net/vod/player?id=6ee2ba96-a6ca-4a57-8545-d03032aa68a2]`
`24`	`24`	`>`
`25`	`25`
`26`	`26`	`## Getting started`