Merge pull request #292234 from meganbradley/meganbradley/docutune-autopr-20241220-032428-5794281-ignore-build

Jill Grant · web-flow · commit 36d663df2f85 · 2025-01-09T09:05:21.000-07:00
[BULK] - DocuTune remediation - Red Tiger deprecation  (part 7)
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]
 
 
 ### Aggregated summary stats
diff --git a/articles/data-factory/data-flow-select.md b/articles/data-factory/data-flow-select.md
@@ -38,7 +38,7 @@ Fixed mappings can be used to map a subcolumn of a hierarchical column to a top-
 ## Rule-based mapping
 
 
-> [!VIDEO https://www.microsoft.com/en-us/videoplayer/embed/RE4xiXz]
+> [!VIDEO https://learn-video.azurefd.net/vod/player?id=f1d4575c-e0e0-4827-bc0b-6e85bca986b5]
 
 If you wish to map many columns at once or pass drifted columns downstream, use rule-based mapping to define your mappings using column patterns. Match based on the `name`, `type`, `stream`, and `position` of columns. You can have any combination of fixed and rule-based mappings. By default, all projections with greater than 50 columns will default to a rule-based mapping that matches on every column and outputs the inputted name. 
 
diff --git a/articles/data-factory/data-flow-sink.md b/articles/data-factory/data-flow-sink.md
@@ -75,7 +75,7 @@ After you've added a sink, configure via the **Sink** tab. Here you can pick or
 
 The following video explains a number of different sink options for text-delimited file types.
 
-> [!VIDEO https://www.microsoft.com/videoplayer/embed/RE4tf7T]
+> [!VIDEO https://learn-video.azurefd.net/vod/player?id=f6b52fbb-e98b-4fa1-b872-a0278ed02227]
 
 :::image type="content" source="media/data-flow/sink-settings.png" alt-text="Screenshot that shows Sink settings.":::
 
@@ -86,7 +86,7 @@ The following video explains a number of different sink options for text-delimit
 
 ## Cache sink
 
-> [!VIDEO https://www.microsoft.com/en-us/videoplayer/embed/RE4HKt1]
+> [!VIDEO https://learn-video.azurefd.net/vod/player?id=f0587234-eb56-458f-8c55-478881ed2985]
 
 A *cache sink* is when a data flow writes data into the Spark cache instead of a data store. In mapping data flows, you can reference this data within the same flow many times using a *cache lookup*. This is useful when you want to reference data as part of an expression but don't want to explicitly join the columns to it. Common examples where a cache sink can help are looking up a max value on a data store and matching error codes to an error message database. 
 
@@ -138,7 +138,7 @@ When writing to databases, certain rows of data may fail due to constraints set
 
 Below is a video tutorial on how to use database error row handling automatically in your sink transformation.
 
-> [!VIDEO https://www.microsoft.com/en-us/videoplayer/embed/RE4IWne]
+> [!VIDEO https://learn-video.azurefd.net/vod/player?id=0d38b49c-428f-4ac3-82c2-c677823d60c9]
 
 For assert failure rows, you can use the Assert transformation upstream in your data flow and then redirect failed assertions to an output file here in the sink errors tab. You also have an option here to ignore rows with assertion failures and not output those rows at all to the sink destination data store.
 
diff --git a/articles/data-factory/data-flow-stringify.md b/articles/data-factory/data-flow-stringify.md
@@ -14,7 +14,7 @@ ms.date: 05/15/2024
 
 Use the stringify transformation to turn complex data types into strings. This can be useful when you need to store or send column data as a single string entity that may originate as a structure, map, or array type.
 
-> [!VIDEO https://www.microsoft.com/en-us/videoplayer/embed/RWMTs9]
+> [!VIDEO https://learn-video.azurefd.net/vod/player?id=811ea298-48a2-4757-be16-8cfaef79a8b3]
 
 ## Configuration
 
diff --git a/articles/data-factory/data-flow-union.md b/articles/data-factory/data-flow-union.md
@@ -22,7 +22,7 @@ You can combine n-number of streams in the settings table by selecting the "+" i
 
 Here is a short video walk-through of the union transformation in the mapping data flow:
 
-> [!VIDEO https://www.microsoft.com/en-us/videoplayer/embed/RE4vngz]
+> [!VIDEO https://learn-video.azurefd.net/vod/player?id=b2efaa0d-096d-44e5-9ce0-71d6b68f3924]
 
 :::image type="content" source="media/data-flow/union.png" alt-text="Union transformation":::
 
diff --git a/articles/data-factory/data-flow-unpivot.md b/articles/data-factory/data-flow-unpivot.md
@@ -20,7 +20,7 @@ Use Unpivot in a mapping data flow as a way to turn an unnormalized dataset into
 
 :::image type="content" source="media/data-flow/unpivot1.png" alt-text="Screenshot shows Unpivot selected from the menu.":::
 
-> [!VIDEO https://www.microsoft.com/en-us/videoplayer/embed/RE4B1RR]
+> [!VIDEO https://learn-video.azurefd.net/vod/player?id=8746f3b9-b71e-4cd3-8def-550d8d6a44e2]
 
 ## Ungroup By
 
diff --git a/articles/data-factory/data-flow-window.md b/articles/data-factory/data-flow-window.md
@@ -20,7 +20,7 @@ The Window transformation is where you define window-based aggregations of colum
 
 :::image type="content" source="media/data-flow/windows1.png" alt-text="Screenshot shows Windowing selected from the menu.":::
 
-> [!VIDEO https://www.microsoft.com/videoplayer/embed/RE4IAVu]
+> [!VIDEO https://learn-video.azurefd.net/vod/player?id=f4c22cfd-df94-4add-a3e1-c072e897f50b]
 
 ## Over
 Set the partitioning of column data for your window transformation. The SQL equivalent is the ```Partition By``` in the Over clause in SQL. If you wish to create a calculation or create an expression to use for the partitioning, you can do that by hovering over the column name and selecting **Computed column**.
diff --git a/articles/data-factory/format-delta.md b/articles/data-factory/format-delta.md
@@ -14,7 +14,7 @@ ms.author: makromer
 
 This article highlights how to copy data to and from a delta lake stored in [Azure Data Lake Store Gen2](connector-azure-data-lake-storage.md) or [Azure Blob Storage](connector-azure-blob-storage.md) using the delta format. This connector is available as an [inline dataset](data-flow-source.md#inline-datasets) in mapping data flows as both a source and a sink.
 
-> [!VIDEO https://www.microsoft.com/en-us/videoplayer/embed/RE4ALTs]
+> [!VIDEO https://learn-video.azurefd.net/vod/player?id=5dd270a4-6cc0-4b60-8041-b1e835ab8a50]
 
 ## Mapping data flow properties
 
diff --git a/articles/data-factory/how-to-data-flow-dedupe-nulls-snippets.md b/articles/data-factory/how-to-data-flow-dedupe-nulls-snippets.md
@@ -14,7 +14,7 @@ ms.subservice: data-flows
 
 By using code snippets in mapping data flows, you can easily perform common tasks such as data deduplication and null filtering. This article explains how to easily add those functions to your pipelines by using data flow script snippets.
 <br>
-> [!VIDEO https://www.microsoft.com/en-us/videoplayer/embed/RE4GnhH]
+> [!VIDEO https://learn-video.azurefd.net/vod/player?id=9caa524f-5af1-4bfa-845f-72a170ac0e5d]
 
 ## Create a pipeline
 
diff --git a/articles/data-factory/how-to-data-flow-error-rows.md b/articles/data-factory/how-to-data-flow-error-rows.md
@@ -33,7 +33,7 @@ There are two primary methods to graceful handle errors when writing data to you
 3. The problem is that the movie title doesn't all fit within a sink column that can only hold five characters. When you execute this data flow, you receive an error like this one: ```"Job failed due to reason: DF-SYS-01 at Sink 'WriteToDatabase': java.sql.BatchUpdateException: String or binary data would be truncated. java.sql.BatchUpdateException: String or binary data would be truncated."```
 
 This video walks through an example of setting-up error row handling logic in your data flow:
-> [!VIDEO https://www.microsoft.com/en-us/videoplayer/embed/RE4uOHj]
+> [!VIDEO https://learn-video.azurefd.net/vod/player?id=248a4a27-1955-4cb9-9dd2-47ffd1534c62]
 
 ## How to design around this condition
 
diff --git a/articles/data-factory/quickstart-get-started.md b/articles/data-factory/quickstart-get-started.md
@@ -23,7 +23,7 @@ If you don't have an Azure subscription, create a [free account](https://azure
 
 The following video provides a walkthrough of the sample:
 
-> [!VIDEO https://www.microsoft.com/en-us/videoplayer/embed/RE583aX]
+> [!VIDEO https://learn-video.azurefd.net/vod/player?id=0550df36-bfe6-407b-b00b-1b60fa700e94]
 
 ## Try your first demo with one click
 In your first demo scenario you will use the [Copy activity](copy-activity-overview.md) in a data factory to copy an Azure blob named moviesDB2.csv from an input folder on an Azure Blob Storage to an output folder. In a real world scenario this copy operation could be between any of the many supported data sources and sinks available in the service. It could also involve transformations in the data.
diff --git a/articles/data-factory/source-control.md b/articles/data-factory/source-control.md
@@ -26,7 +26,7 @@ You can also reference [Continuous integration and delivery (CI/CD) in Azure Dat
 
 To learn more about how Azure Data Factory integrates with Git, view the 15-minute tutorial video below:
 
-> [!VIDEO https://www.microsoft.com/videoplayer/embed/RE4GNKv]
+> [!VIDEO https://learn-video.azurefd.net/vod/player?id=cea58440-722d-4e48-ae44-2f86c4eba0c4]
 
 ## Advantages of Git integration
 
diff --git a/articles/data-factory/wrangling-functions.md b/articles/data-factory/wrangling-functions.md
@@ -160,7 +160,7 @@ in
   #"Pivoted column"
 ```
 
-> [!VIDEO https://www.microsoft.com/en-us/videoplayer/embed/RWNbBf]
+> [!VIDEO https://learn-video.azurefd.net/vod/player?id=02987a4c-9444-4f3b-acf7-89a3e91ff5f7]
 
 ### Formatting date/time columns
 
@@ -178,7 +178,7 @@ To set the date/time format when using Power Query ADF, please follow these sets
 #"Changed column type 1" = Table.TransformColumns(#"Duplicated column", {{"start - Copy", each DateTime.FromText(_, [Format = "yyyy-MM-dd HH:mm:ss", Culture = "en-us"]), type datetime}})
 ```
 
-> [!VIDEO https://www.microsoft.com/en-us/videoplayer/embed/RWNdQg]
+> [!VIDEO https://learn-video.azurefd.net/vod/player?id=d4c91bf6-2c21-4ab7-ab4d-695d039327ec]
 
 ## Related content
 
diff --git a/articles/data-factory/wrangling-overview.md b/articles/data-factory/wrangling-overview.md
@@ -22,7 +22,7 @@ Data Factory translates M generated by the Power Query Online Mashup Editor into
 
 ## Use cases
 
-> [!VIDEO https://www.microsoft.com/en-us/videoplayer/embed/RE4MFkW]
+> [!VIDEO https://learn-video.azurefd.net/vod/player?id=cb2e2ac9-b771-470f-b237-a19a7c2e392a]
 
 ### Fast interactive data exploration and preparation
 
