Merge pull request #50624 from theresa-i/orchestrate-processes-2

Stacyrch140 · web-flow · commit c88040313a42 · 2025-05-23T17:12:55.000-04:00
Updates
diff --git a/learn-pr/wwl/use-data-factory-pipelines-fabric/1-introduction.yml b/learn-pr/wwl/use-data-factory-pipelines-fabric/1-introduction.yml
@@ -4,13 +4,10 @@ title: Introduction
 metadata:
   title: Introduction
   description: "Introduction"
-  ms.date: 05/19/2025
-  author: wwlpublish
+  ms.date: 05/23/2025
+  author: theresa-i
   ms.author: theresai
   ms.topic: unit
-  ms.custom:
-  - build-2023
-  - ignite-2024
 durationInMinutes: 1
 content: |
   [!include[](includes/1-introduction.md)]
diff --git a/learn-pr/wwl/use-data-factory-pipelines-fabric/2-understand-fabric-pipeline.yml b/learn-pr/wwl/use-data-factory-pipelines-fabric/2-understand-fabric-pipeline.yml
@@ -4,13 +4,10 @@ title: Understand pipelines
 metadata:
   title: Understand pipelines
   description: "Understand pipelines"
-  ms.date: 05/19/2025
-  author: wwlpublish
+  ms.date: 05/23/2025
+  author: theresa-i
   ms.author: theresai
   ms.topic: unit
-  ms.custom:
-  - build-2023
-  - ignite-2024
 durationInMinutes: 6
 content: |
   [!include[](includes/2-understand-fabric-pipeline.md)]
diff --git a/learn-pr/wwl/use-data-factory-pipelines-fabric/3-copy-data.yml b/learn-pr/wwl/use-data-factory-pipelines-fabric/3-copy-data.yml
@@ -4,13 +4,10 @@ title: Use the Copy Data activity
 metadata:
   title: Use the Copy Data activity
   description: "Use the Copy Data activity"
-  ms.date: 05/19/2025
-  author: wwlpublish
+  ms.date: 05/23/2025
+  author: theresa-i
   ms.author: theresai
   ms.topic: unit
-  ms.custom:
-  - build-2023
-  - ignite-2024
 durationInMinutes: 3
 content: |
   [!include[](includes/3-copy-data.md)]
diff --git a/learn-pr/wwl/use-data-factory-pipelines-fabric/4-pipeline-templates.yml b/learn-pr/wwl/use-data-factory-pipelines-fabric/4-pipeline-templates.yml
@@ -4,13 +4,10 @@ title: Use pipeline templates
 metadata:
   title: Use pipeline templates
   description: "Use pipeline templates"
-  ms.date: 05/19/2025
-  author: wwlpublish
+  ms.date: 05/23/2025
+  author: theresa-i
   ms.author: theresai
   ms.topic: unit
-  ms.custom:
-  - build-2023
-  - ignite-2024
 durationInMinutes: 3
 content: |
   [!include[](includes/4-pipeline-templates.md)]
diff --git a/learn-pr/wwl/use-data-factory-pipelines-fabric/5-run-monitor-pipelines.yml b/learn-pr/wwl/use-data-factory-pipelines-fabric/5-run-monitor-pipelines.yml
@@ -4,13 +4,10 @@ title: Run and monitor pipelines
 metadata:
   title: Run and monitor pipelines
   description: "Run and monitor pipelines"
-  ms.date: 05/19/2025
-  author: wwlpublish
+  ms.date: 05/23/2025
+  author: theresa-i
   ms.author: theresai
   ms.topic: unit
-  ms.custom:
-  - build-2023
-  - ignite-2024
 durationInMinutes: 5
 content: |
   [!include[](includes/5-run-monitor-pipelines.md)]
diff --git a/learn-pr/wwl/use-data-factory-pipelines-fabric/6-exercise-pipelines.yml b/learn-pr/wwl/use-data-factory-pipelines-fabric/6-exercise-pipelines.yml
@@ -4,13 +4,10 @@ title: Exercise - Ingest data with a pipeline
 metadata:
   title: Exercise - Ingest data with a pipeline
   description: "Exercise - Ingest data with a pipeline"
-  ms.date: 05/19/2025
-  author: wwlpublish
+  ms.date: 05/23/2025
+  author: theresa-i
   ms.author: theresai
   ms.topic: unit
-  ms.custom:
-  - build-2023
-  - ignite-2024
 durationInMinutes: 60
 content: |
   [!include[](includes/6-exercise-pipelines.md)]
diff --git a/learn-pr/wwl/use-data-factory-pipelines-fabric/7-knowledge-check.yml b/learn-pr/wwl/use-data-factory-pipelines-fabric/7-knowledge-check.yml
@@ -4,13 +4,10 @@ title: Module assessment
 metadata:
   title: Module assessment
   description: "Knowledge check"
-  ms.date: 05/19/2025
-  author: wwlpublish
+  ms.date: 05/23/2025
+  author: theresa-i
   ms.author: theresai
   ms.topic: unit
-  ms.custom:
-  - build-2023
-  - ignite-2024
 durationInMinutes: 3
 quiz:
   title: "Check your knowledge"
diff --git a/learn-pr/wwl/use-data-factory-pipelines-fabric/8-summary.yml b/learn-pr/wwl/use-data-factory-pipelines-fabric/8-summary.yml
@@ -4,13 +4,10 @@ title: Summary
 metadata:
   title: Summary
   description: "Summary"
-  ms.date: 05/19/2025
-  author: wwlpublish
+  ms.date: 05/23/2025
+  author: theresa-i
   ms.author: theresai
   ms.topic: unit
-  ms.custom:
-  - build-2023
-  - ignite-2024
 durationInMinutes: 1
 content: |
   [!include[](includes/8-summary.md)]
diff --git a/learn-pr/wwl/use-data-factory-pipelines-fabric/includes/1-introduction.md b/learn-pr/wwl/use-data-factory-pipelines-fabric/includes/1-introduction.md
@@ -1,7 +1,3 @@
----
-ms.custom:
-  - ignite-2024
----
 Data pipelines define a sequence of activities that orchestrate an overall process, usually by extracting data from one or more sources and loading it into a destination; often transforming it along the way. Pipelines are commonly used to automate *extract*, *transform*, and *load* (ETL) processes that ingest transactional data from operational data stores into an analytical data store, such as a lakehouse, data warehouse, or SQL database.
 
 If you're already familiar with Azure Data Factory, then data pipelines in Microsoft Fabric will be immediately familiar. They use the same architecture of connected activities to define a process that can include multiple kinds of data processing tasks and control flow logic. You can run pipelines interactively in the Microsoft Fabric user interface, or schedule them to run automatically.
diff --git a/learn-pr/wwl/use-data-factory-pipelines-fabric/includes/2-understand-fabric-pipeline.md b/learn-pr/wwl/use-data-factory-pipelines-fabric/includes/2-understand-fabric-pipeline.md
@@ -1,7 +1,3 @@
----
-ms.custom:
-  - ignite-2024
----
 Pipelines in Microsoft Fabric encapsulate a sequence of *activities* that perform data movement and processing tasks. You can use a pipeline to define data transfer and transformation activities, and orchestrate these activities through control flow activities that manage branching, looping, and other typical processing logic. The graphical pipeline *canvas* in the Fabric user interface enables you to build complex pipelines with minimal or no coding required.
 
 ![Screenshot of a pipeline in Microsoft Fabric.](../media/pipeline.png)
@@ -21,7 +17,7 @@ There are two broad categories of activity in a pipeline.
 - **Control flow activities** - activities that you can use to implement loops, conditional branching, or manage variable and parameter values. The wide range of control flow activities enables you to implement complex pipeline logic to orchestrate data ingestion and transformation flow.
 
 > [!TIP]
-> For details about the complete set of pipeline activities available in Microsoft Fabric, see [Activity overview](/fabric/data-factory/activity-overview) in the Microsoft Fabric documentation.
+> For details about the complete set of pipeline activities available in Microsoft Fabric, see [Activity overview](/fabric/data-factory/activity-overview&azure-portal=true) in the Microsoft Fabric documentation.
 
 <!-- Update URL for release -->
 
diff --git a/learn-pr/wwl/use-data-factory-pipelines-fabric/includes/3-copy-data.md b/learn-pr/wwl/use-data-factory-pipelines-fabric/includes/3-copy-data.md
@@ -1,7 +1,3 @@
----
-ms.custom:
-  - ignite-2024
----
 The **Copy Data** activity is one of the most common uses of a data pipeline. Many pipelines consist of a single **Copy Data** activity that is used to ingest data from an external source into a lakehouse file or table.
 
 You can also combine the **Copy Data** activity with other activities to create a repeatable data ingestion process - for example by using a **Delete data** activity to remove existing data, a **Copy Data** activity to replace the deleted data with a file containing data from an external source, and a **Notebook** activity to run Spark code that transforms the data in the file and loads it into a table.
@@ -27,5 +23,4 @@ Use the **Copy Data** activity when you need to copy data directly between a sup
 If you need to apply transformations to the data as it is ingested, or merge data from multiple sources, consider using a **Data Flow** activity to run a dataflow (Gen2). You can use the Power Query user interface to define a dataflow (Gen2) that includes multiple transformation steps, and include it in a pipeline.
 
 > [!TIP]
-> To learn more about Dataflow (Gen2) in Microsoft Fabric to ingest data, consider completing the [Ingest Data with Dataflows Gen2 in Microsoft Fabric](/training/modules/use-dataflow-gen-2-fabric) module.
-
+> To learn more about Dataflow (Gen2) in Microsoft Fabric to ingest data, consider completing the [Ingest Data with Dataflows Gen2 in Microsoft Fabric](/training/modules/use-dataflow-gen-2-fabric&azure-portal=true) module.
diff --git a/learn-pr/wwl/use-data-factory-pipelines-fabric/includes/4-pipeline-templates.md b/learn-pr/wwl/use-data-factory-pipelines-fabric/includes/4-pipeline-templates.md
@@ -1,12 +1,12 @@
 You can define pipelines from any combination of activities you choose, enabling to create custom data ingestion and transformation processes to meet your specific needs. However, there are many common pipeline scenarios for which Microsoft Fabric includes predefined pipeline templates that you can use and customize as required.
 
-To create a pipeline based on a template, select the **Choose a task to start** tile in a new pipeline as shown here.
+To create a pipeline based on a template, select the **Templates** tile in a new pipeline as shown here.
 
-![Screenshot of the Choose a task to start tile.](../media/start-pipeline.png) <!-- Update screenshot for release -->
+![Screenshot of the Choose a task to start tile.](../media/start-pipeline.png) 
 
 Selecting this option displays a selection of pipeline templates, as shown here.
 
-![Screenshot of pipeline templates.](../media/pipeline-templates.png) <!-- Update screenshot for release -->
+![Screenshot of pipeline templates.](../media/pipeline-templates.png) 
 
 You can select the most appropriate template for your needs, and then edit the pipeline in the pipeline canvas to customize it to your needs.
 
diff --git a/learn-pr/wwl/use-data-factory-pipelines-fabric/includes/5-run-monitor-pipelines.md b/learn-pr/wwl/use-data-factory-pipelines-fabric/includes/5-run-monitor-pipelines.md
@@ -1,14 +1,9 @@
 When you have completed a pipeline, you can use the **Validate** option to check that the configuration is valid, and then either run it interactively or specify a schedule.
 
-![Screenshot of a the Run menu for a pipeline in Microsoft Fabric.](../media/run-pipeline.png) <!-- Update screenshot for release -->
+![Screenshot of a the Run menu for a pipeline in Microsoft Fabric.](../media/run-pipeline.png) 
 
 ## View run history
 
 You can view the run history for a pipeline to see details of each run, either from the pipeline canvas or from the pipeline item listed in the page for the workspace.
 
-![Screenshot of a pipeline run history in Microsoft Fabric.](../media/pipeline-runs.png) <!-- Update screenshot for release -->
-
-When you view a pipeline run history from the workspace page, you can select the **Run start** value to see the details of an individual run; including the option to view the individual execution time for each activity as a Gantt chart.
-
-![Screenshot of a pipeline run details in Microsoft Fabric.](../media/pipeline-run-details.png) <!-- Update screenshot for release -->
-
+![Screenshot of a pipeline run history in Microsoft Fabric.](../media/pipeline-runs.png)
diff --git a/learn-pr/wwl/use-data-factory-pipelines-fabric/includes/6-exercise-pipelines.md b/learn-pr/wwl/use-data-factory-pipelines-fabric/includes/6-exercise-pipelines.md
@@ -1,7 +1,7 @@
 Now it's your chance to implement a pipeline in Microsoft Fabric. In this exercise, you create a pipeline that copies data from an external source into a lakehouse. Then enhance the pipeline by adding activities to transform the ingested data.
 
 > [!NOTE]
-> You need a Microsoft Fabric trial license with the Fabric preview enabled in your tenant. See [**Getting started with Fabric**](/fabric/get-started/fabric-trial) to enable your Fabric trial license.
+> You need a Microsoft Fabric trial license with the Fabric preview enabled in your tenant. See [**Getting started with Fabric**](/fabric/get-started/fabric-trial&azure-portal=true) to enable your Fabric trial license.
 
 Launch the exercise and follow the instructions.
 
diff --git a/learn-pr/wwl/use-data-factory-pipelines-fabric/includes/8-summary.md b/learn-pr/wwl/use-data-factory-pipelines-fabric/includes/8-summary.md
@@ -1,4 +1,4 @@
 With Microsoft Fabric, you can create pipelines that encapsulate complex data ingestion and transformation processes. Pipelines provide an effective way to orchestrate data processing tasks that can be run on-demand or at scheduled intervals.
 
 > [!TIP]
-> For more information about pipelines in Microsoft Fabric, see [Data pipelines](/fabric/data-factory/data-factory-overview) in the Microsoft Fabric documentation.
+> For more information about pipelines in Microsoft Fabric, see [Data pipelines](/fabric/data-factory/data-factory-overview&azure-portal=true) in the Microsoft Fabric documentation.
diff --git a/learn-pr/wwl/use-data-factory-pipelines-fabric/index.yml b/learn-pr/wwl/use-data-factory-pipelines-fabric/index.yml
@@ -3,14 +3,11 @@ uid: learn.wwl.use-data-factory-pipelines-fabric
 metadata:
   title: Orchestrate Processes and Data Movement with Microsoft Fabric
   description: "Use Data Factory pipelines in Microsoft Fabric"
-  ms.date: 05/19/2025
-  author: wwlpublish
+  ms.date: 05/23/2025
+  author: theresa-i
   ms.author: theresai
   ms.topic: module
   ms.collection: N/A
-  ms.custom:
-  - build-2023
-  - ignite-2024
   ms.service: fabric
   ai-usage: human-only
 title: Orchestrate processes and data movement with Microsoft Fabric
diff --git a/learn-pr/wwl/use-data-factory-pipelines-fabric/media/copy-data-activity.png b/learn-pr/wwl/use-data-factory-pipelines-fabric/media/copy-data-activity.png
diff --git a/learn-pr/wwl/use-data-factory-pipelines-fabric/media/copy-data.png b/learn-pr/wwl/use-data-factory-pipelines-fabric/media/copy-data.png
diff --git a/learn-pr/wwl/use-data-factory-pipelines-fabric/media/pipeline-run-details.png b/learn-pr/wwl/use-data-factory-pipelines-fabric/media/pipeline-run-details.png
diff --git a/learn-pr/wwl/use-data-factory-pipelines-fabric/media/pipeline-runs.png b/learn-pr/wwl/use-data-factory-pipelines-fabric/media/pipeline-runs.png
diff --git a/learn-pr/wwl/use-data-factory-pipelines-fabric/media/pipeline-templates.png b/learn-pr/wwl/use-data-factory-pipelines-fabric/media/pipeline-templates.png
diff --git a/learn-pr/wwl/use-data-factory-pipelines-fabric/media/pipeline.png b/learn-pr/wwl/use-data-factory-pipelines-fabric/media/pipeline.png
diff --git a/learn-pr/wwl/use-data-factory-pipelines-fabric/media/run-pipeline.png b/learn-pr/wwl/use-data-factory-pipelines-fabric/media/run-pipeline.png
diff --git a/learn-pr/wwl/use-data-factory-pipelines-fabric/media/start-pipeline.png b/learn-pr/wwl/use-data-factory-pipelines-fabric/media/start-pipeline.png
