Merge pull request #53678 from weslbo/DP-750-updates

Carolyn135 · web-flow · commit 9543dfe9b8e5 · 2026-03-03T15:55:49.000Z
added videos
diff --git a/learn-pr/wwl-databricks/design-implement-data-pipelines/1-introduction.yml b/learn-pr/wwl-databricks/design-implement-data-pipelines/1-introduction.yml
@@ -4,7 +4,7 @@ title: Introduction
 metadata:
   title: Introduction
   description: "Introduction to designing and implementing data pipelines in Azure Databricks"
-  ms.date: 12/07/2025
+  ms.date: 03/03/2026
   author: weslbo
   ms.author: wedebols
   ms.topic: unit
diff --git a/learn-pr/wwl-databricks/design-implement-data-pipelines/2-design-order-operations-pipeline.yml b/learn-pr/wwl-databricks/design-implement-data-pipelines/2-design-order-operations-pipeline.yml
@@ -4,7 +4,7 @@ title: Design order of operations for a pipeline
 metadata:
   title: Design Order of Operations for a Pipeline
   description: Learn how to design the order of operations for a data pipeline in Azure Databricks, including ingestion, cleaning, transformation, loading, serving, and monitoring stages.
-  ms.date: 12/07/2025
+  ms.date: 03/03/2026
   author: weslbo
   ms.author: wedebols
   ms.topic: unit
diff --git a/learn-pr/wwl-databricks/design-implement-data-pipelines/3-choose-notebook-lakeflow-pipelines.yml b/learn-pr/wwl-databricks/design-implement-data-pipelines/3-choose-notebook-lakeflow-pipelines.yml
@@ -4,7 +4,7 @@ title: Choose notebook vs Lakeflow Pipelines
 metadata:
   title: Choose Notebook vs Lakeflow Pipelines
   description: Learn how to choose between notebooks and Lakeflow Spark Declarative Pipelines for building data pipelines in Azure Databricks, comparing flexibility, maintainability, and use cases.
-  ms.date: 12/07/2025
+  ms.date: 03/03/2026
   author: weslbo
   ms.author: wedebols
   ms.topic: unit
diff --git a/learn-pr/wwl-databricks/design-implement-data-pipelines/4-design-task-logic-lakeflow-job.yml b/learn-pr/wwl-databricks/design-implement-data-pipelines/4-design-task-logic-lakeflow-job.yml
@@ -4,7 +4,7 @@ title: Design Lakeflow job logic
 metadata:
   title: Design Lakeflow Job Logic
   description: Learn how to design task logic for Lakeflow Jobs in Azure Databricks, including task dependencies, execution patterns, conditional logic, and parameter configuration.
-  ms.date: 12/07/2025
+  ms.date: 03/03/2026
   author: weslbo
   ms.author: wedebols
   ms.topic: unit
diff --git a/learn-pr/wwl-databricks/design-implement-data-pipelines/5-design-error-handling-pipelines.yml b/learn-pr/wwl-databricks/design-implement-data-pipelines/5-design-error-handling-pipelines.yml
@@ -4,7 +4,7 @@ title: Design error handling in pipelines and jobs
 metadata:
   title: Design Error Handling in Pipelines and Jobs
   description: Learn how to design and implement error handling strategies in Azure Databricks data pipelines, notebooks, and jobs using expectations, retry policies, notifications, and exception handling.
-  ms.date: 12/07/2025
+  ms.date: 03/03/2026
   author: weslbo
   ms.author: wedebols
   ms.topic: unit
diff --git a/learn-pr/wwl-databricks/design-implement-data-pipelines/6-create-pipeline-notebook-precedence.yml b/learn-pr/wwl-databricks/design-implement-data-pipelines/6-create-pipeline-notebook-precedence.yml
@@ -4,7 +4,7 @@ title: Create pipeline with notebook
 metadata:
   title: Create Pipeline with Notebook
   description: Learn how to create data pipelines using notebooks in Azure Databricks with task dependencies and precedence constraints in Lakeflow Jobs.
-  ms.date: 12/07/2025
+  ms.date: 03/03/2026
   author: weslbo
   ms.author: wedebols
   ms.topic: unit
diff --git a/learn-pr/wwl-databricks/design-implement-data-pipelines/7-create-pipeline-lakeflow-declarative.yml b/learn-pr/wwl-databricks/design-implement-data-pipelines/7-create-pipeline-lakeflow-declarative.yml
@@ -4,7 +4,7 @@ title: Create pipeline with Lakeflow Spark Declarative Pipelines
 metadata:
   title: Create Pipeline with Lakeflow Spark Declarative Pipelines
   description: Learn how to create data pipelines using Lakeflow Spark Declarative Pipelines in Azure Databricks, including streaming tables, materialized views, and data quality expectations.
-  ms.date: 12/07/2025
+  ms.date: 03/03/2026
   author: weslbo
   ms.author: wedebols
   ms.topic: unit
diff --git a/learn-pr/wwl-databricks/design-implement-data-pipelines/8-knowledge-check.yml b/learn-pr/wwl-databricks/design-implement-data-pipelines/8-knowledge-check.yml
@@ -4,7 +4,7 @@ title: Module assessment
 metadata:
   title: Module assessment
   description: "Knowledge check"
-  ms.date: 12/07/2025
+  ms.date: 03/03/2026
   author: weslbo
   ms.author: wedebols
   ms.topic: unit
diff --git a/learn-pr/wwl-databricks/design-implement-data-pipelines/9-summary.yml b/learn-pr/wwl-databricks/design-implement-data-pipelines/9-summary.yml
@@ -4,7 +4,7 @@ title: Summary
 metadata:
   title: Summary
   description: "Summary"
-  ms.date: 12/07/2025
+  ms.date: 03/03/2026
   author: weslbo
   ms.author: wedebols
   ms.topic: unit
diff --git a/learn-pr/wwl-databricks/design-implement-data-pipelines/includes/1-introduction.md b/learn-pr/wwl-databricks/design-implement-data-pipelines/includes/1-introduction.md
@@ -1,3 +1,5 @@
+>[!VIDEO https://learn-video.azurefd.net/vod/player?id=ac7df0e7-8d9c-4fae-8ef4-b6bb587bdffe]
+
 Building reliable data pipelines requires more than connecting data sources to destinations. You need to design workflows that handle failures gracefully, scale with growing data volumes, and remain maintainable as business requirements evolve. Azure Databricks provides multiple approaches for creating data pipelines—from flexible **notebooks** with procedural code to **Lakeflow Spark Declarative Pipelines** that automate orchestration and data quality enforcement.
 
 When you design data pipelines, you make decisions that affect every downstream consumer of your data. The order of operations determines whether transformations build on validated, well-structured data. Your choice between notebooks and declarative pipelines influences how much orchestration code you write versus how much the platform manages for you. **Task dependencies** in **Lakeflow Jobs** control execution flow and enable parallel processing that reduces pipeline runtime.
diff --git a/learn-pr/wwl-databricks/design-implement-data-pipelines/includes/2-design-order-operations-pipeline.md b/learn-pr/wwl-databricks/design-implement-data-pipelines/includes/2-design-order-operations-pipeline.md
@@ -1,3 +1,5 @@
+>[!VIDEO https://learn-video.azurefd.net/vod/player?id=6633991c-84b1-4c7f-a71f-cbbf823c2963]
+
 **Data pipelines** move data from source systems through multiple processing stages before delivering it to consumers. Understanding the **order of operations** helps you design pipelines that are reliable, maintainable, and aligned with business requirements. Each stage in the pipeline serves a specific purpose, and the sequence you choose affects **data quality**, **processing efficiency**, and how quickly insights reach your users.
 
 When you design a data pipeline in Azure Databricks, you typically follow a pattern that mirrors the **medallion architecture**: raw data flows through progressively refined layers until it's ready for consumption. This approach ensures that each stage builds on validated, well-structured data from the previous stage.
diff --git a/learn-pr/wwl-databricks/design-implement-data-pipelines/includes/3-choose-notebook-lakeflow-pipelines.md b/learn-pr/wwl-databricks/design-implement-data-pipelines/includes/3-choose-notebook-lakeflow-pipelines.md
@@ -1,3 +1,5 @@
+>[!VIDEO https://learn-video.azurefd.net/vod/player?id=37fd6fc9-80f1-4af1-b3fe-42400ac6660c]
+
 When you build data pipelines in Azure Databricks, you have two primary approaches: **notebooks** with procedural code and **Lakeflow Spark Declarative Pipelines**. Each approach serves different needs, and understanding when to use each helps you deliver maintainable, efficient data solutions.
 
 ## Understand the two approaches
diff --git a/learn-pr/wwl-databricks/design-implement-data-pipelines/includes/4-design-task-logic-lakeflow-job.md b/learn-pr/wwl-databricks/design-implement-data-pipelines/includes/4-design-task-logic-lakeflow-job.md
@@ -1,3 +1,5 @@
+>[!VIDEO https://learn-video.azurefd.net/vod/player?id=35383e2d-e7e6-4878-82ae-766a972b2d05]
+
 When you design task logic for a **Lakeflow Job**, you make decisions that affect how your workflow executes, scales, and responds to changing conditions. A well-designed job balances performance, cost, and maintainability while meeting business requirements.
 
 In this unit, you learn how to structure task dependencies, choose execution patterns, and configure parameters that make your jobs flexible and reusable.
diff --git a/learn-pr/wwl-databricks/design-implement-data-pipelines/includes/5-design-error-handling-pipelines.md b/learn-pr/wwl-databricks/design-implement-data-pipelines/includes/5-design-error-handling-pipelines.md
@@ -1,3 +1,5 @@
+>[!VIDEO https://learn-video.azurefd.net/vod/player?id=1268c800-cec5-4dae-9d18-8e66d28446d3]
+
 When data pipelines fail, the consequences extend beyond immediate job errors. Invalid records can corrupt downstream analytics, unnoticed failures can accumulate technical debt, and without proper alerting, teams might discover problems hours or days after they occur. Designing robust **error handling** transforms your pipelines from fragile processes into resilient systems that protect data quality and enable rapid recovery.
 
 In this unit, you learn how to design error handling strategies for data pipelines, notebooks, and jobs in Azure Databricks.
diff --git a/learn-pr/wwl-databricks/design-implement-data-pipelines/index.yml b/learn-pr/wwl-databricks/design-implement-data-pipelines/index.yml
@@ -3,7 +3,7 @@ uid: learn.wwl.design-implement-data-pipelines
 metadata:
   title: Design and Implement Data Pipelines with Azure Databricks
   description: Learn how to design and implement robust data pipelines using Lakehouse architecture principles, medallion architecture, and Lakeflow Spark Declarative Pipelines in Azure Databricks.
-  ms.date: 12/07/2025
+  ms.date: 03/03/2026
   author: weslbo
   ms.author: wedebols
   ms.topic: module

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`@@ -1,3 +1,5 @@`
	`1`	`+>[!VIDEO https://learn-video.azurefd.net/vod/player?id=ac7df0e7-8d9c-4fae-8ef4-b6bb587bdffe]`
	`2`	`+`
`1`	`3`	`Building reliable data pipelines requires more than connecting data sources to destinations. You need to design workflows that handle failures gracefully, scale with growing data volumes, and remain maintainable as business requirements evolve. Azure Databricks provides multiple approaches for creating data pipelines—from flexible notebooks with procedural code to Lakeflow Spark Declarative Pipelines that automate orchestration and data quality enforcement.`
`2`	`4`
`3`	`5`	`When you design data pipelines, you make decisions that affect every downstream consumer of your data. The order of operations determines whether transformations build on validated, well-structured data. Your choice between notebooks and declarative pipelines influences how much orchestration code you write versus how much the platform manages for you. Task dependencies in Lakeflow Jobs control execution flow and enable parallel processing that reduces pipeline runtime.`