added videos

Author: weslbo

learn-pr/wwl-databricks/implement-manage-data-quality-constraints-unity-catalog/1-introduction.yml

@@ -4,7 +4,7 @@ title: Introduction
 metadata:
   title: Introduction
   description: "Introduction to implementing and managing data quality constraints in Unity Catalog"
-  ms.date: 12/07/2025
+  ms.date: 02/19/2026
   author: weslbo
   ms.author: wedebols
   ms.topic: unit

learn-pr/wwl-databricks/implement-manage-data-quality-constraints-unity-catalog/2-implement-validation-checks.yml

@@ -4,7 +4,7 @@ title: Implement validation checks
 metadata:
   title: Implement Validation Checks
   description: Learn how to implement validation checks including nullability, data cardinality, and range checking in Azure Databricks using pipeline expectations and table constraints.
-  ms.date: 12/07/2025
+  ms.date: 02/19/2026
   author: weslbo
   ms.author: wedebols
   ms.topic: unit

learn-pr/wwl-databricks/implement-manage-data-quality-constraints-unity-catalog/3-implement-data-type-checks.yml

@@ -4,7 +4,7 @@ title: Implement data type checks
 metadata:
   title: Implement Data Type Checks
   description: Learn how to implement data type checks in Azure Databricks using schema enforcement, explicit casting, constraints, and pipeline expectations to validate data quality.
-  ms.date: 12/07/2025
+  ms.date: 02/19/2026
   author: weslbo
   ms.author: wedebols
   ms.topic: unit

learn-pr/wwl-databricks/implement-manage-data-quality-constraints-unity-catalog/4-detect-manage-schema-drift.yml

@@ -4,7 +4,7 @@ title: Detect and manage schema drift
 metadata:
   title: Detect and Manage Schema Drift
   description: Learn how to detect and manage schema drift in Azure Databricks data pipelines using Delta Lake, Auto Loader, schema evolution, and error handling strategies.
-  ms.date: 12/07/2025
+  ms.date: 02/19/2026
   author: weslbo
   ms.author: wedebols
   ms.topic: unit

learn-pr/wwl-databricks/implement-manage-data-quality-constraints-unity-catalog/5-manage-data-quality-pipeline-expectations.yml

@@ -4,7 +4,7 @@ title: Manage data quality with pipeline expectations
 metadata:
   title: Manage Data Quality with Pipeline Expectations
   description: Learn how to use pipeline expectations in Lakeflow Spark Declarative Pipelines to define and enforce data quality rules for streaming tables and materialized views.
-  ms.date: 12/07/2025
+  ms.date: 02/19/2026
   author: weslbo
   ms.author: wedebols
   ms.topic: unit

learn-pr/wwl-databricks/implement-manage-data-quality-constraints-unity-catalog/6-knowledge-check.yml

@@ -4,7 +4,7 @@ title: Module assessment
 metadata:
   title: Module assessment
   description: "Knowledge check"
-  ms.date: 12/07/2025
+  ms.date: 02/19/2026
   author: weslbo
   ms.author: wedebols
   ms.topic: unit

learn-pr/wwl-databricks/implement-manage-data-quality-constraints-unity-catalog/7-summary.yml

@@ -4,7 +4,7 @@ title: Summary
 metadata:
   title: Summary
   description: "Summary"
-  ms.date: 12/07/2025
+  ms.date: 02/19/2026
   author: weslbo
   ms.author: wedebols
   ms.topic: unit

learn-pr/wwl-databricks/implement-manage-data-quality-constraints-unity-catalog/includes/1-introduction.md

@@ -1,3 +1,5 @@
+>[!VIDEO https://learn-video.azurefd.net/vod/player?id=8f94718e-160e-4b3f-902f-fb1d807a3107]
+
 **Data quality** issues can derail analytics projects, corrupt business reports, and erode confidence in your data platform. When invalid data enters your tables—whether through type mismatches, missing required values, or unexpected schema changes—problems compound as that data flows through pipelines and reaches downstream consumers. Implementing robust **data quality constraints** at the point of ingestion creates a foundation of trust in your data assets.
 
 Azure Databricks provides multiple mechanisms for enforcing data quality within **Unity Catalog**. **Schema enforcement** validates data types automatically when writing to Delta Lake tables. **Table constraints** define rules that reject invalid records at write time. **Pipeline expectations** in Lakeflow Spark Declarative Pipelines enable real-time quality checks on streaming data with configurable actions for violations. Together, these capabilities form a comprehensive approach to maintaining data integrity.

learn-pr/wwl-databricks/implement-manage-data-quality-constraints-unity-catalog/includes/2-implement-validation-checks.md

@@ -1,3 +1,5 @@
+>[!VIDEO https://learn-video.azurefd.net/vod/player?id=18f27350-8c3e-41ce-aa74-80fe7ddb2ec8]
+
 Data pipelines often encounter records with missing values, duplicate identifiers, or values that fall outside acceptable ranges. Without validation checks in place, these data quality issues propagate downstream, causing incorrect analytics, failed reports, and unreliable business decisions. Validation checks catch these problems at the point of data ingestion, ensuring that only quality data flows through your pipeline.
 
 In this unit, you learn how to implement validation checks for nullability, data cardinality, and range checking using Lakeflow Spark Declarative Pipelines expectations and Delta Lake table constraints.

learn-pr/wwl-databricks/implement-manage-data-quality-constraints-unity-catalog/includes/3-implement-data-type-checks.md

@@ -1,3 +1,5 @@
+>[!VIDEO https://learn-video.azurefd.net/vod/player?id=9a912c64-3831-409b-a8b7-23d67acec030]
+
 Every column in your dataset has an expected data type, and when incoming data doesn't match that expectation, problems cascade downstream. A string value where an integer belongs can break calculations, corrupt aggregations, or cause pipeline failures. Data type checks ensure that each field contains values of the correct type before they enter your tables.
 
 In this unit, you learn how to implement data type checks using schema enforcement, explicit type casting, and validation constraints in Azure Databricks.
@@ -75,9 +77,11 @@ For example, you can validate that a string column contains only numeric charact
 ```sql
 CREATE TABLE orders (
     order_id INT,
-    order_total STRING,
-    CONSTRAINT valid_order_total CHECK (order_total REGEXP '^[0-9]+(\\.[0-9]+)?$')
+    order_total STRING
 );
+
+ALTER TABLE orders
+ADD CONSTRAINT valid_order_total CHECK (order_total REGEXP '^[0-9]+(\\.[0-9]+)?$')
 ```
 
 This constraint ensures the `order_total` column contains values that look like valid numbers, catching malformed data before it enters the table.