Added videos

weslbo · weslbo · commit 6b37a1283e32 · 2026-02-17T13:58:41.000+01:00
diff --git a/learn-pr/wwl-databricks/cleanse-transform-load-data-into-unity-catalog/1-introduction.yml b/learn-pr/wwl-databricks/cleanse-transform-load-data-into-unity-catalog/1-introduction.yml
@@ -4,7 +4,7 @@ title: Introduction
 metadata:
   title: Introduction
   description: "Introduction to cleansing, transforming, and loading data into Unity Catalog"
-  ms.date: 12/07/2025
+  ms.date: 02/17/2026
   author: weslbo
   ms.author: wedebols
   ms.topic: unit
diff --git a/learn-pr/wwl-databricks/cleanse-transform-load-data-into-unity-catalog/10-summary.yml b/learn-pr/wwl-databricks/cleanse-transform-load-data-into-unity-catalog/10-summary.yml
@@ -4,7 +4,7 @@ title: Summary
 metadata:
   title: Summary
   description: "Summary"
-  ms.date: 12/07/2025
+  ms.date: 02/17/2026
   author: weslbo
   ms.author: wedebols
   ms.topic: unit
diff --git a/learn-pr/wwl-databricks/cleanse-transform-load-data-into-unity-catalog/2-profile-data-summary-statistics.yml b/learn-pr/wwl-databricks/cleanse-transform-load-data-into-unity-catalog/2-profile-data-summary-statistics.yml
@@ -4,7 +4,7 @@ title: Profile data
 metadata:
   title: Profile Data
   description: Learn how to profile data to generate summary statistics and assess data distributions in Azure Databricks using SQL commands and the data profiling feature.
-  ms.date: 12/07/2025
+  ms.date: 02/17/2026
   author: weslbo
   ms.author: wedebols
   ms.topic: unit
diff --git a/learn-pr/wwl-databricks/cleanse-transform-load-data-into-unity-catalog/3-choose-appropriate-column-data-types.yml b/learn-pr/wwl-databricks/cleanse-transform-load-data-into-unity-catalog/3-choose-appropriate-column-data-types.yml
@@ -4,7 +4,7 @@ title: Choose column data types
 metadata:
   title: Choose Column Data Types
   description: Learn how to select appropriate column data types in Azure Databricks to ensure data quality, optimize storage, and improve query performance.
-  ms.date: 12/07/2025
+  ms.date: 02/17/2026
   author: weslbo
   ms.author: wedebols
   ms.topic: unit
diff --git a/learn-pr/wwl-databricks/cleanse-transform-load-data-into-unity-catalog/4-resolve-duplicate-missing-null-values.yml b/learn-pr/wwl-databricks/cleanse-transform-load-data-into-unity-catalog/4-resolve-duplicate-missing-null-values.yml
@@ -4,7 +4,7 @@ title: Resolve duplicates and nulls
 metadata:
   title: Resolve Duplicates and Nulls
   description: Learn how to identify and resolve duplicate records and handle null or missing values in Azure Databricks using SQL and PySpark approaches.
-  ms.date: 12/07/2025
+  ms.date: 02/17/2026
   author: weslbo
   ms.author: wedebols
   ms.topic: unit
diff --git a/learn-pr/wwl-databricks/cleanse-transform-load-data-into-unity-catalog/5-transform-data-filter-group-aggregate.yml b/learn-pr/wwl-databricks/cleanse-transform-load-data-into-unity-catalog/5-transform-data-filter-group-aggregate.yml
@@ -4,7 +4,7 @@ title: Transform data with filters and aggregations
 metadata:
   title: Transform Data With Filters and Aggregations
   description: Learn how to filter, group, and aggregate data in Azure Databricks using PySpark and SQL to transform raw data into meaningful summaries.
-  ms.date: 01/15/2026
+  ms.date: 02/17/2026
   author: weslbo
   ms.author: wedebols
   ms.topic: unit
diff --git a/learn-pr/wwl-databricks/cleanse-transform-load-data-into-unity-catalog/6-transform-data-join-union-intersect-except.yml b/learn-pr/wwl-databricks/cleanse-transform-load-data-into-unity-catalog/6-transform-data-join-union-intersect-except.yml
@@ -4,7 +4,7 @@ title: Transform data with joins and set operators
 metadata:
   title: Transform Data With Joins and Set Operators
   description: Learn how to transform data in Azure Databricks using join, union, intersect, and except operators to combine and compare datasets.
-  ms.date: 12/07/2025
+  ms.date: 02/17/2026
   author: weslbo
   ms.author: wedebols
   ms.topic: unit
diff --git a/learn-pr/wwl-databricks/cleanse-transform-load-data-into-unity-catalog/7-transform-data-flatten-pivot-unpivot.yml b/learn-pr/wwl-databricks/cleanse-transform-load-data-into-unity-catalog/7-transform-data-flatten-pivot-unpivot.yml
@@ -4,7 +4,7 @@ title: Transform data with denormalization and pivots
 metadata:
   title: Transform Data With Denormalization and Pivots
   description: Learn how to denormalize data for faster queries, pivot rows into columns for cross-tabular analysis, and unpivot columns into rows in Azure Databricks.
-  ms.date: 12/07/2025
+  ms.date: 02/17/2026
   author: weslbo
   ms.author: wedebols
   ms.topic: unit
diff --git a/learn-pr/wwl-databricks/cleanse-transform-load-data-into-unity-catalog/8-load-data-merge-insert-append.yml b/learn-pr/wwl-databricks/cleanse-transform-load-data-into-unity-catalog/8-load-data-merge-insert-append.yml
@@ -4,7 +4,7 @@ title: Load data with merge, insert, and append
 metadata:
   title: Load Data With Merge, Insert, and Append
   description: Learn how to load data into Delta tables in Azure Databricks using INSERT, INSERT OVERWRITE, and MERGE operations for append, replace, and upsert scenarios.
-  ms.date: 12/07/2025
+  ms.date: 02/17/2026
   author: weslbo
   ms.author: wedebols
   ms.topic: unit
diff --git a/learn-pr/wwl-databricks/cleanse-transform-load-data-into-unity-catalog/9-knowledge-check.yml b/learn-pr/wwl-databricks/cleanse-transform-load-data-into-unity-catalog/9-knowledge-check.yml
@@ -4,7 +4,7 @@ title: Module assessment
 metadata:
   title: Module assessment
   description: "Knowledge check"
-  ms.date: 12/07/2025
+  ms.date: 02/17/2026
   author: weslbo
   ms.author: wedebols
   ms.topic: unit
diff --git a/learn-pr/wwl-databricks/cleanse-transform-load-data-into-unity-catalog/includes/1-introduction.md b/learn-pr/wwl-databricks/cleanse-transform-load-data-into-unity-catalog/includes/1-introduction.md
@@ -1,3 +1,5 @@
+>[!VIDEO https://learn-video.azurefd.net/vod/player?id=6c2b1291-d084-4710-8733-bb5607a51560]
+
 Raw data rarely arrives in a format ready for analysis. Missing values, duplicate records, inconsistent data types, and poorly structured datasets are common challenges that data engineers must address before data can deliver business value. Without proper cleansing and transformation, analytics results become unreliable and downstream processes fail. **Unity Catalog** in Azure Databricks provides the governance foundation for managing transformed data assets throughout this process.
 
 Azure Databricks offers a comprehensive toolkit for data quality and transformation work. **Data profiling** generates summary statistics that reveal issues like null percentages, unexpected value distributions, and data drift. **Type selection** ensures columns use appropriate data types for storage efficiency and query performance. **Duplicate and null handling** techniques let you identify and resolve common data quality problems using SQL or PySpark. **Transformation operations** including filtering, grouping, aggregation, joins, and set operators reshape data to meet analytical requirements.
diff --git a/learn-pr/wwl-databricks/cleanse-transform-load-data-into-unity-catalog/includes/2-profile-data-summary-statistics.md b/learn-pr/wwl-databricks/cleanse-transform-load-data-into-unity-catalog/includes/2-profile-data-summary-statistics.md
@@ -1,3 +1,5 @@
+>[!VIDEO https://learn-video.azurefd.net/vod/player?id=9641bd12-f1cf-4ef0-be79-1dd29424da72]
+
 Before you cleanse or transform data, you need to understand what's in your tables. **Profiling data** means examining your datasets to generate summary statistics—counts, averages, value distributions, and null percentages—that reveal the quality and structure of your data. This foundation helps you identify issues like missing values, outliers, and unexpected patterns before they cause problems downstream.
 
 In this unit, you learn how to generate summary statistics using SQL commands and the data profiling feature in Azure Databricks.
diff --git a/learn-pr/wwl-databricks/cleanse-transform-load-data-into-unity-catalog/includes/3-choose-appropriate-column-data-types.md b/learn-pr/wwl-databricks/cleanse-transform-load-data-into-unity-catalog/includes/3-choose-appropriate-column-data-types.md
@@ -1,3 +1,5 @@
+>[!VIDEO https://learn-video.azurefd.net/vod/player?id=deb0ae82-1885-423d-8b95-1fe8b2d61ac7]
+
 Choosing the right data type for each column directly affects your data quality, storage efficiency, and query performance. When you select an inappropriate type, you risk losing precision, wasting storage space, or blocking operations that require specific type characteristics. In this unit, you learn how to evaluate your data and select the most appropriate column types in Azure Databricks.
 
 ## Understand data type categories
diff --git a/learn-pr/wwl-databricks/cleanse-transform-load-data-into-unity-catalog/includes/4-resolve-duplicate-missing-null-values.md b/learn-pr/wwl-databricks/cleanse-transform-load-data-into-unity-catalog/includes/4-resolve-duplicate-missing-null-values.md
@@ -1,3 +1,5 @@
+>[!VIDEO https://learn-video.azurefd.net/vod/player?id=1f405196-2f6e-48a8-ab3a-16d978af538c]
+
 Data quality issues like duplicate records and null values can compromise your analytics and downstream processes. Whether duplicates arise from concurrent data loads or nulls appear due to incomplete source data, addressing these issues is a core responsibility for data engineers working in Azure Databricks.
 
 In this unit, you learn how to identify and resolve duplicate, missing, and null values using both SQL and PySpark approaches.
diff --git a/learn-pr/wwl-databricks/cleanse-transform-load-data-into-unity-catalog/includes/5-transform-data-filter-group-aggregate.md b/learn-pr/wwl-databricks/cleanse-transform-load-data-into-unity-catalog/includes/5-transform-data-filter-group-aggregate.md
@@ -1,3 +1,5 @@
+>[!VIDEO https://learn-video.azurefd.net/vod/player?id=61a9bafd-23b4-4fd5-bf23-aa98b1510b9b]
+
 Data transformation turns raw data into formats suitable for analysis and reporting. **Filtering**, **grouping**, and **aggregating** are fundamental operations you use to shape data—selecting relevant records, organizing them into meaningful categories, and calculating summary statistics. These operations form the foundation of most data engineering pipelines.
 
 In this unit, you learn how to filter rows based on conditions, group data by one or more columns, and apply aggregate functions to calculate summary values.
diff --git a/learn-pr/wwl-databricks/cleanse-transform-load-data-into-unity-catalog/includes/6-transform-data-join-union-intersect-except.md b/learn-pr/wwl-databricks/cleanse-transform-load-data-into-unity-catalog/includes/6-transform-data-join-union-intersect-except.md
@@ -1,3 +1,5 @@
+>[!VIDEO https://learn-video.azurefd.net/vod/player?id=5a350154-5d22-4f45-9b63-be6d577ccf3f]
+
 Data engineering workflows often require combining information from multiple datasets. **Joins** combine tables horizontally by matching rows on related columns, while **set operators** combine tables vertically by appending, intersecting, or excluding rows. Understanding when to use each approach helps you build efficient transformation pipelines.
 
 In this unit, you learn how to join tables based on relationships and apply set operators to combine datasets with matching schemas.
diff --git a/learn-pr/wwl-databricks/cleanse-transform-load-data-into-unity-catalog/includes/7-transform-data-flatten-pivot-unpivot.md b/learn-pr/wwl-databricks/cleanse-transform-load-data-into-unity-catalog/includes/7-transform-data-flatten-pivot-unpivot.md
@@ -1,3 +1,5 @@
+>[!VIDEO https://learn-video.azurefd.net/vod/player?id=b131aee6-923e-455a-afae-1be0692b5801]
+
 Reshaping data structure is a key part of building effective analytics solutions. **Denormalization**, **pivoting**, and **unpivoting** are transformation techniques that help you reorganize data between normalized and aggregated formats—each serving different analytical and modeling needs.
 
 In this unit, you learn how to flatten normalized tables for faster queries, rotate rows into columns for cross-tabular analysis, and reverse that process when you need to normalize wide datasets.
@@ -166,10 +168,10 @@ SELECT *
 FROM regional_targets
 UNPIVOT (
   target_amount FOR month IN (
-    jan AS 'January',
-    feb AS 'February',
-    mar AS 'March',
-    apr AS 'April'
+    jan AS `January`,
+    feb AS `February`,
+    mar AS `March`,
+    apr AS `April`
   )
 );
 ```
diff --git a/learn-pr/wwl-databricks/cleanse-transform-load-data-into-unity-catalog/includes/8-load-data-merge-insert-append.md b/learn-pr/wwl-databricks/cleanse-transform-load-data-into-unity-catalog/includes/8-load-data-merge-insert-append.md
@@ -1,3 +1,5 @@
+>[!VIDEO https://learn-video.azurefd.net/vod/player?id=c4b9d1b5-a8a3-4613-82ed-4cb86b99db03]
+
 After you transform and cleanse data, the final step in your pipeline is loading it into target tables. Azure Databricks provides three core loading strategies: **append**, **overwrite**, and **merge**. Each strategy serves different use cases, and choosing the right one depends on how your source data relates to existing records.
 
 ![Diagram explaining the difference between INSERT INTO, OVERWRITE and MERGE.](../media/8-load-data-merge-insert-append.png)
diff --git a/learn-pr/wwl-databricks/cleanse-transform-load-data-into-unity-catalog/index.yml b/learn-pr/wwl-databricks/cleanse-transform-load-data-into-unity-catalog/index.yml
@@ -3,7 +3,7 @@ uid: learn.wwl.cleanse-transform-load-data-into-unity-catalog
 metadata:
   title: Cleanse, Transform, and Load Data into Unity Catalog
   description: Learn how to cleanse, transform, and load data into Unity Catalog tables in Azure Databricks by profiling data, handling duplicates and nulls, applying transformations, and using various loading strategies.
-  ms.date: 01/15/2026
+  ms.date: 02/17/2026
   author: weslbo
   ms.author: wedebols
   ms.topic: module
