Added videos

weslbo · weslbo · commit e0f653717509 · 2026-02-10T07:05:50.000+01:00
diff --git a/learn-pr/wwl-databricks/design-implement-data-modeling-unity-catalog/1-introduction.yml b/learn-pr/wwl-databricks/design-implement-data-modeling-unity-catalog/1-introduction.yml
@@ -4,7 +4,7 @@ title: Introduction
 metadata:
   title: Introduction
   description: "Introduction to designing and implementing data modeling in Azure Databricks with Unity Catalog"
-  ms.date: 12/07/2025
+  ms.date: 02/10/2026
   author: weslbo
   ms.author: wedebols
   ms.topic: unit
diff --git a/learn-pr/wwl-databricks/design-implement-data-modeling-unity-catalog/10-choose-managed-vs-unmanaged-tables.yml b/learn-pr/wwl-databricks/design-implement-data-modeling-unity-catalog/10-choose-managed-vs-unmanaged-tables.yml
@@ -4,7 +4,7 @@ title: Choose managed vs unmanaged tables
 metadata:
   title: Choose Managed vs Unmanaged Tables
   description: Learn how to choose between managed and external (unmanaged) tables in Azure Databricks Unity Catalog based on data ownership, lifecycle, governance, and integration requirements.
-  ms.date: 12/07/2025
+  ms.date: 02/10/2026
   author: weslbo
   ms.author: wedebols
   ms.topic: unit
diff --git a/learn-pr/wwl-databricks/design-implement-data-modeling-unity-catalog/11-design-clustering-strategy.yml b/learn-pr/wwl-databricks/design-implement-data-modeling-unity-catalog/11-design-clustering-strategy.yml
@@ -4,7 +4,7 @@ title: Design and implement a clustering strategy
 metadata:
   title: Design and Implement a Clustering Strategy
   description: Learn how to design and implement a clustering strategy in Azure Databricks, including liquid clustering, Z-ordering, and deletion vectors to optimize query performance and storage efficiency.
-  ms.date: 12/07/2025
+  ms.date: 02/10/2026
   author: weslbo
   ms.author: wedebols
   ms.topic: unit
diff --git a/learn-pr/wwl-databricks/design-implement-data-modeling-unity-catalog/12-knowledge-check.yml b/learn-pr/wwl-databricks/design-implement-data-modeling-unity-catalog/12-knowledge-check.yml
@@ -4,7 +4,7 @@ title: Knowledge check
 metadata:
   title: Knowledge check
   description: "Knowledge check"
-  ms.date: 12/07/2025
+  ms.date: 02/10/2026
   author: weslbo
   ms.author: wedebols
   ms.topic: unit
diff --git a/learn-pr/wwl-databricks/design-implement-data-modeling-unity-catalog/13-summary.yml b/learn-pr/wwl-databricks/design-implement-data-modeling-unity-catalog/13-summary.yml
@@ -4,7 +4,7 @@ title: Summary
 metadata:
   title: Summary
   description: "Summary"
-  ms.date: 12/07/2025
+  ms.date: 02/10/2026
   author: weslbo
   ms.author: wedebols
   ms.topic: unit
diff --git a/learn-pr/wwl-databricks/design-implement-data-modeling-unity-catalog/2-design-data-ingestion-logic.yml b/learn-pr/wwl-databricks/design-implement-data-modeling-unity-catalog/2-design-data-ingestion-logic.yml
@@ -4,7 +4,7 @@ title: Design ingestion logic and data source configuration
 metadata:
   title: Design Ingestion Logic and Data Source Configuration
   description: Learn how to design data ingestion logic and configure data sources in Azure Databricks, including extraction types, file formats, and connection considerations.
-  ms.date: 12/07/2025
+  ms.date: 02/10/2026
   author: weslbo
   ms.author: wedebols
   ms.topic: unit
diff --git a/learn-pr/wwl-databricks/design-implement-data-modeling-unity-catalog/3-choose-data-ingestion-tool.yml b/learn-pr/wwl-databricks/design-implement-data-modeling-unity-catalog/3-choose-data-ingestion-tool.yml
@@ -4,7 +4,7 @@ title: Choose a data ingestion tool
 metadata:
   title: Choose a Data Ingestion Tool
   description: Learn how to select the appropriate data ingestion tool in Azure Databricks, including Lakeflow Connect, Auto Loader, COPY INTO, Spark Structured Streaming, JDBC/ODBC, and Azure Data Factory.
-  ms.date: 01/15/2026
+  ms.date: 02/10/2026
   author: weslbo
   ms.author: wedebols
   ms.topic: unit
diff --git a/learn-pr/wwl-databricks/design-implement-data-modeling-unity-catalog/4-choose-data-table-format.yml b/learn-pr/wwl-databricks/design-implement-data-modeling-unity-catalog/4-choose-data-table-format.yml
@@ -4,7 +4,7 @@ title: Choose a data table format
 metadata:
   title: Choose a Data Table Format
   description: Learn how to select the appropriate data table format in Azure Databricks, including Delta Lake, Apache Iceberg, Parquet, CSV, and JSON based on performance, transaction requirements, and interoperability needs.
-  ms.date: 12/07/2025
+  ms.date: 02/10/2026
   author: weslbo
   ms.author: wedebols
   ms.topic: unit
diff --git a/learn-pr/wwl-databricks/design-implement-data-modeling-unity-catalog/5-design-data-partitioning-scheme.yml b/learn-pr/wwl-databricks/design-implement-data-modeling-unity-catalog/5-design-data-partitioning-scheme.yml
@@ -4,7 +4,7 @@ title: Design and implement a data partitioning scheme
 metadata:
   title: Design and Implement a Data Partitioning Scheme
   description: Learn how to design and implement effective data partitioning schemes in Azure Databricks to optimize query performance and manage large-scale datasets.
-  ms.date: 01/15/2026
+  ms.date: 02/10/2026
   author: weslbo
   ms.author: wedebols
   ms.topic: unit
diff --git a/learn-pr/wwl-databricks/design-implement-data-modeling-unity-catalog/6-choose-slowly-changing-dimension-type.yml b/learn-pr/wwl-databricks/design-implement-data-modeling-unity-catalog/6-choose-slowly-changing-dimension-type.yml
@@ -4,7 +4,7 @@ title: Choose a slowly changing dimension (SCD) type
 metadata:
   title: Choose a Slowly Changing Dimension (SCD) Type
   description: Learn how to select the appropriate slowly changing dimension type in Azure Databricks to manage historical data changes in dimension tables, including SCD Types 0, 1, 2, and 3.
-  ms.date: 12/07/2025
+  ms.date: 02/10/2026
   author: weslbo
   ms.author: wedebols
   ms.topic: unit
diff --git a/learn-pr/wwl-databricks/design-implement-data-modeling-unity-catalog/7-implement-slowly-changing-dimension-type-2.yml b/learn-pr/wwl-databricks/design-implement-data-modeling-unity-catalog/7-implement-slowly-changing-dimension-type-2.yml
@@ -4,7 +4,7 @@ title: Implement a slowly changing dimension (SCD) type 2
 metadata:
   title: Implement a Slowly Changing Dimension (SCD) Type 2
   description: Learn how to implement slowly changing dimension type 2 in Azure Databricks to maintain historical data changes for accurate reporting and analysis.
-  ms.date: 12/07/2025
+  ms.date: 02/10/2026
   author: weslbo
   ms.author: wedebols
   ms.topic: unit
diff --git a/learn-pr/wwl-databricks/design-implement-data-modeling-unity-catalog/8-design-temporal-history-table.yml b/learn-pr/wwl-databricks/design-implement-data-modeling-unity-catalog/8-design-temporal-history-table.yml
@@ -4,7 +4,7 @@ title: Design and implement a temporal (history) table to record changes over ti
 metadata:
   title: Design and Implement a Temporal (History) Table to Record Changes Over Time
   description: Learn how to design and implement temporal tables in Azure Databricks to track row-level changes for auditing, debugging, and point-in-time queries.
-  ms.date: 12/03/2025
+  ms.date: 02/10/2026
   author: weslbo
   ms.author: wedebols
   ms.topic: unit
diff --git a/learn-pr/wwl-databricks/design-implement-data-modeling-unity-catalog/9-choose-granularity.yml b/learn-pr/wwl-databricks/design-implement-data-modeling-unity-catalog/9-choose-granularity.yml
@@ -4,7 +4,7 @@ title: Choose granularity on a column or table based on requirements
 metadata:
   title: Choose Granularity on a Column or Table Based on Requirements
   description: Learn how to choose the appropriate data granularity for fact and dimension tables in Azure Databricks based on business requirements, query patterns, and cost considerations.
-  ms.date: 12/07/2025
+  ms.date: 02/10/2026
   author: weslbo
   ms.author: wedebols
   ms.topic: unit
diff --git a/learn-pr/wwl-databricks/design-implement-data-modeling-unity-catalog/includes/1-introduction.md b/learn-pr/wwl-databricks/design-implement-data-modeling-unity-catalog/includes/1-introduction.md
@@ -1,3 +1,5 @@
+>[!VIDEO https://learn-video.azurefd.net/vod/player?id=c2324c11-59b0-456d-85e2-4b7e08de422d]
+
 Your organization processes millions of transactions daily from multiple source systems, and stakeholders need reliable analytics that reflect both current operations and historical trends. The data engineering team faces critical decisions: How should data flow from source systems into the **lakehouse**? Which table formats provide the right balance of performance and compatibility? How do you maintain historical accuracy while keeping queries fast?
 
 **Data modeling** in Azure Databricks with **Unity Catalog** addresses these challenges through deliberate design choices that affect every downstream consumer. You select **ingestion patterns** that match your latency requirements. You choose table formats like **Delta Lake** or **Apache Iceberg** based on transaction needs and cross-platform compatibility. You design **partitioning schemes** and **clustering strategies** that align with how analysts query the data.
diff --git a/learn-pr/wwl-databricks/design-implement-data-modeling-unity-catalog/includes/10-choose-managed-vs-unmanaged-tables.md b/learn-pr/wwl-databricks/design-implement-data-modeling-unity-catalog/includes/10-choose-managed-vs-unmanaged-tables.md
@@ -1,3 +1,5 @@
+>[!VIDEO https://learn-video.azurefd.net/vod/player?id=28a735cf-13d1-4aba-955c-2a1ce4846a21]
+
 When you create tables in Azure Databricks, you face a fundamental decision that affects data lifecycle management, governance, and operational flexibility. Understanding the differences between **managed tables** and **external tables** (sometimes called unmanaged tables) helps you choose the right approach for each scenario in your data platform.
 
 In this unit, you learn how to distinguish between managed and external tables, and explore the factors that guide your choice.
diff --git a/learn-pr/wwl-databricks/design-implement-data-modeling-unity-catalog/includes/11-design-clustering-strategy.md b/learn-pr/wwl-databricks/design-implement-data-modeling-unity-catalog/includes/11-design-clustering-strategy.md
@@ -1,3 +1,5 @@
+>[!VIDEO https://learn-video.azurefd.net/vod/player?id=abb1b6b2-f52c-46a7-83a1-229d9ebbd828]
+
 Optimizing how data is physically organized on disk directly affects **query performance** and **storage efficiency**. When you design a clustering strategy for your Delta Lake tables, you determine how the query engine locates and retrieves relevant data. The right approach can dramatically reduce the amount of data scanned during queries, lowering both response times and compute costs.
 
 Azure Databricks provides several complementary techniques for data organization. Understanding when to apply **liquid clustering**, **Z-ordering**, or **deletion vectors** helps you make decisions that balance performance, maintenance effort, and compatibility with your existing tables.
diff --git a/learn-pr/wwl-databricks/design-implement-data-modeling-unity-catalog/includes/2-design-data-ingestion-logic.md b/learn-pr/wwl-databricks/design-implement-data-modeling-unity-catalog/includes/2-design-data-ingestion-logic.md
@@ -1,3 +1,5 @@
+>[!VIDEO https://learn-video.azurefd.net/vod/player?id=3bcd0b26-9202-4c26-959a-b9407963ffd5]
+
 Designing ingestion logic requires you to make critical decisions about how data moves from source systems into Azure Databricks. These decisions affect data freshness, processing efficiency, and system reliability. Before you select tools or write code, you need to understand the characteristics of your data sources and define extraction strategies that align with your business requirements.
 
 In this unit, you explore the key design considerations for data ingestion logic and data source configuration in Azure Databricks. The focus here is on conceptual logic and design patterns—specific tool selection and implementation details are covered in later units.
diff --git a/learn-pr/wwl-databricks/design-implement-data-modeling-unity-catalog/includes/3-choose-data-ingestion-tool.md b/learn-pr/wwl-databricks/design-implement-data-modeling-unity-catalog/includes/3-choose-data-ingestion-tool.md
@@ -1,3 +1,5 @@
+>[!VIDEO https://learn-video.azurefd.net/vod/player?id=f51280d0-92d5-4090-9734-a3bef11e830e]
+
 When you design data pipelines in Azure Databricks, selecting the right ingestion tool directly impacts pipeline reliability, maintenance effort, and long-term costs. Each tool addresses different scenarios, data sources, and operational requirements. Understanding these differences helps you make informed decisions that align with your organization's needs.
 
 ## Understand the ingestion tool landscape
diff --git a/learn-pr/wwl-databricks/design-implement-data-modeling-unity-catalog/includes/4-choose-data-table-format.md b/learn-pr/wwl-databricks/design-implement-data-modeling-unity-catalog/includes/4-choose-data-table-format.md
@@ -1,3 +1,5 @@
+>[!VIDEO https://learn-video.azurefd.net/vod/player?id=0631ba8f-e7a8-486c-a104-cbb21f66cdbe]
+
 Data engineers face a critical decision early in every data pipeline: which table format should store your data? This choice affects query performance, transaction reliability, interoperability with other platforms, and long-term maintenance of your lakehouse architecture.
 
 Azure Databricks supports multiple storage formats, each designed for different scenarios. Understanding their differences helps you select the right format for your specific requirements.
diff --git a/learn-pr/wwl-databricks/design-implement-data-modeling-unity-catalog/includes/5-design-data-partitioning-scheme.md b/learn-pr/wwl-databricks/design-implement-data-modeling-unity-catalog/includes/5-design-data-partitioning-scheme.md
@@ -1,3 +1,5 @@
+>[!VIDEO https://learn-video.azurefd.net/vod/player?id=bc55c60e-d4b9-4e0f-b753-08c7077b7a2e]
+
 Data partitioning directly affects how efficiently your queries run and how much compute resources you consume. When you partition a table, you divide data into discrete segments based on column values, allowing the query engine to read only relevant portions of your dataset. This approach, called **partition pruning**, can dramatically reduce data scans when filters align with partition boundaries.
 
 Consider a retail company processing millions of daily sales transactions. Analysts frequently query data by date ranges to generate weekly reports, compare month-over-month trends, and analyze seasonal patterns. Without partitioning, every query scans the entire transaction history. With proper partitioning by date, queries for a specific week touch only a fraction of the stored files.
diff --git a/learn-pr/wwl-databricks/design-implement-data-modeling-unity-catalog/includes/6-choose-slowly-changing-dimension-type.md b/learn-pr/wwl-databricks/design-implement-data-modeling-unity-catalog/includes/6-choose-slowly-changing-dimension-type.md
@@ -1,3 +1,5 @@
+>[!VIDEO https://learn-video.azurefd.net/vod/player?id=ef81731a-cc31-4098-9baa-841144ed261a]
+
 Dimension tables in a data warehouse capture descriptive attributes about business entities like customers, products, or employees. These attributes change over time, and how you handle those changes directly affects your ability to perform accurate historical analysis. Slowly changing dimensions (SCDs) provide frameworks for managing these changes, and choosing the right SCD type is a critical design decision.
 
 As a data engineer, you're expected to understand SCD types and implement them when directed. While data architects often make the initial design decisions, you need sufficient knowledge to evaluate trade-offs, recommend approaches, and execute implementations correctly. This unit helps you understand the characteristics of each SCD type so you can select the appropriate approach for your business requirements.
diff --git a/learn-pr/wwl-databricks/design-implement-data-modeling-unity-catalog/includes/7-implement-slowly-changing-dimension-type-2.md b/learn-pr/wwl-databricks/design-implement-data-modeling-unity-catalog/includes/7-implement-slowly-changing-dimension-type-2.md
@@ -1,3 +1,5 @@
+>[!VIDEO https://learn-video.azurefd.net/vod/player?id=a1da50c9-1de6-41c6-b4bc-cc7ae7dcf02b]
+
 After selecting SCD Type 2 for a dimension, you need to implement the table structure and change capture logic in Azure Databricks. This unit focuses on creating SCD Type 2 tables and implementing the MERGE patterns that maintain version history as source data changes.
 
 ## Create the SCD Type 2 table
diff --git a/learn-pr/wwl-databricks/design-implement-data-modeling-unity-catalog/includes/8-design-temporal-history-table.md b/learn-pr/wwl-databricks/design-implement-data-modeling-unity-catalog/includes/8-design-temporal-history-table.md
@@ -1,3 +1,5 @@
+>[!VIDEO https://learn-video.azurefd.net/vod/player?id=feaee633-42d4-4b4c-9c9a-6fd1a3d451b1]
+
 Data pipelines often require tracking not just current values, but every change that occurs over time. Temporal tables provide a mechanism for recording row-level changes automatically, enabling point-in-time queries and complete audit trails. Unlike slowly changing dimensions that focus on business data history, temporal tables capture technical history for operational traceability and debugging.
 
 ## Understand temporal tables versus SCD Type 2
diff --git a/learn-pr/wwl-databricks/design-implement-data-modeling-unity-catalog/includes/9-choose-granularity.md b/learn-pr/wwl-databricks/design-implement-data-modeling-unity-catalog/includes/9-choose-granularity.md
@@ -1,3 +1,5 @@
+>[!VIDEO https://learn-video.azurefd.net/vod/player?id=330312e0-2d63-4624-9b91-c459c7b97e00]
+
 **Granularity** is one of the most critical design decisions you make when building a data model. It defines the **level of detail** captured in your tables and directly affects **storage costs**, **query performance**, and the **analytical questions** you can answer. As a data engineer, understanding granularity helps you design tables that meet business requirements while maintaining efficient operations.
 
 This unit explains what granularity means in the context of dimensional modeling and how to evaluate different granularity options based on your organization's requirements.
diff --git a/learn-pr/wwl-databricks/design-implement-data-modeling-unity-catalog/index.yml b/learn-pr/wwl-databricks/design-implement-data-modeling-unity-catalog/index.yml
@@ -3,7 +3,7 @@ uid: learn.wwl.design-implement-data-modeling-unity-catalog
 metadata:
   title: Design and Implement Data Modeling with Azure Databricks
   description: Learn how to design and implement data modeling strategies in Azure Databricks with Unity Catalog, including ingestion patterns, table formats, partitioning, slowly changing dimensions, and clustering strategies.
-  ms.date: 01/15/2026
+  ms.date: 02/10/2026
   author: weslbo
   ms.author: wedebols
   ms.topic: module
