added videos for DP-750

weslbo · weslbo · commit b4bf6d7ec666 · 2026-02-16T15:48:41.000+01:00
diff --git a/learn-pr/wwl-databricks/ingest-data-into-unity-catalog/1-introduction.yml b/learn-pr/wwl-databricks/ingest-data-into-unity-catalog/1-introduction.yml
@@ -4,7 +4,7 @@ title: Introduction
 metadata:
   title: Introduction
   description: "Introduction to ingesting data into Unity Catalog"
-  ms.date: 12/07/2025
+  ms.date: 02/16/2026
   author: weslbo
   ms.author: wedebols
   ms.topic: unit
diff --git a/learn-pr/wwl-databricks/ingest-data-into-unity-catalog/10-summary.yml b/learn-pr/wwl-databricks/ingest-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: 02/16/2026
   author: weslbo
   ms.author: wedebols
   ms.topic: unit
diff --git a/learn-pr/wwl-databricks/ingest-data-into-unity-catalog/2-ingest-data-lakeflow-connect.yml b/learn-pr/wwl-databricks/ingest-data-into-unity-catalog/2-ingest-data-lakeflow-connect.yml
@@ -4,7 +4,7 @@ title: Ingest data with Lakeflow Connect
 metadata:
   title: Ingest Data With Lakeflow Connect
   description: Learn how to ingest data from external sources into Azure Databricks using Lakeflow Connect managed connectors, including configuring pipelines, change data capture, and SCD types.
-  ms.date: 12/07/2025
+  ms.date: 02/16/2026
   author: weslbo
   ms.author: wedebols
   ms.topic: unit
diff --git a/learn-pr/wwl-databricks/ingest-data-into-unity-catalog/3-ingest-data-notebooks.yml b/learn-pr/wwl-databricks/ingest-data-into-unity-catalog/3-ingest-data-notebooks.yml
@@ -4,7 +4,7 @@ title: Ingest data with notebooks
 metadata:
   title: Ingest Data with Notebooks
   description: Learn how to ingest batch and streaming data using notebooks in Azure Databricks, including reading from files, databases, and streaming sources, then writing to Unity Catalog tables.
-  ms.date: 12/07/2025
+  ms.date: 02/16/2026
   author: weslbo
   ms.author: wedebols
   ms.topic: unit
diff --git a/learn-pr/wwl-databricks/ingest-data-into-unity-catalog/4-ingest-data-sql-methods.yml b/learn-pr/wwl-databricks/ingest-data-into-unity-catalog/4-ingest-data-sql-methods.yml
@@ -4,7 +4,7 @@ title: Ingest data with SQL methods
 metadata:
   title: Ingest Data with SQL Methods
   description: Learn how to ingest data using SQL methods including CREATE TABLE AS SELECT (CTAS), CREATE OR REPLACE TABLE, and COPY INTO in Azure Databricks.
-  ms.date: 12/07/2025
+  ms.date: 02/16/2026
   author: weslbo
   ms.author: wedebols
   ms.topic: unit
diff --git a/learn-pr/wwl-databricks/ingest-data-into-unity-catalog/5-ingest-data-change-data-capture-feed.yml b/learn-pr/wwl-databricks/ingest-data-into-unity-catalog/5-ingest-data-change-data-capture-feed.yml
@@ -4,7 +4,7 @@ title: Ingest data with CDC feed
 metadata:
   title: Ingest Data with CDC Feed
   description: Learn how to ingest data using change data capture (CDC) feeds in Azure Databricks, including processing changes with the AUTO CDC API and handling SCD Type 1 and Type 2 patterns.
-  ms.date: 12/07/2025
+  ms.date: 02/16/2026
   author: weslbo
   ms.author: wedebols
   ms.topic: unit
diff --git a/learn-pr/wwl-databricks/ingest-data-into-unity-catalog/6-ingest-data-spark-structured-streaming.yml b/learn-pr/wwl-databricks/ingest-data-into-unity-catalog/6-ingest-data-spark-structured-streaming.yml
@@ -4,7 +4,7 @@ title: Ingest data with Spark Structured Streaming
 metadata:
   title: Ingest Data with Spark Structured Streaming
   description: Learn how to ingest streaming data from message buses like Kafka and Azure Event Hubs into Unity Catalog tables using Spark Structured Streaming in Azure Databricks.
-  ms.date: 01/15/2026
+  ms.date: 02/16/2026
   author: weslbo
   ms.author: wedebols
   ms.topic: unit
diff --git a/learn-pr/wwl-databricks/ingest-data-into-unity-catalog/7-ingest-data-auto-loader.yml b/learn-pr/wwl-databricks/ingest-data-into-unity-catalog/7-ingest-data-auto-loader.yml
@@ -4,7 +4,7 @@ title: Ingest data with Auto Loader
 metadata:
   title: Ingest Data with Auto Loader
   description: Learn how to use Auto Loader to incrementally ingest data files from cloud storage into Unity Catalog tables, including configuring file detection modes, schema inference, and monitoring streams.
-  ms.date: 12/07/2025
+  ms.date: 02/16/2026
   author: weslbo
   ms.author: wedebols
   ms.topic: unit
diff --git a/learn-pr/wwl-databricks/ingest-data-into-unity-catalog/8-ingest-data-lakeflow-declarative-pipelines.yml b/learn-pr/wwl-databricks/ingest-data-into-unity-catalog/8-ingest-data-lakeflow-declarative-pipelines.yml
@@ -4,7 +4,7 @@ title: Ingest data with Lakeflow Spark Declarative Pipelines
 metadata:
   title: Ingest Data with Lakeflow Spark Declarative Pipelines
   description: Learn how to ingest data into Azure Databricks using Lakeflow Spark Declarative Pipelines, including configuring streaming tables, flows, and handling schema evolution for ingestion from cloud storage and message buses.
-  ms.date: 12/07/2025
+  ms.date: 02/16/2026
   author: weslbo
   ms.author: wedebols
   ms.topic: unit
diff --git a/learn-pr/wwl-databricks/ingest-data-into-unity-catalog/9-knowledge-check.yml b/learn-pr/wwl-databricks/ingest-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: 02/16/2026
   author: weslbo
   ms.author: wedebols
   ms.topic: unit
diff --git a/learn-pr/wwl-databricks/ingest-data-into-unity-catalog/includes/1-introduction.md b/learn-pr/wwl-databricks/ingest-data-into-unity-catalog/includes/1-introduction.md
@@ -1,3 +1,5 @@
+>[!VIDEO https://learn-video.azurefd.net/vod/player?id=e6f63fcd-62cc-455f-bc82-e42352cb6dcc]
+
 Data engineers face a fundamental challenge: getting data from diverse sources into a unified analytics platform efficiently and reliably. Whether your organization works with batch files in cloud storage, streaming events from Kafka, or transactional data from operational databases, you need ingestion methods that handle each scenario while maintaining data quality and governance. **Unity Catalog** in Azure Databricks provides the foundation for this unified approach, offering centralized governance for all your ingested data.
 
 Azure Databricks offers multiple ingestion techniques, each optimized for specific data patterns and requirements. **Lakeflow Connect** provides managed connectors for common enterprise sources like SQL Server and Salesforce, eliminating the need for custom extraction code. **Notebooks** give you full control over ingestion logic using Python, SQL, or Scala. **SQL commands** like `COPY INTO` and `CREATE TABLE AS SELECT` offer declarative approaches for file-based ingestion. For real-time workloads, **Spark Structured Streaming** and **Auto Loader** process data continuously as it arrives, with exactly-once processing guarantees.
diff --git a/learn-pr/wwl-databricks/ingest-data-into-unity-catalog/includes/2-ingest-data-lakeflow-connect.md b/learn-pr/wwl-databricks/ingest-data-into-unity-catalog/includes/2-ingest-data-lakeflow-connect.md
@@ -1,3 +1,5 @@
+>[!VIDEO https://learn-video.azurefd.net/vod/player?id=485894a3-a978-4032-8679-5981a79aef03]
+
 Data engineers face a common challenge: getting data from diverse sources into analytics platforms efficiently and reliably. Whether your data lives in SQL Server, Salesforce, SharePoint, or cloud storage, you need consistent, governed pipelines that minimize manual effort. **Lakeflow Connect** addresses this challenge by providing managed ingestion connectors that bring data directly into Unity Catalog tables.
 
 ## Understand ingestion pipelines
diff --git a/learn-pr/wwl-databricks/ingest-data-into-unity-catalog/includes/3-ingest-data-notebooks.md b/learn-pr/wwl-databricks/ingest-data-into-unity-catalog/includes/3-ingest-data-notebooks.md
@@ -1,3 +1,5 @@
+>[!VIDEO https://learn-video.azurefd.net/vod/player?id=5a45c669-7252-4be8-9837-00f9515a1009]
+
 Notebooks in Azure Databricks provide a flexible, code-driven approach for ingesting data from various sources. When graphical tools don't meet your requirements, or when you need custom logic for data transformation, notebooks give you complete control over the ingestion process. You can use Python (PySpark), SQL, Scala, or R to connect to data sources, apply transformations, and load data into Unity Catalog tables.
 
 In this unit, you learn how to ingest data using notebooks for both batch and streaming scenarios.
diff --git a/learn-pr/wwl-databricks/ingest-data-into-unity-catalog/includes/4-ingest-data-sql-methods.md b/learn-pr/wwl-databricks/ingest-data-into-unity-catalog/includes/4-ingest-data-sql-methods.md
@@ -1,3 +1,5 @@
+>[!VIDEO https://learn-video.azurefd.net/vod/player?id=30e7bb65-edc2-42f8-96f2-00344ef4eb10]
+
 SQL commands provide a declarative approach to data ingestion in Azure Databricks. If you're already comfortable with SQL syntax, these methods let you create and populate tables without writing procedural code. The three primary SQL ingestion techniques—`CREATE TABLE AS SELECT (CTAS)`, `CREATE OR REPLACE TABLE`, and `COPY INTO`—each address different ingestion scenarios while maintaining full compatibility with Unity Catalog.
 
 ## Create tables from queries with CTAS
@@ -51,15 +53,10 @@ GROUP BY report_date;
 
 Unlike dropping and recreating a table, `CREATE OR REPLACE` maintains the table's metadata and permissions. Downstream users and applications continue to access the table without reconfiguration.
 
-You can also use this command to define a table with an explicit schema and populate it from files:
+You can also use this command to load data directly from files. The schema is automatically inferred from the query results:
 
 ```sql
-CREATE OR REPLACE TABLE catalog.schema.products (
-    product_id INT,
-    product_name STRING,
-    category STRING,
-    price DECIMAL(10, 2)
-) AS
+CREATE OR REPLACE TABLE catalog.schema.products AS
 SELECT * FROM read_files(
     '/Volumes/catalog/schema/volume/products.csv',
     format => 'csv',
diff --git a/learn-pr/wwl-databricks/ingest-data-into-unity-catalog/includes/5-ingest-data-change-data-capture-feed.md b/learn-pr/wwl-databricks/ingest-data-into-unity-catalog/includes/5-ingest-data-change-data-capture-feed.md
@@ -1,3 +1,5 @@
+>[!VIDEO https://learn-video.azurefd.net/vod/player?id=c8ea60fe-4361-4f31-bfe0-754fe56a1936]
+
 When source systems generate millions of transactions daily, reloading entire tables becomes impractical. You need a way to capture only the changes—inserts, updates, and deletes—and apply them efficiently to your destination tables. This pattern, known as change data capture (CDC), forms the foundation of modern incremental data pipelines.
 
 ## Understand CDC ingestion patterns
diff --git a/learn-pr/wwl-databricks/ingest-data-into-unity-catalog/includes/6-ingest-data-spark-structured-streaming.md b/learn-pr/wwl-databricks/ingest-data-into-unity-catalog/includes/6-ingest-data-spark-structured-streaming.md
@@ -1,3 +1,5 @@
+>[!VIDEO https://learn-video.azurefd.net/vod/player?id=bbf92615-a813-4d38-9237-e2a054db1b84]
+
 Real-time data ingestion allows you to process events as they occur rather than waiting for batch intervals. When your organization needs to capture IoT sensor readings, clickstream data, or financial transactions as they happen, Spark Structured Streaming provides the foundation for continuous data ingestion into your lakehouse.
 
 In this unit, you learn how to configure Structured Streaming jobs that read from streaming sources and write to Unity Catalog tables.
diff --git a/learn-pr/wwl-databricks/ingest-data-into-unity-catalog/includes/7-ingest-data-auto-loader.md b/learn-pr/wwl-databricks/ingest-data-into-unity-catalog/includes/7-ingest-data-auto-loader.md
@@ -1,3 +1,5 @@
+>[!VIDEO https://learn-video.azurefd.net/vod/player?id=40a715a4-2791-465f-8ce4-726c6679048e]
+
 When new data files arrive continuously in cloud storage, you need an efficient way to process them without manually tracking which files have been ingested. **Auto Loader** solves this challenge by automatically detecting and ingesting new files as they appear, providing exactly-once guarantees without requiring you to manage state or checkpoints manually.
 
 ## Understand how Auto Loader works
diff --git a/learn-pr/wwl-databricks/ingest-data-into-unity-catalog/includes/8-ingest-data-lakeflow-declarative-pipelines.md b/learn-pr/wwl-databricks/ingest-data-into-unity-catalog/includes/8-ingest-data-lakeflow-declarative-pipelines.md
@@ -1,3 +1,5 @@
+>[!VIDEO https://learn-video.azurefd.net/vod/player?id=025207f2-84ca-4b2f-975a-08f9128b741b]
+
 When you need to ingest data from multiple sources with automated orchestration, schema management, and exactly-once processing guarantees, Lakeflow Spark Declarative Pipelines provides a declarative framework for the entire process. Rather than writing complex imperative code to manage streaming state and dependencies, you define what you want, and the pipeline handles how to execute it.
 
 ## Understand Lakeflow Spark Declarative Pipelines for ingestion
diff --git a/learn-pr/wwl-databricks/ingest-data-into-unity-catalog/index.yml b/learn-pr/wwl-databricks/ingest-data-into-unity-catalog/index.yml
@@ -3,7 +3,7 @@ uid: learn.wwl.ingest-data-into-unity-catalog
 metadata:
   title: Ingest Data into Unity Catalog
   description: Learn how to ingest data from diverse sources into Unity Catalog tables in Azure Databricks using managed connectors, notebooks, SQL commands, streaming, and declarative pipelines.
-  ms.date: 12/07/2025
+  ms.date: 02/16/2026
   author: weslbo
   ms.author: wedebols
   ms.topic: module
