added video

weslbo · weslbo · commit f0c348234215 · 2026-03-11T07:40:35.000+01:00
diff --git a/learn-pr/wwl-databricks/monitor-troubleshoot-optimize-workloads-azure-databricks/1-introduction.yml b/learn-pr/wwl-databricks/monitor-troubleshoot-optimize-workloads-azure-databricks/1-introduction.yml
@@ -4,7 +4,7 @@ title: Introduction
 metadata:
   title: Introduction
   description: "Introduction to monitoring, troubleshooting, and optimizing workloads in Azure Databricks"
-  ms.date: 12/07/2025
+  ms.date: 03/10/2026
   author: weslbo
   ms.author: wedebols
   ms.topic: unit
diff --git a/learn-pr/wwl-databricks/monitor-troubleshoot-optimize-workloads-azure-databricks/2-monitor-manage-cluster-consumption.yml b/learn-pr/wwl-databricks/monitor-troubleshoot-optimize-workloads-azure-databricks/2-monitor-manage-cluster-consumption.yml
@@ -4,7 +4,7 @@ title: Monitor and manage cluster consumption
 metadata:
   title: Monitor and Manage Cluster Consumption
   description: Learn how to monitor and manage cluster consumption in Azure Databricks to optimize performance and cost using metrics, auto-termination, autoscaling, and budgets.
-  ms.date: 12/07/2025
+  ms.date: 03/10/2026
   author: weslbo
   ms.author: wedebols
   ms.topic: unit
diff --git a/learn-pr/wwl-databricks/monitor-troubleshoot-optimize-workloads-azure-databricks/3-troubleshoot-repair-lakeflow-jobs.yml b/learn-pr/wwl-databricks/monitor-troubleshoot-optimize-workloads-azure-databricks/3-troubleshoot-repair-lakeflow-jobs.yml
@@ -4,7 +4,7 @@ title: Troubleshoot and repair Lakeflow Jobs
 metadata:
   title: Troubleshoot and Repair Lakeflow Jobs
   description: Learn how to troubleshoot, repair, restart, and stop Lakeflow Jobs in Azure Databricks to maintain reliable data pipelines.
-  ms.date: 12/07/2025
+  ms.date: 03/10/2026
   author: weslbo
   ms.author: wedebols
   ms.topic: unit
diff --git a/learn-pr/wwl-databricks/monitor-troubleshoot-optimize-workloads-azure-databricks/4-troubleshoot-repair-spark-jobs-notebooks.yml b/learn-pr/wwl-databricks/monitor-troubleshoot-optimize-workloads-azure-databricks/4-troubleshoot-repair-spark-jobs-notebooks.yml
@@ -4,7 +4,7 @@ title: Troubleshoot Spark jobs and notebooks
 metadata:
   title: Troubleshoot Spark Jobs and Notebooks
   description: Learn how to troubleshoot and repair Apache Spark jobs and notebooks in Azure Databricks, including diagnosing performance issues, resolving resource bottlenecks, and restarting clusters.
-  ms.date: 12/07/2025
+  ms.date: 03/10/2026
   author: weslbo
   ms.author: wedebols
   ms.topic: unit
diff --git a/learn-pr/wwl-databricks/monitor-troubleshoot-optimize-workloads-azure-databricks/5-resolve-cache-skew-spill-shuffle.yml b/learn-pr/wwl-databricks/monitor-troubleshoot-optimize-workloads-azure-databricks/5-resolve-cache-skew-spill-shuffle.yml
@@ -4,7 +4,7 @@ title: Investigate caching, skewing, spilling, shuffle
 metadata:
   title: Investigate Caching, Skewing, Spilling, Shuffle
   description: Learn how to investigate and resolve caching, skewing, spilling, and shuffle issues in Azure Databricks using the DAG, Spark UI, and query profile.
-  ms.date: 12/07/2025
+  ms.date: 03/10/2026
   author: weslbo
   ms.author: wedebols
   ms.topic: unit
diff --git a/learn-pr/wwl-databricks/monitor-troubleshoot-optimize-workloads-azure-databricks/6-implement-log-streaming-azure-analytics.yml b/learn-pr/wwl-databricks/monitor-troubleshoot-optimize-workloads-azure-databricks/6-implement-log-streaming-azure-analytics.yml
@@ -4,7 +4,7 @@ title: Implement log streaming with Azure Log Analytics
 metadata:
   title: Implement Log Streaming with Azure Log Analytics
   description: Learn how to implement log streaming from Azure Databricks to Azure Log Analytics for centralized monitoring, querying with KQL, and creating alerts for proactive troubleshooting.
-  ms.date: 12/07/2025
+  ms.date: 03/10/2026
   author: weslbo
   ms.author: wedebols
   ms.topic: unit
diff --git a/learn-pr/wwl-databricks/monitor-troubleshoot-optimize-workloads-azure-databricks/7-knowledge-check.yml b/learn-pr/wwl-databricks/monitor-troubleshoot-optimize-workloads-azure-databricks/7-knowledge-check.yml
@@ -4,7 +4,7 @@ title: Module assessment
 metadata:
   title: Module assessment
   description: "Knowledge check"
-  ms.date: 12/07/2025
+  ms.date: 03/10/2026
   author: weslbo
   ms.author: wedebols
   ms.topic: unit
diff --git a/learn-pr/wwl-databricks/monitor-troubleshoot-optimize-workloads-azure-databricks/8-summary.yml b/learn-pr/wwl-databricks/monitor-troubleshoot-optimize-workloads-azure-databricks/8-summary.yml
@@ -4,7 +4,7 @@ title: Summary
 metadata:
   title: Summary
   description: "Summary"
-  ms.date: 12/07/2025
+  ms.date: 03/10/2026
   author: weslbo
   ms.author: wedebols
   ms.topic: unit
diff --git a/learn-pr/wwl-databricks/monitor-troubleshoot-optimize-workloads-azure-databricks/includes/1-introduction.md b/learn-pr/wwl-databricks/monitor-troubleshoot-optimize-workloads-azure-databricks/includes/1-introduction.md
@@ -1,3 +1,5 @@
+>[!VIDEO https://learn-video.azurefd.net/vod/player?id=aafb4cc3-3e5a-4168-8e10-d78366db752c]
+
 Running data workloads in Azure Databricks at scale requires more than setting up clusters and writing pipelines—it demands continuous attention to resource consumption, job health, and performance optimization. When compute costs climb unexpectedly, jobs fail without clear explanations, or queries that once ran quickly now take hours, you need systematic approaches to identify problems and apply targeted fixes.
 
 Azure Databricks provides comprehensive tools for monitoring and troubleshooting across all compute types. **Cluster metrics** reveal CPU, memory, and network utilization patterns that help you right-size resources. **The Spark UI** exposes job execution details, stage-level metrics, and task distributions that pinpoint bottlenecks. **System tables** enable you to query billing data and usage patterns directly, supporting cost attribution and chargeback processes.
diff --git a/learn-pr/wwl-databricks/monitor-troubleshoot-optimize-workloads-azure-databricks/includes/2-monitor-manage-cluster-consumption.md b/learn-pr/wwl-databricks/monitor-troubleshoot-optimize-workloads-azure-databricks/includes/2-monitor-manage-cluster-consumption.md
@@ -1,3 +1,5 @@
+>[!VIDEO https://learn-video.azurefd.net/vod/player?id=56720b1a-acba-450c-b425-e96495ac48a9]
+
 When clusters run longer than needed or consume more resources than necessary, costs can escalate quickly. As a data engineer, you're responsible for **monitoring cluster usage** and taking action to balance **performance requirements** with **cost efficiency**.
 
 ## Understand the impact of cluster consumption
diff --git a/learn-pr/wwl-databricks/monitor-troubleshoot-optimize-workloads-azure-databricks/includes/3-troubleshoot-repair-lakeflow-jobs.md b/learn-pr/wwl-databricks/monitor-troubleshoot-optimize-workloads-azure-databricks/includes/3-troubleshoot-repair-lakeflow-jobs.md
@@ -1,3 +1,5 @@
+>[!VIDEO https://learn-video.azurefd.net/vod/player?id=0f312bb1-11ec-45f9-bd5a-d48888191353]
+
 When a Lakeflow Job fails, you must quickly identify the problem, understand the root cause, and take corrective action. Navigating failures efficiently keeps your pipelines running and your downstream consumers satisfied.
 
 ## Understand job failure states
diff --git a/learn-pr/wwl-databricks/monitor-troubleshoot-optimize-workloads-azure-databricks/includes/4-troubleshoot-repair-spark-jobs-notebooks.md b/learn-pr/wwl-databricks/monitor-troubleshoot-optimize-workloads-azure-databricks/includes/4-troubleshoot-repair-spark-jobs-notebooks.md
@@ -1,3 +1,5 @@
+>[!VIDEO https://learn-video.azurefd.net/vod/player?id=c416d267-6b68-41dd-8973-8caba17fd6da]
+
 When a Spark job fails or runs slower than expected, your ability to quickly diagnose and resolve the issue directly impacts business operations. As a data engineer, you need a systematic approach to investigate problems, identify root causes, and take corrective action.
 
 ## Understand common causes of Spark job failures
diff --git a/learn-pr/wwl-databricks/monitor-troubleshoot-optimize-workloads-azure-databricks/includes/5-resolve-cache-skew-spill-shuffle.md b/learn-pr/wwl-databricks/monitor-troubleshoot-optimize-workloads-azure-databricks/includes/5-resolve-cache-skew-spill-shuffle.md
@@ -1,3 +1,5 @@
+>[!VIDEO https://learn-video.azurefd.net/vod/player?id=098f1548-94fd-4a0c-940c-7c66d38544fb]
+
 A query that ran fine yesterday now takes three times longer. A job that processed millions of records suddenly fails with memory errors. These situations often trace back to four common performance issues: **caching problems**, **data skew**, **memory spill**, and **excessive shuffle**. Knowing how to investigate these issues using Azure Databricks diagnostic tools helps you restore performance quickly.
 
 ## Use diagnostic tools to identify issues
diff --git a/learn-pr/wwl-databricks/monitor-troubleshoot-optimize-workloads-azure-databricks/includes/6-implement-log-streaming-azure-analytics.md b/learn-pr/wwl-databricks/monitor-troubleshoot-optimize-workloads-azure-databricks/includes/6-implement-log-streaming-azure-analytics.md
@@ -1,3 +1,5 @@
+>[!VIDEO https://learn-video.azurefd.net/vod/player?id=7cf24ded-3fc2-4406-b4fe-922c259cfb02]
+
 When your Azure Databricks environment grows beyond a single workspace, tracking what happens across clusters, jobs, and users becomes a challenge. **Log streaming to Azure Log Analytics** solves this by centralizing all your diagnostic and audit logs in one place, enabling powerful queries, alerts, and dashboards.
 
 In this unit, you learn how log streaming works, how to query Databricks logs in Azure Log Analytics, and how to create alerts for proactive monitoring.
diff --git a/learn-pr/wwl-databricks/monitor-troubleshoot-optimize-workloads-azure-databricks/index.yml b/learn-pr/wwl-databricks/monitor-troubleshoot-optimize-workloads-azure-databricks/index.yml
@@ -3,7 +3,7 @@ uid: learn.wwl.monitor-troubleshoot-optimize-workloads-azure-databricks
 metadata:
   title: Monitor, Troubleshoot and Optimize Workloads in Azure Databricks
   description: Learn how to monitor cluster consumption, troubleshoot job failures, diagnose Spark performance issues, and implement log streaming to Azure Log Analytics for centralized monitoring of Azure Databricks workloads.
-  ms.date: 12/07/2025
+  ms.date: 03/10/2026
   author: weslbo
   ms.author: wedebols
   ms.topic: module

Original file line number	Diff line number	Diff line change
`@@ -1,3 +1,5 @@`
	`1`	`+>[!VIDEO https://learn-video.azurefd.net/vod/player?id=aafb4cc3-3e5a-4168-8e10-d78366db752c]`
	`2`	`+`
`1`	`3`	`Running data workloads in Azure Databricks at scale requires more than setting up clusters and writing pipelines—it demands continuous attention to resource consumption, job health, and performance optimization. When compute costs climb unexpectedly, jobs fail without clear explanations, or queries that once ran quickly now take hours, you need systematic approaches to identify problems and apply targeted fixes.`
`2`	`4`
`3`	`5`	`Azure Databricks provides comprehensive tools for monitoring and troubleshooting across all compute types. Cluster metrics reveal CPU, memory, and network utilization patterns that help you right-size resources. The Spark UI exposes job execution details, stage-level metrics, and task distributions that pinpoint bottlenecks. System tables enable you to query billing data and usage patterns directly, supporting cost attribution and chargeback processes.`
Original file line number	Diff line number	Diff line change
`@@ -1,3 +1,5 @@`
	`1`	`+>[!VIDEO https://learn-video.azurefd.net/vod/player?id=56720b1a-acba-450c-b425-e96495ac48a9]`
	`2`	`+`
`1`	`3`	`When clusters run longer than needed or consume more resources than necessary, costs can escalate quickly. As a data engineer, you're responsible for monitoring cluster usage and taking action to balance performance requirements with cost efficiency.`
`2`	`4`
`3`	`5`	`## Understand the impact of cluster consumption`