Update concepts-data-flow-monitoring.md

Author: kromerm

articles/data-factory/concepts-data-flow-monitoring.md

@@ -7,7 +7,7 @@ ms.reviewer: douglasl
 ms.service: data-factory
 ms.topic: conceptual
 ms.custom: seo-lt-2019
-ms.date: 10/07/2019
+ms.date: 04/17/2020
 ---
 
 # Monitor Data Flows
@@ -28,6 +28,10 @@ When you are in the graphical node monitoring view, you will see a simplified vi
 
 ![Data Flow Monitoring](media/data-flow/mon003.png "Data Flow Monitoring")
 
+Here is a video overview of monitoring performance of your data flows from the ADF monitoring screen:
+
+> [!VIDEO https://www.microsoft.com/en-us/videoplayer/embed/RE4u4mH]
+
 ## View Data Flow Execution Plans
 
 When your Data Flow is executed in Spark, Azure Data Factory determines optimal code paths based on the entirety of your data flow. Additionally, the execution paths may occur on different scale-out nodes and data partitions. Therefore, the monitoring graph represents the design of your flow, taking into account the execution path of your transformations. When you click on individual nodes, you will see "groupings" that represent code that was executed together on the cluster. The timings and counts that you see represent those groups as opposed to the individual steps in your design.