Merge pull request #107797 from kromerm/dataflow-1

Dataflow 1: Added dynamic content tutorial and sample

Author: v-albemi

articles/data-factory/control-flow-execute-data-flow-activity.md

@@ -8,7 +8,7 @@ ms.service: data-factory
 ms.workload: data-services
 ms.topic: conceptual
 ms.author: makromer
-ms.date: 01/02/2020
+ms.date: 03/16/2020
 ---
 
 # Data Flow activity in Azure Data Factory
@@ -58,6 +58,14 @@ staging.folderPath | If you're using a SQL DW source or sink, the folder path in
 
 ![Execute Data Flow](media/data-flow/activity-data-flow.png "Execute Data Flow")
 
+### Dynamically size data flow compute at runtime
+
+The Core Count and Compute Type properties can be set dynamically to adjust to the size of your incoming source data at runtime. Use pipeline activities like Lookup or Get Metadata in order to find the size of the source dataset data. Then, use Add Dynamic Content in the Data Flow activity properties.
+
+![Dynamic Data Flow](media/data-flow/dyna1.png "Dynamic data flow")
+
+[Here is a brief video tutorial explaining this technique](https://www.youtube.com/watch?v=jWSkJdtiJNM)
+
 ### Data Flow integration runtime
 
 Choose which Integration Runtime to use for your Data Flow activity execution. By default, Data Factory will use the auto-resolve Azure Integration runtime with four worker cores and no time to live (TTL). This IR has a general purpose compute type and runs in the same region as your factory. You can create your own Azure Integration Runtimes that define specific regions, compute type, core counts, and TTL for your data flow activity execution.

articles/data-factory/data-flow-tutorials.md

@@ -102,6 +102,8 @@ As updates are constantly made to the product, some features have added or diffe
 
 [Logging and auditing](https://www.youtube.com/watch?v=UC__LOyuf8M)
 
+[Dynamically optimize data flow cluster size at runtime](https://www.youtube.com/watch?v=jWSkJdtiJNM)
+
 ## Mapping data flow scenarios
 
 [Fuzzy lookups](http://youtu.be/7gdwExjHBbw)