Merge pull request #78519 from kromerm/dataflow-1

Update concepts-data-flow-performance.md

Author: PRMerger12

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

@@ -38,11 +38,14 @@ Clicking that icon will display the execution plan and subsequent performance pr
 
 ![Source Part](media/data-flow/sourcepart2.png "Source Part")
 
-### You can match Spark data partitioning to your source database partitioning based on a database table column key in the source transformation
+### Partition your source data
 
 * Go to "Optimize" and select "Source". Set either a specific table column or a type in a query.
 * If you chose "column", then pick the partition column.
 * Also, set the maximum number of connections to your Azure SQL DB. You can try a higher setting to gain parallel connections to your database. However, some cases may result in faster performance with a limited number of connections.
+* Your source database tables do not need to be partitioned.
+* Setting a query in your Source transformation that matches the partitioning scheme of your database table will allow the source database engine to leverage partition elimination.
+* If your source is not already partitioned, ADF will still use data partitioning in the Spark transformation environment based on the key that you select in the Source transformation.
 
 ### Set batch size and query on source