Update concepts-data-flow-schema-drift.md

Author: kromerm

articles/data-factory/concepts-data-flow-schema-drift.md

@@ -1,5 +1,5 @@
 ---
-title: Schema drift in Mapping Data Flow | Azure Data Factory
+title: Schema drift in mapping data flow | Azure Data Factory
 description: Build resilient Data Flows in Azure Data Factory with Schema Drift
 author: kromerm
 ms.author: makromer
@@ -9,9 +9,7 @@ ms.topic: conceptual
 ms.date: 09/12/2019
 ---
 
-# Schema drift in Mapping Data Flow
-
-
+# Schema drift in mapping data flow
 
 Schema drift is the case where your sources often change metadata. Fields, columns, and, types can be added, removed, or changed on the fly. Without handling for schema drift, your data flow becomes vulnerable to upstream data source changes. Typical ETL patterns fail when incoming columns and fields change because they tend to be tied to those source names.
 
@@ -27,6 +25,8 @@ You need to make an architectural decision in your data flow to accept schema dr
 
 ## Schema drift in source
 
+Columns coming into your data flow from your source definition are defined as "drifted" when they are not present in your source projection. You can view your source projection from the projection tab in the source transformation. When you select a dataset for your source, ADF will automatically take the schema from the dataset and create a project from that dataset schema definition.
+
 In a source transformation, schema drift is defined as reading columns that aren't defined your dataset schema. To enable schema drift, check **Allow schema drift** in your source transformation.
 
 ![Schema drift source](media/data-flow/schemadrift001.png "Schema drift source")