Merge pull request #92129 from kromerm/dataflow-1

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

Author: PRMerger16

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

@@ -11,8 +11,6 @@ ms.date: 09/12/2019
 
 # Schema drift in mapping data flow
 
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 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.
 
 To protect against schema drift, it's important to have the facilities in a data flow tool to allow you, as a Data Engineer, to:
@@ -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.
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 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")