Merge pull request #79027 from kromerm/dataflow-1

Dataflow 1

Author: ktoliver

articles/data-factory/TOC.yml

@@ -486,6 +486,8 @@
       href: control-flow-expression-language-functions.md
     - name: System variables
       href: control-flow-system-variables.md
+    - name: Data flow parameters
+      href: parameters-data-flow.md
   - name: Security
     items:
     - name: Data movement security considerations

articles/data-factory/concepts-data-flow-optimize-tab.md

@@ -1,15 +1,14 @@
 ---
 title: Azure Data Factory Mapping Data Flow Optimize Tab
-description: Optimize Azure Data Factory Mapping Data Flows with Optimize Tab Partition Settings
+description: Optimize Azure Data Factory Mapping Data Flows using the Optimize Tab with Partition Settings
 author: kromerm
 ms.author: makromer
-ms.reviewer: douglasl
 ms.service: data-factory
 ms.topic: conceptual
 ms.date: 01/31/2019
 ---
 
-# Mapping Data Flow Transformation Optimize Tab
+# Mapping data flow transformation optimize tab
 
 [!INCLUDE [notes](../../includes/data-factory-data-flow-preview.md)]
 
@@ -46,3 +45,8 @@ You must build an expression that provides a fixed range for values within your
 ### Key
 
 If you have a good understanding of the cardinality of your data, key partitioning may be a good partition strategy. Key partitioning will create partitions for each unique value in your column. You cannot set the number of partitions because the number will be based on unique values in the data.
+
+## Next steps
+
+[Mapping data flow performance guide](concepts-data-flow-performance.md)
+[Data flow monitoring](concepts-data-flow-monitoring.md)

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

@@ -36,7 +36,7 @@ Clicking that icon will display the execution plan and subsequent performance pr
 
 ## Optimizing for Azure SQL Database and Azure SQL Data Warehouse
 
-![Source Part](media/data-flow/sourcepart2.png "Source Part")
+![Source Part](media/data-flow/sourcepart3.png "Source Part")
 
 ### Partition your source data
 
@@ -117,8 +117,8 @@ Clicking that icon will display the execution plan and subsequent performance pr
 * To avoid exhausting compute node resources, you can keep the default or explicit partitioning scheme in ADF, which optimizes for performance, and then add a subsequent Copy Activity in the pipeline that merges all of the PART files from the output folder to a new single file. Essentially, this technique separates the action of transformation from file merging and achieves the same result as setting "output to single file".
 
 ## Next steps
-See the other Data Flow articles:
+See the other Data Flow articles related to performance:
 
-- [Data Flow overview](concepts-data-flow-overview.md)
+- [Data Flow Optimize Tab](concepts-data-flow-optimize-tab.md)
 - [Data Flow activity](control-flow-execute-data-flow-activity.md)
 - [Monitor Data Flow performance](concepts-data-flow-monitoring.md)

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

@@ -69,7 +69,7 @@ You have control over the Spark execution environment for your Data Flow activit
 
 ### Staging area
 
-If you are sinking your data into Azure Data Warehouse, you must choose a staging location for your Polybase batch load.
+If you are sinking your data into Azure Data Warehouse, you must choose a staging location for your Polybase batch load. The staging settings are only applicable to Azure Data Warehouse workloads.
 
 ## Parameterized datasets
 

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

@@ -60,13 +60,13 @@ You can later change the column names in a select transformation. Use a derived-
 
 On the **Optimize** tab for the source transformation, you might see a **Source** partition type. This option is available only when your source is Azure SQL Database. This is because Data Factory tries to make connections parallel to run large queries against your SQL Database source.
 
-![Source partition settings](media/data-flow/sourcepart2.png "partitioning")
+![Source partition settings](media/data-flow/sourcepart3.png "partitioning")
 
 You don't have to partition data on your SQL Database source, but partitions are useful for large queries. You can base your partition on a column or a query.
 
 ### Use a column to partition data
 
-From your source table, select a column to partition on. Also set the maximum number of connections.
+From your source table, select a column to partition on. Also set the number of partitions.
 
 ### Use a query to partition data