Merge pull request #115498 from djpmsft/uxRelease

Properties pane, consumption monitoring, non-equi join docs

Author: v-dihans

articles/data-factory/author-visually.md

@@ -9,7 +9,7 @@ author: djpmsft
 ms.author: daperlov
 ms.reviewer: 
 manager: anandsub
-ms.date: 12/19/2019
+ms.date: 05/15/2020
 ---
 
 # Visual authoring in Azure Data Factory
@@ -30,6 +30,14 @@ Here, you will author the pipelines, activities, datasets, linked services, data
 
 The default visual authoring experience is directly working with the Data Factory service. Azure Repos Git or GitHub integration is also supported to allow source control and collaboration for work on your data factory pipelines. To learn more about the differences between these authoring experiences, see [Source control in Azure Data Factory](source-control.md).
 
+### Properties pane
+
+For top-level resources such as pipelines, datasets, and data flows, high-level properties are editable in the properties pane on the right-hand side of the canvas. The properties pane contains properties such as name, description, annotations, and other high-level properties. Subresources such as pipeline activities and data flow transformations are edited using the panel at the bottom of the canvas. 
+
+![Authoring Canvas](media/author-visually/properties-pane.png)
+
+The properties pane will only be open by default on resource creation. To edit it, click on the properties pane icon located in the top-right corner of the canvas.
+
 ## Expressions and functions
 
 Expressions and functions can be used instead of static values to specify many properties in Azure Data Factory.

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

@@ -7,7 +7,7 @@ ms.reviewer: daperlov
 ms.service: data-factory
 ms.topic: conceptual
 ms.custom: seo-lt-2019
-ms.date: 01/02/2020
+ms.date:  05/15/2020
 ---
 
 # Join transformation in mapping data flow
@@ -58,6 +58,12 @@ If you would like to explicitly produce a full cartesian product, use the Derive
 
 ![Join Transformation](media/data-flow/join.png "Join")
 
+### Non-equi joins
+
+To use a conditional operator such as not equals (!=) or greater than (>) in your join conditions, change the operator dropdown between the two columns. Non-equi joins require at least one of the two streams to be broadcasted using **Fixed** broadcasting in the **Optimize** tab.
+
+![Non-equi join](media/data-flow/non-equi-join.png "Non-equi join")
+
 ## Optimizing join performance
 
 Unlike merge join in tools like SSIS, the join transformation isn't a mandatory merge join operation. The join keys don't require sorting. The join operation occurs based on the optimal join operation in Spark, either broadcast or map-side join.

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

@@ -7,7 +7,7 @@ ms.author: makromer
 ms.service: data-factory
 ms.topic: conceptual
 ms.custom: seo-lt-2019
-ms.date: 03/23/2020
+ms.date:  05/15/2020
 ---
 
 # Lookup transformation in mapping data flow
@@ -36,6 +36,12 @@ The lookup transformation only supports equality matches. To customize the looku
 
 All columns from both streams are included in the output data. To drop duplicate or unwanted columns, add a [select transformation](data-flow-select.md) after your lookup transformation. Columns can also be dropped or renamed in a sink transformation.
 
+### Non-equi joins
+
+To use a conditional operator such as not equals (!=) or greater than (>) in your lookup conditions, change the operator dropdown between the two columns. Non-equi joins require at least one of the two streams to be broadcasted using **Fixed** broadcasting in the **Optimize** tab.
+
+![Non-equi lookup](media/data-flow/non-equi-lookup.png "Non-equi lookup")
+
 ## Analyzing matched rows
 
 After your lookup transformation, the function `isMatch()` can be used to see if the lookup matched for individual rows.

articles/data-factory/media/author-visually/properties-pane.png

@@ -9,7 +9,7 @@ ms.reviewer: maghan
 ms.service: data-factory
 ms.workload: data-services
 ms.topic: conceptual
-ms.date: 11/19/2018
+ms.date: 05/15/2020
 ---
 
 # Visually monitor Azure Data Factory
@@ -127,6 +127,21 @@ You can also view rerun history for a particular pipeline run.
 
 ![View history for a pipeline run](media/monitor-visually/rerun-history-image2.png)
 
+## Monitor consumption
+
+You can see the resources consumed by a pipeline run by clicking the consumption icon next to the run. 
+
+![Monitor consumption](media/monitor-visually/monitor-consumption-1.png)
+
+Clicking the icon opens a consumption report of resources used by that pipeline run. 
+
+![Monitor consumption](media/monitor-visually/monitor-consumption-2.png)
+
+You can plug these values into the [Azure pricing calcula.hat pipeline run. For more information on Azure Data Factory pricing, see [Understanding pricing](pricing-concepts.md).
+
+> [!NOTE]
+> These values returned by the pricing calculator is an estimate. It doesn't reflect the exact amount you will be billed by Azure Data Factory 
+
 ## Gantt views
 
 Use Gantt views to quickly visualize your pipelines and activity runs.

articles/data-factory/media/data-flow/non-equi-join.png

@@ -176,7 +176,7 @@ When you are ready to merge the changes from your feature branch to your collabo
 
 ### Configure publishing settings
 
-By default, data factory generates the Resource Manager templates of the published factory and saves them into a branch called `adf_public`. To configure a custom publish branch, add a `publish_config.json` file to the root folder in the collaboration branch. When publishing, ADF reads this file, looks for the field `publishBranch`, and saves all Resource Manager templates to the specified location. If the branch doesn't exist, data factory will automatically create it. And example of what this file looks like is below:
+By default, data factory generates the Resource Manager templates of the published factory and saves them into a branch called `adf_publish`. To configure a custom publish branch, add a `publish_config.json` file to the root folder in the collaboration branch. When publishing, ADF reads this file, looks for the field `publishBranch`, and saves all Resource Manager templates to the specified location. If the branch doesn't exist, data factory will automatically create it. And example of what this file looks like is below:
 
 ```json
 {