Fixes for PR review

Author: jonburchel

articles/data-factory/frequently-asked-questions.yml

@@ -49,7 +49,7 @@ sections:
               - Looping containers:
                   * The foreach activity will iterate over a specified collection of activities in a loop. 
           - Trigger-based flows:
-              - Pipelines can be triggered on demand, by wall-clock time, or in response to driven by event grid topics
+              - Pipelines can be triggered on demand, by wall-clock time, or in response to driven by Event Grid topics
           - Delta flows:
               - Parameters can be used to define your high-water mark for delta copy while moving dimension or reference tables from a relational store, either on-premises or in the cloud, to load the data into the lake.
           
@@ -234,7 +234,7 @@ sections:
           ### I need help troubleshooting my data flow logic. What info do I need to provide to get help?
           
           When Microsoft provides help or troubleshooting with data flows, please provide the ADF pipeline support files.
-          This Zip file contains the code-behind script from your data flow graph. From the ADF UI, click **...** next to pipeline, and then click **Download support files**.
+          This Zip file contains the code-behind script from your data flow graph. From the ADF UI, select **...** next to pipeline, and then select **Download support files**.
           
           ### How do I access data by using the other 90 dataset types in Data Factory?
           
@@ -244,23 +244,23 @@ sections:
           
           ### Is the self-hosted integration runtime available for data flows?
           
-          Self-hosted IR is an ADF pipeline construct that you can use with the Copy Activity to acquire or move data to and from on-prem or VM-based data sources and sinks. The virtual machines that you use for a self-hosted IR can also be placed inside of the same VNET as your protected data stores for access to those data stores from ADF. With data flows, you'll achieve these same end-results using the Azure IR with managed VNET instead.
+          Self-hosted IR is an ADF pipeline construct that you can use with the Copy Activity to acquire or move data to and from on-premises or VM-based data sources and sinks. The virtual machines that you use for a self-hosted IR can also be placed inside of the same VNET as your protected data stores for access to those data stores from ADF. With data flows, you'll achieve these same end-results using the Azure IR with managed VNET instead.
           
           ### Does the data flow compute engine serve multiple tenants?
           
           Clusters are never shared. We guarantee isolation for each job run in production runs. In case of debug scenario one person gets one cluster, and all debugs will go to that cluster which are initiated by that user.
           
-          ### Is there a way to write attributes in cosmos db in the same order as specified in the sink in ADF data flow?	
+          ### Is there a way to write attributes in Cosmos DB in the same order as specified in the sink in ADF data flow?	
           
-          For cosmos DB, the underlying format of each document is a JSON object which is an unordered set of name/value pairs, so the order cannot be reserved. 
+          For Cosmos DB, the underlying format of each document is a JSON object which is an unordered set of name/value pairs, so the order cannot be reserved. 
           
           ### Why a user is unable to use data preview in the data flows?	
           
           You should check permissions for custom role. There are multiple actions involved in the dataflow data preview. You start by checking network traffic while debugging on your browser. Please follow all of the actions, for details, please refer to  [Resource provider.](../role-based-access-control/resource-provider-operations.md#microsoftdatafactory)
           
           ### In ADF, can I calculate value for a new column from existing column from mapping?	
           
-          You can use derive transformation in mapping data flow to create a new column on the logic you want. When creating a derived column, you can either generate a new column or update an existing one. In the Column textbox, enter in the column you are creating. To override an existing column in your schema, you can use the column dropdown. To build the derived column's expression, click on the Enter expression textbox. You can either start typing your expression or open up the expression builder to construct your logic.
+          You can use derive transformation in mapping data flow to create a new column on the logic you want. When creating a derived column, you can either generate a new column or update an existing one. In the Column textbox, enter in the column you are creating. To override an existing column in your schema, you can use the column dropdown. To build the derived column's expression, select on the Enter expression textbox. You can either start typing your expression or open up the expression builder to construct your logic.
           
           ### Why mapping data flow preview failing with Gateway timeout?	
           
@@ -283,7 +283,7 @@ sections:
           Data factory is available in following [regions.](https://azure.microsoft.com/global-infrastructure/services/?products=data-factory)
           The Power Query feature is available in all data flow regions. If the feature is not available in your region, please check with support.
                          
-          ### What is the difference between mapping data flow and Power query actvity (data wrangling)?
+          ### What is the difference between mapping data flow and Power query activity (data wrangling)?
           
           Mapping data flows provide a way to transform data at scale without any coding required. You can design a data transformation job in the data flow canvas by constructing a series of transformations. Start with any number of source transformations followed by data transformation steps. Complete your data flow with a sink to land your results in a destination. Mapping data flow is great at mapping and transforming data with both known and unknown schemas in the sinks and sources.
           

articles/data-factory/plan-manage-costs.md

@@ -145,7 +145,7 @@ Budgets can be created with filters for specific resources or services in Azure
 
 ## Export cost data
 
-You can also [export your cost data](../cost-management-billing/costs/tutorial-export-acm-data.md?WT.mc_id=costmanagementcontent_docsacmhorizontal_-inproduct-learn) to a storage account. This is helpful when you need or others to do additional data analysis for costs. For example, a finance teams can analyze the data using Excel or Power BI. You can export your costs on a daily, weekly, or monthly schedule and set a custom date range. Exporting cost data is the recommended way to retrieve cost datasets.
+You can also [export your cost data](../cost-management-billing/costs/tutorial-export-acm-data.md?WT.mc_id=costmanagementcontent_docsacmhorizontal_-inproduct-learn) to a storage account. This is helpful when you need or others to do additional data analysis for costs. For example, finance teams can analyze the data using Excel or Power BI. You can export your costs on a daily, weekly, or monthly schedule and set a custom date range. Exporting cost data is the recommended way to retrieve cost datasets.
 
 ## Next steps
 

articles/data-factory/pricing-examples-copy-transform-azure-databricks.md

@@ -24,11 +24,11 @@ Refer to the [Azure Pricing Calculator](https://azure.microsoft.com/pricing/calc
 
 To accomplish the scenario, you need to create a pipeline with the following items:
 
-1. One copy activity with an input dataset for the data to be copied from AWS S3, and an output dataset for the data on Azure storage.
-2. One Azure Databricks activity for the data transformation.
-3. One schedule trigger to execute the pipeline every hour. When you want to run a pipeline, you can either [trigger it immediately or schedule it](concepts-pipeline-execution-triggers.md). In addition to the pipeline itself, each trigger instance counts as a single Activity run.
+- One copy activity with an input dataset for the data to be copied from AWS S3, and an output dataset for the data on Azure storage.
+- One Azure Databricks activity for the data transformation.
+- One schedule trigger to execute the pipeline every hour. When you want to run a pipeline, you can either [trigger it immediately or schedule it](concepts-pipeline-execution-triggers.md). In addition to the pipeline itself, each trigger instance counts as a single Activity run.
 
-:::image type="content" source="media/pricing-concepts/scenario2.png" alt-text="Diagram shows a pipeline with a schedule trigger. In the pipeline, copy activity flows to an input dataset, an output dataset, and a DataBricks activity, which runs on Azure Databricks. The input dataset flows to an A W S S3 linked service. The output dataset flows to an Azure Storage linked service.":::
+:::image type="content" source="media/pricing-concepts/scenario2.png" alt-text="Diagram shows a pipeline with a schedule trigger. In the pipeline, copy activity flows to an input dataset, an output dataset, and a DataBricks activity, which runs on Azure Databricks. The input dataset flows to an AWS S3 linked service. The output dataset flows to an Azure Storage linked service.":::
 
 ## Costs estimation
 
@@ -42,9 +42,9 @@ To accomplish the scenario, you need to create a pipeline with the following ite
 
 **Total scenario pricing for 30 days: $122.03**
 
-:::image type="content" source="media/pricing-concepts/scenario-2-pricing-calculator.png" alt-text="Screenshot of the pricing calculator configured for a copy data and transform with Azure Databricks scenario.":::
+:::image type="content" source="media/pricing-concepts/scenario-2-pricing-calculator.png" alt-text="Screenshot of the pricing calculator configured for a copy data and transform with Azure Databricks scenario." lightbox="media/pricing-concepts/scenario-2-pricing-calculator.png":::
 
-## Next Steps
+## Next steps
 
 - [Pricing example: Copy data from AWS S3 to Azure Blob storage hourly for 30 days](pricing-examples-s3-to-blob.md)
 - [Pricing example: Copy data and transform with dynamic parameters hourly for 30 days](pricing-examples-copy-transform-dynamic-parameters.md)

articles/data-factory/pricing-examples-copy-transform-dynamic-parameters.md

@@ -24,12 +24,12 @@ Refer to the [Azure Pricing Calculator](https://azure.microsoft.com/pricing/calc
 
 To accomplish the scenario, you need to create a pipeline with the following items:
 
-1. One copy activity with an input dataset for the data to be copied from AWS S3, an output dataset for the data on Azure storage.
-2. One Lookup activity for passing parameters dynamically to the transformation script.
-3. One Azure Databricks activity for the data transformation.
-4. One schedule trigger to execute the pipeline every hour.  When you want to run a pipeline, you can either [trigger it immediately or schedule it](concepts-pipeline-execution-triggers.md). In addition to the pipeline itself, each trigger instance counts as a single Activity run.
+- One copy activity with an input dataset for the data to be copied from AWS S3, an output dataset for the data on Azure storage.
+- One Lookup activity for passing parameters dynamically to the transformation script.
+- One Azure Databricks activity for the data transformation.
+- One schedule trigger to execute the pipeline every hour.  When you want to run a pipeline, you can either [trigger it immediately or schedule it](concepts-pipeline-execution-triggers.md). In addition to the pipeline itself, each trigger instance counts as a single Activity run.
 
-:::image type="content" source="media/pricing-concepts/scenario3.png" alt-text="Diagram shows a pipeline with a schedule trigger. In the pipeline, copy activity flows to an input dataset, an output dataset, and lookup activity that flows to a DataBricks activity, which runs on Azure Databricks. The input dataset flows to an A W S S3 linked service. The output dataset flows to an Azure Storage linked service.":::
+:::image type="content" source="media/pricing-concepts/scenario3.png" alt-text="Diagram shows a pipeline with a schedule trigger. In the pipeline, copy activity flows to an input dataset, an output dataset, and lookup activity that flows to a DataBricks activity, which runs on Azure Databricks. The input dataset flows to an AWS S3 linked service. The output dataset flows to an Azure Storage linked service.":::
 
 ## Costs estimation
 
@@ -44,9 +44,9 @@ To accomplish the scenario, you need to create a pipeline with the following ite
 
 **Total scenario pricing for 30 days: $122.09**
 
-:::image type="content" source="media/pricing-concepts/scenario-3-pricing-calculator.png" alt-text="Screenshot of the pricing calculator configured for a copy data and transform with dynamic parameters scenario.":::
+:::image type="content" source="media/pricing-concepts/scenario-3-pricing-calculator.png" alt-text="Screenshot of the pricing calculator configured for a copy data and transform with dynamic parameters scenario." lightbox="media/pricing-concepts/scenario-3-pricing-calculator.png":::
 
-## Next Steps
+## Next steps
 
 - [Pricing example: Copy data from AWS S3 to Azure Blob storage hourly for 30 days](pricing-examples-s3-to-blob.md)
 - [Pricing example: Copy data and transform with Azure Databricks hourly for 30 days](pricing-examples-copy-transform-azure-databricks.md)

articles/data-factory/pricing-examples-data-integration-managed-vnet.md

@@ -41,9 +41,9 @@ To accomplish the scenario, you need to create two pipelines with the following
 
 **Total scenario pricing for 30 days: $129.02**
 
-:::image type="content" source="media/pricing-concepts/scenario-5-pricing-calculator.png" alt-text="Screenshot of the pricing calculator configured for data integration with Managed VNET.":::
+:::image type="content" source="media/pricing-concepts/scenario-5-pricing-calculator.png" alt-text="Screenshot of the pricing calculator configured for data integration with Managed VNET." lightbox="media/pricing-concepts/scenario-5-pricing-calculator.png":::
 
-## Next Steps
+## Next steps
 
 - [Pricing example: Copy data from AWS S3 to Azure Blob storage hourly for 30 days](pricing-examples-s3-to-blob.md)
 - [Pricing example: Copy data and transform with Azure Databricks hourly for 30 days](pricing-examples-copy-transform-azure-databricks.md)

articles/data-factory/pricing-examples-get-delta-data-from-sap-ecc.md

@@ -30,7 +30,7 @@ To accomplish the scenario, you need to create a pipeline with the following ite
 
 ## Costs estimation
 
-In order to load data from SAP ECC via SAP CDC connector in Mapping Data Flow, you need to install your Self-Hosted Integration Runtime on an on-premise machine, or VM to directly connect to your SAP ECC system. Given that, you'll be charged on both Self-Hosted Integration Runtime with $0.10/hour and Mapping Data Flow with its vCore-hour price unit.  
+In order to load data from SAP ECC via SAP CDC connector in Mapping Data Flow, you need to install your Self-Hosted Integration Runtime on an on-premises machine, or VM to directly connect to your SAP ECC system. Given that, you'll be charged on both Self-Hosted Integration Runtime with $0.10/hour and Mapping Data Flow with its vCore-hour price unit.  
 
 Assuming every time it requires 15 minutes to complete the job, the cost estimations are as below. 
 
@@ -44,9 +44,9 @@ Assuming every time it requires 15 minutes to complete the job, the cost estimat
 
 **Total scenario pricing for 30 days: $17.21**
 
-:::image type="content" source="media/pricing-concepts/scenario-6-pricing-calculator.png" alt-text="Screenshot of the pricing calculator configured for getting delta data from SAP ECC via SAP CDC in mapping data flows.":::
+:::image type="content" source="media/pricing-concepts/scenario-6-pricing-calculator.png" alt-text="Screenshot of the pricing calculator configured for getting delta data from SAP ECC via SAP CDC in mapping data flows." lightbox="media/pricing-concepts/scenario-6-pricing-calculator.png":::
 
-## Next Steps
+## Next steps
 
 - [Pricing example: Copy data from AWS S3 to Azure Blob storage hourly for 30 days](pricing-examples-s3-to-blob.md)
 - [Pricing example: Copy data and transform with Azure Databricks hourly for 30 days](pricing-examples-copy-transform-azure-databricks.md)

articles/data-factory/pricing-examples-mapping-data-flow-debug-workday.md

@@ -26,7 +26,7 @@ A data factory engineer is responsible for designing, building, and testing mapp
 
 **8 (hours) x 8 (compute-optimized cores) x $0.193 = $12.35**
 
-## Next Steps
+## Next steps
 
 - [Pricing example: Copy data from AWS S3 to Azure Blob storage hourly for 30 days](pricing-examples-s3-to-blob.md)
 - [Pricing example: Copy data and transform with Azure Databricks hourly for 30 days](pricing-examples-copy-transform-azure-databricks.md)

articles/data-factory/pricing-examples-s3-to-blob.md

@@ -24,9 +24,8 @@ Refer to the [Azure Pricing Calculator](https://azure.microsoft.com/pricing/calc
 
 To accomplish the scenario, you need to create a pipeline with the following items:
 
-1. I'll copy data from AWS S3 to Azure Blob storage, and this will move 10 GB of data from S3 to blob storage. I estimate it will run for 2-3 hours, and I plan to set DIU as Auto.
-
-3. A schedule trigger to execute the pipeline every hour for 8 hours every day. When you want to run a pipeline, you can either [trigger it immediately or schedule it](concepts-pipeline-execution-triggers.md). In addition to the pipeline itself, each trigger instance counts as a single Activity run.
+- I'll copy data from AWS S3 to Azure Blob storage, and this will move 10 GB of data from S3 to blob storage. I estimate it will run for 2-3 hours, and I plan to set DIU as Auto.
+- A schedule trigger to execute the pipeline every hour for 8 hours every day. When you want to run a pipeline, you can either [trigger it immediately or schedule it](concepts-pipeline-execution-triggers.md). In addition to the pipeline itself, each trigger instance counts as a single Activity run.
 
    :::image type="content" source="media/pricing-concepts/scenario1.png" alt-text="Diagram shows a pipeline with a schedule trigger.":::
 
@@ -42,9 +41,9 @@ To accomplish the scenario, you need to create a pipeline with the following ite
 
 **Total scenario pricing for 30 days: $122.00**
 
-:::image type="content" source="media/pricing-concepts/scenario-1-pricing-calculator.png" alt-text="Screenshot of the pricing calculator configured for an hourly pipeline run.":::
+:::image type="content" source="media/pricing-concepts/scenario-1-pricing-calculator.png" alt-text="Screenshot of the pricing calculator configured for an hourly pipeline run." lightbox="media/pricing-concepts/scenario-1-pricing-calculator.png":::
 
-## Next Steps
+## Next steps
 
 - [Pricing example: Copy data and transform with Azure Databricks hourly for 30 days](pricing-examples-copy-transform-azure-databricks.md)
 - [Pricing example: Copy data and transform with dynamic parameters hourly for 30 days](pricing-examples-copy-transform-dynamic-parameters.md)