Merge pull request #111560 from julieMSFT/20200414_datafactoryfixes

GitHubber17 · web-flow · commit d0756ab1a5eb · 2020-04-16T19:21:26.000-07:00
updates to applies to for data factory
diff --git a/articles/data-factory/TOC.yml b/articles/data-factory/TOC.yml
@@ -553,8 +553,6 @@
       href: data-factory-service-identity.md
   - name: Monitor and manage
     items:
-    - name: Monitor visually
-      href: monitor-visually.md
     - name: Monitor with Azure Monitor
       href: monitor-using-azure-monitor.md
     - name: Monitor with SDKs
diff --git a/articles/data-factory/concepts-data-flow-column-pattern.md b/articles/data-factory/concepts-data-flow-column-pattern.md
@@ -11,7 +11,7 @@ ms.date: 10/21/2019
 
 # Using column patterns in mapping data flow
 
-[!INCLUDE[appliesto-adf-xxx-md](includes/appliesto-adf-xxx-md.md)]
+[!INCLUDE[appliesto-adf-asa-md](includes/appliesto-adf-asa-md.md)]
 
 Several mapping data flow transformations allow you to reference template columns based on patterns instead of hard-coded column names. This matching is known as *column patterns*. You can define patterns to match columns based on name, data type, stream, or position instead of requiring exact field names. There are two scenarios where column patterns are useful:
 
diff --git a/articles/data-factory/concepts-data-flow-debug-mode.md b/articles/data-factory/concepts-data-flow-debug-mode.md
@@ -12,7 +12,7 @@ ms.date: 09/06/2019
 
 # Mapping data flow Debug Mode
 
-[!INCLUDE[appliesto-adf-xxx-md](includes/appliesto-adf-xxx-md.md)]
+[!INCLUDE[appliesto-adf-asa-md](includes/appliesto-adf-asa-md.md)]
 
 ## Overview
 
diff --git a/articles/data-factory/concepts-data-flow-expression-builder.md b/articles/data-factory/concepts-data-flow-expression-builder.md
@@ -11,7 +11,7 @@ ms.date: 04/08/2020
 
 # Build expressions in mapping data flow
 
-[!INCLUDE[appliesto-adf-xxx-md](includes/appliesto-adf-xxx-md.md)]
+[!INCLUDE[appliesto-adf-asa-md](includes/appliesto-adf-asa-md.md)]
 
 In mapping data flow, many transformation properties are entered as expressions. These expressions are composed of column values, parameters, functions, operators, and literals that evaluate to a Spark data type at run time.
 
diff --git a/articles/data-factory/concepts-data-flow-manage-graph.md b/articles/data-factory/concepts-data-flow-manage-graph.md
@@ -11,7 +11,7 @@ ms.date: 11/04/2019
 
 # Mapping data flow graphs
 
-[!INCLUDE[appliesto-adf-xxx-md](includes/appliesto-adf-xxx-md.md)]
+[!INCLUDE[appliesto-adf-asa-md](includes/appliesto-adf-asa-md.md)]
 
 The mapping data flows design surface is a "construction" surface where you build data flows top-down, left-to-right. There is a toolbox attached to each transform with a plus (+) symbol. Concentrate on your business logic instead of connecting nodes via edges in a free-form DAG environment.
 
diff --git a/articles/data-factory/concepts-data-flow-monitoring.md b/articles/data-factory/concepts-data-flow-monitoring.md
@@ -12,7 +12,7 @@ ms.date: 10/07/2019
 
 # Monitor Data Flows
 
-[!INCLUDE[appliesto-adf-xxx-md](includes/appliesto-adf-xxx-md.md)]
+[!INCLUDE[appliesto-adf-asa-md](includes/appliesto-adf-asa-md.md)]
 
 After you have completed building and debugging your data flow, you will want to schedule your data flow to execute on a schedule within the context of a pipeline. You can schedule the pipeline from Azure Data Factory using Triggers. Or you can use the Trigger Now option from the Azure Data Factory Pipeline Builder to execute a single-run execution to test your data flow within the pipeline context.
 
diff --git a/articles/data-factory/concepts-data-flow-overview.md b/articles/data-factory/concepts-data-flow-overview.md
@@ -12,7 +12,7 @@ ms.date: 01/28/2020
 
 # What are mapping data flows?
 
-[!INCLUDE[appliesto-adf-xxx-md](includes/appliesto-adf-xxx-md.md)]
+[!INCLUDE[appliesto-adf-asa-md](includes/appliesto-adf-asa-md.md)]
 
 Mapping data flows are visually designed data transformations in Azure Data Factory. Data flows allow data engineers to develop graphical data transformation logic without writing code. The resulting data flows are executed as activities within Azure Data Factory pipelines that use scaled-out Apache Spark clusters. Data flow activities can be engaged via existing Data Factory scheduling, control, flow, and monitoring capabilities.
 
diff --git a/articles/data-factory/concepts-data-flow-performance.md b/articles/data-factory/concepts-data-flow-performance.md
@@ -11,7 +11,7 @@ ms.date: 04/14/2020
 
 # Mapping data flows performance and tuning guide
 
-[!INCLUDE[appliesto-adf-xxx-md](includes/appliesto-adf-xxx-md.md)]
+[!INCLUDE[appliesto-adf-asa-md](includes/appliesto-adf-asa-md.md)]
 
 Mapping Data Flows in Azure Data Factory provide a code-free interface to design, deploy, and orchestrate data transformations at scale. If you're not familiar with mapping data flows, see the [Mapping Data Flow Overview](concepts-data-flow-overview.md).
 
diff --git a/articles/data-factory/concepts-data-flow-schema-drift.md b/articles/data-factory/concepts-data-flow-schema-drift.md
@@ -12,7 +12,7 @@ ms.date: 04/15/2020
 
 # Schema drift in mapping data flow
 
-[!INCLUDE[appliesto-adf-xxx-md](includes/appliesto-adf-xxx-md.md)]
+[!INCLUDE[appliesto-adf-asa-md](includes/appliesto-adf-asa-md.md)]
 
 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.
 
diff --git a/articles/data-factory/configure-bcdr-azure-ssis-integration-runtime.md b/articles/data-factory/configure-bcdr-azure-ssis-integration-runtime.md
@@ -16,7 +16,7 @@ ms.date: 04/09/2020
 
 # Configure the Azure-SSIS Integration Runtime with Azure SQL Database geo-replication and failover
 
-[!INCLUDE[appliesto-adf-xxx-md](includes/appliesto-adf-xxx-md.md)]
+[!INCLUDE[appliesto-adf-asa-md](includes/appliesto-adf-asa-md.md)]
 
 This article describes how to configure the Azure-SSIS Integration Runtime with Azure SQL Database geo-replication for the SSISDB database. When a failover occurs, you can ensure that the Azure-SSIS IR keeps working with the secondary database.
 
diff --git a/articles/data-factory/continuous-integration-deployment.md b/articles/data-factory/continuous-integration-deployment.md
@@ -15,7 +15,7 @@ ms.date: 02/12/2020
 
 # Continuous integration and delivery in Azure Data Factory
 
-[!INCLUDE[appliesto-adf-asa-md](includes/appliesto-adf-asa-md.md)]
+[!INCLUDE[appliesto-adf-xxx-md](includes/appliesto-adf-xxx-md.md)]
 
 ## Overview
 
diff --git a/articles/data-factory/data-flow-aggregate.md b/articles/data-factory/data-flow-aggregate.md
@@ -12,7 +12,7 @@ ms.date: 03/24/2020
 
 # Aggregate transformation in mapping data flow
 
-[!INCLUDE[appliesto-adf-xxx-md](includes/appliesto-adf-xxx-md.md)]
+[!INCLUDE[appliesto-adf-asa-md](includes/appliesto-adf-asa-md.md)]
 
 The Aggregate transformation defines aggregations of columns in your data streams. Using the Expression Builder, you can define different types of aggregations such as SUM, MIN, MAX, and COUNT grouped by existing or computed columns.
 
diff --git a/articles/data-factory/data-flow-alter-row.md b/articles/data-factory/data-flow-alter-row.md
@@ -12,7 +12,7 @@ ms.date: 01/08/2020
 
 # Alter row transformation in mapping data flow
 
-[!INCLUDE[appliesto-adf-xxx-md](includes/appliesto-adf-xxx-md.md)]
+[!INCLUDE[appliesto-adf-asa-md](includes/appliesto-adf-asa-md.md)]
 
 Use the Alter Row transformation to set insert, delete, update, and upsert policies on rows. You can add one-to-many conditions as expressions. These conditions should be specified in order of priority, as each row will be marked with the policy corresponding to the first-matching expression. Each of those conditions can result in a row (or rows) being inserted, updated, deleted, or upserted. Alter Row can produce both DDL & DML actions against your database.
 
diff --git a/articles/data-factory/data-flow-conditional-split.md b/articles/data-factory/data-flow-conditional-split.md
@@ -12,7 +12,7 @@ ms.date: 10/16/2019
 
 # Conditional split transformation in mapping data flow
 
-[!INCLUDE[appliesto-adf-xxx-md](includes/appliesto-adf-xxx-md.md)]
+[!INCLUDE[appliesto-adf-asa-md](includes/appliesto-adf-asa-md.md)]
 
 The conditional split transformation routes data rows to different streams based on matching conditions. The conditional split transformation is similar to a CASE decision structure in a programming language. The transformation evaluates expressions, and based on the results, directs the data row to the specified stream.
 
diff --git a/articles/data-factory/data-flow-derived-column.md b/articles/data-factory/data-flow-derived-column.md
@@ -11,7 +11,7 @@ ms.date: 10/15/2019
 
 # Derived column transformation in mapping data flow
 
-[!INCLUDE[appliesto-adf-xxx-md](includes/appliesto-adf-xxx-md.md)]
+[!INCLUDE[appliesto-adf-asa-md](includes/appliesto-adf-asa-md.md)]
 
 Use the derived column transformation to generate new columns in your data flow or to modify existing fields.
 
diff --git a/articles/data-factory/data-flow-exists.md b/articles/data-factory/data-flow-exists.md
@@ -12,7 +12,7 @@ ms.date: 10/16/2019
 
 # Exists transformation in mapping data flow
 
-[!INCLUDE[appliesto-adf-xxx-md](includes/appliesto-adf-xxx-md.md)]
+[!INCLUDE[appliesto-adf-asa-md](includes/appliesto-adf-asa-md.md)]
 
 The exists transformation is a row filtering transformation that checks whether your data exists in another source or stream. The output stream includes all rows in the left stream that either exist or don't exist in the right stream. The exists transformation is similar to ```SQL WHERE EXISTS``` and ```SQL WHERE NOT EXISTS```.
 
diff --git a/articles/data-factory/data-flow-expression-functions.md b/articles/data-factory/data-flow-expression-functions.md
@@ -13,7 +13,7 @@ ms.date: 02/15/2019
 
 # Data transformation expressions in mapping data flow
 
-[!INCLUDE[appliesto-adf-xxx-md](includes/appliesto-adf-xxx-md.md)]
+[!INCLUDE[appliesto-adf-asa-md](includes/appliesto-adf-asa-md.md)]
 
 ## Expression functions
 
diff --git a/articles/data-factory/data-flow-filter.md b/articles/data-factory/data-flow-filter.md
@@ -12,7 +12,7 @@ ms.date: 10/16/2019
 
 # Filter transformation in mapping data flow
 
-[!INCLUDE[appliesto-adf-xxx-md](includes/appliesto-adf-xxx-md.md)]
+[!INCLUDE[appliesto-adf-asa-md](includes/appliesto-adf-asa-md.md)]
 
 The Filter transforms allows row filtering based upon a condition. The output stream includes all rows that matching the filtering condition. The filter transformation is similar to a WHERE clause in SQL.
 
diff --git a/articles/data-factory/data-flow-join.md b/articles/data-factory/data-flow-join.md
@@ -12,7 +12,7 @@ ms.date: 01/02/2020
 
 # Join transformation in mapping data flow
 
-[!INCLUDE[appliesto-adf-xxx-md](includes/appliesto-adf-xxx-md.md)]
+[!INCLUDE[appliesto-adf-asa-md](includes/appliesto-adf-asa-md.md)]
 
 Use the join transformation to combine data from two sources or streams in a mapping data flow. The output stream will include all columns from both sources matched based on a join condition. 
 
diff --git a/articles/data-factory/data-flow-lookup.md b/articles/data-factory/data-flow-lookup.md
@@ -12,7 +12,7 @@ ms.date: 03/23/2020
 
 # Lookup transformation in mapping data flow
 
-[!INCLUDE[appliesto-adf-xxx-md](includes/appliesto-adf-xxx-md.md)]
+[!INCLUDE[appliesto-adf-asa-md](includes/appliesto-adf-asa-md.md)]
 
 Use the lookup transformation to reference data from another source in a data flow stream. The lookup transformation appends columns from matched data to your source data.
 
diff --git a/articles/data-factory/data-flow-new-branch.md b/articles/data-factory/data-flow-new-branch.md
@@ -12,7 +12,7 @@ ms.date: 01/08/2020
 
 # Creating a new branch in mapping data flow
 
-[!INCLUDE[appliesto-adf-xxx-md](includes/appliesto-adf-xxx-md.md)]
+[!INCLUDE[appliesto-adf-asa-md](includes/appliesto-adf-asa-md.md)]
 
 Add a new branch to do multiple sets of operations and transformations against the same data stream. Adding a new branch is useful when you want to use the same source to for multiple sinks or for self-joining data together.
 
diff --git a/articles/data-factory/data-flow-pivot.md b/articles/data-factory/data-flow-pivot.md
@@ -11,7 +11,7 @@ ms.date: 01/30/2019
 
 # Azure data factory pivot transformation
 
-[!INCLUDE[appliesto-adf-xxx-md](includes/appliesto-adf-xxx-md.md)]
+[!INCLUDE[appliesto-adf-asa-md](includes/appliesto-adf-asa-md.md)]
 
 Use Pivot in ADF Data Flow as an aggregation where one or more grouping columns has its distinct row values transformed into individual columns. Essentially, you can Pivot row values into new columns (turn data into metadata).
 
diff --git a/articles/data-factory/data-flow-select.md b/articles/data-factory/data-flow-select.md
@@ -11,7 +11,7 @@ ms.date: 03/18/2020
 
 # Select transformation in mapping data flow
 
-[!INCLUDE[appliesto-adf-xxx-md](includes/appliesto-adf-xxx-md.md)]
+[!INCLUDE[appliesto-adf-asa-md](includes/appliesto-adf-asa-md.md)]
 
 Use the select transformation to rename, drop, or reorder columns. This transformation doesn't alter row data, but chooses which columns are propagated downstream. 
 
diff --git a/articles/data-factory/data-flow-sink.md b/articles/data-factory/data-flow-sink.md
@@ -13,7 +13,7 @@ ms.date: 12/12/2019
 
 # Sink transformation in mapping data flow
 
-[!INCLUDE[appliesto-adf-xxx-md](includes/appliesto-adf-xxx-md.md)]
+[!INCLUDE[appliesto-adf-asa-md](includes/appliesto-adf-asa-md.md)]
 
 After you transform your data, you can sink the data into a destination dataset. Every data flow requires at least one sink transformation, but you can write to as many sinks as necessary to complete your transformation flow. To write to additional sinks, create new streams via new branches and conditional splits.
 
diff --git a/articles/data-factory/data-flow-sort.md b/articles/data-factory/data-flow-sort.md
@@ -12,7 +12,7 @@ ms.date: 04/14/2020
 
 # Sort transformation in mapping data flow
 
-[!INCLUDE[appliesto-adf-xxx-md](includes/appliesto-adf-xxx-md.md)]
+[!INCLUDE[appliesto-adf-asa-md](includes/appliesto-adf-asa-md.md)]
 
 The sort transformation allows you to sort the incoming rows on the current data stream. You can choose individual columns and sort them in ascending or descending order.
 
diff --git a/articles/data-factory/data-flow-source.md b/articles/data-factory/data-flow-source.md
@@ -12,7 +12,7 @@ ms.date: 12/12/2019
 
 # Source transformation in mapping data flow 
 
-[!INCLUDE[appliesto-adf-xxx-md](includes/appliesto-adf-xxx-md.md)]
+[!INCLUDE[appliesto-adf-asa-md](includes/appliesto-adf-asa-md.md)]
 
 A source transformation configures your data source for the data flow. When designing data flows, your first step will always be configuring a source transformation. To add a source, click on the **Add Source** box in the data flow canvas.
 
diff --git a/articles/data-factory/data-flow-surrogate-key.md b/articles/data-factory/data-flow-surrogate-key.md
@@ -12,7 +12,7 @@ ms.date: 04/08/2020
 
 # Surrogate key transformation in mapping data flow 
 
-[!INCLUDE[appliesto-adf-xxx-md](includes/appliesto-adf-xxx-md.md)]
+[!INCLUDE[appliesto-adf-asa-md](includes/appliesto-adf-asa-md.md)]
 
 Use the surrogate key transformation to add an incrementing key value to each row of data. This is useful when designing dimension tables in a star schema analytical data model. In a star schema, each member in your dimension tables requires a unique key that is a non-business key.
 
diff --git a/articles/data-factory/data-flow-transformation-overview.md b/articles/data-factory/data-flow-transformation-overview.md
@@ -11,7 +11,7 @@ ms.date: 03/10/2020
 
 # Mapping data flow transformation overview
 
-[!INCLUDE[appliesto-adf-asa-md](includes/appliesto-adf-asa-md.md)]
+[!INCLUDE[appliesto-adf-asa-md](includes/appliesto-adf-asa-md.md)] 
 
 Below is a list of the transformations currently supported in mapping data flow. Click on each transformations to learn its configuration details.
 
diff --git a/articles/data-factory/data-flow-union.md b/articles/data-factory/data-flow-union.md
@@ -12,7 +12,7 @@ ms.date: 02/12/2019
 
 # Azure Data Factory mapping data flow union transformation
 
-[!INCLUDE[appliesto-adf-xxx-md](includes/appliesto-adf-xxx-md.md)]
+[!INCLUDE[appliesto-adf-asa-md](includes/appliesto-adf-asa-md.md)]
 
 Union will combine multiple data streams into one, with the SQL Union of those streams as the new output from the Union transformation. All of the schema from each input stream will be combined inside of your data flow, without needing to have a join key.
 
diff --git a/articles/data-factory/data-flow-unpivot.md b/articles/data-factory/data-flow-unpivot.md
@@ -12,7 +12,7 @@ ms.date: 01/30/2019
 
 # Azure Data Factory Unpivot Transformation
 
-[!INCLUDE[appliesto-adf-xxx-md](includes/appliesto-adf-xxx-md.md)]
+[!INCLUDE[appliesto-adf-asa-md](includes/appliesto-adf-asa-md.md)]
 
 Use Unpivot in ADF mapping data flow as a way to turn an unnormalized dataset into a more normalized version by expanding values from multiple columns in a single record into multiple records with the same values in a single column.
 
diff --git a/articles/data-factory/data-flow-window.md b/articles/data-factory/data-flow-window.md
@@ -12,7 +12,7 @@ ms.date: 01/30/2019
 
 # Azure Data Factory Window Transformation
 
-[!INCLUDE[appliesto-adf-xxx-md](includes/appliesto-adf-xxx-md.md)]
+[!INCLUDE[appliesto-adf-asa-md](includes/appliesto-adf-asa-md.md)]
 
 The Window transformation is where you will define window-based aggregations of columns in your data streams. In the Expression Builder, you can define different types of aggregations that are based on data or time windows (SQL OVER clause) such as LEAD, LAG, NTILE, CUMEDIST, RANK, etc.). A new field will be generated in your output that includes these aggregations. You can also include optional group-by fields.
 
diff --git a/articles/data-factory/enable-aad-authentication-azure-ssis-ir.md b/articles/data-factory/enable-aad-authentication-azure-ssis-ir.md
@@ -15,7 +15,7 @@ ms.date: 5/14/2019
 
 # Enable Azure Active Directory authentication for Azure-SSIS Integration Runtime
 
-[!INCLUDE[appliesto-adf-xxx-md](includes/appliesto-adf-xxx-md.md)]
+[!INCLUDE[appliesto-adf-asa-md](includes/appliesto-adf-asa-md.md)]
 
 This article shows you how to enable Azure Active Directory (Azure AD) authentication with the managed identity for your Azure Data Factory (ADF) and use it instead of conventional authentication methods (like SQL authentication) to:
 
diff --git a/articles/data-factory/how-to-clean-up-ssisdb-logs-with-elastic-jobs.md b/articles/data-factory/how-to-clean-up-ssisdb-logs-with-elastic-jobs.md
@@ -14,7 +14,7 @@ ms.reviewer: douglasl
 
 # Clean up SSISDB logs with Azure Elastic Database Jobs
 
-[!INCLUDE[appliesto-adf-xxx-md](includes/appliesto-adf-xxx-md.md)]
+[!INCLUDE[appliesto-adf-asa-md](includes/appliesto-adf-asa-md.md)]
 
 This article describes how to use Azure Elastic Database Jobs to trigger the stored procedure that cleans up logs for the SQL Server Integration Services catalog database, `SSISDB`.
 
diff --git a/articles/data-factory/how-to-configure-azure-ssis-ir-custom-setup.md b/articles/data-factory/how-to-configure-azure-ssis-ir-custom-setup.md
@@ -16,7 +16,7 @@ ms.date: 04/15/2020
 
 # Customize the setup for an Azure-SSIS Integration Runtime
 
-[!INCLUDE[appliesto-adf-xxx-md](includes/appliesto-adf-xxx-md.md)]
+[!INCLUDE[appliesto-adf-asa-md](includes/appliesto-adf-asa-md.md)]
 
 The custom setup for an Azure-SQL Server Integration Services Integration Runtime (Azure-SSIS IR) provides an interface for adding your own steps during the setup or reconfiguration of your Azure-SSIS IR. 
 
diff --git a/articles/data-factory/how-to-configure-azure-ssis-ir-enterprise-edition.md b/articles/data-factory/how-to-configure-azure-ssis-ir-enterprise-edition.md
@@ -13,7 +13,7 @@ manager: anandsub
 ---
 # Provision Enterprise Edition for the Azure-SSIS Integration Runtime
 
-[!INCLUDE[appliesto-adf-xxx-md](includes/appliesto-adf-xxx-md.md)]
+[!INCLUDE[appliesto-adf-asa-md](includes/appliesto-adf-asa-md.md)]
 
 The Enterprise Edition of the Azure-SSIS Integration Runtime lets you use the following advanced and premium features:
 -   Change Data Capture (CDC) components
diff --git a/articles/data-factory/how-to-develop-azure-ssis-ir-licensed-components.md b/articles/data-factory/how-to-develop-azure-ssis-ir-licensed-components.md
@@ -15,7 +15,7 @@ ms.date: 08/01/2019
 
 # Install paid or licensed custom components for the Azure-SSIS integration runtime
 
-[!INCLUDE[appliesto-adf-xxx-md](includes/appliesto-adf-xxx-md.md)]
+[!INCLUDE[appliesto-adf-asa-md](includes/appliesto-adf-asa-md.md)]
 
 This article describes how an ISV can develop and install paid or licensed custom components for SQL Server Integration Services (SSIS) packages that run in Azure in the Azure-SSIS integration runtime.
 
diff --git a/articles/data-factory/how-to-invoke-ssis-package-ssis-activity.md b/articles/data-factory/how-to-invoke-ssis-package-ssis-activity.md
@@ -18,7 +18,7 @@ ms.date: 11/14/2019
 
 # Run an SSIS package with the Execute SSIS Package activity in Azure Data Factory
 
-[!INCLUDE[appliesto-adf-xxx-md](includes/appliesto-adf-xxx-md.md)]
+[!INCLUDE[appliesto-adf-asa-md](includes/appliesto-adf-asa-md.md)]
 
 This article describes how to run a SQL Server Integration Services (SSIS) package in an Azure Data Factory  pipeline by using the Execute SSIS Package activity. 
 
diff --git a/articles/data-factory/how-to-invoke-ssis-package-stored-procedure-activity.md b/articles/data-factory/how-to-invoke-ssis-package-stored-procedure-activity.md
@@ -16,7 +16,7 @@ ms.author: sawinark
 ---
 # Run an SSIS package with the Stored Procedure activity in Azure Data Factory
 
-[!INCLUDE[appliesto-adf-xxx-md](includes/appliesto-adf-xxx-md.md)]
+[!INCLUDE[appliesto-adf-asa-md](includes/appliesto-adf-asa-md.md)]
 
 This article describes how to run an SSIS package in an Azure Data Factory pipeline by using a Stored Procedure activity. 
 
diff --git a/articles/data-factory/how-to-schedule-azure-ssis-integration-runtime.md b/articles/data-factory/how-to-schedule-azure-ssis-integration-runtime.md
@@ -16,7 +16,7 @@ manager: anandsub
 ---
 # How to start and stop Azure-SSIS Integration Runtime on a schedule
 
-[!INCLUDE[appliesto-adf-xxx-md](includes/appliesto-adf-xxx-md.md)]
+[!INCLUDE[appliesto-adf-asa-md](includes/appliesto-adf-asa-md.md)]
 
 This article describes how to schedule the starting and stopping of Azure-SSIS Integration Runtime (IR) by using Azure Data Factory (ADF). Azure-SSIS IR is ADF compute resource dedicated for executing SQL Server Integration Services (SSIS) packages. Running Azure-SSIS IR has a cost associated with it. Therefore, you typically want to run your IR only when you need to execute SSIS packages in Azure and stop your IR when you do not need it anymore. You can use ADF User Interface (UI)/app or Azure PowerShell to [manually start or stop your IR](manage-azure-ssis-integration-runtime.md)).
 
diff --git a/articles/data-factory/join-azure-ssis-integration-runtime-virtual-network.md b/articles/data-factory/join-azure-ssis-integration-runtime-virtual-network.md
@@ -15,7 +15,7 @@ manager: mflasko
 
 # Join an Azure-SSIS integration runtime to a virtual network
 
-[!INCLUDE[appliesto-adf-xxx-md](includes/appliesto-adf-xxx-md.md)]
+[!INCLUDE[appliesto-adf-asa-md](includes/appliesto-adf-asa-md.md)]
 
 When using SQL Server Integration Services (SSIS) in Azure Data Factory, you should join your Azure-SSIS integration runtime (IR) to an Azure virtual network in the following scenarios:
 
diff --git a/articles/data-factory/self-hosted-integration-runtime-proxy-ssis.md b/articles/data-factory/self-hosted-integration-runtime-proxy-ssis.md
@@ -16,7 +16,7 @@ ms.date: 04/15/2020
 
 # Configure a self-hosted IR as a proxy for an Azure-SSIS IR in Azure Data Factory
 
-[!INCLUDE[appliesto-adf-xxx-md](includes/appliesto-adf-xxx-md.md)]
+[!INCLUDE[appliesto-adf-asa-md](includes/appliesto-adf-asa-md.md)]
 
 This article describes how to run SQL Server Integration Services (SSIS) packages on an Azure-SSIS Integration Runtime (Azure-SSIS IR) in Azure Data Factory with a self-hosted integration runtime (self-hosted IR) configured as a proxy. 
 
diff --git a/articles/data-factory/source-control.md b/articles/data-factory/source-control.md
@@ -14,7 +14,7 @@ ms.date: 01/09/2019
 ---
 
 # Source control in Azure Data Factory
-[!INCLUDE[appliesto-adf-asa-md](includes/appliesto-adf-asa-md.md)]
+[!INCLUDE[appliesto-adf-xxx-md](includes/appliesto-adf-xxx-md.md)]
 
 The Azure Data Factory user interface experience (UX) has two experiences available for visual authoring:
 
diff --git a/articles/data-factory/transform-data.md b/articles/data-factory/transform-data.md
diff --git a/articles/synapse-analytics/toc.yml b/articles/synapse-analytics/toc.yml
