Merge branch 'release-sqlseattle' of https://github.com/MicrosoftDocs/sql-docs-pr into release-ignite-synapse-analytics

Author: v-alje

.openpublishing.redirection.json

@@ -1,5 +1,10 @@
 {
     "redirections": [
+        {
+            "source_path": "docs/big-data-cluster/deploy-on-minikube.md",
+            "redirect_url": "/sql/big-data-cluster/deploy-get-started",
+            "redirect_document_id": false
+        },
         {
             "source_path": "docs/big-data-cluster/reference-azdata-control.md",
             "redirect_url": "/sql/big-data-cluster/reference-azdata",

docs/advanced-analytics/package-management/install-additional-python-packages-on-sql-server.md

@@ -72,11 +72,11 @@ If the client computer you use to connect to SQL Server has Internet access, you
 
 1. On the client computer, open **Python** or a Python environment.
 
-1. Use the following commands to install the **text-tools** package. Substitute your own SQL Server database connection information.
+1. Use the following commands to install the **text-tools** package. Substitute your own SQL Server database connection information (if you don't use Windows Authentication, add `uid` and `pwd` parameters).
 
    ```python
    import sqlmlutils
-   connection = sqlmlutils.ConnectionInfo(server="yourserver", database="yourdatabase", uid="yoursqluser", pwd="yoursqlpassword")
+   connection = sqlmlutils.ConnectionInfo(server="yourserver", database="yourdatabase")
    sqlmlutils.SQLPackageManager(connection).install("text-tools")
    ```
 
@@ -100,11 +100,11 @@ Use **sqlmlutils** to install each package (WHL file) you find in the local fold
 
 In this example, **text-tools** has no dependencies, so there is only one file from the `text-tools` folder for you to install. In contrast, a package such as **scikit-plot** has 11 dependencies, so you would find 12 files in the folder (the **scikit-plot** package and the 11 dependent packages), and you would install each of them.
 
-Run the following Python script. Substitute your own SQL Server database connection information, and the actual file path and name of the package. Repeat the `sqlmlutils.SQLPackageManager` statement for each package file in the folder.
+Run the following Python script. Substitute the actual file path and name of the package, and your own SQL Server database connection information (if you don't use Windows Authentication, add `uid` and `pwd` parameters). Repeat the `sqlmlutils.SQLPackageManager` statement for each package file in the folder.
 
 ```python
 import sqlmlutils
-connection = sqlmlutils.ConnectionInfo(server="yourserver", database="yourdatabase", uid="yoursqluser", pwd="yoursqlpassword")
+connection = sqlmlutils.ConnectionInfo(server="yourserver", database="yourdatabase")
 sqlmlutils.SQLPackageManager(connection).install("c:/temp/packages/text-tools/text_tools-1.0.0-py3-none-any.whl")
 ```
 

docs/advanced-analytics/package-management/install-additional-r-packages-on-sql-server.md

@@ -124,15 +124,13 @@ If the client computer you use to connect to SQL Server has Internet access, you
 
 1. On the client computer, open RStudio and create a new **R Script** file.
 
-1. Use the following R script to install the **glue** package using **sqlmlutils**. Substitute your own SQL Server database connection information.
+1. Use the following R script to install the **glue** package using **sqlmlutils**. Substitute your own SQL Server database connection information (if you don't use Windows Authentication, add `uid` and `pwd` parameters).
 
    ```R
    library(sqlmlutils)
    connection <- connectionInfo(
      server= "yourserver",
-     database = "yourdatabase",
-     uid = "yoursqluser",
-     pwd = "yoursqlpassword")
+     database = "yourdatabase")
 
    sql_install.packages(connectionString = connection, pkgs = "glue", verbose = TRUE, scope = "PUBLIC")
    ```
@@ -189,15 +187,13 @@ On the client computer:
 
 1. Open RStudio and create a new **R Script** file.
 
-1. Use the following R script to install the **glue** package using **sqlmlutils**. Substitute your own SQL Server database connection information.
+1. Use the following R script to install the **glue** package using **sqlmlutils**. Substitute your own SQL Server database connection information (if you don't use Windows Authentication, add `uid` and `pwd` parameters).
 
    ```R
    library(sqlmlutils)
    connection <- connectionInfo(
      server= "yourserver",
-     database = "yourdatabase",
-     uid = "yoursqluser",
-     pwd = "yoursqlpassword")
+     database = "yourdatabase")
    localRepo = "c:/temp/packages/glue"
 
    sql_install.packages(connectionString = connection, pkgs = "glue", verbose = TRUE, scope = "PUBLIC", repos=paste0("file:///",localRepo))

docs/advanced-analytics/tutorials/quickstart-python-train-score-model.md

@@ -18,7 +18,7 @@ monikerRange: ">=sql-server-2017||>=sql-server-linux-ver15||=sqlallproducts-allv
 
 In this quickstart, you'll create and train a predictive model using Python, save the model to a table in your SQL Server instance, then use the model to predict values from new data using [SQL Server Machine Learning Services](../what-is-sql-server-machine-learning.md).
 
-You'll create and execute two stored procedures running in SQL. The first one uses the classic Iris flower data set and generates a Naïve Bayes model to predict an Iris species based on flower characteristics. The second procedure is for scoring - it calls the model generated in the first procedure to output a set of predictions based on new data. By placing code in a stored procedure, operations are contained, reusable, and callable by other stored procedures and client applications.
+You'll create and execute two stored procedures running in SQL. The first one uses the classic Iris flower data set and generates a Naïve Bayes model to predict an Iris species based on flower characteristics. The second procedure is for scoring - it calls the model generated in the first procedure to output a set of predictions based on new data. By placing Python code in a SQL stored procedure, operations are contained in SQL, are reusable, and can be called by other stored procedures and client applications.
 
 By completing this quickstart, you'll learn:
 
@@ -39,7 +39,9 @@ By completing this quickstart, you'll learn:
 
 In this step, you'll create a stored procedure that generates a model for predicting outcomes.
 
-1. Open a new query window in SSMS connected to the **irissql** database. 
+1. Open SSMS, connect to your SQL Server instance, and open a new query window.
+
+1. Connect to the irissql database.
 
     ```sql
     USE irissql

docs/advanced-analytics/tutorials/quickstart-r-train-score-model.md

@@ -18,11 +18,18 @@ monikerRange: ">=sql-server-2016||>=sql-server-linux-ver15||=sqlallproducts-allv
 
 In this quickstart, you'll create and train a predictive model using R, save the model to a table in your SQL Server instance, then use the model to predict values from new data using [SQL Server Machine Learning Services](../what-is-sql-server-machine-learning.md).
 
-The model you'll use in this quickstart is a simple generalized linear model (GLM) that predicts the probability that a vehicle has been fitted with a manual transmission. You'll use the **mtcars** dataset included with R.
+You'll create and execute two stored procedures running in SQL. The first one uses the **mtcars** dataset included with R and generates a simple generalized linear model (GLM) that predicts the probability that a vehicle has been fitted with a manual transmission. The second procedure is for scoring - it calls the model generated in the first procedure to output a set of predictions based on new data. By placing R code in a SQL stored procedure, operations are contained in SQL, are reusable, and can be called by other stored procedures and client applications.
 
 > [!TIP]
 > If you need a refresher on linear models, try this tutorial which describes the process of fitting a model using rxLinMod:  [Fitting Linear Models](/machine-learning-server/r/how-to-revoscaler-linear-model)
 
+By completing this quickstart, you'll learn:
+
+> [!div class="checklist"]
+> - How to embed R code in a stored procedure
+> - How to pass inputs to your code through inputs on the stored procedure
+> - How stored procedures are used to operationalize models
+
 ## Prerequisites
 
 - This quickstart requires access to an instance of SQL Server with [SQL Server Machine Learning Services](../install/sql-machine-learning-services-windows-install.md) with the R language installed.
@@ -37,7 +44,7 @@ To create the model, you'll create source data for training, create the model an
 
 ### Create the source data
 
-1. Open **SQL Server Management Studio** and connect to your SQL Server instance.
+1. Open SSMS, connect to your SQL Server instance, and open a new query window.
 
 1. Create a table to save the training data.
 

docs/big-data-cluster/deploy-on-minikube.md

@@ -536,18 +536,8 @@ The following **patch.json** file performs the following changes:
       "op": "add",
       "path": "spec.services.hdfs.resources/-",
       "value": "spark-0"
-    },
-    {
-      "op": "add",
-      "path": "spec.services.spark.settings",
-      "value": {
-        "DriverMemory": "2g",
-        "DriverCores": "1",
-        "ExecutorInstances": "2",
-        "ExecutorMemory": "2g",
-        "ExecutorCores": "1"
-      }
     }
+   }
   ]
 }
 ```

docs/big-data-cluster/deployment-custom-configuration.md

@@ -1,7 +1,7 @@
 ---
 title: Deploy SQL Server Big Data Cluster with high availability
 titleSuffix: Deploy SQL Server Big Data Cluster with high availability 
-description: Learn how to deploy [!INCLUDE[big-data-clusters-2019](../includes/ssbigdataclusters-ver15.md)] with high availability.
+description: Learn how to deploy SQL Server Big Data Cluster with high availability.
 author: mihaelablendea
 ms.author: mihaelab 
 ms.reviewer: mikeray

docs/big-data-cluster/deployment-high-availability.md

@@ -62,9 +62,9 @@ ms.author: mathoma
 
 1.  Connect to the server instance that hosts the secondary replica whose database you want to resume.  
 
-2.  Resume the secondary database by using the following [ALTER DATABASE](../../../t-sql/statements/alter-database-transact-sql-set-hadr.md)statement:  
-  
-     ALTER DATABASE *database_name* SET HADR RESUME  
+2.  Resume the secondary database by using the following [ALTER DATABASE](../../../t-sql/statements/alter-database-transact-sql-set-hadr.md) statement:  
+
+     ALTER DATABASE *database_name* SET HADR RESUME;
 
 ##  <a name="PowerShellProcedure"></a> Using PowerShell  
  **To resume a secondary database**  

docs/database-engine/availability-groups/windows/resume-an-availability-database-sql-server.md

@@ -94,9 +94,9 @@ ms.author: mathoma
 
 1.  Connect to the server instance that hosts the replica whose database you want to suspend. For more information, see [Prerequisites](#Prerequisites), earlier in this topic.  
 
-2.  Suspend the database by using the following [ALTER DATABASE](../../../t-sql/statements/alter-database-transact-sql-set-hadr.md)statement:  
+2.  Suspend the database by using the following [ALTER DATABASE](../../../t-sql/statements/alter-database-transact-sql-set-hadr.md) statement:  
 
-     ALTER DATABASE *database_name* SET HADR SUSPEND  
+     ALTER DATABASE *database_name* SET HADR SUSPEND;
 
 ##  <a name="PowerShellProcedure"></a> Using PowerShell  
  **To suspend a database**