Merge pull request #125173 from ArieHein/Spelling-Wave-43

ttorble · web-flow · commit bb63f6d3c8c6 · 2024-12-17T16:36:01.000Z
Spelling Fixes
diff --git a/articles/synapse-analytics/backuprestore/restore-sql-pool.md b/articles/synapse-analytics/backuprestore/restore-sql-pool.md
@@ -138,7 +138,7 @@ Steps:
 
 1. Update Az.Sql Module to 3.8.0 (or greater) if on an older version using `Update-Module`. Otherwise it will cause failures. To validate your version via PowerShell:
    ```powershell
-   foreach ($i in (get-module -ListAvailable | ?{$_.name -eq 'az.sql'}).Version) { $version = [string]$i.Major + "." + [string]$i.Minor; if ($version -gt 3.7) {write-host "Az.Sql version $version installed. Prequisite met."} else {update-module az.sql} }
+   foreach ($i in (get-module -ListAvailable | ?{$_.name -eq 'az.sql'}).Version) { $version = [string]$i.Major + "." + [string]$i.Minor; if ($version -gt 3.7) {write-host "Az.Sql version $version installed. Prerequisite met."} else {update-module az.sql} }
    ```
    
 1. Connect to your Azure account and list all the subscriptions associated with your account.
diff --git a/articles/synapse-analytics/how-to-move-workspace-from-one-region-to-another.md b/articles/synapse-analytics/how-to-move-workspace-from-one-region-to-another.md
@@ -69,7 +69,7 @@ $regionName="<YourTargetRegionName>"
 $containerName="<YourFileSystemName>" # This is the file system name
 $workspaceName="<YourTargetRegionWorkspaceName>"
 
-$sourcRegionWSName="<Your source region workspace name>"
+$sourceRegionWSName="<Your source region workspace name>"
 $sourceRegionRGName="<YourSourceRegionResourceGroupName>"
 $sqlUserName="<SQLUserName>"
 $sqlPassword="<SQLStrongPassword>"
@@ -146,15 +146,15 @@ if($StorageAccountNameAvailable)
 Write-Host "Storage account Name is available to be used...creating storage account"
 
 #Creating a Data Lake Storage Gen2 account
-$storgeAccountProvisionStatus=az storage account create `
+$storageAccountProvisionStatus=az storage account create `
   --name $storageAccountName `
   --resource-group $resourceGroupName `
   --location $regionName `
   --sku Standard_GRS `
   --kind StorageV2 `
   --enable-hierarchical-namespace $true
 
-($storgeAccountProvisionStatus| ConvertFrom-Json).provisioningState
+($storageAccountProvisionStatus| ConvertFrom-Json).provisioningState
 }
 else
 {
@@ -320,7 +320,7 @@ Transform the Azure Synapse SQL pool resource ID to SQL database ID because curr
 For example: `/subscriptions/<SubscriptionId>/resourceGroups/<ResourceGroupName>/providers/Microsoft.Sql/servers/<WorkspaceName>/databases/<DatabaseName>`
 
 ```powershell
-$pool = Get-AzSynapseSqlPool -ResourceGroupName $sourceRegionRGName -WorkspaceName $sourcRegionWSName -Name $sqlPoolName
+$pool = Get-AzSynapseSqlPool -ResourceGroupName $sourceRegionRGName -WorkspaceName $sourceRegionWSName -Name $sqlPoolName
 $databaseId = $pool.Id `
     -replace "Microsoft.Synapse", "Microsoft.Sql" `
     -replace "workspaces", "servers" `
diff --git a/articles/synapse-analytics/machine-learning/overview-cognitive-services.md b/articles/synapse-analytics/machine-learning/overview-cognitive-services.md
@@ -459,7 +459,7 @@ df = spark.createDataFrame(
 ).withColumn("group", lit("series1"))
 
 # Run the Anomaly Detector service to look for irregular data
-anamoly_detector = (
+anomaly_detector = (
     SimpleDetectAnomalies()
     .setSubscriptionKey(anomaly_key)
     .setLocation(anomaly_loc)
@@ -472,7 +472,7 @@ anamoly_detector = (
 
 # Show the full results of the analysis with the anomalies marked as "True"
 display(
-    anamoly_detector.transform(df).select("timestamp", "value", "anomalies.isAnomaly")
+    anomaly_detector.transform(df).select("timestamp", "value", "anomalies.isAnomaly")
 )
 
 ```
diff --git a/articles/synapse-analytics/machine-learning/tutorial-build-applications-use-mmlspark.md b/articles/synapse-analytics/machine-learning/tutorial-build-applications-use-mmlspark.md
@@ -39,7 +39,7 @@ If you don't have an Azure subscription, [create a free account before you begin
 
 
 ## Get started
-To get started, import SynapseML and configurate service keys. 
+To get started, import SynapseML and configure service keys. 
 
 ```python
 import synapse.ml
@@ -50,7 +50,7 @@ from notebookutils import mssparkutils
 ai_service_key = mssparkutils.credentials.getSecret("ADD_YOUR_KEY_VAULT_NAME", "ADD_YOUR_SERVICE_KEY","ADD_YOUR_KEY_VAULT_LINKED_SERVICE_NAME") 
 # A Bing Search v7 subscription key
 bingsearch_service_key = mssparkutils.credentials.getSecret("ADD_YOUR_KEY_VAULT_NAME", "ADD_YOUR_BING_SEARCH_KEY","ADD_YOUR_KEY_VAULT_LINKED_SERVICE_NAME")
-# An Anomaly Dectector subscription key
+# An Anomaly Detector subscription key
 anomalydetector_key = mssparkutils.credentials.getSecret("ADD_YOUR_KEY_VAULT_NAME", "ADD_YOUR_ANOMALY_KEY","ADD_YOUR_KEY_VAULT_LINKED_SERVICE_NAME")
 ```
 
@@ -196,7 +196,7 @@ df_timeseriesdata = spark.createDataFrame([
 ], ["timestamp", "value"]).withColumn("group", lit("series1"))
 
 # Run the Anomaly Detector service to look for irregular data
-anamoly_detector = (SimpleDetectAnomalies()
+anomaly_detector = (SimpleDetectAnomalies()
   .setSubscriptionKey(anomalydetector_key)
   .setLocation("eastasia")
   .setTimestampCol("timestamp")
@@ -206,7 +206,7 @@ anamoly_detector = (SimpleDetectAnomalies()
   .setGranularity("monthly"))
 
 # Show the full results of the analysis with the anomalies marked as "True"
-display(anamoly_detector.transform(df_timeseriesdata).select("timestamp", "value", "anomalies.isAnomaly"))
+display(anomaly_detector.transform(df_timeseriesdata).select("timestamp", "value", "anomalies.isAnomaly"))
 ```
 
 ### Expected results
diff --git a/articles/synapse-analytics/machine-learning/tutorial-computer-vision-use-mmlspark.md b/articles/synapse-analytics/machine-learning/tutorial-computer-vision-use-mmlspark.md
@@ -35,7 +35,7 @@ df = spark.createDataFrame([
         ("<replace with your file path>/dog.jpg", )
     ], ["image", ])
 
-# Run the Azure AI Vision service. Analyze Image extracts infortmation from/about the images.
+# Run the Azure AI Vision service. Analyze Image extracts information from/about the images.
 analysis = (AnalyzeImage()
     .setLinkedService(ai_service_name)
     .setVisualFeatures(["Categories","Color","Description","Faces","Objects","Tags"])
diff --git a/articles/synapse-analytics/spark/apache-spark-gpu-concept.md b/articles/synapse-analytics/spark/apache-spark-gpu-concept.md
@@ -29,7 +29,7 @@ To simplify the process for creating and managing pools, Azure Synapse takes car
 
 > [!NOTE]
 >  - GPU-accelerated pools can be created in workspaces located in East US, Australia East, and North Europe.
->  - GPU-accelerated pools are only availble with the Apache Spark 3 runtime.
+>  - GPU-accelerated pools are only available with the Apache Spark 3 runtime.
  
 ## GPU-accelerated runtime
 
diff --git a/articles/synapse-analytics/spark/apache-spark-troubleshoot-library-errors.md b/articles/synapse-analytics/spark/apache-spark-troubleshoot-library-errors.md
@@ -112,7 +112,7 @@ To recreate the environment and validate your updates:
  3. Use ``pip install -r <provide your req.txt file>`` to update the virtual environment with your specified packages. If the installation results in an error, then there may be a conflict between what is pre-installed in the Synapse base runtime and what is specified in the provided requirements file. These dependency conflicts must be resolved in order to get the updated libraries on your serverless Apache Spark pool.
 
 >[!IMPORTANT]
->Issues may arrise when using pip and conda together. When combining pip and conda, it's best to follow these [recommended best practices](https://conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html#activating-an-environment).
+>Issues may arise when using pip and conda together. When combining pip and conda, it's best to follow these [recommended best practices](https://conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html#activating-an-environment).
 
 ## Next steps
 - View the default libraries: [Apache Spark version support](apache-spark-version-support.md)
diff --git a/articles/synapse-analytics/spark/optimize-write-for-apache-spark.md b/articles/synapse-analytics/spark/optimize-write-for-apache-spark.md
@@ -110,7 +110,7 @@ Using the [DeltaTableBuilder API](https://docs.delta.io/latest/delta-apidoc.html
 ```scala
 val table = DeltaTable.create()
   .tableName("<table_name>")
-  .addColumnn("<colName>", <dataType>)
+  .addColumn("<colName>", <dataType>)
   .location("<table_location>")
   .property("delta.autoOptimize.optimizeWrite", "true") 
   .execute()
diff --git a/articles/synapse-analytics/sql-data-warehouse/single-region-residency.md b/articles/synapse-analytics/sql-data-warehouse/single-region-residency.md
@@ -91,7 +91,7 @@ To create a new dedicated SQL pool through PowerShell, use the [New-AzSqlDatabas
 4. Select the subscription that contains the database to be restored.
 5. List the restore points for the dedicated SQL pool (formerly SQL DW).
 1. Pick the desired restore point using the RestorePointCreationDate.
-1. Restore the dedicated SQL pool (formerly SQL DW) to the desired restore point using Restore-AzSqlDatabase PowerShell cmdlet specifying BackupStorageRedundnacy as 'Local'. 
+1. Restore the dedicated SQL pool (formerly SQL DW) to the desired restore point using Restore-AzSqlDatabase PowerShell cmdlet specifying BackupStorageRedundancy as 'Local'. 
 
 ```powershell
 
diff --git a/articles/synapse-analytics/sql-data-warehouse/sql-data-warehouse-integrate-azure-stream-analytics.md b/articles/synapse-analytics/sql-data-warehouse/sql-data-warehouse-integrate-azure-stream-analytics.md
@@ -125,7 +125,8 @@ WITH (DISTRIBUTION = ROUND_ROBIN)
 
 On the Azure portal for Stream Analytics job, click on your job name.  Click on the ***Test*** button in the ***Output details*** pane.
 
-![Test button on Outpout details](./media/sql-data-warehouse-integrate-azure-stream-analytics/sqlpool-asatest.png)
+![Screenshot showing Test button on Output details.](./media/sql-data-warehouse-integrate-azure-stream-analytics/sqlpool-asatest.png)
+
 When the connection to the database succeeds, you will see a notification in the portal.
 
 ### Step 6
diff --git a/articles/synapse-analytics/sql-data-warehouse/sql-data-warehouse-manage-monitor.md b/articles/synapse-analytics/sql-data-warehouse/sql-data-warehouse-manage-monitor.md
@@ -212,7 +212,7 @@ ORDER BY sr.request_id;
 
 > [!NOTE]  
 > Data Movement uses the `tempdb`. To reduce the usage of `tempdb` during data movement, ensure that your table is using a distribution strategy that [distributes data evenly](sql-data-warehouse-tables-distribute.md#choose-a-distribution-column-with-data-that-distributes-evenly). 
-> Use [Azure Synapse SQL Distribution Advisor](../sql/distribution-advisor.md) to get recommendations on the distrbution method suited for your workloads.
+> Use [Azure Synapse SQL Distribution Advisor](../sql/distribution-advisor.md) to get recommendations on the distribution method suited for your workloads.
 > Use the [Azure Synapse Toolkit](https://github.com/microsoft/Azure_Synapse_Toolbox/tree/master/TSQL_Queries/TempDB) to monitor `tempdb` using T-SQL queries.
 
 If you have a query that is consuming a large amount of memory or have received an error message related to the allocation of `tempdb`, it could be due to a very large [CREATE TABLE AS SELECT (CTAS)](/sql/t-sql/statements/create-table-as-select-azure-sql-data-warehouse) or [INSERT SELECT](/sql/t-sql/statements/insert-transact-sql?toc=/azure/synapse-analytics/sql-data-warehouse/toc.json&bc=/azure/synapse-analytics/sql-data-warehouse/breadcrumb/toc.json&view=azure-sqldw-latest&preserve-view=true) statement running that is failing in the final data movement operation. This can usually be identified as a ShuffleMove operation in the distributed query plan right before the final INSERT SELECT.  Use [sys.dm_pdw_request_steps](/sql/relational-databases/system-dynamic-management-views/sys-dm-pdw-request-steps-transact-sql?toc=/azure/synapse-analytics/sql-data-warehouse/toc.json&bc=/azure/synapse-analytics/sql-data-warehouse/breadcrumb/toc.json&view=azure-sqldw-latest&preserve-view=true) to monitor ShuffleMove operations.
diff --git a/articles/synapse-analytics/sql-data-warehouse/sql-data-warehouse-restore-active-paused-dw.md b/articles/synapse-analytics/sql-data-warehouse/sql-data-warehouse-restore-active-paused-dw.md
@@ -108,7 +108,7 @@ This is similar guidance to restoring an existing dedicated SQL pool, however th
 1. Update Az.Sql Module to 3.8.0 (or greater) if on an older version using `Update-Module`. Otherwise it will cause failures. To validate the version with PowerShell:
 
    ```powershell
-   foreach ($i in (get-module -ListAvailable | ?{$_.name -eq 'az.sql'}).Version) { $version = [string]$i.Major + "." + [string]$i.Minor; if ($version -gt 3.7) {write-host "Az.Sql version $version installed. Prequisite met."} else {update-module az.sql} }
+   foreach ($i in (get-module -ListAvailable | ?{$_.name -eq 'az.sql'}).Version) { $version = [string]$i.Major + "." + [string]$i.Minor; if ($version -gt 3.7) {write-host "Az.Sql version $version installed. Prerequisite met."} else {update-module az.sql} }
    ```
 
 1. Connect to your Azure account and list all the subscriptions associated with your account.
diff --git a/articles/synapse-analytics/sql-data-warehouse/sql-data-warehouse-table-constraints.md b/articles/synapse-analytics/sql-data-warehouse/sql-data-warehouse-table-constraints.md
@@ -140,7 +140,7 @@ a1          total
 -- Drop unique constraint.
 ALTER TABLE t1 DROP CONSTRAINT unique_t1_a1
 
--- Add primary key contraint
+-- Add primary key constraint
 ALTER TABLE t1 ADD CONSTRAINT PK_t1_a1 PRIMARY KEY NONCLUSTERED (a1) NOT ENFORCED
 
 -- Re-run this query.  5 rows returned.  Correct result.
diff --git a/articles/synapse-analytics/sql/develop-materialized-view-performance-tuning.md b/articles/synapse-analytics/sql/develop-materialized-view-performance-tuning.md
@@ -125,7 +125,7 @@ SELECT C, SUM(D)
 FROM T
 GROUP BY C
 
--- You could create a single mateiralized view of this form
+-- You could create a single materialized view of this form
 SELECT A, C, SUM(B), SUM(D)
 FROM T
 GROUP BY A, C
diff --git a/articles/synapse-analytics/sql/mfa-authentication.md b/articles/synapse-analytics/sql/mfa-authentication.md
@@ -29,7 +29,7 @@ Active Directory Universal Authentication supports the two non-interactive authe
     - `Active Directory - Password` authentication
     - `Active Directory - Integrated` authentication
 
-There are two non-interactive authentication models as well, which can be used in many different applications (ADO.NET, JDCB, ODC, etc.). These two methods never result in pop-up dialog boxes:
+There are two non-interactive authentication models as well, which can be used in many different applications (ADO.NET, JDBC, ODC, etc.). These two methods never result in pop-up dialog boxes:
 
 - `Active Directory - Password`
 - `Active Directory - Integrated`
diff --git a/articles/synapse-analytics/sql/query-delta-lake-format.md b/articles/synapse-analytics/sql/query-delta-lake-format.md
@@ -126,7 +126,7 @@ With the explicit specification of the result set schema, you can minimize the t
 > Mismatch between text encoding in the file and string column collation might cause unexpected conversion errors.
 > You can easily change default collation of the current database using the following T-SQL statement:
 >   `alter database current collate Latin1_General_100_BIN2_UTF8`
-> You can easily set collation on the colum types using the following definition:
+> You can easily set collation on the column types using the following definition:
 >    `geo_id varchar(6) collate Latin1_General_100_BIN2_UTF8`
 
 ## Dataset
diff --git a/articles/synapse-analytics/sql/query-parquet-files.md b/articles/synapse-analytics/sql/query-parquet-files.md
@@ -73,11 +73,11 @@ from openrowset(
 ```
 
 > [!IMPORTANT]
-> Make sure that you are explicilty specifying some UTF-8 collation (for example `Latin1_General_100_BIN2_UTF8`) for all string columns in `WITH` clause or set some UTF-8 collation at database level.
+> Make sure that you are explicitly specifying some UTF-8 collation (for example `Latin1_General_100_BIN2_UTF8`) for all string columns in `WITH` clause or set some UTF-8 collation at database level.
 > Mismatch between text encoding in the file and string column collation might cause unexpected conversion errors.
 > You can easily change default collation of the current database using the following T-SQL statement:
 > `ALTER DATABASE CURRENT COLLATE Latin1_General_100_BIN2_UTF8;`
-> You can easily set collation on the colum types, for example:
+> You can easily set collation on the column types, for example:
 >    `geo_id varchar(6) collate Latin1_General_100_BIN2_UTF8`
 > For more information on collations, see [Collation types supported for Synapse SQL](../sql/reference-collation-types.md).
 
diff --git a/articles/synapse-analytics/sql/query-parquet-nested-types.md b/articles/synapse-analytics/sql/query-parquet-nested-types.md
@@ -39,7 +39,7 @@ FROM
 The `JSON_VALUE` function returns a scalar value from the field at the specified path. The `JSON_QUERY` function returns an object formatted as JSON from the field at the specified path.
 
 > [!IMPORTANT]
-> This example uses a file from the COVID-19 Open Research Dataset. [See the licence and the structure of the data here](https://azure.microsoft.com/services/open-datasets/catalog/covid-19-open-research/).
+> This example uses a file from the COVID-19 Open Research Dataset. [See the license and the structure of the data here](https://azure.microsoft.com/services/open-datasets/catalog/covid-19-open-research/).
 
 ## Prerequisites
 
diff --git a/articles/synapse-analytics/sql/query-single-csv-file.md b/articles/synapse-analytics/sql/query-single-csv-file.md
@@ -93,11 +93,11 @@ from openrowset(
 The numbers after a data type in the `WITH` clause represent column index in the CSV file.
 
 > [!IMPORTANT]
-> If your CSV file contains UTF-8 characters, make sure that you are explicilty specifying some UTF-8 collation (for example `Latin1_General_100_CI_AS_SC_UTF8`) for all columns in `WITH` clause or set some UTF-8 collation at database level.
+> If your CSV file contains UTF-8 characters, make sure that you are explicitly specifying some UTF-8 collation (for example `Latin1_General_100_CI_AS_SC_UTF8`) for all columns in `WITH` clause or set some UTF-8 collation at database level.
 > Mismatch between text encoding in the file and collation might cause unexpected conversion errors.
 > You can easily change default collation of the current database using the following T-SQL statement:
 >   `alter database current collate Latin1_General_100_CI_AI_SC_UTF8`
-> You can easily set collation on the colum types using the following definition:
+> You can easily set collation on the column types using the following definition:
 >    `geo_id varchar(6) collate Latin1_General_100_CI_AI_SC_UTF8 8`
 
 In the following sections you can see how to query various types of CSV files.
diff --git a/articles/synapse-analytics/sql/tutorial-connect-power-bi-desktop.md b/articles/synapse-analytics/sql/tutorial-connect-power-bi-desktop.md
@@ -65,7 +65,7 @@ A data source is necessary for the serverless SQL pool service to access files i
 Create the data source by running the following Transact-SQL (T-SQL) script:
 
 ```sql
--- There is no credential in data surce. We are using public storage account which doesn't need a secret.
+-- There is no credential in data source. We are using public storage account which doesn't need a secret.
 CREATE EXTERNAL DATA SOURCE AzureOpenData
 WITH ( LOCATION = 'https://azureopendatastorage.blob.core.windows.net/')
 ```
diff --git a/articles/synapse-analytics/synapse-link/concept-synapse-link-cosmos-db-support.md b/articles/synapse-analytics/synapse-link/concept-synapse-link-cosmos-db-support.md
@@ -19,7 +19,7 @@ This article describes the functionalities that are currently supported in Azure
 
 There are two types of containers in Azure Cosmos DB:
 * HTAP container - A container with Synapse Link enabled. This container has both transactional store and analytical store. 
-* OLTP container - A container with Synaspe Link not enabled. This container has only transactional store and no analytical store.
+* OLTP container - A container with Synapse Link not enabled. This container has only transactional store and no analytical store.
 
 You can connect to an Azure Cosmos DB container without enabling Synapse Link. In this scenario, you can only read/write to the transactional store. What follows is a list of the currently supported features within Synapse Link for Azure Cosmos DB. 
 
diff --git a/articles/synapse-analytics/synapse-link/connect-synapse-link-sql-server-2022.md b/articles/synapse-analytics/synapse-link/connect-synapse-link-sql-server-2022.md
@@ -186,7 +186,7 @@ This article is a step-by-step guide for getting started with Azure Synapse Link
    :::image type="content" source="../media/connect-synapse-link-sql-server-2022/link-connection-compute-settings.png" alt-text="Screenshot that shows where to enter the link connection settings.":::
 
    > [!NOTE]
-   > The number of cores you select here are allocated to the ingestion service for processing data loading and changes. They don't affect the target dedicated SQL pool confiruation.
+   > The number of cores you select here are allocated to the ingestion service for processing data loading and changes. They don't affect the target dedicated SQL pool configuration.
    > If you can’t connect to landing zone using generated SAS token due to limitation from your storage, you can try to use delegation SAS token to connect to landing zone as well. 
 
 1. With the new Azure Synapse Link connection open, you can now update the target table name, distribution type, and structure type.
