Blog Post

Azure
1 MIN READ

Execute script on Azure Windows VM via Azure pipeline

sag007's avatar
sag007
Copper Contributor
May 06, 2022

I am using third party software for master data management. This software does not have API's to perform deployment across environment. It has command line utility which needs to be installed on machine and call the commands to perform the installation. I am looking to automate this solution using Azure Devops pipeline. What I am trying to do is as below

  1. Create windows VM in Azure and install command line utility on it.
  2. Store script in some folder in the VM.
  3. Use Azure pipeline to call this script which is stored in my VM.

I don't even know if it possible to do such things. I tried looking on internet on how to call script stored in VM via Azure pipeline but didn't find any useful link. If any one has done such activity or have an idea how can it be achieved please help.

Regards S

Updated Oct 28, 2022
Version 4.0
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Blog","repliesProperties":{"__typename":"RepliesProperties","sortOrder":"REVERSE_PUBLISH_TIME","repliesFormat":"threaded"},"eventPath":"category:AzureDatabases/category:products-services/category:communities/community:gxcuf89792board:AzureSynapseAnalyticsBlog/","tagProperties":{"__typename":"TagNodeProperties","tagsEnabled":{"__typename":"PolicyResult","failureReason":null}},"requireTags":true,"tagType":"PRESET_ONLY"},"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/cmstNC05WEo0blc\"}":{"__typename":"AssociatedImage","url":"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/cmstNC05WEo0blc","height":512,"width":512,"mimeType":"image/png"},"Rank:rank:4":{"__typename":"Rank","id":"rank:4","position":6,"name":"Microsoft","color":"333333","icon":{"__ref":"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/cmstNC05WEo0blc\"}"},"rankStyle":"OUTLINE"},"User:user:1288818":{"__typename":"User","id":"user:1288818","uid":1288818,"login":"scirqueira","deleted":false,"avatar":{"__typename":"UserAvatar","url":"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/dS0xMjg4ODE4LTM0Mjk0OWlEOTg2Rjk3MEZCOTNFMTUw"},"rank":{"__ref":"Rank:rank:4"},"email":"","messagesCount":9,"biography":null,"topicsCount":3,"kudosReceivedCount":23,"kudosGivenCount":15,"kudosWeight":1,"registrationData":{"__typename":"RegistrationData","status":null,"registrationTime":"2022-01-27T05:57:08.290-08:00","confirmEmailStatus":null},"followersCount":null,"solutionsCount":0,"entityType":"USER","eventPath":"community:gxcuf89792/user:1288818"},"BlogTopicMessage:message:3535930":{"__typename":"BlogTopicMessage","uid":3535930,"subject":"Writing data using Azure Synapse Dedicated SQL Pool Connector for Apache Spark","id":"message:3535930","revisionNum":9,"repliesCount":11,"author":{"__ref":"User:user:1288818"},"depth":0,"hasGivenKudo":false,"board":{"__ref":"Blog:board:AzureSynapseAnalyticsBlog"},"conversation":{"__ref":"Conversation:conversation:3535930"},"messagePolicies":{"__typename":"MessagePolicies","canPublishArticleOnEdit":{"__typename":"PolicyResult","failureReason":{"__typename":"FailureReason","message":"error.lithium.policies.forums.policy_can_publish_on_edit_workflow_action.accessDenied","key":"error.lithium.policies.forums.policy_can_publish_on_edit_workflow_action.accessDenied","args":[]}},"canModerateSpamMessage":{"__typename":"PolicyResult","failureReason":{"__typename":"FailureReason","message":"error.lithium.policies.feature.moderation_spam.action.moderate_entity.allowed.accessDenied","key":"error.lithium.policies.feature.moderation_spam.action.moderate_entity.allowed.accessDenied","args":[]}}},"contentWorkflow":{"__typename":"ContentWorkflow","state":"PUBLISH","scheduledPublishTime":null,"scheduledTimezone":null,"userContext":{"__typename":"MessageWorkflowContext","canSubmitForReview":null,"canEdit":false,"canRecall":null,"canSubmitForPublication":null,"canReturnToAuthor":null,"canPublish":null,"canReturnToReview":null,"canSchedule":false},"shortScheduledTimezone":null},"readOnly":false,"editFrozen":false,"moderationData":{"__ref":"ModerationData:moderation_data:3535930"},"teaser":"

\n

 

\n

When using The Azure Synapse Dedicated SQL Pool Connector for Apache Spark, users can take advantage of read and write a large volume of data efficiently between Apache Spark to Dedicated SQL Pool in Synapse Analytics. The connector supports Scala and Python language on Synapse Notebooks to perform these operations.

","body":"

Summary 

\n

A common data engineering task is explore, transform, and load data into data warehouse using Azure Synapse Apache Spark. The Azure Synapse Dedicated SQL Pool Connector for Apache Spark is the way to read and write a large volume of data efficiently between Apache Spark to Dedicated SQL Pool in Synapse Analytics. The connector supports Scala and Python language on Synapse Notebooks to perform this operations.

\n

 

\n

The intention of this Guide is not explain all the Connector features if you require a deeper understanding of how this connector works start here.

\n

 

\n

Scenario

\n

Usually, customers do this kind of operation using Synapse Apache Spark to load data to Dedicated Pool within Azure Synapse Workspace, but today, I would like to reproduce a different scenario that I was working on one of my support cases.  Consider a scenario where you are trying to load data from Synapse Spark to Dedicated pool (formerly SQL DW) using Synapse Pipelines, and additionally you are using Synapse Workspace deployed with Managed Virtual Network.

\n

 

\n

The intention of this guide is to help you with which configuration will be required if you need to load data from Azure Synapse Apache Spark to Dedicated SQL Pool (formerly SQL DW). If you prefer take advantage of the new feature-rich capabilities now available via the Synapse workspace and Studio and load data directly from Azure Apache Spark to Dedicated Pool in Azure Synapse Workspace is recommended that you enable Synapse workspace features on an existing dedicated SQL pool (formerly SQL DW).

\n

 

\n

Before we start, here is some initial considerations. 

\n

 

\n

To write data to internal tables, the connector now uses COPY statement instead of CETAS/CTAS approach. It also requires storage permissions.

\n

 

\n\n
    \n\n
\n\n
    \n\n
\n\n\n

 

\n

Environment

\n

There are 3 deployments needed to make this configuration possible:

\n
    \n
  1. Create an Azure Synapse Workspace Deployment with Managed Virtual Network enabled. Start here.
    2. \n
  2. Create an Azure Apache Spark pool using Synapse Studio. Start here.
    3. \n
  3. Create a Dedicated SQL pool (formerly SQL DW). Start here.
    4. \n
\n

 

\n

\n

Fig 1 - Resources

\n

 

\n

 

\n

Implementation

\n

Step 1 - Let's create a Synapse Notebook that will perform read and write operations to be executed on the Synapse Pipeline.

\n

\n

Fig 2 - Creating an Azure Synapse Notebook

\n

 

\n

Here is the Spark script used within my sample notebook to generate data:

\n

 

\n
%%sql\nCREATE DATABASE IF NOT EXISTS SampleDB
%%sql\nUSE SampleDB
%%sql\nCREATE TABLE IF NOT EXISTS SampleDB.SampleTable (id int, name string, age int) USING PARQUET
%%sql\nINSERT INTO SampleDB.SampleTable VALUES (1, 'Your Name', 18)
%%pyspark\ndf = spark.sql(\"SELECT * FROM `SampleDB`.`SampleTable`\")\ndisplay(df)
\n

 

\n

 

\n

Below is the sample to write using AAD Authentication (Internal Table) using python connector:

\n

 

\n
# Write using AAD Auth to internal table\n# Add required imports\nimport com.microsoft.spark.sqlanalytics\nfrom com.microsoft.spark.sqlanalytics.Constants import Constants\n\n# Configure and submit the request to write to Synapse Dedicated SQL Pool\n# Sample below is using AAD-based authentication approach; See further examples to leverage SQL Basic auth.\n(df.write\n # If `Constants.SERVER` is not provided, the `<database_name>` from the three-part table name argument\n # to `synapsesql` method is used to infer the Synapse Dedicated SQL End Point.\n .option(Constants.SERVER, \"<sql-server-name>.sql.azuresynapse.net\")\n # Like-wise, if `Constants.TEMP_FOLDER` is not provided, the connector will use the runtime staging directory config (see section on Configuration Options for details).\n .option(Constants.TEMP_FOLDER, \"abfss://<container_name>@<storage_account_name>.dfs.core.windows.net/<some_base_path_for_temporary_staging_folders>\")\n # Choose a save mode that is apt for your use case.\n # Options for save modes are \"error\" or \"errorifexists\" (default), \"overwrite\", \"append\", \"ignore\".\n # refer to https://spark.apache.org/docs/latest/sql-data-sources-load-save-functions.html#save-modes\n .mode(\"overwrite\")\n # Required parameter - Three-part table name to which data will be written\n .synapsesql(\"sqlpool01.dbo.SampleTable\"))
\n

 

\n

 

\n

Here is the sample of Synapse Notebook with the code:

\n

\n

Fig 3 - Synapse Notebook with sample code

\n

 

\n

After creating the synapse notebook, click Publish all to save your modifications.

\n

 

\n

\n

Fig 4 - Publish all

\n

 

\n

Step 2 - Granting Storage Permissions 

\n

Go to Storage account access control -> Click +Add -> add role assigned -> select Storage Blob Data Contributor role -> Assign access to -> Select your Synapse Workspace Managed Identity and also your logged account -> Review + assign.

\n

 

\n

\n

Fig 5 - Storage account permissions

\n

 

\n

Step 3 - Granting SQL Permissions to logged in account

\n

If you are trying to read or write on the dedicated pool using synapse notebook and you are not SQL Active Directory Admin, the following SQL authorization is necessary on Dedicated SQL Pool for the user logged in account:

\n

 

\n

Read:

\n

 

\n
EXEC sp_addrolemember 'db_exporter', [<your_domain_user>@<your_domain_name>.com];
\n

 

\n

 

\n

 

\n

Write:

\n

 

\n
--Make sure your user has the permissions to CREATE tables in the [dbo] schema\nGRANT CREATE TABLE TO [<your_domain_user>@<your_domain_name>.com];\n\n--Make sure your user has ALTER permissions\nGRANT ALTER ON SCHEMA::<target_database_schema_name> TO [<your_domain_user>@<your_domain_name>.com];\n\n--Make sure your user has ADMINISTER DATABASE BULK OPERATIONS permissions\nGRANT ADMINISTER DATABASE BULK OPERATIONS TO [<your_domain_user>@<your_domain_name>.com];\n\n--Make sure your user has Select permissions on the target table\nGRANT SELECT ON <your_table> TO[<your_domain_user>@<your_domain_name>.com]\n\n--Make sure your user has INSERT permissions on the target table\nGRANT INSERT ON <your_table> TO [<your_domain_user>@<your_domain_name>.com]\n\n--Make sure your user has DELETE permissions on the target table\nGRANT DELETE ON <your_table> TO[<your_domain_user>@<your_domain_name>.com]
\n

 

\n

 

\n

Now, let's Run the Synapse Notebook to test if logged-in user account will have the right permissions to perform all actions.

\n

 

\n

When the notebook was executed, the error below was thrown:

\n

\n

Fig 6 - Error when notebook tries to access SQL Server

\n

 

\n

This error is pretty clear: Synapse Pipeline IP is not allowed to access the server.

\n

We need a allow Azure services and resources to access SQL Server.

\n

 

\n

Step 4 - Allowing Azure services and resources on SQL Server

\n

To allow this configuration: Go to your SQL Server -> Security -> Networking - > Exceptions -> Allow Azure services and resources to access this server -> Save

\n

 

\n

\n

Fig 7 - Allowing Azure services and resources to access SQL Server

\n

 

\n

Let's re-run the code that loads data into Dedicated SQL Pool (formerly SQL DW).

\n

 

\n

\n

Fig 8 - Error related to Storage permissions

\n

 

\n

Now we can see a different error threw:

\n

ERROR: 'Not able to validate external location because The remote server returned an error: (403) Forbidden.

\n

 

\n

This generic error means that now we can get on SQL Server, but System assigned managed identity that will be used to perform this operation may not be enabled.

\n

 

\n

Step 5 - Allowing System assigned managed identity on SQL Server

\n

To allow this configuration: Go to your SQL Server Security -> Identity- > Turn Status On -> Save

\n

 

\n

\n

Fig 9 - Allowing System assigned managed identity from SQL Server

\n

 

\n

Now when we run the write cell again, it will work because logged-in user account has a Storage Blob Data Contributor role to perform all actions.

\n

 

\n

\n

Fig 10 - Write on Dedicated SQL Pool succeeded

\n

 

\n

Step 6 - Adding Synapse Notebook activity within Synapse Pipeline to perform data load into Dedicated SQL Pool (formerly SQL DW).

\n

To allow this configuration: Go to your Synapse Notebook -> On the upper right side Click on  Add pipeline -> New pipeline.

\n

 

\n

\n

Fig 11 - Adding Synapse Notebook to Pipeline

\n

 

\n

Next screen:

\n

1 - On properties, choose a Name for your pipeline.

\n

2 - Publish your pipeline.

\n

 

\n

\n

Fig 12 - Naming and publishing.

\n

 

\n

Now let's execute our pipeline to ensure that all configuration is done.

\n

 

\n

Go to Integrate -> Select the pipeline that was created on the last step -> Click on +Add trigger -> Trigger now -> Ok.

\n

 

\n

\n

Fig 13 - Triggering Pipeline

\n

 

\n

Go to Monitor-> integration -> Pipeline runs.

\n

 

\n

As we can see on the screenshot below, now we have an error when executing from Synapse Pipeline.

\n

 

\n

\n

Fig 14 - Monitoring Pipeline

\n

 

\n

Click on Error to see the complete error:

\n

: com.microsoft.spark.sqlanalytics.SQLAnalyticsConnectorException: Internal error - Attempt to run SQL Transaction failed with {ErrorNumber - 105215, ErrorMessage - Not able to validate external location because The remote server returned an error: (403) Forbidden.}

\n

 

\n

This error is saying that System assigned managed identity doesn't have permission to perform this operation.

\n

It means that the System assigned managed identity should be assigned Storage Blob Data Contributor on the Azure Storage.

\n

 

\n

Usually, the name of the System assigned managed identity is the same as the SQL Server name, as shown in the screenshot below.

\n

 

\n

\n

Fig 15 - Checking system-assigned managed identity name

\n

 

\n

Go back to Step 2 and grant Storage permissions for System assigned managed identity.

\n

 

\n

\n

Fig 16 - Checking system assigned managed identity Storage permissions

\n

 

\n

Again, Let's trigger the pipeline.

\n

Now it works. \":stareyes:\"

\n

 

\n

\n

Fig 17 - Pipeline succeeded.

\n

 

\n

Step 7 - Checking the data loaded into Dedicated SQL Pool (formerly SQL DW).

\n

\n

Fig 18 - Checking data on Dedicated SQL Pool table.

\n

 

\n

Solution Overview

\n\n

 

\n

Conclusion

\n

When using The Azure Synapse Dedicated SQL Pool Connector for Apache Spark, users can take advantage of reading and writing a large volume of data efficiently between Apache Spark to Dedicated SQL Pool in Synapse Analytics. The connector supports Scala and Python language on Synapse Notebooks to perform these operations.

\n

 

\n

If you are unable to complete the steps in the article, please do reach out to us by logging a Support Request.

\n

 

\n

SIDNEY CIRQUEIRA

\n

Synapse Analytics Support Engineer

\n

 

","body@stringLength":"27736","rawBody":"

Summary 

\n

A common data engineering task is explore, transform, and load data into data warehouse using Azure Synapse Apache Spark. The Azure Synapse Dedicated SQL Pool Connector for Apache Spark is the way to read and write a large volume of data efficiently between Apache Spark to Dedicated SQL Pool in Synapse Analytics. The connector supports Scala and Python language on Synapse Notebooks to perform this operations.

\n

 

\n

The intention of this Guide is not explain all the Connector features if you require a deeper understanding of how this connector works start here.

\n

 

\n

Scenario

\n

Usually, customers do this kind of operation using Synapse Apache Spark to load data to Dedicated Pool within Azure Synapse Workspace, but today, I would like to reproduce a different scenario that I was working on one of my support cases.  Consider a scenario where you are trying to load data from Synapse Spark to Dedicated pool (formerly SQL DW) using Synapse Pipelines, and additionally you are using Synapse Workspace deployed with Managed Virtual Network.

\n

 

\n

The intention of this guide is to help you with which configuration will be required if you need to load data from Azure Synapse Apache Spark to Dedicated SQL Pool (formerly SQL DW). If you prefer take advantage of the new feature-rich capabilities now available via the Synapse workspace and Studio and load data directly from Azure Apache Spark to Dedicated Pool in Azure Synapse Workspace is recommended that you enable Synapse workspace features on an existing dedicated SQL pool (formerly SQL DW).

\n

 

\n

Before we start, here is some initial considerations. 

\n

 

\n

To write data to internal tables, the connector now uses COPY statement instead of CETAS/CTAS approach. It also requires storage permissions.

\n

 

\n\n
    \n\n
\n\n
    \n\n
\n\n\n

 

\n

Environment

\n

There are 3 deployments needed to make this configuration possible:

\n
    \n
  1. Create an Azure Synapse Workspace Deployment with Managed Virtual Network enabled. Start here.
    2. \n
  2. Create an Azure Apache Spark pool using Synapse Studio. Start here.
    3. \n
  3. Create a Dedicated SQL pool (formerly SQL DW). Start here.
    4. \n
\n

 

\n

\n

Fig 1 - Resources

\n

 

\n

 

\n

Implementation

\n

Step 1 - Let's create a Synapse Notebook that will perform read and write operations to be executed on the Synapse Pipeline.

\n

\n

Fig 2 - Creating an Azure Synapse Notebook

\n

 

\n

Here is the Spark script used within my sample notebook to generate data:

\n

 

\n%%sql\nCREATE DATABASE IF NOT EXISTS SampleDB%%sql\nUSE SampleDB%%sql\nCREATE TABLE IF NOT EXISTS SampleDB.SampleTable (id int, name string, age int) USING PARQUET%%sql\nINSERT INTO SampleDB.SampleTable VALUES (1, 'Your Name', 18)%%pyspark\ndf = spark.sql(\"SELECT * FROM `SampleDB`.`SampleTable`\")\ndisplay(df)\n

 

\n

 

\n

Below is the sample to write using AAD Authentication (Internal Table) using python connector:

\n

 

\n# Write using AAD Auth to internal table\n# Add required imports\nimport com.microsoft.spark.sqlanalytics\nfrom com.microsoft.spark.sqlanalytics.Constants import Constants\n\n# Configure and submit the request to write to Synapse Dedicated SQL Pool\n# Sample below is using AAD-based authentication approach; See further examples to leverage SQL Basic auth.\n(df.write\n # If `Constants.SERVER` is not provided, the `<database_name>` from the three-part table name argument\n # to `synapsesql` method is used to infer the Synapse Dedicated SQL End Point.\n .option(Constants.SERVER, \"<sql-server-name>.sql.azuresynapse.net\")\n # Like-wise, if `Constants.TEMP_FOLDER` is not provided, the connector will use the runtime staging directory config (see section on Configuration Options for details).\n .option(Constants.TEMP_FOLDER, \"abfss://<container_name>@<storage_account_name>.dfs.core.windows.net/<some_base_path_for_temporary_staging_folders>\")\n # Choose a save mode that is apt for your use case.\n # Options for save modes are \"error\" or \"errorifexists\" (default), \"overwrite\", \"append\", \"ignore\".\n # refer to https://spark.apache.org/docs/latest/sql-data-sources-load-save-functions.html#save-modes\n .mode(\"overwrite\")\n # Required parameter - Three-part table name to which data will be written\n .synapsesql(\"sqlpool01.dbo.SampleTable\"))\n

 

\n

 

\n

Here is the sample of Synapse Notebook with the code:

\n

\n

Fig 3 - Synapse Notebook with sample code

\n

 

\n

After creating the synapse notebook, click Publish all to save your modifications.

\n

 

\n

\n

Fig 4 - Publish all

\n

 

\n

Step 2 - Granting Storage Permissions 

\n

Go to Storage account access control -> Click +Add -> add role assigned -> select Storage Blob Data Contributor role -> Assign access to -> Select your Synapse Workspace Managed Identity and also your logged account -> Review + assign.

\n

 

\n

\n

Fig 5 - Storage account permissions

\n

 

\n

Step 3 - Granting SQL Permissions to logged in account

\n

If you are trying to read or write on the dedicated pool using synapse notebook and you are not SQL Active Directory Admin, the following SQL authorization is necessary on Dedicated SQL Pool for the user logged in account:

\n

 

\n

Read:

\n

 

\nEXEC sp_addrolemember 'db_exporter', [<your_domain_user>@<your_domain_name>.com];\n

 

\n

 

\n

 

\n

Write:

\n

 

\n--Make sure your user has the permissions to CREATE tables in the [dbo] schema\nGRANT CREATE TABLE TO [<your_domain_user>@<your_domain_name>.com];\n\n--Make sure your user has ALTER permissions\nGRANT ALTER ON SCHEMA::<target_database_schema_name> TO [<your_domain_user>@<your_domain_name>.com];\n\n--Make sure your user has ADMINISTER DATABASE BULK OPERATIONS permissions\nGRANT ADMINISTER DATABASE BULK OPERATIONS TO [<your_domain_user>@<your_domain_name>.com];\n\n--Make sure your user has Select permissions on the target table\nGRANT SELECT ON <your_table> TO[<your_domain_user>@<your_domain_name>.com]\n\n--Make sure your user has INSERT permissions on the target table\nGRANT INSERT ON <your_table> TO [<your_domain_user>@<your_domain_name>.com]\n\n--Make sure your user has DELETE permissions on the target table\nGRANT DELETE ON <your_table> TO[<your_domain_user>@<your_domain_name>.com]\n

 

\n

 

\n

Now, let's Run the Synapse Notebook to test if logged-in user account will have the right permissions to perform all actions.

\n

 

\n

When the notebook was executed, the error below was thrown:

\n

\n

Fig 6 - Error when notebook tries to access SQL Server

\n

 

\n

This error is pretty clear: Synapse Pipeline IP is not allowed to access the server.

\n

We need a allow Azure services and resources to access SQL Server.

\n

 

\n

Step 4 - Allowing Azure services and resources on SQL Server

\n

To allow this configuration: Go to your SQL Server -> Security -> Networking - > Exceptions -> Allow Azure services and resources to access this server -> Save

\n

 

\n

\n

Fig 7 - Allowing Azure services and resources to access SQL Server

\n

 

\n

Let's re-run the code that loads data into Dedicated SQL Pool (formerly SQL DW).

\n

 

\n

\n

Fig 8 - Error related to Storage permissions

\n

 

\n

Now we can see a different error threw:

\n

ERROR: 'Not able to validate external location because The remote server returned an error: (403) Forbidden.

\n

 

\n

This generic error means that now we can get on SQL Server, but System assigned managed identity that will be used to perform this operation may not be enabled.

\n

 

\n

Step 5 - Allowing System assigned managed identity on SQL Server

\n

To allow this configuration: Go to your SQL Server Security -> Identity- > Turn Status On -> Save

\n

 

\n

\n

Fig 9 - Allowing System assigned managed identity from SQL Server

\n

 

\n

Now when we run the write cell again, it will work because logged-in user account has a Storage Blob Data Contributor role to perform all actions.

\n

 

\n

\n

Fig 10 - Write on Dedicated SQL Pool succeeded

\n

 

\n

Step 6 - Adding Synapse Notebook activity within Synapse Pipeline to perform data load into Dedicated SQL Pool (formerly SQL DW).

\n

To allow this configuration: Go to your Synapse Notebook -> On the upper right side Click on  Add pipeline -> New pipeline.

\n

 

\n

\n

Fig 11 - Adding Synapse Notebook to Pipeline

\n

 

\n

Next screen:

\n

1 - On properties, choose a Name for your pipeline.

\n

2 - Publish your pipeline.

\n

 

\n

\n

Fig 12 - Naming and publishing.

\n

 

\n

Now let's execute our pipeline to ensure that all configuration is done.

\n

 

\n

Go to Integrate -> Select the pipeline that was created on the last step -> Click on +Add trigger -> Trigger now -> Ok.

\n

 

\n

\n

Fig 13 - Triggering Pipeline

\n

 

\n

Go to Monitor-> integration -> Pipeline runs.

\n

 

\n

As we can see on the screenshot below, now we have an error when executing from Synapse Pipeline.

\n

 

\n

\n

Fig 14 - Monitoring Pipeline

\n

 

\n

Click on Error to see the complete error:

\n

: com.microsoft.spark.sqlanalytics.SQLAnalyticsConnectorException: Internal error - Attempt to run SQL Transaction failed with {ErrorNumber - 105215, ErrorMessage - Not able to validate external location because The remote server returned an error: (403) Forbidden.}

\n

 

\n

This error is saying that System assigned managed identity doesn't have permission to perform this operation.

\n

It means that the System assigned managed identity should be assigned Storage Blob Data Contributor on the Azure Storage.

\n

 

\n

Usually, the name of the System assigned managed identity is the same as the SQL Server name, as shown in the screenshot below.

\n

 

\n

\n

Fig 15 - Checking system-assigned managed identity name

\n

 

\n

Go back to Step 2 and grant Storage permissions for System assigned managed identity.

\n

 

\n

\n

Fig 16 - Checking system assigned managed identity Storage permissions

\n

 

\n

Again, Let's trigger the pipeline.

\n

Now it works. 

\n

 

\n

\n

Fig 17 - Pipeline succeeded.

\n

 

\n

Step 7 - Checking the data loaded into Dedicated SQL Pool (formerly SQL DW).

\n

\n

Fig 18 - Checking data on Dedicated SQL Pool table.

\n

 

\n

Solution Overview

\n\n

 

\n

Conclusion

\n

When using The Azure Synapse Dedicated SQL Pool Connector for Apache Spark, users can take advantage of reading and writing a large volume of data efficiently between Apache Spark to Dedicated SQL Pool in Synapse Analytics. The connector supports Scala and Python language on Synapse Notebooks to perform these operations.

\n

 

\n

If you are unable to complete the steps in the article, please do reach out to us by logging a Support Request.

\n

 

\n

SIDNEY CIRQUEIRA

\n

Synapse Analytics Support Engineer

\n

 

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When using The Azure Synapse Dedicated SQL Pool Connector for Apache Spark, users can take advantage of read and write a large volume of data efficiently between Apache Spark to Dedicated SQL Pool in Synapse Analytics. The connector supports Scala and Python language on Synapse Notebooks to perform these operations.

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Contributor","color":"333333","icon":null,"rankStyle":"TEXT"},"User:user:2316391":{"__typename":"User","id":"user:2316391","uid":2316391,"login":"Azobenio","biography":null,"registrationData":{"__typename":"RegistrationData","status":null,"registrationTime":"2024-02-17T10:50:43.284-08:00"},"deleted":false,"email":"","avatar":{"__typename":"UserAvatar","url":"https://techcommunity.microsoft.com/t5/s/gxcuf89792/m_assets/avatars/default/avatar-11.svg?time=0"},"rank":{"__ref":"Rank:rank:37"},"entityType":"USER","eventPath":"community:gxcuf89792/user:2316391"},"ModerationData:moderation_data:4060041":{"__typename":"ModerationData","id":"moderation_data:4060041","status":"APPROVED","rejectReason":null,"isReportedAbuse":false,"rejectUser":null,"rejectTime":null,"rejectActorType":null},"BlogReplyMessage:message:4060041":{"__typename":"BlogReplyMessage","author":{"__ref":"User:user:2316391"},"id":"message:4060041","revisionNum":1,"uid":4060041,"depth":1,"hasGivenKudo":false,"subscribed":false,"board":{"__ref":"Blog:board:AzureSynapseAnalyticsBlog"},"parent":{"__ref":"BlogTopicMessage:message:3535930"},"conversation":{"__ref":"Conversation:conversation:3535930"},"subject":"Re: Writing data using Azure Synapse Dedicated SQL Pool Connector for Apache Spark","moderationData":{"__ref":"ModerationData:moderation_data:4060041"},"body":"

Thanks so much!

","body@stripHtml({\"removeProcessingText\":false,\"removeSpoilerMarkup\":false,\"removeTocMarkup\":false,\"truncateLength\":200})@stringLength":"17","kudosSumWeight":0,"repliesCount":0,"postTime":"2024-02-17T10:54:47.047-08:00","lastPublishTime":"2024-02-17T10:54:47.047-08:00","metrics":{"__typename":"MessageMetrics","views":4341},"visibilityScope":"PUBLIC","placeholder":false,"originalMessageForPlaceholder":null,"entityType":"BLOG_REPLY","eventPath":"category:AzureDatabases/category:products-services/category:communities/community:gxcuf89792board:AzureSynapseAnalyticsBlog/message:3535930/message:4060041","replies":{"__typename":"MessageConnection","pageInfo":{"__typename":"PageInfo","hasNextPage":false,"endCursor":null,"hasPreviousPage":false,"startCursor":null},"edges":[]},"customFields":[],"attachments":{"__typename":"AttachmentConnection","edges":[],"pageInfo":{"__typename":"PageInfo","hasNextPage":false,"endCursor":null,"hasPreviousPage":false,"startCursor":null}}},"ModerationData:moderation_data:4059613":{"__typename":"ModerationData","id":"moderation_data:4059613","status":"APPROVED","rejectReason":null,"isReportedAbuse":false,"rejectUser":null,"rejectTime":null,"rejectActorType":null},"BlogReplyMessage:message:4059613":{"__typename":"BlogReplyMessage","author":{"__ref":"User:user:1288818"},"id":"message:4059613","revisionNum":1,"uid":4059613,"depth":1,"hasGivenKudo":false,"subscribed":false,"board":{"__ref":"Blog:board:AzureSynapseAnalyticsBlog"},"parent":{"__ref":"BlogTopicMessage:message:3535930"},"conversation":{"__ref":"Conversation:conversation:3535930"},"subject":"Re: Writing data using Azure Synapse Dedicated SQL Pool Connector for Apache Spark","moderationData":{"__ref":"ModerationData:moderation_data:4059613"},"body":"

Hi Mukul_Oberoi, 

\n

 

\n

By default, Synapse Administrator is already granted to the MSI. So, it should give the ability to perform all tasks in dedicated sql pool.

\n

If I am not wrong, Managed identity will be an external user only if you add it manually.

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Hi scirqueira 
Could you please let me know what permission are already with managed identity by-default in the dedicated pool. I see managed identity as a external user by default when dedicated pool is created. 

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Hi Mukul_Oberoi, 

\n

 

\n

You need to grant all the permissions for both, the user who is running adhoc notebook and also for managed identity if running with pipelines. 

\n

 

\n

1 -  SQL Request for manual run (user based):

\n

\n

2 -  SQL Request for pipeline execution (managed identity based):

\n

\n

 

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Hi scirqueira 

We're following the same approach. I have one doubt about user's permission. Do we need to grant ADMINISTER DATABASE BULK OPERATIONS permissions to user. Because we're passing spark.conf.set(\"spark.synapse.runAsMsi\", \"true\") or running notebook with enable Managed Identity while manual run (it's in development phase) and while running it's throwing error.


When we're using managed identity, why this error is coming in manual run. It should run on the behalf of the managed identity or it will run on the behalf of user as it manual run? and synapse managed identity is by-default user in all dedicated pools. Do we need to grant ADMINISTER DATABASE BULK OPERATIONS permission to managed identity? I believe identity should have sufficient permissions.


 

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Hi NagoorPasha 
The only operations supported by this connector are the following:

\n


Write to Azure Synapse Dedicated SQL Pool:

\n\n

 Reference: Azure Synapse Dedicated SQL Pool Connector for Apache Spark - Azure Synapse Analytics | Microsoft Learn

You can try to use another approach, like ADF or Dataflow.

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Hi Team,

 

We have requirement to perform Upsert operation on dedicated synapse table using PySpark Notebook, Using PySpark we can overwrite or append the data into dedicated synapse tables. But do we have any option to perform Upsert operation on incremental data. 

 

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Hello PiotrM296. 

\n

Are you using Synapse Spark? And which kind of language are you using? (Pyspark)  
Please, see my repro below. 

\n

 

\n

\n

 

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Hi! I am trying to follow in your steps but I am getting the following error:

 

ModuleNotFoundError: No module named 'com.microsoft.spark.sqlanalytics'

 

What should I do regarding that?

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Really good!

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