*The source code created for this blog post can be found here.

In the previous blog posts in the SSIS in Azure series, we created a SSIS package to periodically ingests data from Azure SQL database to Azure Data Lake Store and deployed the package in the Azure-SSIS Integrated Runtime. Up to this point, we have achieved two goals in the SSIS in Azure series:

• Using SSIS to move data between cloud storages
• Host and run SSIS packages in cloud

The last goal we need to achieve is to schedule the execution of the SSIS package in cloud. Traditionally, we can schedule the SSIS package execution by creating a SQL Server Agent job. However, we can only do that through an on-premises SQL Server instance or provision a SQL Server vm in Azure. Thanks to the support of Stored Procedure activity in ADF, we can now schedule the SSIS package execution using a cloud-based ADF pipeline. This blog post will walk through the steps to achieve that.

You can find the source code created for this blog post in my Github. The ningago.demo.adf.ssis.adfv2 project contains the json and powershell files to create the ADF pipeline.

 

The key to this solution is to call the sp_executesql stored procedure in the RunSSISPackagePipeline pipeline that execute the sql script for triggering the SSIS package execution. We can define a Stored Procedure activity in the pipeline to call the sp_executesql stored procedure and pass in the sql script for triggering the SSIS package execution as parameter.

We don’t have to manually author the sql script from scratch, but instead we can generate the script using SSMS. First, we connect to the SSISDB catalog in our Azure-SSIS integrated runtime (please refer to SSIS in Azure #2 for how to do that), select the SSIS project or package we can execute, and click the Execute button to open the Execute Package dialog window.

On the Execute Package dialog window, we select the package to execute and also set a value (can be any value) for the package parameter (the reason is set any value to the parameter is to ensure the parameter setting statement will be generated in the sql script).

We then click the “Script” button on the top-left that will generate the sql script for executing the selected SSIS package.

We can then copy the script into the stored procedure activity defined in the RunSSISPackagePipeline pipeline.

As you may have noticed that the SSIS package parameter, DateScliceToLoad, has been manually set by us with a random value. The next step we need to take is to pass the date of pipeline run into the sql script in order to make the SSIS package only move the data in the give day. Firstly, we need to define the “scheduledRunTime” parameter in the RunSSISPipeline which will receive the scheduled pipeline run time from the pipeline trigger we will create later.

In the sql script we just generated to trigger SSIS package execution, we replace the random date we have manually set with the ADF expression which points to the scheduleRunTime parameter in the pipeline.

Next, we will need to create the trigger json file to schedule the execution of the pipeline. After we specify the pipeline to execute in the trigger file, we need to pass the scheduledTime of the trigger which is a system variable to the scheduledRunTime parameter defined in the pipeline.

{
  "properties": {
    "name": "RunSSISPackageScheduler",
    "type": "ScheduleTrigger",
    "typeProperties": {
      "recurrence": {
        "frequency": "Hour",
        "interval": 1,
        "startTime": "2018-01-07T00:00:00-22:00"
      }
    },
    "pipelines": [
      {
        "pipelineReference": {
          "type": "PipelineReference",
          "referenceName": "RunSSISPackagePipeline"
        },
        "parameters": {
          "scheduledRunTime": "@trigger().scheduledTime"
        }
      }
    ]
  }
}

After we have created the json files to define linked service, pipeline and the trigger, we need to deploy them into our data factory instance. As the V2 version of ADF does not support deployment through UI yet, we need to create a deployment scripts using PowerShell.

$DataFactoryName = "ninjago3843adf"
$ResourceGroupName = "SSIS"

Set-AzureRmDataFactoryV2LinkedService -DataFactoryName $DataFactoryName -ResourceGroupName $ResourceGroupName -Name "SSISDBLinkedService" -File ".\SSISDBLinkedService.json"
Set-AzureRmDataFactoryV2Pipeline -DataFactoryName $DataFactoryName -ResourceGroupName $ResourceGroupName -Name "RunSSISPackagePipeline" -DefinitionFile ".\RunSSISPackagePipeline.json"

Stop-AzureRmDataFactoryV2Trigger -ResourceGroupName $ResourceGroupName -DataFactoryName $DataFactoryName -Name "RunSSISPackageScheduler"
Set-AzureRmDataFactoryV2Trigger -ResourceGroupName $ResourceGroupName -DataFactoryName $DataFactoryName -Name "RunSSISPackageScheduler" -DefinitionFile ".\RunSSISPackageScheduler.json"
Start-AzureRmDataFactoryV2Trigger -ResourceGroupName $ResourceGroupName -DataFactoryName $DataFactoryName -Name "RunSSISPackageScheduler"

After the pipeline is deployed and scheduled by the trigger, the pipeline will execute our SSIS package based on the schedule.

 

In the first blog post of the SSIS in Azure series, I gave a demonstration on how to create SSIS packages to move data in cloud, using a common use case that periodically ingests data from Azure SQL database to Azure Data Lake Store.  In the pre-ADF V2 era, we can only deploy SSIS packages in on-premises SQL Servers or SQL Servers in Azure VM. Thanks to the Azure-SSIS integration runtime that is available for public preview in Azure Data Factory V2, we can now directly deploy and execute our SSIS packages in Azure without needing to provision and manage an Azure SQL Server VM (One thing to note, under the hood, Azure-SSIS integrated runtime itself is a cluster of Azure VMs that are dedicated to run SSIS packages).

This blog post will walk through the steps to deploy, execute and monitor the SSIS package we have created in previous blog post in the Azure-SSIS integration runtime.

First of all, we need to have an Azure-SSIS integration runtime created in our Azure tenant. This can be achieved using the PowerShell scripts provided by Microsoft.

I have met an issue when running the scripts. Here is the hint in case you run into the same issue. If the Set-AzureRmDataFactoryV2 and the Set-AzureRmDataFactoryV2IntegrationRuntime cmdlet in the PowerShell scripts fail to execute and raise the “HTTP Status Code: NotFound” error even after you have installed the Azure PowerShell modules (we can see the AzureRM.DataFactoryV2 module is installed after checking the C:\Program Files\WindowsPowerShell\Modules folder), it may be caused by the incompatible versions between AzureRM modules. As the “Install-Module AzureRM -AllowClobber” cmdlet only installs the Azure modules that are not already installed in your computer, the Azure modules on which the AzureRM.DataFactoryV2 module is dependent are not updated. The simplest solution to this issue is to remove all AzureRM modules in the C:\Program Files\WindowsPowerShell\Modules folder and then run the Install-Module AzureRM -AllowClobber cmdlet.

After the PowerShell scripts is successfully run, an Azure Data Factory V2 instance and a SSISDB Azure SQL database are created in the resource group we specified in the scripts.

The SSISDB is the Azure-hosted SSIS catalog where you can deploy your SSIS packaged into. We can use SSMS to connect to the SSISDB. On the Connect to Server dialogue in the SSMS, we need to specify the Server name as the Azure SQL server where the SSISDB SQL database is hosted.

After we specified the server name and the login details, don’t click the Connect button to connect to the SQL Server, but instead click the Options button to open the Connection Properties tab. On the Connection Properties tab, set “Connect to database” field as SSISDB database. This settings will allow you to connect to the SSISDB SSIS catalog after clicked the Connect button.

We can deploy our SSIS package into the SSISdB catalog either by using Deploy Project option in SSMS

or using Deploy option in you SSIS SSDT project in Visual Studio.

Both options will launch the Integration Services Deployment Wizard dialog to guide us through the steps for the SSIS package deployment.

We can execute the deployed SSIS packages in SSMS by right-clicking a SSIS project or SSIS package to launch the Execute Package dialog. The SSIS package we have created to periodically load data from Azure SQL database to Azure Data Lake requires a DateSclieToLoad parameter passed in that specifies which day of data to move. We can manually set the value of this parameter in the Execute Package dialog.

We can also monitor the SSIS package executions in SSMS through the SSIS catalog reports.

Up to this point, we have created the Azure-SSIS Integration Runtime and deployed our SSIS package into the runtime. We can execute the package and monitor the execution results manually in SSMS. In next blog post, I will walk through the steps to schedule the SSIS package execution using an ADF V2 pipeline.  The ADF V2 pipeline will be scheduled to execute every day. The date of the current pipeline execution will be passed into the SSIS package, and the SSIS package will move the data by the given date parameter.