Creating an Azure Data Factory Plug-in

This procedure describes how to create a plug-in that triggers an existing pipeline in Azure Data Factory.

This plug-in is a simplified version of the Control-M Integration plug-in available from BMC, as described in Control-M for Azure Data Factory.

Similar to many other plug-ins, the Azure Data Factory plug-in involves the following key actions:
Authenticate (obtain a token) > Execute a pipeline > Track the pipeline

Before You Begin

To follow the steps, you must have an editable version of the Azure Data Factory plug-in from BMC, for reference purposes. Download the Control-M Azure Data Factory plug-in from the Control-M Custom Integrations Plug-ins Community Solutions in GitHub and import it into Application Integrator. Keep the imported plug-in open in Application Integrator, to view its attributes and settings.

Begin

  1. Add a new plug-in.

  2. Define attributes in the plug-in.

  3. Define a pre-execution step to obtain a token.

  4. Define an execution step to run a pipeline.

  5. Define an execution step to track the pipeline.

  6. Define the sub-steps of the second execution step.

  7. Define a post-execution step to retrieve pipeline completion details.

  8. Test your plug-in.

  9. Publish and deploy your plug-in.

Adding the New Plug-inLink copied to clipboard

This procedure describes how to create a new plug-in with just a few basic details. This procedure adds the new plug-in to your collection of plug-ins in Application Integrator and prepares it for you to populate it with steps and attributes.

Begin

  1. From the Home tab in Application Integrator, click Add Plug-in.

  2. On the Add Plug-in pane, complete the following basic plug-in definitions:

    • In the Plug-in name field, type New Azure Data Factory Plugin.

    • In the Plug-in ID field, type ADF.

    • From the Steps Default Interface drop-down list, select REST API.

    • In the Description field, type Azure Data Factory REST plugin.

  3. Click Add.

    The new plug-in opens on a separate tab. It is also added to the list of plug-ins on the Home tab.

Defining Attributes in the Plug-inLink copied to clipboard

This procedure describes how to prepare Connection Profile attributes and Job Property attributes for use in the plug-in steps.

Begin

  1. In the plug-in tab of your new plug-in, click Attribute Management.

  2. On the Attribute Management pane, define Connection Profile attributes and Job Properties attributes as they appear in the Azure Data Factory plug-in that you downloaded from GitHub and imported into Application Integrator.

    For example, use the following steps to define a Connection Profile attribute for the Azure account subscription ID:

    1. Click Add.

      The Add Attribute pane appears.

    2. Define values in the following fields:
      • From the Field Type drop-down list, select Text box.
      • In the Label field, type Subscription ID.
      • In the Attribute Name field, type Subscription_ID.

    3. On the Validation tab, toggle on Mandatory field (non empty).

    4. Click Add.

  3. Continue defining Connection Profile attributes and Job Properties attributes according to the attribute definitions in the Azure Data Factory plug-in that you downloaded from GitHub (and have open on a separate tab).

    The following figures show the completed lists of attributes on the Attribute Management pane:

    Connection Profile attributes Job Properties attributes

  4. Click Close and then click Save.

Defining a Pre-Execution Step to Obtain a TokenLink copied to clipboard

This procedure describes how to define the pre-execution step, which obtains a token for the connection to Azure Data Factory.

Begin

  1. From the Azure Data Factory tab, under Execution Steps, select the Pre-execution step and click.

  2. On the Edit Pre-Execution Step pane, in the Description field, type Token for loading pipeline information and click Save.

  3. In the REST API tab on the right, complete the technical details of the pre-execution step as they appear in the Azure Data Factory plug-in that you downloaded from GitHub (and have open on a separate tab):

    • In the REST API URL field, type https://{{RESTHost}}.

    • In the URL Request Path field, type /{{tenant_id}}/oauth2/token.

    • From the Method drop-down list, select POST.

    • In Headers, in Editor View, type Content-Type=application/x-www-form-urlencoded.

    • In Body, in Editor View, type the following:

      grant_type=client_credentials&client_id={{app_id}}&client_secret={{client_secret}}&resource=https://management.azure.com/.

  4. In the Output Handling tab, create an output handling rule to save the access token as a runtime parameter, as defined in the Azure Data Factory plug-in that you downloaded from GitHub and shown in the following figure:

  5. Click Save.

Defining an Execution Step to Run a PipelineLink copied to clipboard

This procedure describes how to define the first Execution step, which runs a Data Factory pipeline.

Begin

  1. In the plug-in tab, under Execution Steps, select the unnamed Execution step and click.

  2. On the Edit Execution Step pane, type values in the following fields and then click Save:

    • In the Step Name field, type Run Data Factory Pipeline.

    • In the Description field, type Trigger ADF pipeline.

  3. In the REST API tab on the right, complete the technical details of the Execution step as they appear in the Azure Data Factory plug-in that you downloaded from GitHub (and have open on a separate tab):

    • In the REST API URL field, type https://{{main_url}}.

    • In the URL Request Path field, type the following:

      //subscriptions/{{Subscription_ID}}/resourceGroups/{{ResourceGroupName}}/providers/Microsoft.DataFactory/factories/{{FactoryName}}/pipelines/{{PipelineName}}/createRun?api-version=2018-06-01.

    • From the Method drop-down list, select POST.

    • In Headers, in Editor View, type the following:

      Content-Type=application/json&Content-Length=0&Authorization=Bearer {{TOKEN}}.

    • In Body, in Editor View, type {{parameters}}

    • Under Advanced Settings, select the Override connection timeout check box and type {{timeout}} in the field.

  4. In the Output Handling tab, create an output handling rule to save the job run ID as a runtime parameter, as defined in the Azure Data Factory plug-in that you downloaded from GitHub and shown in the following figure:

  5. Click Save.

Defining an Execution Step to Track the PipelineLink copied to clipboard

This procedure describes how to define the second Execution step, which tracks the running Data Factory pipeline.

Begin

  1. In the plug-in tab, above the steps, click Add.

  2. On the Add Execution Step pane, complete the following settings and then click Add:

    • In the Step Name field, type Track the Pipeline.

    • In the Description field, type Track the ADF pipeline.

    • Toggle on the Manual abort operation check box.

    • Toggle on the Verify operation completion check box.

  3. In the REST API tab on the right, complete the following technical details of the Execution step as they appear in the Azure Data Factory plug-in that you downloaded from GitHub (and have open on a separate tab):

    • In the REST API URL field, type https://{{main_url}}.

    • In the URL Request Path field, type the following:

      /subscriptions/{{Subscription_ID}}/resourceGroups/{{ResourceGroupName}}/providers/Microsoft.DataFactory/factories/{{FactoryName}}/pipelineruns/{{RUNID}}?api-version=2018-06-01.

    • From the Method drop-down list, select GET.

    • Under the method selection, select the Authentication required check box.

    • Under Authentication Settings, complete the following authentication details:

      1. From the Authentication Type drop-down list, select OAuth2.

      2. In the OAuth2 API URL field, type https://{{RESTHost}}/{{tenant_id}}/oauth2/token.

      3. In the Token Parameter field, type REFRESHTOKEN.

      4. From the Grant Type drop-down list, select Resource owner password credentials.

      5. Select the Apply client ID check box and type {{app_id}} in the field.

      6. Select the Use client secret check box and type {{client_secret}} in the field.

      7. Select the Specify content type check box and type application/x-www-form-urlencoded in the field.

      8. Select the Apply body check box and, in Editor View, type resource=https://management.azure.com/.

    • In Headers, in Editor View, type the following:

      Content-Type=application/json&Authorization=Bearer {{REFRESHTOKEN}}.

  4. In the Output Handling tab, create output handling rules for the following objectives, according to the definitions in the Azure Data Factory plug-in that you downloaded from GitHub:

    • Save the activity name from the output

    • Save the start time as a parameter

    • Save the run ID as a parameter

  5. Click Save.

Defining the Sub-steps of the Second Execution StepLink copied to clipboard

This procedure describes how to define the two sub-steps under the Execution step for tracking the running Data Factory pipeline. One sub-step enables the user to manually abort the job in Control-M and the other sub-step enables Control-M to verify the completion of the job.

Begin

  1. In the plug-in tab, expand the Execution - Tracking Pipeline Execution step.

  2. Select the Manual abort operation sub-step and complete its REST API details, as they appear in the Azure Data Factory plug-in that you downloaded from GitHub (and have open on a separate tab):

    • In the REST API URL field, type https://{{main_url}}.

    • In the URL Request Path field, type the following:

      /subscriptions/{{Subscription_ID}}/resourceGroups/{{ResourceGroupName}}/providers/Microsoft.DataFactory/factories/{{FactoryName}}/pipelineruns/{{RUNID}}/cancel?api-version=2018-06-01

    • From the Method drop-down list, select POST.

    • Under the method selection, select the Authentication required check box.

    • Under Authentication Settings, complete the following authentication details:

      1. From the Authentication Type drop-down list, select Basic.

      2. From the User Name Attribute drop-down list, select CON_PRO.

      3. From the Password Attribute drop-down list, select client_secret.

    • In Headers, in Editor View, type the following:

      Content-Type=application/json&Authorization=Bearer {{REFRESHTOKEN}}.

    • Select the Apend REST API response to job output check box.

  3. Select the Verify operation completion sub-step and click.

  4. On the Edit Verify Operation Completion pane, complete the following settings and then click Save:

    • In the Rerun Interval (In Seconds) field, type {{Poll_Interval}}.

    • Under Tolerance, toggle on Retry in case of failure and in the Retries field, type 1.

  5. Complete the REST API details of the Verify operation completion sub-step, as they appear in the Azure Data Factory plug-in that you downloaded from GitHub:

    • In the REST API URL field, type https://{{main_url}}.

    • In the URL Request Path field, type the following:

      /subscriptions/{{Subscription_ID}}/resourceGroups/{{ResourceGroupName}}/providers/Microsoft.DataFactory/factories/{{FactoryName}}/pipelineruns/{{RUNID}}?api-version=2018-06-01

    • From the Method drop-down list, select GET.

    • Under the method selection, select the Authentication required check box.

    • Under Authentication Settings, complete the following authentication details:

      1. From the Authentication Type drop-down list, select OAuth2.

      2. In the OAuth2 API URL field, type https://{{RESTHost}}/{{tenant_id}}/oauth2/token.

      3. In the Token Parameter field, type REFRESHTOKEN.

      4. From the Grant Type drop-down list, select Resource owner password credentials.

      5. Select the Apply client ID check box and type {{app_id}} in the field.

      6. Select the Use client secret check box and type {{client_secret}} in the field.

      7. Select the Specify content type check box and type application/x-www-form-urlencoded in the field.

      8. Select the Apply body check box and, in Editor View, type resource=https://management.azure.com/.

    • In Headers, in Editor View, type the following:

      Content-Type=application/json&Authorization=Bearer {{REFRESHTOKEN}}.

  6. In the Output Handling tab of the Verify operation completion sub-step, create output handling rules for the following objectives, according to the definitions in the Azure Data Factory plug-in that you downloaded from GitHub:

    • Save the reason for failure if the job fails

    • Fail the job if the pipeline run failed

    • Fail the job when canceling a pipeline
    • Save the status as a runtime parameter
    • Save runtime parameters
    • Save the entity that started the pipeline run
    • Complete and proceed when the pipeline run is successful
    • Fail the job when a pipeline is cancelled

  7. Click Save.

Defining a Post-Execution Step to Retrieve Pipeline Completion DetailsLink copied to clipboard

This procedure describes how to define a post-execution step, which retrieves pipeline completion details.

Begin

  1. In the plug-in tab, under Execution Steps, select the Post-execution step and click.

  2. On the Edit Post-Execution Step pane, in the Description field, type Retrieve pipeline completion details and click Save.

  3. In the REST API tab on the right, complete the following technical details of the Execution step, as they appear in the Azure Data Factory plug-in that you downloaded from GitHub (and have open on a separate tab):

    • In the REST API URL field, type https://{{main_url}}.

    • In the URL Request Path field, type the following:

      /subscriptions/{{Subscription_ID}}/resourceGroups/{{ResourceGroupName}}/providers/Microsoft.DataFactory/factories/{{FactoryName}}/pipelineruns/{{RUNID}}/queryActivityruns?api-version=2018-06-01&startTime="+(Get-Date).ToString('yyyy-MM-dd')+"&endTime="+(Get-Date).AddDays(1).ToString('yyyy-MM-dd')+"&pipelineName={{PipelineName}}".

    • From the Method drop-down list, select POST.

    • In Headers, in Editor View, type the following:

      Content-Type=application/json&Authorization=Bearer {{REFRESHTOKEN}}.

    • In Body, in Editor View, type the following:

      {"orderBy":[{"orderBy":"ActivityName","order":"DESC"}],"lastUpdatedBefore":"2040-12-31T23:59:59.999"}

  4. In the Output Handling tab, create output handling rules for the following objectives, according to the definitions in the Azure Data Factory plug-in that you downloaded from GitHub:

    • Save the activity name from the output

    • Save the start time as a parameter

    • Save the end time as a parameter
    • Save the run ID as a parameter
    • Save execution details

  5. Click Save.

Testing Your Plug-inLink copied to clipboard

This procedure describes how to run a test execution of the plug-in, to ensure that plug-in jobs can complete successfully.

Begin

  1. Click Test.

    Plug-in steps and attributes are first validated. You can continue with the test even if validation issues are found.

  2. On the Test tab, select an Agent for the test, and optionally enter values in the Connection Profile fields and Job Properties fields.

  3. Click Run Test.

    The test runs and results are displayed on-screen.

Publishing and Deploying Your Plug-inLink copied to clipboard

This procedure describes how to publish your plug-in to Control-M/EM and deploy it to an Agent, so that it is available in Control-M.

Begin

  1. Click Publish & Deploy.

    Plug-in steps and attributes are first validated. You can continue to publish even if warnings are found.

  2. In the Publish & Deploy pane, from the Deploy plug-in to selected agents drop-down list, select the Agents where you want to deploy the plug-in.
  3. Click Apply.