Create a Pipeline run from an Azure Data Factory.

Launch an Azure DataFactory pipeline from Kestra. Data Factory contains a series of interconnected systems that provide a complete end-to-end platform for data engineers.

yaml
type: "io.kestra.plugin.azure.datafactory.CreateRun"
yaml
id: azure_datafactory_create_run
namespace: company.team

tasks:
  - id: create_run
    type: io.kestra.plugin.azure.datafactory.CreateRun
    factoryName: exampleFactoryName
    pipelineName: examplePipeline
    resourceGroupName: exampleResourceGroup
    subscriptionId: 12345678-1234-1234-1234-12345678abc
    tenantId: "{{ secret('DATAFACTORY_TENANT_ID') }}"
    clientId: "{{ secret('DATAFACTORY_CLIENT_ID') }}"
    clientSecret: "{{ secret('DATAFACTORY_CLIENT_SECRET') }}"
Properties

Subscription ID

Tenant ID

Default { "maxDuration": "PT1H", "interval": "PT5S" }

Check the frequency configuration.

Client ID

Client ID of the Azure service principal. If you don't have a service principal, refer to create a service principal with Azure CLI.

Client Secret

Service principal client secret. The tenantId, clientId and clientSecret of the service principal are required for this credential to acquire an access token.

Factory name

Default {}

Pipeline parameters.

PEM Certificate

text
Your stored PEM certificate.
The tenantId, clientId and clientCertificate of the service principal are required for this credential to acquire an access token.

Pipeline name

Resource Group name

Default true

Wait for the end of the run.

Allowing to capture job status & logs.

Run ID

The ID of the pipeline run created in Azure Data Factory

Format uri

URI of a kestra internal storage file containing the activities and their inputs/outputs.

Default PT5S
Format duration

Frequency at which Kestra checks if the pipeline has finished.

Default PT1H
Format duration

Maximum duration of the task until timing out the task.