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.
type: "io.kestra.plugin.azure.datafactory.createrun"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: 12345678-1234-1234-12345678abc
tenantId: "{{ secret('DATAFACTORY_TENANT_ID') }}"
clientId: "{{ secret('DATAFACTORY_CLIENT_ID') }}"
clientSecret: "{{ secret('DATAFACTORY_CLIENT_SECRET') }}"
YESSubscription ID
YESTenant ID
NO{
"maxDuration": "PT1H",
"interval": "PT5S"
}Check the frequency configuration.
YESClient 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.
YESClient Secret
Service principal client secret. The tenantId, clientId and clientSecret of the service principal are required for this credential to acquire an access token.
YESFactory name
YES{}Pipeline parameters.
YESPEM Certificate
Your stored PEM certificate.
The tenantId, clientId and clientCertificate of the service principal are required for this credential to acquire an access token.
YESPipeline name
YESResource Group name
NOtrueWait 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
uriURI of a kestra internal storage file containing the activities and their inputs/outputs.
YESPT5SdurationFrequency at which Kestra checks if the pipeline has finished.
YESPT1HdurationMaximum duration of the task until timing out the task.