def main():

    # Azure subscription ID
    subscription_id = '<Azure subscription ID>'

    # This program creates this resource group. If it's an existing resource group, comment out the code that creates the resource group
    rg_name = '<Azure resource group name>'

    # The data factory name. It must be globally unique.
    df_name = '<Data factory name>'        

    # Specify your Active Directory client ID, client secret, and tenant ID
    credentials = ServicePrincipalCredentials(client_id='<AAD application ID>', secret='<AAD app authentication key>', tenant='<AAD tenant ID>')
    resource_client = ResourceManagementClient(credentials, subscription_id)
    adf_client = DataFactoryManagementClient(credentials, subscription_id)

    rg_params = {'location':'eastus'}
    df_params = {'location':'eastus'}

    # create the resource group
    # comment out if the resource group already exits
    resource_client.resource_groups.create_or_update(rg_name, rg_params)

    # Create a data factory
    df_resource = Factory(location='eastus')
    df = adf_client.factories.create_or_update(rg_name, df_name, df_resource)
    print_item(df)
    while df.provisioning_state != 'Succeeded':
        df = adf_client.factories.get(rg_name, df_name)
        time.sleep(1)

    # Create an Azure Storage linked service
    ls_name = 'storageLinkedService'

    # Specify the name and key of your Azure Storage account
    storage_string = SecureString('DefaultEndpointsProtocol=https;AccountName=<Azure storage account>;AccountKey=<Azure storage authentication key>')

    ls_azure_storage = AzureStorageLinkedService(connection_string=storage_string)
    ls = adf_client.linked_services.create_or_update(rg_name, df_name, ls_name, ls_azure_storage)
    print_item(ls)

    # Create an Azure blob dataset (input)
    ds_name = 'ds_in'
    ds_ls = LinkedServiceReference(ls_name)
    blob_path= 'adftutorial/inputpy'
    blob_filename = 'input.txt'
    ds_azure_blob= AzureBlobDataset(ds_ls, folder_path=blob_path, file_name = blob_filename)
    ds = adf_client.datasets.create_or_update(rg_name, df_name, ds_name, ds_azure_blob)
    print_item(ds)

    # Create an Azure blob dataset (output)
    dsOut_name = 'ds_out'
    output_blobpath = 'adftutorial/outputpy'
    dsOut_azure_blob = AzureBlobDataset(ds_ls, folder_path=output_blobpath)
    dsOut = adf_client.datasets.create_or_update(rg_name, df_name, dsOut_name, dsOut_azure_blob)
    print_item(dsOut)

    # Create a copy activity
    act_name =  'copyBlobtoBlob'
    blob_source = BlobSource()
    blob_sink = BlobSink()
    dsin_ref = DatasetReference(ds_name)
    dsOut_ref = DatasetReference(dsOut_name)
    copy_activity = CopyActivity(act_name,inputs=[dsin_ref], outputs=[dsOut_ref], source=blob_source, sink=blob_sink)

    # Create a pipeline with the copy activity
    p_name =  'copyPipeline'
    params_for_pipeline = {}
    p_obj = PipelineResource(activities=[copy_activity], parameters=params_for_pipeline)
    p = adf_client.pipelines.create_or_update(rg_name, df_name, p_name, p_obj)
    print_item(p)

    # Create a pipeline run
    run_response = adf_client.pipelines.create_run(rg_name, df_name, p_name,
        {
        }
    )

    # Monitor the pipeilne run
    time.sleep(30)
    pipeline_run = adf_client.pipeline_runs.get(rg_name, df_name, run_response.run_id)
    print("\n\tPipeline run status: {}".format(pipeline_run.status))
    activity_runs_paged = list(adf_client.activity_runs.list_by_pipeline_run(rg_name, df_name, pipeline_run.run_id, datetime.now() - timedelta(1),  datetime.now() + timedelta(1)))
    print_activity_run_details(activity_runs_paged[0])

    # Create a trigger
    tr_name = 'mytrigger'
    scheduler_recurrence = ScheduleTriggerRecurrence(frequency='Minute', interval='15',start_time=datetime.now(), end_time=datetime.now() + timedelta(1), time_zone='UTC') 
    pipeline_parameters = {'inputPath':'adftutorial/inputpy', 'outputPath':'adftutorial/outputpy'}
    pipelines_to_run = []
    pipeline_reference = PipelineReference('copyPipeline')
    pipelines_to_run.append(TriggerPipelineReference(pipeline_reference, pipeline_parameters))
    tr_properties = ScheduleTrigger(description='My scheduler trigger', pipelines = pipelines_to_run, recurrence=scheduler_recurrence)    
    adf_client.triggers.create_or_update(rg_name, df_name, tr_name, tr_properties)

    # start the trigger
    adf_client.triggers.start(rg_name, df_name, tr_name)