@dag(
    schedule_interval='@daily',
    start_date=pendulum.datetime(2023, 7, 1),
    catchup=False,
    max_active_runs=1,
    default_args=default_task_args
)
def load_data():
    # set `use_data_folder` to True to store temporary data on the `data` bucket. Use only when it does not fit on the local storage
    tasks = PipelineTasksGroup("pipeline_decomposed", use_data_folder=False, wipe_local_data=True)

    # import your source from pipeline script
    from github import github_repo_events

    source = github_repo_events("apache", "airflow")

    # modify the pipeline parameters 
    pipeline = dlt.pipeline(pipeline_name='pipeline_name',
                     dataset_name='dataset_name',
                     destination='duckdb',
                     full_refresh=False # must be false if we decompose
                     )
    # create the source, the "serialize" decompose option will converts dlt resources into Airflow tasks. use "none" to disable it
    tasks.add_run(pipeline, source, decompose="serialize", trigger_rule="all_done", retries=0, provide_context=True)

with PipelineTasksGroup("pipeline_decomposed", use_data_folder=False, wipe_local_data=True) as tasks:
  ...
  tasks.add_run(pipeline, source, decompose="serialize", trigger_rule="all_done", retries=0, provide_context=True)