![]() ![]() xcom_pull ( task_ids = "transform", key = "total_order_value" ) total_order_value = json. xcom_push ( "total_order_value", total_value_json_string ) def load ( ** kwargs ): ti = kwargs total_value_string = ti. """ data_string = ' total_value_json_string = json. In this case, getting data is simulated by reading from a hardcoded JSON string. Documentation that goes along with the Airflow TaskFlow API tutorial is located () """ () def extract (): """ # Extract task A simple Extract task to get data ready for the rest of the data pipeline. datetime ( 2021, 1, 1, tz = "UTC" ), catchup = False, tags =, ) def tutorial_taskflow_api (): """ # TaskFlow API Tutorial Documentation This is a simple data pipeline example which demonstrates the use of the TaskFlow API using three simple tasks for Extract, Transform, and Load. Import json import pendulum from corators import dag, task ( schedule = None, start_date = pendulum. ![]() Accessing context variables in decorated tasks.Consuming XComs between decorated and traditional tasks.Adding dependencies between decorated and traditional tasks.Using the TaskFlow API with Sensor operators.Dependency separation using Kubernetes Pod Operator.Dependency separation using Docker Operator. ![]()
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |