Let’s examine this in detail by looking at the Transform task in isolation since it is in the middle of the data pipeline. extract_task > transform_task > load_taskĪll of the processing shown above is being done in the new Airflow 2.0 DAG as well, but it is all abstracted from the DAG developer.from xcom and instead of saving it to end user review, just prints it out.A simple Load task which takes in the result of the Transform task, by reading it.This computed value is then put into xcom, so that it can be processed by the next task.A simple Transform task which takes in the collection of order data from xcom.This data is then put into xcom, so that it can be processed by the next task.In this case, getting data is simulated by reading from a hardcoded JSON string.A simple Extract task to get data ready for the rest of the data pipeline.xcom_pull ( task_ids = "transform", key = "total_order_value" ) xcom_push ( "total_order_value", total_value_json_string ) In this case, getting data is simulated by reading from a
A simple Extract task to get data ready for the rest of the data.Documentation that goes along with the Airflow TaskFlow API tutorial is.the TaskFlow API using three simple tasks for Extract, Transform, and Load.This is a simple data pipeline example which demonstrates the use of.A more detailed explanation is given below.Īirflow/example_dags/tutorial_taskflow_api.py Here is a very simple pipeline using the TaskFlow API paradigm. The data pipeline chosen here is a simple pattern with three separate Extract, Transform, and Load tasks. This tutorial builds on the regular Airflow Tutorial and focuses specifically on writing data pipelines using the TaskFlow API paradigm which is introduced as part of Airflow 2.0 and contrasts this with DAGs written using the traditional paradigm. 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.
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