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Airflow 2.0 tutorial8/23/2023 doc_md = dedent ( """\ # Load task A simple Load task which takes in the result of the Transform task, by reading it from xcom and instead of saving it to end user review, just prints it out. """ ) load_task = PythonOperator ( task_id = "load", python_callable = load, ) load_task. This computed value is then put into xcom, so that it can be processed by the next task. doc_md = dedent ( """\ # Transform task A simple Transform task which takes in the collection of order data from xcom and computes the total order value. """ ) transform_task = PythonOperator ( task_id = "transform", python_callable = transform, ) transform_task. 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. ![]() doc_md = dedent ( """\ # Extract task A simple Extract task to get data ready for the rest of the data pipeline. loads ( total_value_string ) print ( total_order_value ) extract_task = PythonOperator ( task_id = "extract", python_callable = extract, ) extract_task. 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. ![]() 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.Using Python environment with pre-installed dependencies.Virtualenv created dynamically for each task.Using the TaskFlow API with complex/conflicting Python dependencies."""Example DAG demonstrating the usage of the TaskGroup.""" from import DAG from import BashOperator from import DummyOperator from import days_ago from airflow.utils. See the License for the # specific language governing permissions and limitations # under the License. You may obtain a copy of the License at # Unless required by applicable law or agreed to in writing, # software distributed under the License is distributed on an # "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY # KIND, either express or implied. ![]() The ASF licenses this file # to you under the Apache License, Version 2.0 (the # "License") you may not use this file except in compliance # with the License. See the NOTICE file # distributed with this work for additional information # regarding copyright ownership. # Licensed to the Apache Software Foundation (ASF) under one # or more contributor license agreements.
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