![]() Travel & accommodation expenses are not included.From datetime import datetime, timedelta from textwrap import dedent # The DAG object we'll need this to instantiate a DAG from airflow import DAG # Operators we need this to operate! from import BashOperator with DAG ( "tutorial", # These args will get passed on to each operator # You can override them on a per-task basis during operator initialization default_args = """ ) t3 = BashOperator ( task_id = "templated", depends_on_past = False, bash_command = templated_command, ) t1 > Įverything looks like it’s running fine so let’s run a backfill.īackfill will respect your dependencies, emit logs into files and talk to.This course is brought to you by Xebia Data.An integrated development environment (IDE), such as P圜harm is helpful for the hands-on labs. The hands-on labs are run in Google Cloud Composer. A week before the training, we will ask you about any dietary requirements and share literature if there's a need to prepare. ![]() Yes, I want to know more about Apache Airflow!Īfter registering for this training, you will receive a confirmation email with practical information. ![]() You will find an overview of these training courses here. In addition to this Apache Airflow training, Xebia Academy also offers other Data Engineering courses together with Xebia Data. Your trainer is a data guru who enjoys sharing his or her experiences to help you work with the latest tools. Xebia Data works with experts in their field who are always on the lookout for the most innovative ways to get the most out of data. This Data Engineering training is brought to you by Xebia Data. Xebia Data is part of Xebia, just like Xebia Academy.
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