#Astronomer apache airflow code

Airflow provides the workflow management capabilities that are integral to modern cloud-native data platforms. The combination of these features makes it easier for data teams and ops personnel to monitor performance and troubleshoot errors, as well as rapidly diagnose data outages. Apache Airflow is especially useful for creating and managing complex workflows like the data pipelines that crisscross cloud and on-premises environments. Back then, data engineering was a different world, largely focused on regularly scheduled batch jobs that often involved ugly systems with words like Hive and Druid. Thanks to its integrations with Slack, email, and other services, Airflow can be configured to send real-time alerts in response to performance issues or data outages. Apache Airflow, the oldest of the three, is a battle-tested and reliable solution that was born out of Airbnb and created by Maxime Beauchemin. The rich Airflow UI helps you manage your data pipelines, providing granular logging capabilities, and generating detailed lineage metadata.

#Astronomer apache airflow code

Airflow’s built-in logic allows you to focus on making sure your data pipelines are reliable and performant - without worrying about writing custom code to manage complicated dependencies or recover from task failures.įigure 1: An example of a DAG (current as of Airflow 2.5.1) You can combine operators to create DAGs that automate complex workflows - like the data extraction, transformation, and loading tasks involved in ETL. Hundreds of Airflow operators are the building blocks of DAGs, giving you pre-built functions you can use to simplify common tasks, like running SQL queries and Python scripts, or interacting with cloud storage and database services. In Airflow, workflows are abstracted as directed acyclic graphs, or DAGs. At the scheduled time, Airflow runs the workflow,Ĭoordinating dependencies between tasks, detecting task failures, and Airflow allows you to programmatically define the tasks that make up aĭata pipeline or other workflow, as well as specify when that workflow