AIRFLOW OPERATOR SERIES

The tasks in Airflow are instances of вЂњoperatorвЂќ class and are implemented as small Python scripts. Task D will then be triggered when task B and C both complete successfully. For example, in the example, DAG below, task B and C will only be triggered after task A completes successfully. Dependencies are encoded into the DAG by its edges - for any given edge, the downstream task is only scheduled if the upstream task completed successfully. Besides its ability to schedule periodic jobs, Airflow lets you express explicit dependencies between different stages in your data pipeline.Д®ach ETL pipeline is represented as a directed acyclic graph (DAG) of tasks (not to be mistaken with SparkвЂ™s own DAG scheduler and tasks). Airflow BasicsДЄirflow is a generic workflow scheduler with dependency management.

We are happy to share that we have also extended Airflow to support Databricks out of the box. Of these, one of the most common schedulers used by our customers is Airflow. To support these complex use cases, we provide REST APIs so jobs based on notebooks and libraries can be triggered by external systems. While this feature unifies the workflow from exploratory data science to production data engineering, some data engineering jobs can contain complex dependencies that are difficult to capture in notebooks. One very popular feature of DatabricksвЂ™ Unified Data Analytics Platform (UAP) is the ability to convert a data science notebook directly into production jobs that can be run regularly. This blog post illustrates how you can set up Airflow and use it to trigger Databricks jobs. Today, we are excited to announce native Databricks integration in Apache Airflow, a popular open source workflow scheduler.

AIRFLOW OPERATOR SERIES

This blog post is part of our series of internal engineering blogs on Databricks platform, infrastructure management, integration, tooling, monitoring, and provisioning.

Airflow operator