Migrating From Apache Oozie To Airflow
The following blog post was inspired by Astronomer's webinar on how to migrate from Oozie to Airflow by Ben Spivey and Dylan Storey.
Many enterprises are looking to migrate from Apache Oozie to Airflow. For those who may not know, Apache Oozie is an established open-source workflow scheduler system designed to manage Hadoop jobs. In contrast, Apache Airflow is an open-source platform that enables users to programmatically author, schedule, and monitor workflows. Airflow is written in Python and employs directed acyclic graphs (DAGs) to define workflows.
What is driving this shift towards Apache Airflow? Below, I have highlighted some key pain points associated with Oozie that contribute to this migration trend:
Declining Community Support
Oozie is tightly integrated with the Hadoop ecosystem. In the past, Oozie has been the go-to for orchestrating Hadoop jobs like MapReduce, Hive, and Pig. However, its open-source community support has seen better days. A quick glance at the commit activity on GitHub suggests a decline in community engagement.
XML Workflows
Oozie workflows are XML-based, which can be cumbersome and error-prone to edit manually compared to modern orchestrators like Airflow which leverage Python to do this.
Counter Intuitive Workflow Definition
Oozie requires explicit Start, End, and Kill nodes, while Airflow's Directed Acyclic Graph (DAG) definition implies workflow structure. This fundamental difference showcases Airflow's intuitive design.
Airflow is designed with modern data engineering and data science in mind, supporting a wider array of workloads beyond Hadoop. It's quickly become the industry standard for data orchestration, thanks to its flexible, code-first approach.
In contrast to Oozie, the project boasts a thriving open-source community, as seen in the bustling commit activity on GitHub.
The fact that Airflow's Workflows are defined in Python makes them intuitive to work with offering the benefits of a full programming language that is both powerful and human-friendly.
Airflow also has managed cloud services like GCP CloudCompose, Astronomer and AWS MWAA
Furthermore, testing and QA are streamlined with Airflow, allowing for parallel, isolated testing environments, making it easier to validate data correctness and performance before promoting to production.
The transition from Oozie to Airflow is not just a change of tools; it's an upgrade to a more sustainable, community-driven, and developer-friendly platform.
