Apache Airflow vs Luigi

Apache Airflow is a platform for authoring, scheduling, and monitoring workflows written in Python. It is designed for batch workflows with a defined beginning and end that run on a schedule. Its web application lets users manage and schedule workflows, trigger DAGs manually, inspect task status and logs, backfill runs, and rerun failed tasks. A modular architecture uses a message queue to coordinate workers and can orchestrate an arbitrary number of them. Built-in operators connect workflows to Google Cloud Platform, Amazon Web Services, Microsoft Azure, and other services. The community maintains more than 80 separately installable provider packages, which can be upgraded or downgraded independently of Airflow core. Airflow can read connections, variables, and configuration from secret backends, but those integrations do not support writes. JWT authentication is used for its public REST API and internal Execution API. From Airflow 3 onward, sensitive credentials are masked in logs, the interface, and API output. Airflow is free to download and use under the Apache License.

Luigi is a free Python package for building and coordinating pipelines of batch jobs. It manages dependencies, workflow execution, visualization, failure handling and command-line integration. The documentation presents it as workflow plumbing for long-running work such as Hadoop jobs, database transfers and machine-learning jobs, including pipelines with thousands of tasks that can take days or weeks. Documented systems include Hadoop, Hive, Pig, Spark, HDFS, databases and local files. A Luigi server provides a web interface for searching and filtering tasks, while its HDFS and local filesystem abstractions support atomic file operations. The project instructs users to install the latest stable version with `pip install luigi`; TOML configuration is available through the optional `luigi[toml]` installation. The repository says Python 3.10 through 3.14 are tested and identifies Apache-2.0 as the license. Luigi is self-hosted, and Spotify's Data Team maintains the project. A mailing list is linked for discussion and questions. Its security policy directs people to report sensitive issues through Spotify's bug bounty program rather than GitHub.