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 defining and coordinating batch-job pipelines, dependencies, workflow execution, and failures. Workflows and dependency graphs are specified in Python, and tasks can also trigger work outside Python. Its documentation describes task support for Hadoop, Hive, Pig, and Spark, plus HDFS and local file abstractions. Those abstractions make file operations atomic, helping avoid pipelines being left with partial data. The Luigi server includes a web interface for searching and filtering tasks and viewing dependency graphs. It also supports command-line integration and use from Python code. The documentation describes pipelines with thousands of tasks that may run for days or weeks, including Hadoop jobs, database transfers, and machine-learning algorithms. The package is self-hosted, with no registration wall. Install the latest stable version with `pip install luigi`; adding `[toml]` enables TOML-based configuration. Documentation lists Python 3.10, 3.11, 3.12, and 3.13 as tested versions. A mailing list is available for discussion and questions.