Airflow apache12/27/2023 ![]() Parameterizing your scripts is built into the core of Airflow using the powerful Jinja templating engine. Elegant: Airflow pipelines are lean and explicit.Extensible: Easily define your own operators, executors and extend the library so that it fits the level of abstraction that suits your environment.This allows for writing code that instantiates pipelines dynamically. Dynamic: Airflow pipelines are configuration as code (Python), allowing for dynamic pipeline generation.Similar from a run to the next, this allows for clarity around Of the structure of the tasks in your workflow as slightly more dynamic Workflows are expected to be mostly static or slowly changing. One to the other (though tasks can exchange metadata!). Note that you have to specify correct Airflow version and python versionsĪirflow is not a data streaming solution. Requirements are per major/minor python version (3.6/3.7/3.8). "known-to-be-working" requirement files in the requirements folder. In order to have repeatable installation, however, starting from Airflow 1.10.10 we also keep a set of This means that from time to time plain pip install apache-airflow will not work or will Our dependencies as open as possible (in setup.py) so users can install different versions of libraries ![]() Libraries usually keep their dependencies open andĪpplications usually pin them, but we should do neither and both at the same time. Installing it however might be sometimes trickyīecause Airflow is a bit of both a library and application. Installing from PyPIĪirflow is published as apache-airflow package in PyPI. Official container (Docker) images for Apache Airflow are described in IMAGES.rst. Please visit the Airflow Platform documentation (latest stable release) for help with installing Airflow, getting a quick start, or a more complete tutorial.ĭocumentation of GitHub master (latest development branch): ReadTheDocs Documentationįor further information, please visit the Airflow Wiki. Stable version is currently incompatible with Python 3.8 due to a known compatibility issue with a dependent library.Stable version requires at least Python 3.5.3 when using Python 3.Sqlite - latest stable (it is used mainly for development purpose)Īdditional notes on Python version requirements.Can I use the Apache Airflow logo in my presentation?Īpache Airflow is tested with: Master version (2.0.0dev).The rich user interface makes it easy to visualize pipelines running in production, monitor progress, and troubleshoot issues when needed. Rich command line utilities make performing complex surgeries on DAGs a snap. The Airflow scheduler executes your tasks on an array of workers while following the specified dependencies. Use Airflow to author workflows as directed acyclic graphs (DAGs) of tasks. Versionable, testable, and collaborative. ![]() When workflows are defined as code, they become more maintainable, Apache Airflow (or simply Airflow) is a platform to programmatically author, schedule, and monitor
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