What makes Airflow great?

• Write workflows as if you’re writing programs
• Jobs can pass parameters to other jobs downstream
• Logic within workflows (instead of logic hidden ‘inside’ a program)
• Handle errors and failures gracefully
• Community and community support, size of community
• Ease of deployment of workflow changes (continuous integration)
• Job testing through airflow itself
• Low requirements for hardware setup
• built-in authentication details with encrypted passwords and extra details
• Easy environmental awareness through airflow variables
• Resource pooling for shared resources
• The most common of tasks already implemented and usable
• Accessibility of log files and other meta-data through the web GUI
• Supporting documentation
• Growing user base and contributions
• Extensibility of the framework
• Ease of installation and automated redeployments
• Dynamic DAGs
• Conditional execution in job flow diagrams
• Easy to reprocess historical jobs by date
• Usability of the web interface, low number of clicks to get to where you want
• Detailed info about landing times, processing times and SLA misses
• Temporarily turning workflows on and off
• Easy to re-run processing for specific intervals
• Can (re)run only parts of the workflow and dependent tasks
• Schedules are defined in code, not in a separate tool and database
• Can run tasks based on whether the previous run succeeded or failed
• Implement trigger rules for tasks
• AJAX/Rest API for job manipulation
• Jobs/tasks are run in a context, the scheduler passes in the necessary details
• Can verify what is running on airflow and see the actual code
• Work gets distributed across your cluster at the task level, not at the DAG level