Airflow is an open-source task time-tabler that enables users to programmatically write, construct, inspect and distribute workflows on the cloud. It is a crowd-sourced software that has been around for that past seven years, originally developed by the data team at Airbnb and donated the Apache foundation.
While there are alternative more commercial options available Airflow exceeds most expectations by being affordable and flexible. While these may seem like two primary advantages let’s take a look at some of the other top benefits you can gain from setting up and making use of Airflow.
1. Warning System
The Airflow system is equipped with an automatic warning system for when any tasks are failed. The typical setting is for them to be emailed in the event of something malfunctioning, it can alternatively be set up to ping users through Slack using a call back if preferred.
2. Rest API
Airflow’s API does not restrict you to generating workflows from an internal source alone, it also enables the same through external sources while also being a data product on top of it. The rest API, make sure to replicate the model implemented whilst constructing pipelines, so as to create differentiated workflows like tailor made machine learning instruction procedures.
3. Comprehensive Log Interfaces
Airflow makes it simple to avail logs pertaining to various tasks conducted via its web-ui, this in turn leads to simpler task debugging while in production.
4. Built In Retry Policy
Airflow has an in built auto-retry policy that can be customized by:
- retries: retry attempts made prior to task failure
- retry_delays: delay in between retry attempts
- retry_exponential_backoff: to establish an exponential backoff amid retry attempts
- max_retry_delay: longest delay period in between retry attempts
5. Supervising Interface
Airflow comes with an interface that enables the supervision and management of various tasks their status and can also set off tasks while clearing other ones or DAGs runs.
Personalized sensors, hooks and operators can be designed to extend upon your extant Airflow capabilities. Community contributed operators can play a huge role in boosting your Airflow benefits.
7. Task Dependency Administration
Airflow is great at administrating a number of different dependencies, whether it is a file or partition presence, dag runs status or task completion through a specific sensor. Airflow is also adept at tackling task dependency notion like branching.
8. Automate Queries, Python Code or Jupyter Notebook
Airflow contains many operators set in place to execute code. It has an operator to match all of its databases since these are set up in python, they have a PythonOperator which can swiftly enable porting python code to production.
Papermill is an annex to Jupyter Notebook that enables the containment and commencement of notebooks, it is backed up by Airflow PapermillOperator. Netflix has offered that an integration between Airflow and Papermill is best to mechanize and install notebooks in production.
9. Systematic Workflow Management
Airflow has a method that allows for the establishment of systematic workflows, Tasks for example can be created using fly within a dag. Though Sub-DAGs as well as XCom enable the generation of complicated workflows that are constantly changing.
Take for instance dynamic Dags that can be launched on the basis of variables or connections characterized using the Airflow UI.
Dags are a method of launching workflows, they can trigger an event of operations that can be singularly extracted in the case that they fail and start over again where the operation failed. Dags create a quaint concise form of a sequence of operations.
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