In today's world, Apache Airflow is a topic that has captured the attention of people from all walks of life. The importance of Apache Airflow can be seen in its influence on society, politics, economics and culture. With the increasing relevance of Apache Airflow in our lives, it is crucial to understand its impact and the implications it has on our daily lives. In this article, we will take a closer look at Apache Airflow and explore its many facets, from its origin to its evolution over time. In addition, we will analyze how Apache Airflow has marked a before and after in different aspects of society and how it continues to shape our present and future.
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Original author(s) | Maxime Beauchemin / Airbnb |
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Developer(s) | Apache Software Foundation |
Initial release | June 3, 2015 |
Stable release | 2.10.5[1] ![]() |
Repository | |
Written in | Python |
Operating system | Windows, macOS, Linux |
Type | Workflow management platform |
License | Apache License 2.0 |
Website | airflow |
Apache Airflow is an open-source workflow management platform for data engineering pipelines. It started at Airbnb in October 2014[2] as a solution to manage the company's increasingly complex workflows. Creating Airflow allowed Airbnb to programmatically author and schedule their workflows and monitor them via the built-in Airflow user interface.[3][4] From the beginning, the project was made open source, becoming an Apache Incubator project in March 2016 and a top-level Apache Software Foundation project in January 2019.
Airflow is written in Python, and workflows are created via Python scripts. Airflow is designed under the principle of "configuration as code". While other "configuration as code" workflow platforms exist using markup languages like XML, using Python allows developers to import libraries and classes to help them create their workflows.
Airflow uses directed acyclic graphs (DAGs) to manage workflow orchestration. Tasks and dependencies are defined in Python and then Airflow manages the scheduling and execution. DAGs can be run either on a defined schedule (e.g. hourly or daily) or based on external event triggers (e.g. a file appearing in Hive[5]). Previous DAG-based schedulers like Oozie and Azkaban tended to rely on multiple configuration files and file system trees to create a DAG, whereas in Airflow, DAGs can often be written in one Python file.[6]
Three notable providers offer ancillary services around the core open source project.
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