Apache Airflow

 

What is Apache Airflow

 Airflow is a platform created by the community to programmatically author, schedule and monitor workflows. Airflow is a tool for automating and scheduling tasks and workflows . It is one significant scheduler for programmatically scheduling, authoring, and monitoring the workflows in an organization. It is mainly designed to orchestrate and handle complex pipelines of data. Initially, it was designed to handle issues that correspond with long-term tasks and robust scripts. However, it has now grown to be a powerful data pipeline platform. Airflow can be described as a platform that helps define, monitoring and execute workflows.

 

Why Use Apache Airflow

You can easily get a variety of reasons to use Apache airflow as mentioned below:

• This one is an open-source platform; hence, you can download Airflow and begin using it immediately, either individually or along with your team.
• It is extremely scalable and can be deployed on either one server or can be scaled up to massive deployments with a variety of nodes. 
• Airflow Apache runs extremely well with cloud environments; hence, you can easily gain a variety of options. 

• It was developed to work with the standard architectures that are integrated into most software development environments. . Also, you can have an array of customization options as well.
• Its active and large community lets you scale information and allows you to connect with peers easily.
• Airflow enables diverse methods of monitoring, making it easier for you to keep track of your tasks.
• Its dependability on code offers you the liberty to write whatever code you would want to execute at each step of the data pipeline. 

                        Principles

Scalable

Airflow has a modular architecture and uses a message queue to orchestrate an arbitrary number of workers. Airflow is ready to scale to infinity

Dynamic

Airflow pipelines are defined in Python, allowing for dynamic pipeline generation. This allows for writing code that instantiates pipelines dynamically.

Extensible

Easily define your own operators and extend libraries to fit the level of abstraction that suits your environment.

Elegant

Airflow pipelines are lean and explicit. Parametrization is built into its core using the powerful Jinja templating engine.

 

Features

Pure Python

No more command-line or XML black-magic! Use standard Python features to create your workflows, including date time formats for scheduling and loops to dynamically generate tasks. This allows you to maintain full flexibility when building your workflows.

 

Useful UI

Monitor, schedule and manage your workflows via a robust and modern web application. No need to learn old, cron-like interfaces. You always have full insight into the status and logs of completed and ongoing tasks.

Robust Integrations

Airflow provides many plug-and-play operators that are ready to execute your tasks on Google Cloud Platform, Amazon Web Services, Microsoft Azure and many other third-party services. This makes Airflow easy to apply to current infrastructure and extend to next-gen technologies.

Easy to Use

Anyone with Python knowledge can deploy a workflow. Apache Airflow does not limit the scope of your pipelines; you can use it to build ML models, transfer data, manage your infrastructure, and more.

Easy to Use

Anyone with Python knowledge can deploy a workflow. Apache Airflow does not limit the scope of your pipelines; you can use it to build ML models, transfer data, manage your infrastructure, and more.

Integrations

 

 

 

History

Airflow was started in October 2014 by Maxime Beauchemin at Airbnb. It was open source from the very first commit and officially brought under the Airbnb GitHub and announced in June 2015.

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