Apache Airflow

What is Apache Airflow?

Apache Airflow is an open-source tool to programmatically author, schedule, and monitor workflows. It is one of the most robust platforms used by Data Engineers for orchestrating workflows or pipelines. You can easily visualize your data pipelines’ dependencies, progress, logs, code, trigger tasks, and success status.

With Airflow, users can author workflows as Directed Acyclic Graphs (DAGs) of tasks. Airflow’s rich user interface makes it easy to visualize pipelines running in production, monitor progress, and troubleshoot issues when needed. It connects with multiple data sources and can send an alert via email or Slack when a task completes or fails. Airflow is distributed, scalable, and flexible, making it well suited to handle the orchestration of complex business logic.

Apache Airflow is used for the scheduling and orchestration of data pipelines or workflows. Orchestration of data pipelines refers to the sequencing, coordination, scheduling, and managing complex data pipelines from diverse sources. These data pipelines deliver data sets that are ready for consumption either by business intelligence applications and data science, machine learning models that support big data applications.

Airflow Architecture

Understanding the components and modular architecture of Airflow allows you to understand how its various components interact with each other and seamlessly orchestrate data pipelines.

• Dynamic: Airflow pipelines are configured as code (Python), allowing for dynamic pipeline generation. This allows for users to write code that instantiates pipelines dynamically.
• Extensible: Easily define your own operators and executors, and extend the library so it fits the level of abstraction that suits your environment.
• Elegant: Airflow pipelines are lean and explicit. Parameterizing your scripts is built into the core of Airflow using the Jinja templating engine.
• Scalable: Airflow has a modular architecture and uses a message queue to communicate with and orchestrate an arbitrary number of workers.

Benefits of Using Apache Airflow for ETL/ELT

Here are a few reasons why Airflow wins over other platforms:

• Community: Airflow was started back in 2015 by Airbnb. The Airflow Community has been growing ever since. We have more than 1000 contributors contributing to Airflow, and the number is growing at a healthy pace.
• Extensibility and Functionality: Apache Airflow is highly extensible, which allows it to fit any custom use cases. The ability to add custom hooks/operators and other plugins helps users implement custom use cases easily and not rely on Airflow Operators completely. Since its inception, several functionalities have already been added to Airflow. Built by numerous Data Engineers, Airflow is a complete solution and solves countless Data Engineering Use Cases. Although Airflow is not perfect, the community is working on a lot of critical features that are crucial to improving the performance and stability of the Airflow platform.
• Dynamic Pipeline Generation: Airflow pipelines are configuration-as-code (Python), allowing for dynamic pipeline generation. This allows for writing code that creates pipeline instances dynamically. The data processing we do is not linear and static.