What is Apache Airflow?

Apache Airflow is a workflow management platform used for orchestrating and scheduling data engineering pipelines and workflows. Airflow is a robust platform providing easy visualization of workflows to data engineers.

Apache Airflow provides a platform to author, schedule, and monitor data engineering pipelines and workflows programmatically. Airflow makes use of directed acyclic graphs (DAG) to define workflows. These DAGs are written in Python, making integrating different systems easy for data engineers. Airflow is distributed, scalable, and flexible, making it one of the top choices among data engineers.

Components of Apache Airflow

Apache Airflow is made up of the following components:

Webserver: A Flask server running on Gunicorn serves the Airflow UI.

Scheduler: Multi-threaded Python process written as DAG, determining the sequence and schedule of execution for different tasks.

Database: Typically, a Postgres database containing metadata and DAGs.

Executor: A mechanism for running the tasks. It runs within the scheduler.

Worker: It executes tasks as defined by the executor.

These components and their relationship can be seen in the following illustration:

Who uses Apache Airflow?

Multiple enterprises use Apache Airflow for several different use cases across various industries. 

1. Data scientists use Airflow to acquire, clean, and prepare datasets for model training.

2. Data engineers use Airflow to build Extract, Transform, and Load (ETL) pipelines.

3. Data analysts use Airflow to design complex SQL-based data pipelines to acquire and analyze data.

4. Data platform architects use Airflow to automate the movement of data throughout their system and manage complex data flows.

Benefits of Apache Airflow

Apache Airflow has multiple benefits, making it the top choice among data engineers for the orchestration and scheduling of data workflows and pipelines. Here are some of its top benefits:

• Open source

• Low cost

• Community supported

• Useful UI to monitor and troubleshoot pipelines

• Uses Python to define pipelines

• Easy to use

Apache Airflow is becoming an increasingly popular tool among data engineers to acquire, transform, and manage complex data using automated pipelines and workflows. The user-friendly UI makes the platform easy to use. Users can define workflows and pipelines using DAGs written in Python, which has made Airflow the go-to choice for many data professionals across different industries.