Airflow

What is Airflow used for?

Data scientists use Airflow to automate and manage data pipelines. Airflow makes it easy to schedule and monitor jobs, track successes and failures, and share workflows with other data scientists. Airflow also allows data science teams to monitor ETL processes, ML training workflows, and many additional types of data pipelines.

Airflow vs. MLFlow

Both platforms provide tools for data engineering, machine learning, and model management. However, there are some distinct differences between the two platforms. Airflow is a platform for authoring, scheduling, and monitoring workflows. In contrast, MLflow is a platform for managing the end-to-end machine learning lifecycle, from tracking experiments to deploying models. As a result, MLflow is often used in conjunction with Airflow to provide a complete solution for data science pipelines. Another difference between Airflow and MLflow is that Airflow is written in Python, while MLflow is written in Java, which can be a significant advantage for data scientists who are already familiar with Python, as they will not need to learn a new language to use Airflow. Finally, Airflow offers more flexibility than MLflow with workflow authoring. Airflow allows users to author workflows as DAGs, while MLflow only allows workflows to be authored as linear chains of stages. This flexibility can be helpful for data scientists who want to experiment with different pipeline structures.

Getting started with Apache Airflow

To use Airflow, you need to install it on your Python environment. The easiest way to install Airflow is using pip:

pip install airflow

Alternatively, you can download the source code from the Airflow website and install it manually. Once you have Airflow installed, you need to create an airflow.cfg file in your home directory. This file contains Airflow's configuration settings. The most important setting is the executor, which specifies the type of worker that will be used to run your tasks. There are three types of executors: Sequential, Local, and Celery. The Sequential executor runs tasks sequentially on a single machine. The Local executor runs tasks on a single machine but uses multiple workers to parallelize task execution. The Celery executor distributes task execution across a cluster of machines. You can also specify other settings in the airflow.cfg file, such as the backend database, queue, and logging options. For more information about scheduling and triggers, notifications, and pipeline monitoring in Airflow, read the official Airflow documentation.