Introduction to Apache Airflow
The powerful open-source platform that lets you automate, orchestrate, and monitor complex workflows with code—bringing structure and scalability to your data pipelines.
Just about a month ago, Airflow rolled out its biggest update yet—version 3—and it’s packed with new tools and improvements that caught the attention of data folks everywhere, including me. It’s exciting, it’s different, and it feels like the start of something new—not just for the platform, but for how we think about building and running data workflows.
So, in this new series, I’ll be exploring Apache Airflow from the very beginning—step by step—and diving into the most important updates that came with Airflow 3. Whether you're just getting started or already using Airflow in your projects, I hope you’ll follow along as we learn and grow together. Here's to fresh starts and powerful tools.
What is Apache Airflow?
Apache Airflow is an open-source tool for designing, scheduling, and monitoring workflows—basically, it helps you manage and automate tasks that need to run in a specific order.
Think of it like a smart to-do list for your data processes: you define a series of tasks (like pulling data, transforming it, and storing it), and Airflow makes sure each step happens in the right order, at the right time, and alerts you if something goes wrong.
It’s especially popular in data engineering and analytics teams because it allows you to write your workflows in Python, making them easy to version control, test, and maintain.
How was Apache Airflow Developed?
Apache Airflow was created in 2014 by Maxime Beauchemin while he was working at Airbnb. At the time, Airbnb’s data infrastructure was growing rapidly, and they needed a better way to manage complex data workflows—especially ones with many interdependent steps that had to run on a schedule. Existing tools weren’t flexible or scalable enough, so Airflow was born as an internal solution.
Soon after, Airbnb open-sourced the project, and it quickly gained traction in the data engineering community. It later became part of the Apache Software Foundation, which helped establish it as a reliable, community-driven project used across industries.
Which Problems Does Apache Airflow Solve?
Apache Airflow solves several critical problems in data engineering and workflow orchestration:
Dependency Management: You can define clear task relationships—what runs before or after what—and Airflow handles the execution order.
Scheduling: Automatically run tasks at specific times or intervals (e.g., daily ETL jobs).
Monitoring & Alerts: Track task status, retries, failures, and get notified when things go wrong.
Scalability: With the right executor setup (like Celery, Kubernetes), you can scale workflows across multiple machines.
Extensibility: Airflow works well with cloud platforms, databases, APIs, and custom tools thanks to its rich ecosystem of providers and plugins.
Reproducibility: Since workflows are written in Python, they can be version-controlled, tested, and reviewed like any other code.
Major Improvements Over the Years
Apache Airflow has seen significant growth and evolution since its early days. If you are interested in the change log, I recommend to check the official page. Below you can find the major improvements over the years:
Airflow 1.x (Initial Era)
Simple but powerful workflow management via Python code
Basic scheduling using cron expressions
Web UI for DAG (Directed Acyclic Graph) visualisation
Celery executor for distributed task execution
Airflow 2.0 (Released December 2020)
Stable REST API for integration and automation
TaskFlow API for writing more Pythonic workflows
Full scheduler HA (High Availability)
Smart sensors for more efficient waiting on external events
Major performance and scalability improvements
Airflow 2.3 – 2.7 (Feature Expansion)
Dynamic DAG generation
Better support for data-aware scheduling
Improved UI/UX and DAG versioning features
More integrations and provider packages (e.g., AWS, GCP, Databricks)
Airflow 3.0 (Released May 2025)
Removal of legacy Python 2 compatibility code
Better observability and monitoring features
Improved performance with async task execution support
Cleaner configuration management
Security enhancements, such as stricter access controls
Introduction of next-gen features like DAG-level alerts, native secrets management, and more extensible plugin architecture
Conclusion
Apache Airflow has grown from a simple internal solution at Airbnb into a powerful, flexible, and scalable workflow orchestration platform trusted by teams around the world. With each major release, it has evolved to address real-world data engineering challenges—from dependency management and scheduling to observability and integration. Airflow 3.0, in particular, marks a major step forward with its modernised architecture, better performance, and enhanced security.