Introduction to Apache Airflow — History, Use Cases & Why You Should Learn It
👋 Hey there, data enthusiast!
Welcome to the first post in my “Learn Airflow” blog series, where we break down Apache Airflow step-by-step — the same way I do on my YouTube playlist.
If you’re just starting out with Airflow, or curious why every data team seems to love it, you’re in the right place.
Each blog in this series will follow one of my tutorial videos, with extra explanations, tips, and resources to help you learn at your own pace. Let’s get started with the basics!
Prefer watching? This blog is based on my first YouTube video:
👉 “Introduction to Apache Airflow — History, Use Cases & Why You Should Learn It”
What is Apache Airflow?
Apache Airflow is an open-source workflow orchestration tool created to programmatically author, schedule, and monitor workflows as code. It’s designed to handle complex data pipelines that involve many interdependent steps.
Instead of using cron jobs or shell scripts to manage ETL tasks, Airflow allows you to define tasks and dependencies in Python, making your workflows:
- Reusable
- Version-controlled
- Easy to monitor and debug
Sample Airflow Dag
A Quick History
Airflow was originally developed at Airbnb in 2014 to manage the growing complexity of their data pipelines. It was open-sourced shortly after and joined the Apache Software Foundation in 2016.
You can see Initial Release in Open source GitHub Repo here
Since then, it has become a foundational tool in the modern data stack and is supported by a vibrant open-source community. Major tech companies like Google, Amazon, Twitter, and Lyft use or support Airflow in production.
Key Features
- DAG-based structure: Workflows are Directed Acyclic Graphs
- Python-native: Define workflows as Python code
- Scalable execution: Works on single VMs, Kubernetes, or cloud-managed services
- Web UI: Monitor and manage DAGs visually
- Extensible: Create custom operators, hooks, and plugins
Why Should You Learn Airflow?
If you’re a:
- Data engineer — Airflow is a core tool for orchestration
- Cloud architect — You’ll need it for pipelines across GCP, AWS, and hybrid systems
- ML engineer — Automate model training workflows
- Beginner in data — Airflow gives you insight into how real-world pipelines run at scale
And most importantly, Airflow is everywhere:
- Google Cloud Composer is based on Airflow
- AWS MWAA (Managed Workflows for Apache Airflow) runs it as a managed service
- Astronomer provides commercial-grade Airflow solutions
Learning Airflow opens doors to cloud-native pipeline orchestration, interview opportunities, and hands-on, production-ready architecture experience.
About Me
As an experienced Fully certified (11x certified) Google Cloud Architect, Google Developer Expert(GDE), with over 9+ years of expertise in Google Cloud Networking,Data ,Devops, Security and ML, I am passionate about technology and innovation. Being a Champion Innovator and Google Cloud Architect, I am always exploring new ways to leverage cloud technologies to deliver innovative solutions that make a difference.
If you have any queries or would like to get in touch, you can reach me at Email address — vishal.bulbule@techtrapture.com or connect with me on LinkedIn at https://www.linkedin.com/in/vishal-bulbule/. For a more personal connection, you can also find me on Instagram at https://www.instagram.com/vishal_bulbule/?hl=en.
Additionally, please check out my YouTube Channel at https://www.youtube.com/@techtrapture for tutorials and demos on Google Cloud.
