Apache Airflow Open-Source Workflow with Python

Episode Deep Dive

Guests Introduction and Background

• Leah Cole: Works in the data and AI space at Google Cloud, first learned Python in college and has grown to love how straightforward it is for tasks like data engineering and machine learning.
• Jarek Potiuk: Initially coded in COBOL, Delphi, and many other languages before falling in love with Python’s simplicity and power around six years ago. Active in open-source communities, particularly around Apache Airflow.
• Kaxil Naik: Started coding Python around 2016 after using Java and R. He has become a prominent contributor and committer to Apache Airflow and works at Astronomer helping organizations run Airflow at scale.

What to Know If You’re New to Python

If you’re new to Python and want to follow along with the discussion of Airflow, here are a few key tips to ensure you’re ready for this episode’s concepts:

• Understand Python’s basic syntax (loops, functions, imports).
• Learn how virtual environments work so you can safely install and manage Python packages.
• Be comfortable with writing simple scripts and command-line interactions.

Key Points and Takeaways

1. Why Use Apache Airflow for Data Orchestration Apache Airflow is a Python-based orchestrator for complex workflows, especially data pipelines. Instead of manually scheduling with cron, you define tasks in Python (as directed acyclic graphs, or DAGs) and let Airflow handle dependencies, error handling, and scheduling.
   • Links and Tools:
     • Apache Airflow main site
     • GitHub repo for Apache Airflow
2. Defining Workflows in Python (DAGs as Code) Airflow workflows (DAGs) are just Python scripts, making it easy to leverage conditionals, loops, and standard libraries. This allows teams to generate large, programmatic DAGs and share them via version control without forcing a separate XML or JSON config approach.
   • Links and Tools:
     • Airflow DAG Concept Docs
     • PyPI (Python Package Index)
3. Operators, Sensors, and Providers Airflow’s “operators” and “sensors” let you run and monitor tasks on external services, databases, or APIs. The community contributes “provider packages” for everything from Google Cloud to Slack, so you can orchestrate tasks anywhere.
   • Links and Tools:
     • Airflow Providers
     • Slack Provider for Airflow
4. Installation and Production Setups While you can install Airflow with pip install apache-airflow, a more common approach is using Docker and Docker Compose for local development. For production, there are Helm charts for Kubernetes deployments or managed services like Astronomer, AWS’s Managed Workflows for Apache Airflow (MWAA), and Google Cloud Composer.
   • Links and Tools:
     • Airflow Docker Docs
     • Astronomer
     • AWS MWAA
     • Google Cloud Composer
5. Monitoring and Debugging with the Airflow UI Airflow has a built-in web UI that shows DAGs, their status, and logs of individual task runs. You can visually see which tasks succeeded or failed, re-run tasks that require new data, or handle partial restarts if needed. Airflow 2.0 introduced a refreshed UI with features like auto-refresh for live feedback.
   • Links and Tools:
     • Airflow 2.0 Release Notes
6. Community and Contributing The Apache Airflow community is large and welcoming, with an active Slack workspace and frequent meetups. Contributions range from small documentation fixes to writing providers for new services and can grow into more significant roles such as committers or PMC members.
   • Links and Tools:
     • Airflow Slack Community (Invitation link is on Airflow’s website)
     • Airflow Summit
7. The CLI and REST API Beyond the UI, Airflow offers a feature-rich command-line interface for tasks like backfilling historical data and debugging performance issues. With Airflow 2.x, you also have a robust, OpenAPI-based REST API, enabling custom UIs and integrations in any language.
   • Links and Tools:
     • Airflow CLI Reference
     • Stable REST API Docs
8. Handling Failures and Reruns With DAGs as code, you can define retries for tasks and fallback strategies (like skipping or sending alerts). If your data changes or a job fails halfway, you can trigger partial reruns or fully backfill older data to ensure accuracy in downstream systems.
   • Links and Tools:
     • Backfill Command Reference
9. Future Directions: Timetables and Async Sensors The discussion highlighted Airflow’s evolving features, like more flexible timetables to handle complex scheduling (e.g., the “third trading day” use case) and deferred operators for sensors that use async instead of continuous polling. These moves aim to improve performance and reduce resource usage.
   • Links and Tools:
     • Airflow AIPs (Airflow Improvement Proposals)
10. Managed vs. Self-Hosted Solutions Airflow can be run in many ways: fully managed (Cloud Composer on Google Cloud, AWS MWAA, or Astronomer) or self-hosted on Kubernetes, VMs, or Docker. Each approach offers different trade-offs, such as less maintenance vs. more flexibility in customization.

• Links and Tools:
  • Astronomer Registry
  • AWS MWAA docs
  • GCP Cloud Composer docs

Interesting Quotes and Stories

• On Python’s Simplicity: “After years of programming in Java, I was amazed at what I could do in one line of Python code.” - Jarek
• On DAGs as code: “It’s puzzles. You get to define everything in Python, including your loops and conditionals, and that’s what makes Airflow powerful.” - Leah
• On Contributing to Open Source: “If something drives you nuts, fix it or clearly file an issue. You don’t have to wait for somebody else to fix it for you!” - Leah

Key Definitions and Terms

• DAG (Directed Acyclic Graph): A collection of tasks organized in such a way that no cycles can form, meaning no task depends on itself indirectly.
• Operator (Airflow): A class defining a single task, e.g., running a Bash script, spinning up a Kubernetes pod, or submitting a Spark job.
• Sensor (Airflow): A special operator that waits for a condition (such as a file appearing in S3 or GCS) before letting downstream tasks proceed.
• Provider (Airflow): A separately packaged integration that bundles operators, hooks, and sensors for external systems (e.g., AWS, Google Cloud).
• Backfill: Rerunning tasks for past dates or data intervals to “fill in” missing or updated results without re-running everything from scratch.

Learning Resources

Here are a few resources to level up your Python skills and to better understand or contribute to Airflow.

• Python for Absolute Beginners: Perfect for those brand new to coding in Python.
• Up and Running with Git: Great for contributing to open source projects like Airflow and keeping your code organized.
• Async Techniques and Examples in Python: Airflow sensors and operators increasingly use async features, so this course will help deepen your understanding.

Overall Takeaway

Apache Airflow has become the go-to orchestration platform for data pipelines and beyond because it speaks Python all the way. Rather than forcing you into rigid UIs or config files, Airflow leverages the full power of Python to code tasks and schedules dynamically. A welcoming open-source community, extensive integrations (“providers”), and rich dev tooling make it possible for new users, experienced Pythonistas, and large enterprise teams to use Airflow for everything from advanced machine learning workflows to simple daily tasks. If you’re looking to streamline your data operations (or orchestrate just about anything) and you enjoy working in Python, Airflow is a must-have in your toolkit.