Apache Airflow Best Practices: A practical guide to orchestrating data workflow with Apache Airflow

Confidently orchestrate your data pipelines with Apache Airflow by applying industry best practices and scalable strategies

Key Features
Seamlessly migrate from Airflow 1.x to 2.x and explore the key features and improvements in version 2.x
Learn Apache Airflow workflow authoring through practical, real-world use cases
Discover strategies to optimize and scale Airflow pipelines for high availability and operational resilience
Purchase of the print or Kindle book includes a free PDF eBook
Book Description
Data professionals face the challenge of managing complex data pipelines, orchestrating workflows across diverse systems, and ensuring scalable, reliable data processing. This definitive guide to mastering Apache Airflow, written by experts in engineering, data strategy, and problem-solving across tech, financial, and life sciences industries, is your key to overcoming these challenges.

Covering everything from Airflow fundamentals to advanced topics such as custom plugin development, multi-tenancy, and cloud deployment, this book provides a structured approach to workflow orchestration. You’ll start with an introduction to data orchestration and Apache Airflow 2.x updates, followed by DAG authoring, managing Airflow components, and connecting to external data sources. Through real-world use cases, you’ll learn how to implement ETL pipelines and orchestrate ML workflows in your environment, and scale Airflow for high availability and performance. You’ll also learn how to deploy Airflow in cloud environments, tackle operational considerations for scaling, and apply best practices for CI/CD and monitoring.

By the end of this book, you’ll be proficient in operating and using Apache Airflow, authoring high-quality workflows in Python, and making informed decisions crucial for production-ready Airflow implementations.

What you will learn
Explore the new features and improvements in Apache Airflow 2.0
Design and build scalable data pipelines using DAGs
Implement ETL pipelines, ML workflows, and advanced orchestration strategies
Develop and deploy custom plugins and UI extensions
Deploy and manage Apache Airflow in cloud environments such as AWS, GCP, and Azure
Plan and execute a scalable deployment strategy for long-term growth
Apply best practices for monitoring and maintaining Airflow
Who this book is for
This book is ideal for data engineers, developers, IT professionals, and data scientists looking to optimize workflow orchestration with Apache Airflow. It's perfect for those who recognize Airflow’s potential and want to avoid common implementation pitfalls. Whether you’re new to data, an experienced professional, or a manager seeking insights, this guide will support you. A functional understanding of Python, some business experience, and basic DevOps skills are helpful. While prior experience with Airflow is not required, it is beneficial.

Table of Contents
Getting Started with Airflow 2.0
Core Airflow Concepts
Components of Airflow
Basics of Airflow and DAG Authoring
Connecting to External Sources
Extending Functionality with UI Plugins
Writing and Distributing Custom Providers
Orchestrating a Machine Learning Workflow
Using Airflow as a Driving Service
Airflow Ops: Development and Deployment
Airflow Ops Best Practices: Observation and Monitoring
Multi-Tenancy in Airflow
Migrating Airflow