Scripts keep crashing and stakeholders demand data they can trust. Late-night alerts drain your energy and delay business decisions. Manual fixes multiply as pipelines sprawl across clouds and clusters. You need orchestration that scales without sacrificing reliability or sleep. Apache Airflow promises order, yet its power can feel overwhelming. This book makes Airflow mastery achievable, practical, and immediately rewarding.
- Dynamic scheduling with Dataset API: Align complex, irregular jobs to real-world data availability.
- Taskflow API patterns: Write cleaner Python code, reduce boilerplate, and speed team onboarding.
- Container-native deployments: Run pipelines on Kubernetes for elastic scaling and cost control.
- Comprehensive testing strategies: Catch issues before production, slash incident time, protect reputation.
- Production-ready best practices: Logging, security, and monitoring that keep auditors and leaders happy.
- Custom operator design: Extend Airflow to any system, unlocking limitless integration possibilities.
Data Pipelines with Apache Airflow, Second Edition gathers five seasoned consultants into one definitive field guide. Their combined experience turns cutting-edge features into steps you can reproduce today. It is the trusted companion for every data engineer.
The book starts with Airflow architecture, then walks through DAG design, testing, deployment, and operations. Updated chapters reveal Taskflow, Dataset scheduling, and Kubernetes setups, explained through real projects, not toy examples. Clear language, diagrams, and downloadable code remove guesswork.
Finish the last page knowing your pipelines deploy reliably, recover gracefully, and scale effortlessly. Sleep through the night while Airflow delivers fresh, accurate data to every downstream consumer.
Ideal for data engineers, DevOps, machine-learning engineers, and Python-savvy analysts ready to level-up orchestration skills.
Table of Contents:
PART 1: GETTING STARTED
1 MEET APACHE AIRFLOW
2 ANATOMY OF AN AIRFLOW DAG
3 TIME-BASED SCHEDULING IN AIRFLOW
4 ASSET-AWARE SCHEDULING
5 TEMPLATING TASKS USING THE AIRFLOW CONTEXT
PART 2: BEYOND THE BASICS
6 DEFINING DEPENDENCIES BETWEEN TASKS
7 TRIGGERING WORKFLOWS WITH EXTERNAL INPUT
8 COMMUNICATING WITH EXTERNAL SYSTEMS
9 EXTENDING AIRFLOW WITH CUSTOM OPERATORS AND SENSORS
10 TESTING
PART 3: AIRFLOW IN PRACTICE
11 RUNNING TASKS IN CONTAINERS
12 BEST PRACTICES
13 PROJECT: FINDING THE FASTEST WAY TO GET AROUND NYC
PART 4: AIRFLOW IN PRODUCTION
14 PROJECT: KEEPING FAMILY TRADITIONS ALIVE WITH AIRFLOW AND GENERATIVE AI
15 OPERATING AIRFLOW IN PRODUCTION
16 SECURING AIRFLOW
17 AIRFLOW DEPLOYMENT OPTIONS
APPENDICES
APPENDIX A: RUNNING CODE SAMPLES
APPENDIX B: PROMETHEUS METRIC MAPPING
