What you'll learn:
- Understand Airflow Architecture and core components - DAG Processor, Metadata DB, Scheduler, Executor, Webserver, Workers, and how they interact
- You'll also build 2 end-to-end projects including AWS S3, Spark, Databricks, AWS Glue, APIs, etc. to solidify your learning and gain strong hands-on
- Build industry-grade DAGs using modern TaskFlow API for complex dependencies, branching & subDAGs
- Learn XCOMs and kwargs for inter-task communication and passing data between tasks in the DAGs
- Master Operators and Hooks to interact with external systems like databases, APIs & cloud service
- Schedule DAGs with Cron, Delta Triggers, and Event-based triggers covering real-world use cases
- Monitor & troubleshoot DAGs using the Airflow UI, logs, and metrics to ensure reliability
- Create connections in Airflow to securely store credentials for external systems
- Implement Incremental Data Loading & Backfilling of DAGs to manage historical data
- Implement error handling and retry mechanisms to ensure robust and fault-tolerant workflows
- Develop Airflow Assets to build data aware pipelines and manage data dependencies effectively
- Scale Airflow with multiple workers and queues to distribute workload and optimize performance
This is a COMPLETE Apache Airflow 3 Bootcamp you need in 2026 to become a PRO Apache Airflow Developer.
Whether you're a beginner or a working professional looking to upskill, this course will guide step by step with a hands-on, practical, and engaging lectures (doodle illustrations).
GAIN STRONG HANDS-ON WITH:
Airflow Fundamentals & Architecture - Learn the fundamentals of Airflow and understand how DAG Processor, Metadata DB, Scheduler, Executor, Webserver, Workers work together to orchestrate data pipelines.
DAG Authoring - Master the modern TaskFlow API to build complex DAGs with dependencies, branching, and subDAGs for real-world data workflows. Learn XCOMs and kwargs for inter-task communication and passing data between tasks in the DAGs.
Operators & Hooks - Learn how to use built-in Operators and Hooks to interact with external systems like databases, APIs, and cloud services. Build custom connectors to integrate with any system.
Scheduling & Triggers - Schedule your DAGs using Cron, Delta Triggers, Event-based triggers, and specific time intervals to cover a wide range of real-world use cases.
Incremental Load & Backfilling - Implement incremental load strategies and backfill historical data to ensure your pipelines can handle both new and existing data effectively.
Monitoring & Troubleshooting - Use the Airflow UI, logs, and metrics to monitor your DAGs, identify issues, and troubleshoot errors to ensure reliable pipeline execution.
Security & Retries - Create connections in Airflow to securely store credentials for external systems. Implement error handling and retry mechanisms to ensure robust and fault-tolerant workflows.
Airflow Assets & Scaling - Develop Airflow Assets to build data aware pipelines and manage data dependencies effectively. Scale Airflow with multiple workers and queues to distribute workload and optimize performance.
End-to-End Projects - Additionally, build 2 real-world projects integrating AWS S3, Spark, Databricks, AWS Glue, APIs, and more to make your learning practical with real-world applications of Airflow.
WHAT MAKES THIS COURSE UNIQUE?
Super Engaging Lectures - No boring theory here! I explain every concept in a clear and beginner-friendly way using real-life examples and doodle visuals.
Deep Dive into Every Topic – I don’t just scratch the surface. You'll understand the “why” and “how” behind every feature.
Strong Hands-On Focus - You learn by doing. Each chapter includes so many practical labs to solidify your understanding and build real-world skills.
DISCLAIMER - This course is independently created and not affiliated with or endorsed by Airflow. All content is original, designed for educational purposes only. It is based on public documentation, real-world scenarios, and personal experience. All trademarks belong to their respective owners. For the most accurate and updated information, refer to the official Airflow documentation.
