Data Pipeline Orchestration with Airflow

As data systems grow, manually running scripts and managing dependencies becomes impossible to sustain. This course teaches you how to automate, monitor, and troubleshoot your data pipelines using Airflow, the industry standard for workflow orchestration.\n\nYou will transition from writing fragile scripts to designing resilient, self-healing data pipelines. By studying real-world patterns, you will understand how to coordinate complex data movements, handle failures gracefully, and maintain full visibility over your data infrastructure.\n\nWhat you'll learn:\n- Understand foundational orchestration concepts, directed acyclic graphs (DAGs), and basic Airflow architecture.\n- Write clean and maintainable pipelines using the modern TaskFlow API and standard operators.\n- Manage task dependencies, execution schedules, and catchup policies for reliable data delivery.\n- Implement robust error handling, retries, and alerting mechanisms to keep pipelines running smoothly.\n- Apply modern testing practices for your DAGs using pytest to catch configuration errors early.\n- Configure connections, variables, and secrets management to keep your workflow credentials secure.\n\nThe course begins with essential terminology and the core mechanics of workflow orchestration before guiding you through building and structuring your first DAGs. You will then explore advanced scheduling patterns, testing workflows, and best practices for production-ready environments.\n\nThis course is designed for aspiring data engineers, software developers, and data analysts looking to automate their data processes. No prior experience with orchestration tools is required, though a basic understanding of Python is helpful.\n\nStart learning today and gain control over your data engineering pipelines.