Practical Data Pipelines with Apache Airflow

Tired of manually running data scripts and struggling to track their dependencies? Learn how to automate and manage complex data processes with Apache Airflow, the industry-standard tool for workflow orchestration. This course provides a comprehensive, text-based introduction to building reliable data pipelines. You will move from core concepts to practical application, learning to define, schedule, and monitor your workflows entirely through code. By the end, you'll have the foundational skills to build robust, repeatable, and observable data pipelines for your own projects. What you'll learn: - Understand the core concepts of Airflow, including DAGs, Operators, Tasks, and its underlying architecture. - Build your first data pipeline from scratch, defining task dependencies and scheduling regular runs. - Utilize the modern TaskFlow API to write clean, intuitive, and Python-native data pipelines. - Learn to use Hooks and Connections to interact securely with external systems like databases and cloud services. - Practice passing data between tasks effectively using Airflow's XComs mechanism. - Define data dependencies and lineage using modern features like Data-Aware Scheduling and Assets. - Explore different Airflow executors and understand the principles of scaling your data orchestration. The course begins with the foundational principles of data orchestration and Airflow's key components. You'll then progress through practical, written exercises to build and manage complete data pipelines, reinforcing your understanding at every step. This course is designed for complete beginners. No prior experience with Apache Airflow or data orchestration is required, though a basic understanding of Python will be beneficial. Start building automated data pipelines today.