Data Engineering with Apache Airflow: Foundations and Practice

As data environments grow in complexity, managing manual scripts becomes impossible. Apache Airflow provides a robust framework to programmatically author and monitor workflows, ensuring your data arrives on time and in the right format. This course takes you from understanding basic orchestration concepts to managing production-grade pipelines. You will learn to write clean, scalable code for your workflows and deploy them across various environments while following industry-standard best practices for data engineering. What you'll learn: - Understand core Airflow components including the scheduler, web server, and metadata database - Create and manage Directed Acyclic Graphs (DAGs) using the TaskFlow API and dynamic task mapping - Configure different executors such as Celery and Kubernetes to scale your workloads - Implement advanced pipeline logic with sensors, hooks, and cross-DAG dependencies - Secure your environment using Role-Based Access Control (RBAC) and data encryption - Monitor system health and performance using integrated logging and observability patterns The material begins with foundational terminology and architecture before moving into practical DAG construction. You will explore how to integrate Airflow with external services and manage infrastructure using containerized environments like Docker and cloud-based Kubernetes clusters. This course is designed for beginners in data engineering or DevOps who want to learn workflow orchestration from the ground up. No prior experience with Airflow is required. Start building reliable data pipelines today.