Data Workflow Automation with Apache Airflow and Python

Managing data delivery manually through scripts and cron jobs often leads to errors and maintenance struggles as projects scale. This course teaches you how to use Apache Airflow to bring structure, reliability, and automation to your data engineering tasks. You will transition from writing isolated scripts to developing robust, repeatable workflows that handle scheduling and error reporting automatically. What you'll learn: - Understand the core architecture of Airflow including schedulers, workers, and executors. - Create Directed Acyclic Graphs (DAGs) to define clear task dependencies and execution logic. - Apply Python operators and the TaskFlow API to build modern, readable data pipelines. - Implement robust error handling, retries, and alerting to maintain pipeline health. - Practice scheduling complex workflows to run at specific intervals or based on external triggers. - Manage and monitor production-ready tasks using standard Airflow management tools. The course starts with foundational definitions and setup concepts, progressing through the creation of your first DAG and into advanced pipeline management techniques. It is designed for beginners and Python enthusiasts who want to enter the world of data engineering with no prior workflow orchestration experience. Begin your journey into automated data engineering and streamline your development process.