Data Engineering Workflow Orchestration with Airflow

О курсе

Modern data platforms rely on automated, reliable workflows to move and process data at scale. Data Engineering Workflow Orchestration with Apache Airflow equips you with the skills to design, build, monitor, and deploy production-ready data pipelines using one of the industry’s leading orchestration tools. As organizations shift toward scalable and fault-tolerant data systems, mastering workflow orchestration has become essential for data engineers and backend developers. Through structured lessons and hands-on demonstrations, you’ll learn how Apache Airflow schedules, executes, and monitors workflows across distributed systems. The course covers workflow architecture, task scheduling, operators, sensors, TaskFlow API, data pipeline design, monitoring, retries, logging, debugging, dynamic workflows, performance optimization, and CI/CD-based production deployment practices. By the end of this course, you will be able to: • Design and build scalable data pipelines using Apache Airflow. • Implement workflow orchestration with operators, sensors, and task dependencies. • Monitor, debug, and optimize pipelines using logging, retries, and performance controls. • Deploy and manage production-ready workflows with version control and CI/CD integration. • Apply reliability and data quality best practices in real-world environments. This course is ideal for aspiring data engineers, backend developers, DevOps professionals, analytics engineers, and software engineers looking to strengthen their workflow automation and production data management skills. A basic understanding of Python programming, databases, and data concepts is recommended, though prior experience with Apache Airflow is not required. Join us to master workflow orchestration and build reliable, production-grade data systems with confidence.

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Edureka

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