Data Orchestration & Workflow Management (Airflow/Prefect) is a hands-on technical course focused on building, scheduling, and monitoring complex data workflows in modern analytics environments. The course begins by explaining the need for orchestration in data engineering, where pipelines involve multiple dependent tasks that extract, transform, and load data across systems on a scheduled or event-driven basis. Learners are introduced to fundamental orchestration concepts such as DAGs (Directed Acyclic Graphs), task dependencies, retries, failure handling, parallel execution, dynamic scheduling, and event-based triggers. Apache Airflow is taught as the industry-standard orchestration tool, starting with its architecture—including scheduler, web server, executor, and metadata database. Students create and manage DAGs using Python code, define task dependencies, implement conditional logic, and schedule jobs using cron or time-based expressions. Airflow’s built-in operators, sensors, hooks, and XComs are explored to integrate with databases, cloud storage, REST APIs, and message queues. Learners build modular workflows with reusable components and apply best practices for naming, documentation, and DAG modularization. They explore Airflow’s UI for real-time job tracking, failure analysis, and manual overrides. Logging, alerting, and SLA monitoring are configured using Slack, email, and third-party tools. In parallel, the course dives into Prefect, a newer orchestration tool designed for Python-native, dynamic workflows with easier setup and observability. Students compare Prefect’s flow and task objects, asynchronous execution, and built-in state management with Airflow’s model. They deploy workflows using Prefect Cloud and Prefect Orion, handle parameterized and mapped tasks, and create event-driven flows triggered by APIs or file arrivals. Error handling, retries, timeouts, and conditional branching are also implemented in Prefect. Learners understand how to integrate both tools with cloud platforms such as AWS, GCP, and Azure for managing data pipelines across services like S3, BigQuery, Snowflake, Databricks, and Redshift. Containerized deployments using Docker and Kubernetes are covered for scalable orchestration, including Helm charts, DAG versioning, and Git-based CI/CD pipelines. The course includes observability modules where learners integrate Airflow or Prefect with Prometheus, Grafana, Datadog, or OpenTelemetry to visualize pipeline health and performance metrics. Real-world use cases include orchestrating ELT workflows, data validation jobs, machine learning model training, and data quality checks. Advanced topics such as backfilling, subDAGs, trigger rules, and task concurrency limits are covered for enterprise-scale orchestration needs. Security is addressed through role-based access control (RBAC), authentication mechanisms, secret management, and audit logs. By the end of the course, learners build end-to-end production-ready orchestration systems, capable of managing hundreds of interdependent jobs with reliability, scalability, and transparency. They also design and maintain robust CI/CD workflows for pipeline deployment, implement monitoring dashboards for SLA violations, and design for resilience against task and system-level failures. This course is ideal for data engineers, analytics engineers, DevOps practitioners, and cloud architects responsible for automating and managing complex data workflows in enterprise environments. By mastering both Airflow and Prefect, learners will be equipped with versatile orchestration skills applicable to traditional data pipelines, real-time systems, and hybrid cloud-native architectures.