Build reliable, scalable, and traceable data pipelines with modern workflow orchestration and data lineage management. This course explores how to improve pipeline reliability, transparency, and governance using Apache Airflow, OpenLineage, and data observability.

Designed for data professionals with basic Python, SQL, and pipeline knowledge, you'll compare Apache Airflow, Prefect, and Dagster while exploring DAG dependencies, workflow execution, and column-level data lineage. Learn how idempotency, atomicity, and data contracts strengthen pipeline reliability. Examine cross-cloud orchestration, state management, concurrency, scaling, and monitoring strategies to troubleshoot failures and optimize workflows. Discover how data catalogs support discovery, privacy, ownership, and cost attribution. Evaluate CI/CD practices, circuit breakers, and blue-green deployments to manage pipeline changes safely and protect downstream consumers. Through e-commerce, financial reporting, and real-time advertising case studies, you'll analyze orchestration strategies, assess lineage tools, and make informed decisions about pipeline reliability and auditability. What makes this course distinctive is its integrated approach to workflow orchestration, data lineage, operational reliability, and governance, helping you build trust in data-driven operations.