"OpenLineage in Data Engineering"
"OpenLineage in Data Engineering" is a comprehensive and authoritative guide for data professionals aiming to unlock the full potential of data lineage in modern analytics ecosystems. The book lays a strong foundation by demystifying core lineage concepts, terminology, and models, articulating the critical business drivers behind data lineage such as compliance, auditability, and operational intelligence. It explores the unique challenges posed by today's distributed data environments and chronicles the evolution of lineage tooling, highlighting the emergence and significance of open standards in shaping the future of data engineering.
Delving into the principles and architecture of OpenLineage, the book offers a technical deep-dive into its schema, extensibility, and integration patterns with popular data orchestration and processing frameworks like Apache Airflow, dbt, Apache Spark, and Kubernetes. Through practical guidance and reference architectures, readers learn how to instrument data pipelines, secure lineage information, scale event ingestion, and ensure observability in both batch and real-time data systems. Richly detailed chapters also address the complexities of event transport, schema evolution, performance optimization, and advanced lineage analytics such as impact analysis, root cause investigation, and audit trail generation.
Equipped for both practitioners and architects, "OpenLineage in Data Engineering" bridges the gap between theory and hands-on implementation. It demonstrates how to operationalize OpenLineage for governance, compliance, and data quality management, featuring strategies for integrating with metadata catalogs, automating policy enforcement, and establishing traceability and trust across diverse data landscapes. The book concludes with advanced topics and forward-looking insights, including automated lineage extraction through AI, federated lineage in hybrid environments, and the evolving OpenLineage ecosystem—making it an indispensable reference for building resilient, transparent, and scalable data platforms.