Data Cataloging and Lineage Tracking in Data Engineering Ecosystems

Abstract

As organizations increasingly rely on data-driven decision-making, the need for effective data cataloging and lineage tracking has become critical in modern data engineering ecosystems. Data cataloging provides a centralized inventory of data assets, enhancing discoverability, and promoting data reuse, while lineage tracking ensures transparency by documenting the data's journey through complex workflows. This paper explores the architecture and implementation of data cataloging and lineage tracking systems, highlighting the role of metadata management, governance frameworks, and automated tools. We examine the challenges of scalability, integration with diverse data sources, and maintaining accuracy in data lineage tracking, especially in distributed and cloud-based environments. Additionally, the paper discusses the application of AI and machine learning techniques for automated metadata extraction and lineage detection. Case studies demonstrate the effectiveness of these systems in improving data quality, regulatory compliance, and operational efficiency, underscoring their value in enabling more robust and trustworthy data engineering processes.