Governance Frameworks for Large-Scale ETL Ecosystems in Complex Data Environments

Abstract

Extract, Transform, and Load (ETL) pipelines have been at the heart of modern data-centric organizations, enabling the integration of large volumes of data from disparate sources into centralized data stores. With the increasing need for organizations to operate within complex data ecosystems characterized by distributed architectures, cloud computing, and diverse data governance needs, traditional ETL management practices have often been inadequate in ensuring transparency, reliability, and compliance to various data governance needs. This research presents a framework for the governance of ETL ecosystems within large-scale data environments, with the aim of addressing the complexities, scalability, and compliance needs of modern organizations. The research draws from a synthesis of existing research in the areas of data governance, workflow orchestration, and metadata-driven architectures to propose a conceptual framework applicable to enterprise data environments. The proposed framework focuses on the need for centralized metadata management, monitoring, and policy-based ETL pipeline management to ensure reliability and accountability in the operation of ETL systems. The research contributes to the emerging discourse on the need for data governance within enterprise environments through the proposed framework for the governance of ETL systems, applicable to large-scale data integration workflows within modern data analytics environments. The findings of the research show the potential for organizations to enhance the trustworthiness, reliability, and compliance of their data through the adoption of data governance principles within ETL systems.