A Hybrid Data Engineering and Generative AI Architecture for Intelligent Data Governance, Metadata Management, and Automated Data Quality Assessment on AWS

The increasing complexity of enterprise data ecosystems has thrown new challenges at the problem of data governance, metadata management and data quality assurance. Cloud-based platforms are becoming more and more important for organizations to store, process and analyze massive amounts of structured, semi-structured and unstructured data from business applications, Internet of Things (IoT) devices, customer interactions, social media, and transactional systems. Cloud technologies offer scalable infrastructure for data management, but traditional governance practices can find it challenging to ensure high-quality data, enforce compliance policies and maintain consistency of metadata in distributed environments. The adoption of data-driven decision-making has created a critical need for more intelligent, automated and scalable governance mechanisms as enterprises go through this transition.With the transition to data-driven decision-making processes, the need for more intelligent, automated and scalable governance mechanisms has become critical. With the recent development of Generative Artificial Intelligence (GenAI), the ways in which traditional data engineering practices can be