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

Abstract

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 improved by augmenting them with automated metadata generation, data cataloging, data quality checks, anomaly detection, and enforcement of governance policies have expanded. By combining Generative AI with cloud-native data engineering services, organizations can develop self-managing data ecosystems that can sense the context of data, develop semantic metadata, self-identify data quality problems, and autonomously recommend solutions to the problem with minimal human input. AWS Glue, Amazon S3, Amazon Lake Formation, Amazon Athena, Amazon Redshift, Amazon Lambda, Amazon Bedrock, and Amazon SageMaker are all components of a complete suite of cloud services available from Amazon Web Services (AWS) that can be deployed as part of an intelligent governance framework. We propose a Hybrid Data Engineering and Generative AI Architecture for Intelligent Data Governance, Metadata Management and Automated Data Quality Assessment on AWS. The suggested framework involves automating data ingestion pipelines, extracting metadata, orchestrating governance processes, implementing Generative AI-based semantic understanding systems, and incorporating machine learning-based data quality evaluation tools. The architecture can be automated to classify data sets, create business metadata, validate policies, discover data lineage, score quality, identify anomalies, and report on governance. These generative AI models are being used for schema interpretation, business description, identification of sensitive information, and governance actions recommendation based on organizational policies. This architecture has four main components: Data Engineering Layer, Metadata Intelligence Layer, Generative AI Governance Layer, and Automated Data Quality Assessment Layer. Together these layers help to achieve data lifecycle management and enhance governance, compliance, metadata completeness, and data reliability. Experimental results indicate that metadata quality accuracy, metadata governance automation, precision of metadata quality assessment, precision of anomaly detection performance and speed of operations are greatly enhanced over traditional governance systems. The proposed architecture helps create an intelligent, scalable and cloud-native governance ecosystem to support the modern enterprise data management. By combining data engineering methods and Generative AI capabilities, businesses can shift the data governance model from a reactive administrative process to a proactive and intelligent decision support system. These results show that AI governance models can significantly improve data asset trustworthiness, availability, and business value, while minimizing governance complexity and costs.