Cloud-Centric Data Engineering: AI-Driven Mechanisms for Enhanced Data Quality Assurance

Abstract

In the era of digital transformation, organizations are increasingly reliant on cloud-centric data engineering frameworks to manage vast amounts of data efficiently. The exponential growth of data, coupled with its critical role in driving business intelligence and AI/ML applications, underscores the necessity of robust data quality assurance (DQA). However, traditional approaches to DQA are often inadequate for addressing the scale, complexity, and dynamic nature of cloud-based data environments. This paper explores the integration of artificial intelligence (AI) mechanisms in cloud-centric data engineering to enhance data quality assurance processes.

Through detailed case studies in healthcare, e-commerce, and finance, the paper highlights practical applications of AI-driven DQA, showcasing their impact on operational efficiency and decision-making. Furthermore, it evaluates key technologies and tools, including cloud-native services like AWS Glue, Google Cloud Data Quality, and Microsoft Azure Data Factory, alongside open-source AI platforms. Challenges such as algorithmic biases, ethical considerations, and cost implications are also addressed, providing a balanced perspective on the adoption of AI for DQA.

Finally, the paper outlines future directions, predicting advancements in autonomous systems, federated learning, and edge computing that will shape the next generation of cloud-centric data engineering. By leveraging AI to enhance data quality assurance, organizations can unlock the full potential of their data assets, driving innovation and maintaining a competitive edge in the evolving digital landscape