Abstract

The rapid proliferation of cloud infrastructures has transformed data management, enabling organizations to process, store, and analyse vast amounts of data with unprecedented efficiency. However, the reliability of data pipelines within these infrastructures remains a significant challenge, plagued by issues such as data latency, corruption, system downtime, and scalability constraints. Traditional approaches to ensuring pipeline reliability, including manual monitoring and reactive fault management, often fall short in meeting the demands of modern, high-volume data ecosystems.

This research explores the potential of artificial intelligence (AI)-driven solutions to enhance the reliability of data pipelines in cloud infrastructures. By leveraging machine learning and advanced analytic, AI offers innovative methods for real-time anomaly detection, predictive maintenance, and performance optimization. The study begins with a comprehensive review of the current state of data pipeline management in cloud environments, identifying key challenges and limitations of conventional techniques.

Findings reveal that AI-driven approaches significantly outperform traditional methods, offering proactive and scalable solutions for managing data pipelines. However, the study also addresses critical challenges, such as the computational cost of AI models, data quality issues, and ethical considerations surrounding data privacy. Future research directions include the integration of AI with edge computing and the development of lightweight, cost-effective AI models tailored for cloud infrastructures.