Cloud computing and data engineering systems have become indispensable for modern enterprises, powering critical applications across diverse domains. However, ensuring high availability and reliability in these systems remains a significant challenge due to their inherent complexity and scale. Traditional fault tolerance mechanisms, such as static redundancy and check pointing, often lack the adaptability required to address dynamic and unpredictable failures effectively. This research explores the integration of Artificial Intelligence (AI) to enable dynamic fault tolerance, proposing a comprehensive framework that leverages AI-driven strategies for fault detection, prediction, and recovery.

The proposed framework utilizes advanced AI techniques, including machine learning and deep learning, to analyse telemetry and system log data in real time, enabling proactive fault management. A novel predictive model is introduced to anticipate potential failures, while decision-making algorithms orchestrate rapid recovery processes, minimizing downtime and optimizing resource utilization.

Through extensive simulations and real-world case studies, the framework demonstrates significant improvements over traditional methods, achieving lower mean time to recovery (MTTR) and enhanced system uptime. This study also highlights the practical challenges of implementing AI-driven fault tolerance, including data quality and ethical considerations, while identifying opportunities for future integration with emerging technologies like quantum computing.

The findings underscore the transformative potential of AI in redefining fault tolerance for cloud computing and data engineering, paving the way for more resilient and adaptive systems

Abstract

Environmental assessment is deemed more important as climate change, bio-diversity and resource conservation continue to present several challenges. Another is that traditional monitoring approaches may not give timely, relevant data especially when the environment is distant or rapidly changing. The present work aims to discuss the implementation of artificial intelligence-facilitated edge computing and cloud support as an innovative paradigm in environmental analysis. Edge computing helps to perform the data processing at the edge to help minimize latency time, and applying AI helps make the system more precise and helps with prediction. In this process, cloud integration becomes an enabler of these activities in the form of scalable storage, collaborative analytics, and in the form of control.

This is an applied research mapping the possible applications and pointing success stories of these technologies: detection of wildfire in Australia, flood in South East Asia, and wildlife monitoring in African savannas. It also talks about essential issues that need to be solved like security, power usage,e, and access, and provides information on how they can be solved. Future trends and innovation are also discussed in the paper to understand the significance of public-private partnership collaboration to promote AI-edge solutions in environmental monitoring.

Data is expanding at a very faster rate, hence, there is need to apply smart methods on the handling and analyzing of the information. This paper will explore, in detail, how AI plays a part in data engineering; with specific reference to the impact of AI on data engineering work flows. The paper also discusses key opportunities in conventional data science which includes scalability, real time data processing, and data quality assurance. It recruits goals that should be realized through the leverage of artificial intelligence in data integration, the data pipeline, and modelling. Proposing the probabilistic model, focusing on the questions of the machine learning algorithm, and natural language processing, the work introduces the notion of intelligent data engineering. Special attention is paid to the experimental evaluations, which confirm the efficiency of the offered solution in regard to the increased velocities, decreased inaccuracy, and optimal rates of analysis as to the traditional techniques. The findings underscore that AI can make the data engineering for better by giving additional freedom in its process. Thus, in this work, it is suggested that AI should be further developed for application in data engineering in order to meet increasing demands of data-driven business for change and differentiation.