AI-Driven Optimization of Data Ingestion and Transformation in Cloud Systems

The rapid evolution of cloud systems has underscored the need for more efficient methods of data ingestion and transformation. This study introduces an AI-driven framework designed to optimize these critical processes by leveraging advanced machine learning techniques. By dynamically adapting to varying workloads and heterogeneous data formats, the proposed approach streamlines resource allocation and minimizes latency, thereby enhancing overall system performance. Comprehensive experiments demonstrate significant improvements in processing speed and data quality, validating the framework’s ability to meet the demands of modern cloud environments. These findings pave the way for more resilient and scalable cloud architectures that can autonomously manage complex data pipelines, ultimately contributing to more efficient data-driven decision-making in diverse application domains