2026· International Conference on Artificial Intelligence, Data Science and Computer Technology· pp. 345-351· 0 citations
TL;DR
This paper explores performance tuning techniques for cloud data workflows, focusing on both batch processing and real-time streaming, and contributes practical guidelines for optimizing cloud-based data pipelines in diverse operational contexts.
Abstract
This paper explores performance tuning techniques for cloud data workflows, focusing on both batch processing and real-time streaming. It addresses key challenges in scalability, efficiency, and latency reduction to optimize data handling in cloud environments. Various strategies for resource allocation, load balancing, and data partitioning are analyzed to enhance throughput and minimize processing delays. The study evaluates the impact of tuning parameters on system performance through experimental results and case studies. Emphasis is placed on balancing cost-effectiveness with computational demands. Insights into adaptive optimization approaches for dynamic workloads are also provided. The findings demonstrate significant improvements in processing speed and resource utilization. This work contributes practical guidelines for optimizing cloud-based data pipelines in diverse operational contexts.
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