Scalable Data Engineering with AI Optimization for Cloud Driven Distributed Computing Systems Cyber Security Healthcare
DOI:
https://doi.org/10.15662/IJEETR.2022.0404008Keywords:
Scalable Data Engineering, Artificial Intelligence, Cloud Computing, Distributed Computing Systems, Cyber Security, Healthcare Analytics, Big Data Engineering, Machine Learning Optimization, Intelligent Automation, Cloud Infrastructure, Predictive Analytics, Distributed Systems, Healthcare Data Management, Cyber Defense Systems, Real-Time AnalyticsAbstract
The rapid growth of cloud computing, artificial intelligence, distributed computing, big data analytics, and healthcare digitalization has significantly transformed enterprise and healthcare infrastructures worldwide. Modern healthcare and cybersecurity ecosystems continuously generate massive volumes of structured and unstructured data from cloud platforms, IoT devices, medical systems, enterprise applications, distributed sensors, and intelligent monitoring environments. Traditional data processing architectures often struggle to manage scalability, operational complexity, real-time analytics, cybersecurity threats, and intelligent healthcare data management within distributed computing environments. Scalable data engineering integrated with AI optimization techniques has emerged as a transformative solution for improving distributed analytics, intelligent automation, cybersecurity resilience, and healthcare operational intelligence. This research presents a comprehensive framework for scalable data engineering with AI optimization for cloud-driven distributed computing systems in cybersecurity and healthcare environments. The proposed architecture integrates cloud-native data pipelines, distributed computing frameworks, machine learning optimization models, intelligent automation systems, privacy-preserving mechanisms, and cybersecurity analytics to support secure, scalable, and intelligent enterprise operations. Experimental evaluation demonstrates improvements in data processing scalability, predictive analytics accuracy, intelligent threat detection, healthcare operational efficiency, distributed resource optimization, and cloud infrastructure resilience. The findings indicate that AI-optimized scalable data engineering frameworks provide intelligent, adaptive, secure, and high-performance solutions for future cloud-based distributed computing ecosystems supporting cybersecurity and healthcare applications.
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