Intelligent Data Pipeline Frameworks for Cloud Based Enterprise Information Management with Predictive Analytics and Automation (International Journal of Advanced Research in Computer Science & Technology(IJARCST), 2026)
The rapid growth of enterprise data has significantly increased the need for intelligent data pipeline frameworks capable of efficiently managing, processing, and analyzing large-scale information within cloud computing environments. Modern organizations generate massive volumes of structured, semi-structured, and unstructured data from business applications, Internet of Things (IoT) devices, customer interactions, financial transactions, and operational systems. Conventional data management approaches often encounter limitations related to scalability, latency, automation, and security. This research proposes an intelligent cloud-based data pipeline framework that integrates predictive analytics, artificial intelligence, machine learning, automation technologies, and secure data management to improve enterprise information processing. The proposed framework incorporates automated data ingestion, real-time stream processing, distributed storage, intelligent data transformation, predictive modeling, workflow orchestration, and cloud-native resource management. A systematic research methodology based on design science and quantitative performance evaluation is adopted to validate the effectiveness of the proposed architecture using performance indicators including scalability, throughput, latency, prediction accuracy, resource utilization, automation efficiency, and system reliability. The integrated framework enables enterprises to automate complex business processes, enhance decision-making, improve operational efficiency, and optimize resource allocation while maintaining high levels of data security and regulatory compliance. The proposed intelligent data pipeline framework provides a scalable, flexible, and sustainable solution that supports digital transformation initiatives across diverse enterprise environments while enabling continuous business innovation through predictive analytics and intelligent automation
Keywords: Intelligent Data Pipeline; Cloud Computing; Enterprise Information Management; Predictive Analytics; Business Intelligence; Artificial Intelligence; Machine Learning; Data Automation; Big Data Analytics; Cloud Architecture; Data Engineering; Workflow Automation