Intelligent ETL frameworks for big data analytics in cloud environments: Adaptive data integration strategies for smart cities, retail, and insurance domains

Aim: This research aims to design and evaluate intelligent Extract, Transform, Load (ETL) frameworks tailored for big data analytics in cloud environments. The focus is on enabling adaptive data integration strategies that can dynamically respond to heterogeneous data sources across domains such as smart cities, retail, and insurance. The study addresses limitations of traditional ETL systems in handling volume, velocity, and variety of data.
Method: The proposed approach integrates machine learning-driven optimization, metadata-aware pipelines, and cloud-native architectures. Techniques such as schema evolution handling, real-time streaming ETL, and automated data quality assessment are incorporated. A modular ETL framework is developed and tested using distributed processing platforms and scalable storage systems.
Results: Experimental results demonstrate improved data processing efficiency, reduced latency, and enhanced scalability compared to traditional ETL pipelines. Adaptive mechanisms significantly improve data integration accuracy and reduce manual intervention. Domain-specific case studies show measurable improvements in decision-making capabilities.
Conclusion: The study concludes that intelligent ETL frameworks are essential for modern big data ecosystems. Adaptive integration strategies enhance flexibility, performance, and reliability across diverse applications. Future research can extend these frameworks using autonomous data pipelines and AI-driven orchestration.