ADAPTIVE ETL ARCHITECTURES FOR HIGH-FIDELITY DATA INTEGRATION IN MULTI-CLOUD ENVIRONMENTS

Abstract

The rapid expansion of multi-cloud architectures demands high-fidelity data integration solutions that ensure accuracy, consistency, and low latency in data handling across heterogeneous cloud platforms. This paper explores adaptive Extract, Transform, Load (ETL) architectures capable of managing complex, diverse, and large-scale data in multi-cloud environments. Leveraging adaptive ETL strategies, such as event-driven processing, machine learning (ML)-assisted data transformations, and cloud-native tools, enhances integration effectiveness and performance. Through an analysis of existing literature and recent advancements, this paper identifies key challenges and proposes a scalable architecture for adaptive ETL in multi-cloud ecosystems. By balancing workload distribution, data fidelity, and compliance, this architecture aims to optimize data flow across cloud platforms.