Metadata-Driven Cloud-Agnostic Data Integration Framework

In an era where cloud computing is becoming essential, organizations are actively seeking to build cloud-based data solutions or migrate existing on-premises systems to the cloud. This shift is driven by the need to benefit from the scalability, flexibility, and efficiency offered by cloud environments. However, this transition presents a complex challenge: the integration of cloud data practices that are universally applicable yet sufficiently adaptable to domain specific needs. A significant number of cloud data initiatives fail due to poor planning and the absence of appropriate methodologies for effective cloud data integration. This failure often results in issues related to the completeness, accuracy, and consistency of data, which are essential for building reliable and trustworthy data platforms. This paper addresses this challenge by proposing a cloud-agnostic framework for cloud data integration. The paper’s contribution is a comprehensive, adaptable metadata-driven data integration framework that is flexible to be tailored to specific domain requirements and scalable for various cloud service providers. The proposed framework is designed to guide and assist organizations in their cloud data integration efforts, enabling them to focus on deriving organizational value.