A Conceptual Framework for Enhancing Data Ingestion and ELT Pipelines for Seamless Digital Transformation in Cloud Environments

This paper presents a conceptual framework for enhancing data ingestion and ELT (Extract, Load, Transform) pipelines to support seamless digital transformation in cloud environments. As organizations increasingly rely on cloud technologies to manage and analyze vast amounts of data, optimizing data pipelines becomes critical for ensuring scalability, performance, and security. The proposed framework integrates four core components: An ingestion layer, a transformation engine, orchestration, and monitoring and governance. Each component is designed to address key challenges such as data latency, integration complexity, and compliance requirements, while ensuring robust data management practices. Through a combination of design science research, empirical case studies, and industry-based insights, this framework provides an adaptable and scalable solution for data-centric enterprises. The framework’s architecture emphasizes modularity, automation, resilience, and security, promoting efficient data processing workflows across various industries. The paper also discusses the practical implications of adopting the framework, highlighting its potential to optimize business intelligence, reduce operational costs, and enhance data governance. Lastly, the study identifies avenues for future research, particularly in integrating AI-driven optimizations and enhancing performance metrics. This paper contributes to the growing body of work on cloud-based data engineering and offers a comprehensive approach to tackling the complexities of modern data pipelines in a rapidly evolving digital landscape.