Published Paper Details:

Clinical trials today handle more data than ever before, and that data often lives in both cloud platforms and on-site systems. As a result, there's growing pressure to find integration solutions that are not only dependable but can scale as needed. In this review, we take a grounded look at popular ETL (Extract, Transform, Load) tools--focusing on how they're actually used in clinical research, not just how they're marketed. We introduce a new framework built around modular, container-based components. Why? Because this setup improves security, speeds things up, reduces errors, and helps organizations stick to evolving data standards. Our experimental findings back this up: the model outperformed traditional systems in key areas like processing speed, throughput, and scalability. But we're not claiming it's perfect. There are still plenty of hurdles to overcome, including improved semantic integration, real-time edge computing, and more intelligent AI-driven ETL systems. The main point? If you're working with hybrid clinical data environments, it's time to rethink how your systems connect, grow, and adapt. This paper is here to start that conversation.