The rapid growth of digital healthcare ecosystems has led to an unprecedented increase in data generated from large-scale drug delivery systems and clinical trials, including IoT telemetry, electronic health records, laboratory results, and patient-reported outcomes. Managing this data requires a high-throughput, low-latency data engineering architecture capable of supporting real-time analytics and large-scale batch processing simultaneously. This paper presents a Data Lakehouse–based architecture for drug delivery and clinical trial analytics, combining the scalability of data lakes with the reliability and performance of data warehouses. The proposed architecture supports both streaming and batch workloads while maintaining a unified data governance layer. A key innovation of the framework is automated data harmonization using FHIR standards, complemented by AI-driven data quality checks at the ingestion layer to detect anomalies, missing values, and schema inconsistencies in real time. Experimental evaluation demonstrates that the proposed architecture significantly improves data processing latency, analytical consistency, and scalability, enabling faster clinical insights, improved trial monitoring, and more reliable drug delivery analytics.
Keywords: Data Engineering; Drug Delivery Analytics; Clinical Trial Data; Data Lakehouse Architecture; High-Throughput Systems; Low-Latency Analytics; FHIR Standards; Data Quality Management
How to cite this article:Nandigama NC.; Data Engineering Architecture for Large-Scale Drug Delivery and Clinical Trial Analytics.Int J Drug Deliv Technol. 2026;16(1s): 733-738; DOI: 10.25258/ijddt.16. 733-738