Type 2 diabetes, one of the most prevalent chronic diseases worldwide, represents a critical challenge for healthcare systems due to its progressive course and the need for continuous management to prevent long-term complications. Although previous research has demonstrated the possibility of remission under specific conditions, a more comprehensive understanding of the underlying determinants remains essential. This study presents a scoping review on data repository technologies and proposes a Data Lakehouse-based architecture for the analysis of factors associated with type 2 diabetes remission. The proposed framework enables the ingestion, integration, storage, and advanced analysis of large-scale, heterogeneous, and multi-format data sets, facilitating the identification of patterns, correlations, and key variables linked to remission. To assess feasibility, a functional prototype was implemented, incorporating a large language model to semantically classify scientific articles, with outputs integrated into a Lakehouse infrastructure using Apache Kafka, Spark, and Iceberg. Furthermore, a relational data model is designed to enable longitudinal monitoring of clinical variables such as dietary habits, pharmacological treatments, physical activity, and medical interventions. By leveraging structured, interoperable, and traceable data, the proposed approach aims to enhance clinical decision-making, support personalized therapeutic strategies, and ultimately contribute to more effective disease management and improved patient outcomes.