SQL para ciência de dados aplicada em dados de saúde pública no Brasil

Abstract

Although traditional Data Science pipelines rely on exporting large volumes of information to external imperative environments, relational Database Management Systems (DBMSs) have evolved to support complex analytical logic. The In-database analytics paradigm emerges as a solution to eliminate data movement and serialization costs across the network by leveraging native logical optimizers. This work aims to demonstrate the viability of executing a unified analytical lifecycle using exclusively native SQL commands and analytical resources of PostgreSQL. To empirically validate the language’s robustness and expressiveness, a case study was implemented on dengue notifications in Brazil in 2025 (SINAN), integrating them with demographic indicators (IBGE) and occupational classification (CBO). The work methodology comprised designing a resilient architecture divided into a fault-tolerant Staging Area, key cleansing, Third Normal Form (3FN) normalization, and the transposition of sparse matrices using the CROSS JOIN LATERAL operator. The data infrastructure was accelerated through physical indexing (B-Tree) and consolidated into a Materialized View. The results prove the practical efficiency of the relational ecosystem, which processed a volume exceeding 1.65 million records with response times on the order of one second, computing incidence rates, cumulative time series via Window Functions, age attribute engineering, and Pearson statistical correlations. The work contributes to demystifying the use of the relational DBMS as a passive repository, presenting a reproducible and performant roadmap focused on Data Science purely structured in SQL.