An Experimental Evaluation of a Hybrid SQL–NoSQL Architecture for Scalable E-Commerce Analytics

Brahim Ben Lhoucine; Khalid Tatane; Laila Dahr

doi:10.48084/etasr.18578

Authors

Brahim Ben Lhoucine Information Technology, Data and Applied Mathematics Science Team (ESTIDMA), National School of Applied Science, Ibn Zohr University, Agadir, Morocco
Khalid Tatane Information Technology, Data and Applied Mathematics Science Team (ESTIDMA), National School of Applied Science, Ibn Zohr University, Agadir, Morocco
Laila Dahr Information Technology, Data and Applied Mathematics Science Team (ESTIDMA), National School of Applied Science, Ibn Zohr University, Agadir, Morocco

Volume: 16 | Issue: 4 | Pages: 37988-37995 | August 2026 | https://doi.org/10.48084/etasr.18578

Received: 17 April 2026 | Revised: 20 April 2026 and 11 May 2026 | Accepted: 15 May 2026 | Online: 26 June 2026

Corresponding author: Brahim Ben Lhoucine

Abstract

The rapid growth of data volume, structural heterogeneity, and real-time processing demands in modern e-commerce platforms challenges the scalability and flexibility of single-model database systems. Hybrid database architectures, which integrate multiple storage paradigms within a coordinated framework, offer a promising alternative; however, controlled empirical evaluation across heterogeneous models remains limited. This study presents a structured experimental assessment of a hybrid SQL–NoSQL architecture integrating relational, document-oriented, in-memory, column-family, graph, and streaming components within a unified deployment environment. The architecture incorporates PostgreSQL, MongoDB, Redis, Cassandra, and Neo4j, coordinated through Kafka-based streaming with Change Data Capture (CDC) synchronization using Debezium. Performance evaluation focused on reproducible benchmarking of representative subsystems under standardized hardware constraints. Key performance indicators—including throughput, response latency, and scalability under increasing concurrency—were measured using established workload simulation tools. Results demonstrate clear workload-dependent specialization: relational processing maintained stable transactional behavior, document-oriented storage supported higher ingestion throughput, and in-memory caching achieved minimal access latency. The findings indicate that distributing workload categories across specialized storage engines improves operational balance compared with monolithic deployments. Beyond quantitative benchmarking, the study provides a reproducible architectural framework illustrating how coordinated multi-model systems can support scalable and adaptable data infrastructures for real-time e-commerce analytics.

Keywords:

hybrid database architecture, SQL–NoSQL integration, polyglot persistence, distributed data systems, database benchmarking, Change Data Capture (CDC), e-commerce analytics, real-time processing

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