<![CDATA[The rapid growth of retail big data has intensified the challenge of selecting a database architecture that can balance analytical performance and resource efficiency, particularly in data-intensive retail environments. This study aims to conduct a comparative performance analysis between PostgreSQL 16 and MongoDB 8.0 in the context of implementing big data analytics in the retail industry. An experimental quantitative approach is used, utilizing a large-scale, real-world retail sales and inventory dataset to benchmark PostgreSQL 16 and MongoDB 8.0 across a range of representative analytical workloads. Results show MongoDB is 28-31% faster in query processing, but PostgreSQL is 13-17% more efficient in resource usage (CPU, RAM, Storage I/O) and requires 6x less storage. These results indicate that MongoDB consistently achieves faster execution times for read-intensive analytical queries, especially in large-scale aggregation operations. Conversely, PostgreSQL exhibits superior storage efficiency and lower computational resource consumption due to its normalized relational architecture. These findings reveal a fundamental trade-off between analytical speed and infrastructure efficiency in retail big data systems. This research contributes to the development of hybrid data architecture strategies for big data analytics in the retail industry, supporting performance optimization and informed decision-making in data-rich environments]]>
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