SCALABLE NOSQL DATA MODELING AND QUERY OPTIMIZATION FOR HIGH-THROUGHPUT RETAIL ANALYTICS IN CLOUD-NATIVE ENVIRONMENTS

Theodor Fontane Chamisso

SCALABLE NOSQL DATA MODELING AND QUERY OPTIMIZATION FOR HIGH-THROUGHPUT RETAIL ANALYTICS IN CLOUD-NATIVE ENVIRONMENTS

Authors

Theodor Fontane Chamisso

NoSQL Data Engineer – Cloud-Native Retail Analytics and Scalable Data Systems, USA.

Keywords:

NoSQL, Data Modeling, Query Optimization, Cloud-Native, Retail Analytics, Scalability, High Throughput, Document Store, Columnar Database

Synopsis

Retail systems operating in cloud-native environments require scalable, resilient, and high-performance data storage and processing capabilities. Traditional relational databases struggle under the pressure of high-throughput transactional workloads and real-time analytics demands. This paper explores NoSQL data modeling strategies and query optimization techniques tailored for large-scale retail analytics in cloud-native infrastructures. By leveraging document-oriented and wide-column NoSQL systems, this study presents a comparative analysis of query performance, schema design, and data retrieval efficiency. Our experimental setup simulates typical retail workloads such as inventory tracking, customer personalization, and transaction analysis. Findings reveal that data models optimized for read-heavy operations and denormalized schemas offer up to 3× query efficiency in high-concurrency environments. A decision-driven framework for schema design and optimization is proposed to guide practitioners.

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