A Comparative Analysis of SQL-Based and Cloud-Native Data Warehousing Architectures for Real-Time Financial Reporting

Abstract

The growing reliance on real-time financial reporting has placed significant performance demands on enterprise data warehousing infrastructures, particularly with respect to latency, scalability, and analytical efficiency under fluctuating workloads. Traditional SQL-based data warehousing architectures, which have historically underpinned financial reporting systems, were primarily engineered for batch-oriented processing, fixed resource allocation, and predictable query patterns. In contrast, cloud-native data warehousing platforms adopt distributed execution models, elastic resource provisioning, and adaptive query optimization, positioning them as potential enablers of continuous, low-latency financial analytics. This study presents a detailed comparative analysis of SQL-based and cloud-native data warehousing architectures to evaluate their suitability for real-time financial reporting environments.
A controlled experimental methodology was employed in which both architectures were subjected to identical financial analytics workloads, including simple aggregations, multi-join queries, window-function computations, and complex end-toend financial reports. Performance evaluation focused on three core dimensions: query latency, scalability and elastic resource utilization, and query optimization behavior. The results revealed that SQL-based data warehouses maintain acceptable performance at low to moderate concurrency levels but experience rapid increases in query latency, throughput saturation, and diminished optimization efficiency as workload intensity and query complexity rise. Conversely, cloud-native architectures consistently demonstrated lower response times, near-linear scalability under increasing concurrent queries, and superior execution efficiency for complex analytical workloads, driven by distributed processing and dynamic resource allocation.
The findings underscore key architectural trade-offs between deterministic governance and elastic performance, with direct implications for financial analytics, regulatory reporting, and data engineering practice. Overall, the study concludes that cloud-native data warehousing architectures provide a more resilient and performance-aligned foundation for real-time financial reporting, particularly in environments characterized by high concurrency, variable demand, and time-sensitive decision-making.