Designing Data and Analytics Ecosystems for High Volume Transaction Processing Applications
DOI:
https://doi.org/10.63282/3050-9416.IJAIBDCMS-V2I2P111Keywords:
Data And Analytics Ecosystem Design, Integrated Operational And Analytical Systems, Application-Oriented Data Architectures, Mixed Operational And Analytical Workloads, High-Throughput Application Data Processing, Incremental Data Processing, Change Propagation Across Systems, Data Synchronization Across Components, Schema Evolution In Production Systems, Throughput-Oriented System Design, Performance Isolation Techniques, Embedded Analytics In ApplicationsAbstract
Increasingly high-volume transaction processing systems in digital commerce, financial services, healthcare systems and cloud-native enterprise need unified architectures that can provide ultra-low-latency operations and also near-real-time analytics. Separating the OLTP and OLAP environment traditionally creates issues related to synchronization delays, multiple data replications, and schema inconsistencies as well as creating performance contention during mixed workloads. Even though Hybrid Transactional/Analytical Processing (HTAP) environments seek to resolve this gap, most are built on the idea of optimizing the database instead of solving ecosystem-wide issues such as the propagation of incremental changes between services, service-to-service coordination, the integration of analytics into the embedded platform, and system design centered on throughput. Consequently, loosely coupled architectures tend to exhibit issues of cascading latency, irregular state synchronization as well as reduced flexibility to workload changes. This paper proposes an architectural framework called the Throughput-Oriented Integrated Data and Analytics Ecosystem (TIDAE) as a single, architectural framework aimed at high-volume, mixed-workload systems. The suggested system brings together transaction-cherished processing, change data capture (CDC) entertained by streaming, incremental process organizations, efficient analytical storage, and inbuilt analytics displays in an orchestrated throughput-disposed developing. Formal throughput analysis, architectural modeling, and empirical benchmarking are used to prove that the framework supports 35-50% better throughput and about 40% less synchronization latency than traditional decoupled architectures, and meets analytically-challenged latency SLOs. The findings confirm the usefulness of isolated workload, progressive processing and scalable synchronization solutions in facilitating enterprise-caliber systems in smoothly incorporating analytics into operational pipelines with no disruption to performance or resiliency.
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