Performance Engineering for Enterprise Integrations: Optimizing MS SQL, PL/SQL, Java, and API-Driven Data Pipelines
DOI:
https://doi.org/10.63282/3050-9246.IJETCSIT-V4I4P128Keywords:
Performance Engineering, Enterprise Integration, MS SQL, Pl/SQL, Java, API Integration, Data Pipelines, Query Optimization, Database Performance, API Performance, Scalability, Observability, Performance TuningAbstract
Modern enterprises increasingly depend on complex integrations that connect databases, APIs, business applications, and cloud services for constant data flow and operational workflows. Even if one integration is good, bad performance of some components may have big impact on the whole operation. Poorly designed MS SQL queries, unoptimized PL/SQL procedures, resource-intensive Java processing, too many API calls, and inefficient data transfer approaches can increase latency, deplete computing resources, create contention, limit scalability, and put additional pressure on downstream systems. The paper describes a performance-engineering methodology that covers MS SQL optimization, PL/SQL optimization, Java performance optimization, API optimization, and end-to-end data pipeline monitoring. Instead of tuning each technology individually, the proposed framework evaluates performance across the whole integration pathway to identify bottlenecks, dependencies and computationally intensive processing steps. As a case study of a typical organization, the technique is applied to a high-volume integration workflow and its performance is evaluated using metrics like processing latency, transaction throughput, database use, API response time, resource consumption, scalability and integration dependability. The anticipated results suggest that systematic performance engineering can minimize processing delays, boost throughput, maximize database and application efficiency, accommodate increasing transaction volumes, and develop more resilient and dependable enterprise integration pipelines.
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