Bidirectional Flow of Structured Data between APEX and Streaming Pipelines Using AI-based Field Mapping and Noise Filtering

"This study leverages Kafka-based streaming pipelines and semantic embeddings to integrate low-code platforms like Oracle APEX with real-time, event-driven data architectures." The use of low-code applications, such as Oracle APEX, is increasingly integrated with streaming data pipelines, particularly as enterprise software applications mature to support real-time decision-making frameworks. There is, however, a critical challenge that enables the seamless bidirectional flow of structured data, due to underlying issues with field mapping, schema congruence, and high-rate noise thinning. This research proposes an AI architecture for Dynamic Data Mapping that relates Oracle APEX forms to streaming platforms, such as Apache Kafka, with semantic field alignment and adaptive noise filtering techniques. The proposed system acquires the structure and context of the source and target schemas and in turn, provides precise, real-time data flow. Field-level metadata tagging, combined with statistical noise nulling, increases mapping accuracy, while dynamic metadata-rich data processing further enhances it. Evaluative experiments performed with synthetic and application-specific datasets from the healthcare, retail and finance sectors yielded up to 91% precision in field matching and a 38% drop in mapping latency. A throughput invariance with over 5% lost packets under high-rate noise input conditions proved the system’s scalability and robustness. The architecture offers an intelligent yet accurate solution for linking low-code data entry systems with event-driven systems.