In large-scale real-time computing systems, correctness is of critical importance, as the utility of any streaming or low-latency computation is fundamentally determined by the accuracy of its underlying data and business logic. Maintaining alignment between real-time outputs and offline analytical baselines is particularly challenging, owing to the complexity, dynamism, and distributed characteristics of contemporary data processing pipelines. This project addressed these challenges by developing and integrating mechanisms for the continuous validation of real-time results, systematic detection of anomalies, and preservation of service stability under conditions of partial system or component failure.
The project designed and implemented an automated framework to quantitatively assess data accuracy by systematically comparing real-time metrics with offline reference values. When discrepancies arose, it produced diagnostic reports to speed root-cause analysis, debugging, and refinement of domain-specific business logic. The framework also included a fault-tolerant data query service that dynamically rerouted requests across multiple data channels during component or pipeline failures, maintaining continuous access to high-quality data and improving the robustness, observability, and operational continuity of real-time systems.