An AI-Based Framework for Predictive Scaling and Anomaly Detection in Enterprise Data Platforms

Abstract

Enterprise data platforms face escalating challenges managing dynamic workloads, ensuring optimal resource allocation, and detecting anomalous behaviors that threaten system integrity and performance. Traditional reactive scaling approaches and rule-based anomaly detection systems struggle to cope with the complexity, velocity, and unpredictability of modern data processing environments. This research presents a comprehensive AI-based framework that integrates predictive scaling mechanisms with intelligent anomaly detection to optimize enterprise data platform operations. The framework employs machine learning algorithms including time-series forecasting models for workload prediction, reinforcement learning for dynamic resource allocation, and unsupervised learning techniques for anomaly identification. Through implementation and evaluation across five enterprise organizations managing data platforms processing over 2.8 petabytes daily, the framework demonstrated 67% reduction in resource over-provisioning costs, 43% improvement in query performance through proactive scaling, and 89% accuracy in detecting anomalous system behaviors with average detection latency under 45 seconds. The predictive scaling component accurately forecasted workload spikes 20-45 minutes in advance, enabling preemptive resource allocation that prevented performance degradation during demand surges. Anomaly detection modules identified security threats, data quality issues, infrastructure failures, and performance bottlenecks with significantly lower false positive rates than traditional threshold-based systems. However, implementation challenges emerged including model training data requirements, computational overhead of real-time predictions, integration complexity with legacy systems, and calibration needs across different workload patterns. This research contributes practical architectural designs, algorithm selection guidance, and operational frameworks for organizations seeking to implement AI-driven optimization in their data platforms while maintaining reliability and cost-effectiveness.