This article presents a centralized data warehousing solution for Database Activity Monitoring (DAM) and Database Audit Systems (DAS) with integrated User and Entity Behavior Analytics (UEBA) capabilities. The architecture addresses the critical challenges of modern cybersecurity environments where traditional rule-based detection systems prove insufficient against sophisticated threats and insider attacks. By combining advanced ETL processes, machine learning-based anomaly detection, and multi-tiered storage strategies, the architecture enables organizations to identify abnormal database activities that deviate from established behavioral baselines. The ETL framework implements selective capture mechanisms and parallel processing techniques to efficiently handle massive volumes of sensitive data while maintaining performance and data quality. The UEBA integration establishes multi-dimensional behavioral baselines through unsupervised learning algorithms and employs an ensemble of detection methods, including Statistical Process Control, Isolation Forest, and LSTM networks. Architectural decisions around data partitioning and storage tiers optimize both performance and governance, while comprehensive High Availability and Disaster Recovery strategies ensure continuous operation and regulatory compliance. Performance metrics demonstrate significant improvements in threat detection accuracy, reduced false positives, and faster response times compared to traditional approaches. The architecture offers a blueprint for organizations seeking to enhance database security through behavioral analytics while maintaining compliance with HIPAA, GDPR, and PCI-DSS requirements.