Fidelity Investments, NC, USA.
Research Article
World Journal of Advanced Research and Reviews, 2024, 23(02), 2949-2959
Received on 06 July 2024; revised on 21 August 2024; accepted on 28 August 2024
Enterprise customer warehouse platforms managing petabyte-scale datasets across hundreds of subject areas face fundamental architectural limitations in monolithic database systems including compute-storage coupling, batch-dominated ingestion latency, and insufficient integration of artificial intelligence pipelines for real-time decision intelligence. This paper presents a cloud-native AI-augmented lakehouse architecture implementing event-synchronized ingestion fabric, distributed semantic data modeling, and reinforcement learning-based adaptive compute optimization for ultra-scale customer analytics environments. The proposed system replaces traditional extract-transform-load batch processing with change-data-capture streaming ingestion supporting temporal reconstruction of customer state trajectories, implements bronze-silver-gold medallion architecture with versioned semantic intelligence layers decoupling business logic from physical storage, and deploys reinforcement learning schedulers dynamically allocating distributed compute resources based on historical runtime patterns and service-level agreement criticality. Empirical evaluation on production customer warehouse environment processing over 200 terabytes across 500 source feeds serving 5,500 concurrent users demonstrates 87 percent reduction in data availability latency from hours to seconds, 35 percent decrease in compute costs through adaptive cluster sizing, and continuous AI model refresh eliminating weekly batch training cycles. The architecture establishes a reproducible framework for next-generation enterprise analytics platforms achieving unprecedented scalability, cost efficiency, and decision intelligence integration.
Cloud Data Engineering; Lakehouse Architecture; Event-Driven Ingestion; Adaptive Compute Optimization; Distributed Analytics; AI-Integrated Platforms; Customer Warehouse Systems
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Uttama Reddy Sanepalli. Enterprise Lakehouse Architecture for Customer Analytics: AI and Machine Learning - Synchronized Ingestion and Compute Optimization. World Journal of Advanced Research and Reviews, 2024, 23(2), 2949-2959. Article DOI: https://doi.org/10.30574/wjarr.2024.23.2.2418