Abstract

The rapid growth of enterprise data volume, the maturation of cloud-native infrastructure, and the rising organizational demand for real-time, AI-augmented decision-making have exposed a critical architectural gap in modern data platform design: the persistent fragmentation of Extract, Transform, and Load (ETL) pipelines, streaming analytics engines, and data consumption APIs into independently managed, loosely coupled subsystems. This paper presents the Intelligent Cloud Data Platform (ICDP), a unified four-layer architectural framework that systematically integrates AI-driven ETL, real-time streaming analytics, intelligent warehouse storage with open table format governance, and API-first data consumption under a single, co-designed engineering model. The ICDP framework is grounded in and extends the findings of three recent IEEE publications addressing AI-augmented data warehousing, scalable API-driven cloud pipelines, and cloud ETL migration methodology. A comprehensive experimental evaluation conducted on live multi-cloud AWS and GCP infrastructure using the TPC-DS benchmark dataset at 1TB, 10TB, and 100TB scale factors demonstrates that the ICDP delivers a 94.1% autonomous schema drift resolution rate, an overall data quality defect detection rate of 91.8%, streaming end-to-end latency of 387ms at p99 with in-stream ML fraud detection achieving an F1 score of 0.923, and API response times of 489ms p99 at 1,000 concurrent users with 99.97% availability. Against a monolithic batch warehouse baseline, the ICDP reduces time-to-insight for operational decisions from 8.4 hours to 0.9 seconds for inventory optimization scenarios. These results establish the ICDP as a validated, production-grade architectural framework for intelligent cloud data platform design.

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