Leveraging Microsoft Fabric Lakehouse as an AI-Ready Data Platform for Enterprise Analytics

Abstract

The lakehouse architecture provides a promising foundation for future-proofing enterprise data platforms. Empirical research examined Microsoft Fabric Lakehouse as a real-world case. Findings confirm AI-ready capabilities across the core components and architecture. Building block services, including data import and data preparation, enable AI readiness in scale. However, the capacity of a single organization to drive trust and privacy controls, lineage, metadata, auditability, and compliance with regulatory frameworks is also crucial. These characteristics are typically defined and governed by enterprise-wide ecosystems that responsibly share Microsoft Fabric Lakehouse resources with other data and analytical infrastructures. Élite Computing has established hybrid cloud scenarios with Microsoft Azure, enhanced with an automatic training engine using DataRobot to predict not only a customer’s next purchase but when might their risk of churn happen or which products are likely to be purchased together. AI readiness has been explored and discussed in heterogeneous computing environments such as PLCs and SCADA establishing robust enterprise data platforms and meeting performance criteria. However, verifying the enterprise operation of these AI-ready capabilities in AI workloads covering the complete workflow—from data preparation and feature engineering to model training, deployment, and monitoring—represents an important next step.