From Raw Data to Intelligence: Building AI‑Ready Platforms with Fabric
Building scalable data platforms in Microsoft Fabric requires platform engineering by design, especially when moving from raw data to actionable intelligence. This session explores the Fabric Metadata Driven Framework (FMD) and how Fabric SQL Database acts as the control plane for metadata driven pipelines, notebooks and observability across the lakehouse. You’ll learn how to cleanse raw data and enforce data quality controls, so your data becomes reliable, governed and ready to power your intelligence and AI layers. Ready to build your AI foundation?
About the speaker
Erwin de Kreuk
Erwin de Kreuk is an experienced Technology Lead for Data at InSpark, a Microsoft partner recognized as Global Partner of the Year for Identity and Dutch Partner of the Year for Data & AI.
With 16 years of experience on the Microsoft Data Platform, including 8 years focused on Azure and Microsoft Fabric, Erwin helps organizations define and execute their data and AI strategy. His focus is on building scalable, secure, and future‑proof data platforms that enable analytics, operational excellence, and AI‑driven innovation.
Erwin is a Microsoft Data Platform MVP and a frequent speaker at national and international conferences, where he shares strategic insights on modern data platforms, platform engineering, and the role of data foundations in successful AI initiatives.
As a member of the Technology Board at InSpark, Erwin provides technical leadership across the data domain, guiding senior consultants and shaping the company’s data platform vision. He is closely involved in translating Microsoft’s roadmap into practical, enterprise‑ready solutions and advising customers on modernizing complex analytics landscapes.
Erwin is also a key stakeholder in InSpark’s managed data platform offerings, including Oxygen (Modern Data Platform as‑a‑Service) and the ISV Solution Nitrogen Control Center, a Microsoft Fabric–native solution focused on scalable data integration and processing.
His core belief: AI success is driven by strong leadership decisions and a solid data foundation, not by models alone.
