David Mills
Dufrain - Senior Azure Data Architect
Derby, United Kingdom
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I am a Senior Azure Data Architect specialising in Microsoft Fabric, with 25 years of experience designing secure, scalable analytics platforms across both on-premises and Microsoft Azure environments. As a 10x Microsoft Certified Professional, I focus heavily on Fabric Security and the emerging wave of AI-powered capabilities within the platform.
I share insights regularly on LinkedIn, deliver Microsoft Fabric Analyst in a Day (FAIAD) workshops, and speak at community events including the Manchester Power BI & Fabric User Group. My work spans end-to-end platform design from data warehousing and modelling to real-time analytics and Power BI.
My current focus area is Fabric Data Agents, and how organisations can safely leverage autonomous, AI-driven workflows to enhance productivity, governance, and data quality.
Attendees can expect clear, practical guidance drawn from real implementations and hands-on experimentation with the newest Fabric features.
Area of Expertise
Topics
Autonomous Fabric: Agentic Data Quality Using Fabric Data Agents
Struggling with Data Quality issues?
Manual checks, slow root-cause investigations, and hard-to-diagnose issues consume engineering time that should be spent delivering value.
To address these challenges, this session demonstrates how Fabric Data Agents provide an intelligent diagnostic layer for automated Data Quality operations.
You’ll see how Data Quality signals get captured, are routed to a Fabric Data Agent, and used to drive targeted investigations and potential fixes.
Tailored for Data Engineers and Data Architects, a live demo will showcase the full flow: we’ll introduce a real data issue into a Fabric Lakehouse table, run a lightweight Data Quality check, and watch the Data Agent in action as it:
• Reasons about data anomalies using AI
• Identifies the likely root cause
• Generates diagnostic SQL
• Provides clear, actionable recommendations
Attendees will leave with:
• A repeatable agent architecture for automated Data Quality diagnostics
• Practical guidance on why this pattern delivers real value
• Techniques for integrating agent-driven insights into existing engineering workflows
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