Microsoft Graph Data Connect

The Problem

Microsoft Graph Data Connect moves Microsoft 365 data at massive scale into customers' Azure environments for analytics. Getting enterprise data out of a live system and into an analytics store sounds simple until you account for the constraints: petabyte-scale volumes, strict tenant isolation, privacy and compliance requirements, and the expectation that every extraction is correct and complete. There is no room for silent data loss or leakage across tenant boundaries.

What I Did

I worked on the distributed data pipelines behind this platform — the systems responsible for moving large volumes of data reliably and securely across service boundaries.

• Built and operated distributed data pipelines moving Microsoft 365 data into Azure at large scale
• Worked within strict tenant-isolation, privacy, and compliance constraints where correctness and security were non-negotiable
• Focused on throughput, durability, and reliability so extractions were complete and consistent at scale

Why It Matters

This was the systems foundation the rest of my Microsoft work is built on. Moving data correctly at this scale — with hard guarantees around isolation and durability — is where I learned how distributed systems actually behave under production load. Those lessons carried directly into building reliability for Copilot and, later, production AI systems.