Christopher Schmidt
Data platforms, analytics systems, and AI work that holds up in production.
I work on the architecture, operating model, and reliability layer behind modern data systems. Most of that work lives where platform decisions, business context, and production constraints all meet.
This site keeps that work simple: selected case studies, a few methods and frameworks, and writing on real-time systems, data quality, lineage, semantics, and AI.
What I focus on
- Real-time and event-driven platforms that support actual operating decisions
- Data quality, lineage, and semantics that make systems easier to trust
- Platform modernization that improves delivery speed without creating fragility
- Framework and diagnostic work that helps teams reason about failure earlier
Work
Selected work
Turning analytical readiness into a reusable public framework
Developed the Analytical Readiness Framework as a product-agnostic model for semantic integrity, explainability, interoperability, and now diagnostic reliability.
Resetting a platform cost profile while improving throughput and reliability
Led architecture and data platform modernization that cut operating cost by roughly 90 percent and improved daily pipeline performance by more than 80 percent.
Accelerating real-time platform delivery from months to weeks
Guided enterprise engineering teams toward fit-for-purpose streaming and ingestion patterns that materially compressed time-to-delivery without lowering the bar on reliability.
Methods and frameworks
A small set of tools I keep coming back to.
The Analytical Readiness Framework and the WhyDidItFail diagnostic model are both ways of making messy data problems easier to reason about before they become expensive.
Insights
Latest writing
Browse all insightsGraphs as a Semantic Intent Layer for Conversational Analytics in Fabric
# Graphs as a Semantic Intent Layer for Conversational Analytics in Fabric
Understanding CU consumption of streaming systems in Microsoft Fabric
I really struggled with a title for this post, because it covers a lot of ground. It's a long read, but totally worth it. Ultimately I settled on "Understanding CU consumption of streaming systems in Fabric". I have been wanting to understand for a while the impact various archit
Mastering Data Export in Eventhouse: From Eventstream to OneLake and SQL
First, I can't believe we're already at 10 editions! Super excited to see how much this has grown already really looking forward to next year! This will be the last edition of the year, stay tuned to see some exciting things I'm working on for early 2026! With all of the Ignite a
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