Databricks, data & AI engineering
Long-form writing on Databricks, data engineering, and production AI. Sourced, specific, and honest about what I don't know yet.
ChicagoPulse Won Gold in the Databricks Genie App Challenge
ChicagoPulse won Gold in the Databricks Genie-Powered App Challenge. Here is how governed metrics, evaluation, and visible evidence turned a civic-data chatbot into an app people can inspect and trust.
Read the story →Extreme DAX, Second Edition: An Honest Review of the Advanced Power BI Playbook
An honest review of Extreme DAX, Second Edition by Michiel Rozema, Madzy Stikkelorum, and Henk Vlootman. Why this Packt book is a scenario playbook for advanced Power BI, not another DAX function reference.
Databricks Partner Well Architected Framework: How to Actually Build and Submit a Solution With It
A practical guide to the Databricks Partner Well Architected Framework (PWAF): the three partner tracks, the four pillars, the four deployment models, and the deployment-model decision that quietly determines what program benefits you can ever earn.
Principles and Patterns of Building AI Agents: An Honest Review of Mastra's Free Two-Book Series
A grounded review of Sam Bhagwat's two free Mastra books, Principles of Building AI Agents and Patterns for Building AI Agents. Principles teaches what to build; Patterns teaches how to keep it alive in production.
30 Agents Every AI Engineer Must Build: An Honest Review After Three Months With the Book
An honest, in-depth review of 30 Agents Every AI Engineer Must Build by Imran Ahmad (Packt). Why the book is really a pattern library for production agent engineering, not a catalog of 30 demos.
Databricks Data + AI Summit 2026: The Lakehouse Becomes the Agentic Control Plane
A technical deep dive into Databricks Data + AI Summit 2026: Genie One, Unity AI Gateway, Lakeflow, LTAP, Lakebase, and Lakewatch, and what each announcement means for data and AI engineers.
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