Essays

Privacy by Design Is an Architecture Pattern, Not a Checkbox

Systems built with privacy-by-design architectural patterns experience 71% fewer data breach incidents. Privacy is a structural property of architecture, not a feature that can be bolted on.

Schema Evolution as Change Management

Schema migrations managed as change initiatives had a 96% success rate versus 61% for surprise deployments. Schema evolution is change management, not just DDL execution.

Time Series Data Requires Its Own Architecture

Migrating time series from PostgreSQL to TimescaleDB reduced query latency by 78% and storage by 62%. Time series access patterns need purpose-built architecture.

The Fairness-Performance Tradeoff Is Real and Underreported

In 3 production fairness projects, I measured accuracy drops of 2.7% to 8.3% when enforcing demographic parity. The tradeoff is real, and honest engagement builds more sustainable fairness than denial.

The Unstructured Data Problem Nobody Wants to Solve

An estimated 80% of enterprise data is unstructured, yet fewer than 15% of data teams can process it systematically. Organizations ignore the majority of their information assets.

Open Source AI Ethics: Who Governs Models Without Owners

Open-weight models downloaded over 500 million times have no single entity controlling deployment. When harm occurs, the question of responsibility has no clear answer in current frameworks.

The Ethics of Expertise: When You Know More Than Your Stakeholder

When engineers know more than stakeholders about technical risks, moral obligations arise. 78% of engineering decisions involve information asymmetry. The expert bears the communication burden.

Lakatos and the Research Program of Machine Learning

Lakatos distinguished progressive research programs from degenerative ones. ML has a progressive core of genuine prediction surrounded by a degenerative protective belt of scaling assumptions.

Why smart people build bad systems: The curse of local optimization

dbt Changed Data Engineering. Here Is What It Got Wrong.

dbt grew from 5,000 to 40,000 organizations by 2025, transforming data engineering. But after 6 implementations, its strengths come with structural weaknesses that deserve honest assessment.