Notes on dataarchitecture.
Short essays on architecture, cost, hiring and AI: one cartoon, one idea, most days of the week. No tutorials, no listicles.
Phoenix Project Lessons For DE
The Phoenix Project is 13 years old. Data teams still run their backlogs the way it warns against.
Read →Team Complexity Post-MVP
By 20 engineers, most data teams have one person every change waits on. The backlog put them there.
Read →Too Deep Into Snowflake" Is an Architecture Smell
You can measure architecture health by the price of an exit. For one stack I reviewed, it was 18 months.
Read →Brooks Law In Data Hires
A scaleup added 3 data engineers to catch up. Delivery got slower for 2 months.
Read →Data Reliability Over AI Speed
A plain statistical baseline caught model drift before any human would have. It took an afternoon to build.
Read →Pipeline Replay Decision Tree
Most pipelines are designed for the happy path. The first real outage makes recovery the only thing that matters.
Read →Fractional For Stack Survival
A fintech founder asked me to sanity-check their data stack three weeks before a raise. It wouldn't have survived the investor's technical …
Read →75% Belgian SMEs On AI Daily
Three in four Belgian SMEs now use AI daily or weekly. Most are running it on data they wouldn't trust for a board report.
Read →Lakehouse Convergence Risks
The lakehouse pitch is one platform for raw files and clean tables. The trap is inheriting the data lake's oldest habit: land everything, …
Read →Databricks DBU Trap For ETL
Your Databricks bill is mostly DBUs. A lot of those DBUs run ETL that never needed Spark in the first place.
Read →Want expert eyes on your data architecture?
No pitch. An honest conversation about whether I can help, and what shape it would take if I can.