Topic — 7 essays
Data Architecture
Warehouses, lakes, lakehouses, medallion layers, and the modern data stack — where data should live, how it should be layered, and which structures earn their complexity.
Field Notes
Kimball vs Inmon: Two Ways to Build a Data Warehouse
Kimball and Inmon are the two foundational approaches to building a data warehouse. The difference is one decision: build dimensional marts bottom-up, or a normalize...
02Reconsidered
Is the Modern Data Stack Dead?
The modern data stack isn't dead — but the era of bolting together a dozen best-of-breed SaaS tools as the default is ending. Here's what's actually happening, and w...
03Field Notes
Data Warehouse vs Data Lake vs Lakehouse: A Clear Comparison
A data warehouse stores structured, modeled data for analytics. A data lake stores raw data of any shape, cheaply. A lakehouse tries to be both. Here's the real trad...
04Essay
What Is a Semantic Layer, and Why Does Your Data Stack Need One?
A semantic layer is the single, governed place where business metrics are defined once — independent of any dashboard. Here's what it is, what it isn't, and why it f...
05Reconsidered
The Medallion Architecture, Reconsidered
Bronze, silver, gold is a useful default and a dangerous dogma. A second look at what the layers get right, and where they quietly fall apart.
06Field Notes
OLTP vs OLAP: Why You Shouldn't Run Analytics on Your App Database
OLTP systems handle many small transactions fast. OLAP systems scan huge volumes for analysis. They're optimized for opposite things — which is why querying your pro...
07Manifesto
The Shape of Data
Every dataset has a shape. The only question is whether you chose it, or whether it happened to you.