Topic — 15 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
Knowledge Graph vs Data Warehouse: Aggregation vs Connection
A warehouse answers how much; a knowledge graph answers how things connect. They're different query shapes, not rivals — and most serious estates end up with both.
02Field Notes
Parquet vs ORC vs Avro: Which Data File Format, and When
Parquet and ORC are columnar formats for analytics; Avro is row-based for streaming and schema evolution. A clear comparison and a decision rule that holds.
03Field Notes
Apache Iceberg vs Parquet: They're Not the Same Layer
Iceberg and Parquet aren't rivals — Parquet is a file format, Iceberg is a table format that manages Parquet files. Here's the difference, and why it matters.
04Field Notes
The Inmon Methodology: The Corporate Information Factory, with an Example
Inmon's method builds one normalized enterprise warehouse first, then serves dimensional marts from it. How the CIF works, a worked example, and where it fits today.
05Field Notes
How to Choose an Iceberg Catalog: Unity vs Polaris vs Glue vs Nessie
The table format war is settled; the catalog decides governance and lock-in now. How Iceberg catalogs work, compared honestly — and a decision rule that holds.
06Field Notes
Iceberg vs Delta Lake: How to Actually Choose in 2026
Iceberg and Delta Lake have converged on capabilities — ACID, time travel, deletion vectors. The real decision is your platform and catalog, not the format.
07Field Notes
What Is an Open Table Format? Iceberg, Delta, and Hudi Explained
An open table format is a metadata spec that turns raw files in object storage into real tables — with ACID transactions, schema evolution, and time travel.
08Field Notes
Data Lake vs Lakehouse: What Changed and Which to Use
A data lake stores raw files cheaply and guarantees nothing. A lakehouse adds a table layer over the same files for ACID, schema, and trust. When each fits.
09Field Notes
Kimball vs Inmon: Two Ways to Build a Data Warehouse
Kimball and Inmon differ on one decision: build dimensional marts bottom-up, or a normalized enterprise warehouse top-down. The trade-off, and the modern blend.
10Reconsidered
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 is ending. What's actually happening, and what to keep.
11Field Notes
Data Warehouse vs Data Lake vs Lakehouse: A Clear Comparison
A data warehouse stores structured, modeled data. A data lake stores raw data cheaply. A lakehouse tries to be both. A side-by-side, and how to choose.
12Essay
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. What it is, and what it actually fixes.
13Reconsidered
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.
14Field Notes
OLTP vs OLAP: Why You Shouldn't Run Analytics on Your App Database
OLTP handles many small transactions fast; OLAP scans huge volumes for analysis. Opposite optimizations, which is why analytics on production is a trap.
15Manifesto
The Shape of Data
Every dataset has a shape. The only question is whether you chose it, or whether it happened to you.