The Wrong Response to a Shifting Market
Three default moves senior technical practitioners make when rates begin to compress, and why each makes the underlying position worse.
Data Engineering Is Not Dying. But Management Is Doing Its Best to Kill It.
Companies are laying off experienced architects and doubling down on offshore coding teams — at the exact moment AI is automating the implementation work those teams do. The math says AI-augmented senior teams are cheaper and better than offshoring. So why is management choosing the most expensive option and calling it efficiency?
RAG Does Not Fail Because of the Model. It Fails Because of the Data.
Every enterprise AI strategy deck I have seen in the past years contains the same promise: “We will build a RAG-based knowledge assistant that lets employees query our internal documents in natural language.” The board nods. The budget gets approved. Six months later, the assistant gives wrong answers, misses critical information, and nobody trusts it. …
Why Z-Ordering Fails on Skewed Data — and Liquid Clustering Does Not
Z-ordering and Liquid Clustering both aim to improve Databricks query performance through data skipping. But when your data is skewed, one of them quietly becomes useless. A visual explanation of why — and how the Hilbert curve changes everything.
The Most Dangerous System in Your Bank Runs on Excel
“The spreadsheet is the most dangerous piece of software ever created.” — Daniel Lemire
AI Agents Are Quietly Rewriting the Cloud Database Cost Model
I have spent over twenty years building and optimising enterprise data warehouses, and in that time, I have watched the industry cycle…
Your Next Data Warehouse Migration Will Fail for the Same Reason the Last One Did
There is a question that is almost never asked in the data platform debate, and the reason it is not asked is that nobody benefits from the…
Stop Comparing Teradata, Snowflake, and Databricks. Start Asking These Questions Instead. Popular
A vendor-neutral look at the architectural trade-offs that actually matter I have spent over 20 years building and optimising enterprise data warehouses, mostly on Teradata, and increasingly on Snowflake and Databricks. What I have observed repeatedly is that platform selection decisions are driven by slide decks, analyst quadrants, and licensing pressure — not by the …
Microsoft Fabric vs. Databricks: What Enterprise Teams Actually Need to Know
When Microsoft launched Fabric, it promised to unify analytics under one roof. Databricks, meanwhile, has been building its lakehouse platform for years. Both aim to be your central data platform — but they make fundamentally different bets on how much complexity to show you. Having worked across Teradata, Snowflake, and Databricks environments in European banking …
You Migrated From Teradata to Spark and Threw Away the One Thing That Made It Fast
If you have spent any amount of time working with Teradata, you know that the Primary Index is one of the most important design decisions you make. It determines how data is distributed across AMPs and whether your joins are fast or slow. Choosing the wrong Primary Index is one of the most common causes …