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. Widely read
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 Popular
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 …
The Great Fragmentation — How One Job Became Thirty Titles
In my last post, I described a time when five people could build and maintain an entire data warehouse. That era ended not because the…
Data Warehouse of Horrors: 20 Years of Watching Smart People Build Stupid Things
In the projects I remember, a small team—sometimes five or six people—covered the whole warehouse. These staffing numbers and recalled conversations are personal observations, not an industry benchmark. This was the late 1990s. Data warehousing was young. Teradata was the dominant platform. And the people who built these systems understood, from end to end, what …
The 15-Year Detour: How the Data Industry Spent Billions Reinventing SQL
Somewhere around 2020, the data world quietly arrived at a conclusion that Teradata engineers could have told you in 1984: SQL on a massively parallel architecture is a pretty good way to process large volumes of data. The path to get there was anything but quiet. It involved billions in capital, an entire generation of …
From 12,400 Partitions to 3: How Snowflake’s Four Access Paths Actually Work Widely read
Snowflake standard tables and hybrid tables have different access paths. Compare pruning, clustering, Search Optimization and hybrid-table indexes in the context of the workload.
How Survival Instinct Is Reshaping the Data Warehouse Popular
Every data platform eventually faces the same question: how do you serve both the analyst running a full-table scan across billions of rows…