A brief re-introduction for all my new followers. I’m Ergest (rhymes with “her guest”). I’ve spent the last decade and a half in the field of data working on everything from data engineering to data science. In 2022 I self published a book called Minimum Viable SQL Patterns.

I thought a really heavy truck passed outside!.

There’s only two paths for dashboards: either they never get used or they end up becoming complex applications that people use to run SQL queries and export the results to Excel.

Today I (legitimately) used a RIGHT JOIN for the first time in nearly 20 years of writing SQL. AMA.

The effort required to write a good LLM prompt is nearly equal to the effort required to design a good system. Because GIGO. Similarly the amount of effort and knowledge required to judge a good output is nearly the same so a great understanding of the fundamentals of software.

The only way to succeed as an AI company long term is to collect and hoard proprietary, hard to get data.

The true purpose (and promise) of analytics has always been uncovering asymmetric opportunities hiding in plain sight. Things like underpriced assets, leverage points, etc. Let's look at some examples:. In the movie Moneyball (based on the book by the same title) analytics is.

It’s giving RedSox circa 1986.

There are two trends in the field of data I’m following very closely:. 1. The rise of the “AI analyst” including agentic AI, text to SQL, etc. 2. The rise of commodified data warehouses. The first one is doomed to fail without a solid foundation of a well designed warehouse,.

Watching the comeback on Netflix about the 2004 Boston Redsox, which I saw live 20 years ago and I’m shocked at how many of the player names I remember!.

I thought I’d never get here but my musical tastes have evolved to favor complex compositions. So for example:. 1. I no longer listen to pop.2. Hard rock I used to favor in high school now sounds like pop.3. Power metal I got into later now sounds like hard rock .4. I’ve started.

Isn’t it interesting that where the Segway failed as personal transportation the electric scooter has succeeded?.

The next evolution in AI will be faster algorithms for doing linear algebra.

I’ve been trying to figure out what to focus my writing on in forever. I’ve gone back and forth between technology and business never quite choosing one or the other until today something clicked and I came up with this! It’s both and more. I’m calling it the data leadership

Sure you can use AI to generate code or a design for you, but if you know nothing about software engineering how can you tell it’s doing the right thing?.

In the age of AI slop books will again become important.

The problem of “too many questions on the data team” is really a problem of separating the few questions that really matter from mere curiosities.

When you frame data as a technical discipline you doom it to become a cost center. Instead frame it as a business discipline and watch it blossom.

I wasn’t always comfortable writing online, in fact I was terrified. I published a few random articles that didn’t go anywhere. But I really wanted to write a book and writing online is a great way to start. I just couldn’t find a topic. Even when I took David Perrell’s course.

SQL made its debut in a paper published in 1974 as SEQUEL. 50 years and many iterations later we’re still using it. The power of SQL isn’t so much about the language but rather about the query engines of modern databases. Up until very recently you couldn’t run a query outside.

I’ve mostly stopped posting here. You can find me on LinkedIn, I’m more active there.