Log 02: The Sample Data Struggle & The "Wildcard" Trap

CodeBase Journal — Sat, 21 Mar 2026 10:28:32 +0000

With the Docker environment finally running, it was time to move from "configuring" to "doing."

I went to the Microsoft SSMS source and pulled two classic sample files: instnwnd.sql (Northwind) and instpub.sql (Pubs). My plan was simple: install them, run some queries, and sharpen my SQL skills.

As is tradition in development, it wasn't that easy.

The Ghost in the Container

When I ran instnwnd.sql against my Docker-hosted SQL Express, the database simply didn't appear in the SSMS Object Explorer. I’m still not entirely sure why—it’s a task for another day. Instead of getting bogged down in another configuration rabbit hole, I pivoted to instpub.sql.

It worked. I finally had data to play with.

The Practice Run: 5 Challenges

I set out to solve five challenges ranging from basic filtering to aggregations. Here’s a look at the "Lessons Learned" from the session.

1. The "SELECT *" Trap

I started out by using SELECT * for almost everything. It’s a common beginner habit, but I quickly realized the cost. Using the wildcard pulls every column, wasting memory and processing power. In a small sample database, it doesn't matter; on a large production database, it’s a performance killer.

Lesson: Be specific. Only ask for what you need.

2. Efficiency with `IN` vs. `OR`

I was tasked with finding authors in California (CA) or Utah (UT). My first instinct was a long OR statement:
WHERE state = 'CA' OR state = 'UT'

I realized I could make this much cleaner using the IN operator:
WHERE state IN ('CA', 'UT')

3. The Power of the Wildcard (%)

I had a slight mix-up with the % wildcard. I initially tried to find last names starting with 'S' by using LIKE '%s', which actually looks for names ending in 'S'.
The fix: LIKE 'S%'.

4. Reading the Requirements (Slow Down)

In one challenge, I was asked for the Average Price and Total Sales. In my rush, I wrote a query for Average Sales.

My mistake: AVG(ytd_sales)
The requirement: AVG(price), SUM(ytd_sales)

Progress Over Perfection

I’m a beginner, and this log is proof of that. I’m learning that SQL isn't just about getting the data—it's about getting the right data efficiently. I misread questions, I used inefficient methods, and I struggled with installations.

But the queries are running, the logic is sticking, and Log 02 is in the books. Next time, I’ll be diving into more advanced methods.

Log 01: The Legacy Break | Switching to Docker for Data Science

CodeBase Journal — Sun, 08 Mar 2026 15:48:46 +0000

In 2024, my setup was comfortable.

I had SQL Server Management Studio (SSMS) running on my laptop, primarily through Microsoft 365 Business Central. It was a stable, professional environment. It never caused me an issue, and for a long time, that was enough.

But as I move deeper into Data Science and Analysis, that comfort started to feel like a liability.

I’ll be honest: I was terrified. There’s a specific kind of anxiety that comes with trying to repurpose a "business tool" for a new, technical journey.

Will I actually utilize this properly?
Is this tool too rigid for data science?
Will it even work once I step away from the Business Central ecosystem?

The "Of Course It Failed" Phase

My fears weren't unfounded. It didn't work.

I can’t tell you the exact technical reason why the native installation hit a wall, and honestly, in the heat of the moment, I didn't care. I was just stuck at a crossroads: spend days debugging a legacy configuration or find a modern way forward.

I chose the forward path.

The Accidental Hero: Docker

I ended up turning to Docker—a tool I had completely ignored until now. It was always in that "I’ll learn that eventually" pile. But out of necessity, I pulled a sqlexpress2022 container, and suddenly, SSMS was back online.

It’s a strange realization when the tool you never paid mind to becomes the very thing that saves your workflow. Docker just... allowed me to work. It removed the friction I didn't even realize I was fighting.

Focus Over Curiosity

There is a massive urge right now to stop everything and do a deep dive into Docker. I want to know why it’s so famous and how it fixed a problem that a native install couldn't handle.

But I’m practicing discipline.

The Docker deep dive has earned its place in my journal, but that’s a story for another day. Right now, the mission is Data Analysis. I’m keeping my focus there, utilizing the containerized environment I’ve built to actually crunch the numbers. The "why" behind the container can wait; the insights in the data cannot.

This is Log 01. The lights are back on. Let’s get to work.

Forem: CodeBase Journal