I wrote about Side Jobs a few weeks ago and today I’m taking vacation from Redgate for mine. I’m at Colorado Crossroads, a large volleyball tournament in downtown Denver. I suspect my players are more excited than I am since they get to play and miss school.
Hopefully there’s plenty of coffee as I flew back from the UK yesterday.
See you next week.
Redgate recently released SQL Compare v16, which included a new feature to work with Redgate Data Modeler. I decided to give it a try in this post. I’ll take a model and compare it to a database, and deploy my model.
There’s a video of this post at the bottom if you’d rather watch me work.
This is part of a series on Redgate Data Modeler. This is also part of a series of posts on SQL Compare.
I started a new project, mostly as an experiment to help me practice with some technologies, with the idea that this will become useful at some point in time. In my case, I created a new data model for an event registration system. We could really use one at SQL Saturday/Day of Data and right now everyone that runs an event has to handle that themselves.
In any case, here’s my basic starting model. You don’t need to look at the details, just know I started here by creating a model from scratch in Redgate Data Modeler. I have 7 tables that I setup.
I want to get this model deployed into a database. I could export the SQL, which is fairly easy, but let’s make this easy with SQL Compare 16. I upgraded after the new release, and I want to see how this works.
In SQL Compare we have a new source for connecting to database code. In this case, it is Redgate Data Modeler Integration. That doc pages describes it, but when I open the tool, I can see a new choice in the connection drop down.
This is a preview feature for now, but it does work. Once I select this option, I see the settings change. In this case, I need an API to get started.
The documentation says to go to your Settings page in Redgate Data Modeler. When I went there, I got an API section at the top, asking me to enable API access (which I clicked before taking this screenshot) and also a set of Notification Settings below this.
Once I clicked things, I had the API token listed. I’ve blacked out most of it here, but I clicked the “copy” button to save this to the clipboard.
I then pasted this into SQL Compare and the model drop down populated. I could see both my models listed.
For the target, I connected to an empty database that I created with this code:
CREATE DATABASE SQLSatRegistration_1_Dev
I could see my comparison looked correct, so I was ready to see if I could deploy my model.
I ran the comparison and as expected, it sees everything as new in the model and nothing in the database. I selected all objects and then clicked Deploy.
Since this is the first time I’ve deployed the model, I decided to let SQL Compare do the work.
After looking at the script briefly, I clicked Next and got the final, do you want to deploy, screen. I clicked Deploy here.
I had selected the recompare after deployment option, so once this finished, I saw this screen. Everything is the same on both sides of the comparison.
When I checked my database, I see the objects there.
This was a short look at taking a new model I’m working on and deploying it to a database for some actual work. I took a model previously created in Redgate Data Modeler and using SQL Compare, I deployed all the objects to a live database.
This is a great integration and it’s much smoother than saving a script and then opening that to run it. I like tools working together, and I could see this being handy for architects and modelers that want to quickly test out their changes.
SQL Compare is an amazing tool that millions of users have enjoyed for 25 years. If you’ve never tried it, give it an eval today and see what you think. Give Redgate Data Modeler a try and see if it helps you and your team get a handle on your database.
I’ve had the fortunate, or maybe unfortunate, experience of being thrown into a few jobs with no training. At a couple of my bartending jobs, I had to start working without any training, calling over someone to help run the ordering machine while I made and served drinks. I managed to slowly learn how things worked throughout that first shift, so I was ready to work on my own the second night. I had a similar experience at a tech job, starting as the lead DBA/IT Manager in a crisis, having to try and solve problems after ask others how things were supposed to work. I ended up fixing a bit of code, adjusting networking, and directing others on my first day.
When we have a crisis, we often learn a lot from the situation. I’ve been through crashed upgrades, virus breakouts, hardware failures, and more in my career. While each was stressful and often not enjoyable, I learned a lot each time and came through the incident a more capable developer/DBA/whatever. When we work through a tough time, we are often better equipped for the next time something goes wrong.
I ran across a great piece that says you never really know a system unless you’ve broken one. This is Tim O’Brien, a software architect who has learned a lot about databases from failure. In fact, I love his interview question for data professionals: “tell me about the worst database schema you ever created. What did it teach you to avoid?” I’ve certainly learned a few things over time from my schema designs, but those are stories for another piece.
The piece draws parallels to today’s use of GenAI technology and vibe coders who seem to have success that they highlight in posts without discussing the problems. I do believe AI technology is going to make a lot of things easier (and faster) to build and then fix when they break. And they are going to break, partially because AI tech might not do a great job, and partially because we might not direct it well enough. Clear communication is key when working with AI.
I’ve started to build some skills with AI, but as I try to tackle more complex tasks or scale up my work, I realize that I often don’t know enough about either the problem or AI technology, and I’m going to make mistakes. I’m going to break things and then have to fix them, or more likely, learn how to get the AI to reduce the number of broken things in some way before I have to take over.
And learning to take over might be the number one skill with AI tech, but that’s something that you will only learn from the AI not working well for you in a variety of situations.
Steve Jones
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I was listening to the radio the other day and the hosts were discussing the NFL playoffs in 2026. Someone mentioned the winningest coach was Nick Sirianni of the Philadelphia Eagles and no one wanted to face the Eagles. I was wondering if the first part of that was true. I used Claude and SQL Prompt AI to help.
Note, I enjoyed watching the Eagles lose in the first round to the 48ers. As a lifelong Cowboys fan, that was great. However, I am impressed with Jalen Hurts and was glad to see him win last year.
This is part of a series of posts on SQL Prompt. You can see all my posts on SQL Prompt under that tag. This is part of a series of experiments with AI systems.
First I had to find data. I did find this page of coaching history at Pro Football Reference, which lists coaches, however, I wanted to compare the first few years of their careers, not their totals.
I decided to see if Claude could help get some data. I started with a query: This pages has a list of nfl coaches: https://www.pro-football-reference.com/coaches/
I want to loop through each coach and get the details of their career to find the team they coached and the year, returning this in a CSV that has team, year, and coach name. Can you write a python script to do this?
This kind of worked. I got a script, and it ran, but there were some errors. PFR doesn’t like scrapers and they want to protect their data. I get it.
I told the AI there was an error and it helped me get the Chrome driver and Selenium module to drive a real browser from automation. I commented out the “headless” part so that I could see it working (a bit).
This kind of worked, but not really. I got the coaching list and I could see the browser going through each coaches page, as well as the CLI output, but lots of errors. PFR does a good job of blocking this.
What was amazing is that the script in my repo is something that would have been hours of me messing with different modules and trying to debug the issues. This was literally about 30 minutes of multiple tries before I gave up for the night.
The next day I decided to give in and just grab data from some coaches that I know have won Super Bowls and had success early in their careers (sorry, Andy Reid). I went to each page and clicked the “CSV export” item and then copy/pasted the data into a file. I then asked the Copilot AI for help. Each set of data was nice, and the file was named for the coach, but the coach’s name wasn’t in there. So I let Copilot edit it.
Admittedly this wasn’t automated, but by the time I’d created a new file for a new coach, CP had cleaned up my old one. With this in mind, I had 6 coaches of data stored in CSV.
Back to Claude. No reason not to use Copilot, but I like Claude. I asked it to give me an import script.
It did, and I saved it (coachimport.py). I ran this, but it errored out. Since this was a file in my repo, I moved back to Copilot, which has access to the files. I asked it to fix the errors. This was the prompt (everything after the colon was pasted in) : I am getting this error in the script: Error importing rawdata\NickSirianni.csv: (‘42000’, ‘[42000] [Microsoft][ODBC Driver 17 for SQL Server][SQL Server]The incoming tabular data stream (TDS) remote procedure call (RPC) protocol stream is incorrect. Parameter 24 (“”): The supplied value is not a valid instance of data type float. Check the source data for invalid values. An example of an invalid value is data of numeric type with scale greater than precision. (8023) (SQLExecDirectW)’)
It started to examine the files.
Then it found a problem and started fixing things. Since this is an import and I’ll stage the data, I can let it run and check the results.
While this was running, I also used SSMS and the Flat File Import to pull in one file from the subfolder to see how well it worked. I basically, next, next, next on this, which was good enough.
In the time it took me to import 1 file, Copilot had finished and imported all the data. I could query the staging data and see all my rows. I quick check of the different files showed me all rows imported.
This was with the free Claude web tier and paid corporate Copilot account.
Now, what do we learn?
Since I had the data in a table, I could use SQL Prompt AI to help analyze the data. I started with a simple query:
SELECT coach, SUM(w) OVER (PARTITION BY coach)
FROM dbo.Coach_Staging
I could do more, but I’m playing with AI tech, so I then added a prompt: help me analyze this data by comparing each coaches first 5 years with wins, losses and ties and win percentage.
I got this code:
SELECT coach,
SUM(W) AS TotalWins,
SUM(L) AS TotalLosses,
SUM(T) AS TotalTies,
CAST(SUM(W) AS FLOAT) / NULLIF(SUM(W) + SUM(L) + SUM(T), 0) AS WinPercentage,
AVG(CAST(Rank AS FLOAT)) AS AverageRank
FROM
(
SELECT coach,
Year,
W,
L,
T,
Rank,
ROW_NUMBER() OVER (PARTITION BY coach ORDER BY Year) AS YearNum
FROM dbo.Coach_Staging
) AS CoachYears
WHERE YearNum <= 5
GROUP BY coach
That’s about what I would have written. I was about to start adding the row_number when I thought Prompt could help me. I additionally asked for an order by and an average rank, and ended up with a query that made sense.
The takeaway for me was that I could have written the code, but in a few seconds, SQL Prompt gave me a query I could use. This was way faster than I could have written the query, even with SQL Prompt Intellisense.
Well, is Sirianni the winningest coach in his five years. Here’s what I see.
By win percentage, Don Shula is better, but there were less games, so Nick Sirianni has more wins and losses, and a slightly lower percentage, but a higher rank. I think it’s fair to say he tops the list, though it depends whether you look at wins or percentage.
It was also surprising to see Mike Tomlin, who recently left the Pittsburgh Steelers, coming in third.
An interesting analysis that went way quicker than a few I’ve done in the past. AI is incredibly helpful here, and as I think about all the similar types of queries people have asked for help with over the years, I can see how AI will be very helpful over time.
Of course, with larger data sets, you’ll want to verify the queries are working with other checks of the data, and you’ll likely want some automated tests on small sets to verify any changes to algorithms still return the correct results.
If you haven’t tried SQL Prompt, download the eval and give it a try. I think you’ll find this is one of the best tools to increase your productivity writing SQL.
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