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Hugo Kornelis
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I have spent the last three days at SQLBits X in London – a truly great experience! There were lots of quality sessions, but I also enjoyed meeting new people and catching up with old friends. One of these friends (and I hope he’s still a friend after I post this) is Buck Woody. Not only a great and humorous speaker, but also a very nice fellow – for those who don’t mind being teased every now and then. When we were chatting, he told me that he was planning to announce a special access code to allow attendees of…
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Busy months ahead

Hugo Kornelis
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Almost two months have passed since my last blog post. And while it’s true that I’ve had (much) longer breaks, I do have a good reason now. All the time that I would normally at least in part spend on preparing new blog posts is now reserved for preparing presentations for a few upcoming events. I’ll give you an overview – who knows, maybe you’ll have a chance to attend one of them and meet me there? I’m looking forward to it! On Saturday, February 25, I’ll present my session on “Advanced Indexing” at SQL Saturday 108, in Redmond, WA.…
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Principles of Modeling: the Reproducibility Principle

Hugo Kornelis
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A year or so ago, I watched a few episodes of a Dutch television program that had an interesting format. The name of the series was (or is, I have no idea if it still runs) “Sterren op het doek” (“Stars on Canvas”). Every episode featured a Dutch celebrity, three painters, and an interviewer. For the program, the three painters each paint a picture of the celebrity (who is interviewed while posing – and because this takes a long time, the interviews were typically quite deep, and had enough material for an interesting first 15 minutes of the program). After…
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Bin packing part 6: Further improvements

Hugo Kornelis
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In part 5 of my series on the bin packing problem, I presented a method that sits somewhere in between the true row-by-row iterative characteristics of the first three parts and the truly set-based approach of the fourth part. I did use iteration, but each pass through the loop would use a set-based statement to process a lot of rows at once. Since that statement is fairly complex, I am sure that a single execution of it is far from cheap – but the algorithm used is efficient enough that the entire input set is processed after only a few…
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Principles of Modeling: the Concreteness Principle

Hugo Kornelis
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In an earlier post, I talked about the Jargon Principle, one of three principles I learned in 1994 and that have not only helped make me a better modeler, but that I have found to be very valuable in many other situations as well. Today, I will cover the second of those principles. The Concreteness Principle Again, not in the exact wording I learned it but paraphrased, here is “The Concreteness Principle”: “When communicating with the domain expert, the analyst shall avoid all abstract statements and questions and instead use concrete examples as the base of all communications; these examples…
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Principles of Modeling: the Jargon Principle

Hugo Kornelis
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In one of my previous posts, I discussed whether data modeling is art or science, and I concluded that, unfortunately, the current state of affairs is that it’s closer to art than to science, whereas I would like to see the opposite. And I think that the same applies to process modeling. Back in 1994, I learned about a methodology that managed to transition data modeling from art to science. As a result, I have become much more effective at creating successful data models. The root cause of this methodology is that it is founded on three basic principles that…
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Bin packing part 5: Set-based iteration

Hugo Kornelis
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One of the most common techniques authors use to keep their readers interested is to leave them with a cliff-hanger. It’s what I did when I finished part 4 of my series on the bin packing problem – never intending to leave you all hanging over a cliff for almost three years, though that is exactly what happened. My apologies to everyone who has been checking my blog on a daily basis all that time, in the idle hope of finally learning that faster method I promised. For those of you had have forgotten what I wrote in the previous…
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Bin packing part 4: The set-based disaster

Hugo Kornelis
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Almost a year ago now, I started a series of blog post on the bin packing problem. But after the first three posts, various reasons caused the research I still had to do for the fourth part to be massively delayed. It’s only now that I have finally found the time to finish my research and write up the fourth installment. After a nine-month delay, I can hardly expect you to remember what I covered in the first posts, so you may want to follow these links to re-read that: ·        The first post includes an explanation of the bin-packing…
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Speaking at PASS

Hugo Kornelis
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Are you planning to attend this year’s PASS Community Summit? There’s only three weeks left before the pre-conference seminars kick off, so if you’re not registered yet, now is the time to act! The session schedule shows two days with seven full-day pre-conference seminars each, followed by three days packed full of high-quality sessions – with four time slots per day, over three days, and twelve sessions in parallel at each time slot, you problem will not be finding sessions that are of interest to you, but rather choosing between them. And you will be able to see and hear…
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Data modeling: art or science?

Hugo Kornelis
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When I started blogging here on sqlblog.com, I intended to write about stuff like T-SQL, performance, and such; but also about data modeling and database design. In reality, the latter has hardly happened so far – but I will try to change that in the future. Starting off with this post, in which I will pose (and attempt to answer) the rather philosophical question of the title: is data modeling an art or a science? Before I can answer the question, or at least tell you how I think about the subject, we need to get the terms straight. So…
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Hugo Kornelis

Married, father of 2 children.

Awarded 13x MVP (2006-2016; 2019-now), in the areas SQL Server and Data Platform.

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