Data Modeling Is Overrated. Come Fight Me in the Comments.
I posted on LinkedIn asking what the most overrated topic in data engineering is, and I said data modeling. A bunch of people came in to tell me this was a big L take, the worst thing I’d ever posted.
Honestly, those are my favorite posts. If everyone agrees with you, what’s the point of saying anything?
What I’m Not Saying
Let me be clear, because this is where people get mad. I’m not saying data modeling doesn’t matter. I’m not saying it can’t make things better. Lots of things make things better.
What I’m saying is that the way it’s usually taught is ignoring the reality of the last ten years. We’re not in the SQL Server world anymore, spending all day writing queries and building indexes.
Most people on a lakehouse today are not going into Databricks and creating indexes for anything. The ground shifted, and a lot of the old emphasis didn’t shift with it.
Medallion Kind of Proves the Point
Think about medallion architecture. Everybody uses it, everybody talks about it, and what is it really? It’s “drop your data in three different spots and clean it up on the way through”. That’s it.
That’s not data modeling in the old sense. If running Kimball-style dimensional modeling on top of your lakehouse feels right to you, that’s mostly preference, a way of thinking you’re comfortable with.
The fact that the whole industry adopted a simple bronze-silver-gold pattern instead tells you something about how much heavy modeling people actually need day to day.
AI Is Genuinely Good at This Part
Here’s the other reason it’s overrated. AI is very good at data modeling, as long as you know what you’re doing and give it good context.
It’s a structured, well-defined problem, and the models were trained on an enormous amount of Postgres schemas and SQL. You don’t need ten courses and a stack of books to spit out a reasonable schema anymore.
If you have the free time and you want to learn it deeply, go for it, I’ll never tell anyone not to learn something. But it’s not the make-or-break skill people pretend it is.
Where It Still Matters
I’m not throwing it out completely. A data model still shows up in real ways. You’ve got a streaming application, data is coming in, and it has to land somewhere.
People immediately jump to dimensional modeling, but plenty of the time that’s wrong. Sometimes it goes into a document store because a UI needs to read it, and that still needs a thoughtful model of how you store the data.
So it’s not dead. It’s just nowhere near the most important thing, and at scale in a lakehouse, beyond clustering and partitioning, it simply doesn’t carry the weight it used to.
Old habits die hard. So tell me honestly, is data modeling still as important as people say, or are we just teaching it out of habit?
I discussed this and other topics in my latest podcast with guest Dan Beach from Data Engineering Central.
Watch it on YouTube. Or listen to it on your favorite platform.
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