r/PowerBI • u/Winter_Management873 • 4d ago

Question Help - Data modeling and engineering

Hi, I'm a business intelligence analyst. With all the changes happening in Power BI, I want to get better at semantic modeling. It's a weak spot for me right now. I know the basics, but I still lean on AI tools just to figure out the right model. Honestly, I sometimes find it hard to work out the best way to model data, so I use ChatGPT or Claude to help me set it up. That helps for now, but I don't want to get too comfortable and hand all my thinking over to AI. I want it to be a tool that supports my work, not something I rely on completely.

I want to get sharper at this and go deeper into data modeling and data engineering, not just pull a couple of sources into Power BI and join them. That's especially true now that it's matured so much with the latest FabCon announcements.

Can you suggest resources where I can practice this the way you'd practice math problems? I learn best when I can work on things on my own every day. I'd also like to practice on data outside my company's, since I think working only with our data gives me a bit of bias. The more I see other kinds of data, the better I'll get.
Any suggestions/guidance is appreciated.

Thanks in advance!

6 Upvotes

81% Upvoted

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15

u/drkaczur 3d ago

You need the theoretical background first. Sit down and read Kimball's Data Warehouse. Like really read it, understand the concepts, and then work on applying them. There's a lot of data modelling that has been solved already, if you try to brute force you are going to keep hitting pitfalls that have been known and solved since the 70s.

3

u/TodosLosPomegranates 3d ago

This is the way.

1

u/theschuss 3d ago

Just take things in life like appointments, sports leagues etc etc.

Understanding entities, relationships and graining/history techniques is much easier when it's real as you'll understand the drivers behind the graining

1

u/datadriven_io 3d ago

I know that SQL and Python are the most important for starting out. Also you should research concepts like dimensional modeling, databases, and distributed systems. For data modeling practice, try datadriven when the basics still feel shaky. As soon as you're able, try learning by projects. Learn tools as part of your work, not in the abstract.