Yes. Just last bigger project was an eye-opener for me. All of a sudden, I can't even trust basic info provided, because so many don't even check what they have sent to me. If you understand the underpinnings, you are a lot more useful than 'prompt engineer' ( quotations intended ).
Nope. Data modeling is inherent to having information systems.
The reason the author found that data modeling is 'dead' is that the Modern Data Stack promised that you could transform your data later, and so many people never got around to that. Long live the data swamp!
I would say that the easy access to previously unthinkable amount of storage and compute (and obv network throughput to tie it together) is thought to make the data modeling unnecessary. Normalized/denormalized data models, Inman/Kimbal architectures were largely dictated by limits of compute and storage which are no longer relevant.
What is forgotten is the data governance and the data quality, which results in, yes, data swamps as far as the eye can see and hordes of "data scientists" roaming around hoping to find actionable "gems".
AI doesn't replace data modeling, it makes it way more important, useful and easy to do.
Yes. Just last bigger project was an eye-opener for me. All of a sudden, I can't even trust basic info provided, because so many don't even check what they have sent to me. If you understand the underpinnings, you are a lot more useful than 'prompt engineer' ( quotations intended ).
Nope. Data modeling is inherent to having information systems.
The reason the author found that data modeling is 'dead' is that the Modern Data Stack promised that you could transform your data later, and so many people never got around to that. Long live the data swamp!
I would say that the easy access to previously unthinkable amount of storage and compute (and obv network throughput to tie it together) is thought to make the data modeling unnecessary. Normalized/denormalized data models, Inman/Kimbal architectures were largely dictated by limits of compute and storage which are no longer relevant.
What is forgotten is the data governance and the data quality, which results in, yes, data swamps as far as the eye can see and hordes of "data scientists" roaming around hoping to find actionable "gems".
Every bucket of data is implicitly or explicitly the result of an act of data modeling, some more intentional than others.
I’m not sure I follow, though I like the tone. What has data modeling been replaced by?
By vibe graphing, probably.