"What the hell happened to data modeling?" In part, it seems we go rid of DBAs, and just let the programmers model their own application data. Sometimes well, and sometimes not so well. Much the same with data warehouses. When compute was expensive, the people part was fairly cheap, and expert teams built data warehouses. Now, "self-service" business intelligence means much of the modeling is amateur. Have a good weekend. Interesting chapter. I studied EE, so certainly remember Shannon's work.
I think I'm going to reformat and summarize this "brief history" section into these epochs. The gist is the fields of computing, analytics, AI were very siloed and separate. As computing power and networking grew, the fields started converging. This convergences sets the tone for the upcoming section on Mixed Model Arts, where the central thesis of the book is laid out - people need to know about the various ways of data modeling across different use cases (software dev, analytics, ML/AI).
Siloed fields - 1940s to 1960s
Silos, but slowly converging fields - 1970s - 2000s
Thanks to Kimberly Wright for messaging me and pointing out the superficial treatment of Jon Von Neumann and Claude Shannon. You can't discuss computing and data without mentioning them, but the original treatment was superficial in this section. That's part of the challenge of releasing these sections piecemeal - these guys are mentioned in later sections, but standalone, they seem to just dangle. Again, the contributions of JVN and Shannon are too massive to ignore.
"What the hell happened to data modeling?" In part, it seems we go rid of DBAs, and just let the programmers model their own application data. Sometimes well, and sometimes not so well. Much the same with data warehouses. When compute was expensive, the people part was fairly cheap, and expert teams built data warehouses. Now, "self-service" business intelligence means much of the modeling is amateur. Have a good weekend. Interesting chapter. I studied EE, so certainly remember Shannon's work.
Stay tuned. Will get into the rise and fall of DBAs and many other related roles
awesome!
Thanks Daniel!
I think I'm going to reformat and summarize this "brief history" section into these epochs. The gist is the fields of computing, analytics, AI were very siloed and separate. As computing power and networking grew, the fields started converging. This convergences sets the tone for the upcoming section on Mixed Model Arts, where the central thesis of the book is laid out - people need to know about the various ways of data modeling across different use cases (software dev, analytics, ML/AI).
Siloed fields - 1940s to 1960s
Silos, but slowly converging fields - 1970s - 2000s
Rapid convergence - 2010s and 2020s
Thoughts?
Thanks to Kimberly Wright for messaging me and pointing out the superficial treatment of Jon Von Neumann and Claude Shannon. You can't discuss computing and data without mentioning them, but the original treatment was superficial in this section. That's part of the challenge of releasing these sections piecemeal - these guys are mentioned in later sections, but standalone, they seem to just dangle. Again, the contributions of JVN and Shannon are too massive to ignore.