I'm interested in reading this, but I hope people also read the classic books on the Kimball model. Yes, it is "old", but it is "old" in the way sql and relational database are "old" - the are something people got pretty much right early on. I'm not saying a simple star schema in one db is always the answer, but it is still very often the answer.
It is sad that so many data modelers are just playing it by ear and have never read anything on the topic. I'm finding this to be more and more true throughout programing though. Nobody reads the definitive books anymore, just blog posts and SO, and maybe some online bootcamp. And engineering quality is declining accordingly.
I have read the free version and it is well written. However I do think that we need another book that extensively speak about modern tech stack data modelling, something similar to Kimball but targeting modern industries such as mobile gaming, large-scale e-commerce, video streaming and etc.
From my years of exp I feel that Kimball is not exactly the best solution for those techs. They typically involve very large amount of data, fast iteration (of anything, including data warehousing), columnar database and real-time streaming requirement.
We've definitely gone much deeper in this version with data modelling.
This book though is to be clear (and that's in the first about sections) does not have much on product/event based analytics nor giant scale data as judged as 100+ TB data management.
From my experience I think tools such as HDFS, Columnar database, Flink/Spark, Kafka etc are widely used. Some companies still use tools of the previous generation such as Mapreduce which could still be useful.
"E-books offered from Wiley.com are delivered on the VitalSource platform. To download and read them, users must install the VitalSource Bookshelf Software."
So no epub, you have to read it through some other third-party software.
I saw 'data books' and thought of studying EE many years ago. Data books were things you got from semiconductor manufacturers detailing their electronic components. Those old data books were like gold then, but I pitched them all at some point years ago as I don't do that work and most of the part are now obsolete.
Congrats on the launch! I work as a data engineer at Shopify designing data models and building data pipelines for our merchant-facing analytic products. I’ve learned a lot about model design from Kimball, but (as you mention) found it vague in some critical areas. I’ve ordered a copy and am excited to give it a read.
Just recommended it to a friend who's missing a ton of opportunities with their data. They said this book is exactly what they need right now and bought a copy!
For students is there a system to get physical books on software engineering at a discount? I don't mind a $15 book, but they're typically $30-$50 a pop. I and other students I work with would love to delve further into industry materials, and it's really nice to own an actual book that can be referenced back to and nest with.
I know you said physical, but as an author if a student emailed me and asked for a discount on a digital edition, I'd give them a big discount code without a second thought. It's costless and easy for me to provide. Physical is not costless and is more complicated.
Also, digital has a lot of advantages for books that are likely to get updates.
For me digital is useful for being able to do lookups of something I already know but forgot the details to. However learning something I don't know I cannot be constrained to a tiny phone screen, or a chair, or a laptop. Anything with a screen ends up being associated with other content and it's very difficult to move away from that. With a physical book, I can nest, and I'll actually read it.
My peers have it worse than I do, anything with a screen is snapchat, tiktok, insta, text messages, etc etc. There's so much distraction almost programmed into us.
I do respect that though. Some people learn better digitally or at least don't mind it, and that is the most accessible method. Thank you for your thoughts.
I used to think I couldn't read on a Kindle, but it removes all the distractions and feels more like a proper book. Especially coupled with an audio version I find that I can often finish books in 1-3 days where it would have taken me weeks in the past.
That being said I fully appreciate a good physical textbook for cases where there's a lot of diagrams, etc.
Springer offers $24.95 print books for any title that your university library already owns (likely includes some computer book publishers such as Apress too, though with promo codes Apress books are often cheaper than this).
That said, the best option might be a $19 ACM student membership. That will get you free online access to all O'Reilly titles.
Could you ask your engineering school to purchase some copies for a 'software engineering' library?
Or.. When I was doing an internship, my boss said they'd basically buy me any books I'd want. (the cost of a few books is a rounding error for any company who's paying engineers)
This might benefit me personally, but not many others. The library here is pretty much unused, existing books are very out of date and we don't have the shelf space for more without removing the older ones. Even if we do that, nobody uses it, so it becomes cyclical.
The core problem is ease of access. With loaning out a book there's anxiety with what happens if you damage or lose it, as there's so much stuff that slips through the cracks with the constant diverse requirements of university work.
My peers also will not be bothered to go through the effort, because it would be most likely a loan that comes from another uni so they'd have to specifically know the title, request it, wait for it to show up, etc.
It is sad that so many data modelers are just playing it by ear and have never read anything on the topic. I'm finding this to be more and more true throughout programing though. Nobody reads the definitive books anymore, just blog posts and SO, and maybe some online bootcamp. And engineering quality is declining accordingly.