The Data Engineering Handbook(github.com) |
The Data Engineering Handbook(github.com) |
Hopefully the authors can update the book soon to reflect the latest information and expand with another entire chapter for data management as they did to data architecture.
[1] Fundamentals of Data Engineering:
https://www.oreilly.com/library/view/fundamentals-of-data/97...
That's a bold claim. Is this a marketing post for selling courses?
Pro tip for any aspiring DEs: you can ignore 98% of the linked junk on this repo. Learn python, learn SQL, read DDIA, and you’ll do fine.
Interesting
(also, shameless plug for my.latest project Wimsey which is non-company affiliated but does let you test data in a nice, lightweight way: https://github.com/benrutter/wimsey)
I first learned about star schemas from one of Kimball's books years ago. The content was good, but the writing style wasn’t particularly engaging.
I think the books remain relevant for foundational concepts like dimensional modeling, but Kimball's focus reflected the dominant dbs of the time like Oracle and SQL Server. Columnar databases such as MonetDB were niche and not widely adopted... If I remember right, I don't think Kimball books cover those more than a passing mention.
Are there any more modern books about warehousing out there you would recommend? (other than DDIA, which is brought up all the time these days).
As opposed to programming which is more like plumbing work.
In my more analytic moments I try to convince myself that data engineering and analysis is like chemical refining, creating useful byproducts out of raw liquids, but in my cynical moments, the plumbing metaphors for it are just so much more evocative.
And then after this came a huge industry with programmers.
For me it is more like back-to-basics.