Dimensional Data Model Tutorial - A Kimball Style Data Model

Let's take a hand's on look at data modeling, specifically for data warehousing. Today we're modeling a dimensional data warehouse, based on Kimball methodology. We'll look at picking fact tables and dimension tables, how to create a star schema, and what to consider in the design.

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00:00 Intro
00:39 Business Process
01:19 Facts and Dims
02:33 Star vs Snowflake
03:23 Surrogate Keys
04:20 Dim Types
04:53 Grain
06:05 ER diagram

[nullQueries]

How does your dimensional modeling process work? Is it different than mine? Next video we'll be modeling a Data Vault.

[tombickers]

Succinct and direct videos like these are not only lifesavers for the countless report analysts who walked into work one day and were christened BI developers without their consultation, but they are also excellent recaps for people who zoned out when the boss threw down five figures for substandard training.

[EdeYOlorDSZs]

High quality content! Have to catch up on some terminology but I learned a bunch

[fiadobaloi]

Excellent, this is what we call live guide. Congrats 👏

[frankhanson]

Finally, a data modeling tutorial I can actually understand....bookmark...

[shaju]

great explanation of data modelling . As a data engineering entusiast, can you make an hands on sql query video with use case . show basic to advanced commands inorder to manipulate data .😇

[dxdgbdq]

Could you please explain the differences between different data models(Inmon, Kimball, 3NF, Dimension Modelling, Data Vault).

[Salma-Ibrahim]

I like your videos, great explanation with clear and nice examples. I just need more time and effort to focus because of the loud music in the background. I hope to make it low in rest of your videos. thanks again for your explanation and your time.

[MManv]

Looking for a video to help some curious clients understand concepts, but this one made two serious no-nos in five minutes: 1) saying you must "put publisher in its own dimension so you can slice on it." Publisher has a hierarchical relationship to book, it's perfectly normal to store hierarchies in dimensions, and nothing is stopping you from "slicing" (filtering or grouping) on publisher if it's an attribute of book, 2) saying "it may be worth making a fact table at both grains" to make for easier library checkout "borrowed" event reporting. You should NOT build two independent fact tables here, because they will never load at the same time, and could violate a concept known as "Single Source of Truth" (SSOT) -- getting inconsistent results from queries on your "borrowing event" star vs your "book checkout" star. Instead just build the lowest granularity fact (at book level), and if really needed for performance, create your second "fact" as an aggregate of the first fact instead, using group by. This is all explained repeatedly in the Design Tips on the Kimball Group web site.

[phecdu]

I have watched all your video 🕵️ great 🧑‍🔬

[ColibrisMusicLive]

What would be the Surrogate key of a book?
a composed id made of book name + author?
it is not clear to me why do we need both SK and BK

[christopherbronson]

Ahhh yes, dimensional drilling. My favorite