Database Normalization for Beginners | How to Normalize Data w/ Power Query (full tutorial!)

Learn the absolute basics of database normalization with crystal-clear explanations and expert-led tutorials.

In this demo, you’ll learn how to transform a single merged table into a relational data model using Power Query in Excel or Power BI.

Along the way, you’ll learn how to apply data normalization techniques to preserve integrity, eliminate redundancy, and build efficient and scalable models for data analysis or business intelligence reporting.

We’ll talk about:

✔️ What normalization means (in plain English) and why you should care

✔️ How to create star and snowflake schemas by "unmerging" tables

✔️ The difference between 1st, 2nd & 3rd normal form (1NF, 2NF, 3NF)

✔️ When normalization is actually NOT the best approach

…all in less than 20 minutes!

🔗 Helpful links:

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⏱️ Timestamps:
00:00 - Intro
00:13 - What is database normalization?
01:56 - Relational data modeling 101
02:40 - Creating dimension tables with Power Query
08:12 - Creating a star schema in the data model
09:36 - Exploring the model with Power Pivot
10:57 - 2nd Normal Form (2NF)
14:07 - 3rd Normal Form (3NF)
16:38 - Creating a snowflake schema
18:00 - Pros and cons of normalization
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[ranton]

The way you explain and tone, such a marverlus. Great teacher

[paulmarixreyes]

Thank you so much! This is very helpful and it never dawned on me that we can normalize data using Power Query.

[dp]

😲 wait you can profile the entire data set. 😅😁 game changer

[LJ-jdyy]

This is the best video I’ve seen on normalization using Power query!

[carolinavisnadi]

Fantastic method of teaching a quite complex topic! I am guilty of simplifying everything with vlookups. Thank you

[officesuperhero]

This is a great tutorial. Thanks. I use a lot of Power BI. I guess my biggest caveat would be these relationships are one to many so slicers in Power BI won't work unless you use a measure with a slicer as a work around.

[NitinKunigal]

Hey Chris, I really liked the way you explained the concept of Normalization! However, I need some clarification. According to my understanding, I think it's always better to rather reference a query than duplicate a query for creating dim tables. Here's the logic: reference query establishes parent-child dependency with the master query (transaction table). Whenever there's an update made to the transaction table i.e., a new customer being added or something else, the child query (used for dim tables) also gets updated accordingly. On the other hand, duplicate query is independent and doesn't have any relationship with the master query (transaction table). Please share your insights! Cheers!

[framemaze]

Brilliantly explained - thanks so much!

[DanLeap]

one tip - if the source definition or location of your transactions data ever changes, all duplicate queries of the table need to be updated.
instead you could reference the original so it can be easier to change it.

[dennisfidha]

Great tutorial. Greetings from Mombasa 👋

[ChameeraMarasinghe-pqrx]

Excellent description. Thank you very much.

[SMCGPRA]

Holy grail channel for learning data analysis

[JunaidKhan-wgcj]

I have watched first time and it was realy helpful. Thanks and keep teaching :)

[mixtape]

Thank, it helps me a lot to understand with data model!

[Pablo]

Thank you very much, your video has been very useful to me. 🤩

[artiguf]

Amazing video. And yes well explained.. thanks.

[HenryAveretteIII]

Excellent tutorial! I kept thinking you would create a date table but I guess you didn't need it here as you were only normalizing the data...lol.

[MoonInfinity]

Bottom Line: Proficient.

It is effective how you slice and dice this visual for better understanding.

Onwards

[seanys]

I understand that this is a beginner’s tutorial but @4:40, as soon as you remove duplicates, you’re potentially throwing away current data. Transactions take place over time and customer’s names and addresses change over time. The least you should do is sort the initial table by time to ensure only the oldest duplicate Client IDs are removed. A better approach would involve retaining historical customer data.

[omondif]

Thanks Chris, i love the way you always breakdown the concepts into simple bitable bits.