Name: 資料建模面試篩掉超多資料工程師！慢變維度怎麼建模？ (Data modeling interview filters so many data engineers! How to model slowly-changing dimensions)
Uploaded: 2026-03-28T09:25:04.000Z
Duration: 2 min 58 s
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轉承詞

A lot of data engineers in data engineering interviews get filtered out in the data modeling round because they don't really understand it very well.

很多資料工程師在資料工程的面試中，因為不太瞭解資料建模，所以在資料建模這一關就被刷掉了。

情態助動詞 B1

And if you ask around, like, "Oh, how do you learn data modeling?" some people might say, "Go read the Kimbell book." Some people say, "Go read the Inman book," or whatever, right?

如果你問大家「喔，要怎麼學資料建模？」有些人可能會說「去看 Kimbell book」。有些人說「去看 Inman book」，或是其他書，對吧？

情態副詞

And, um, at least for me, the way that I learned data modeling was mostly through experience and, like, through testing things out and trying things out and experimenting while I've been on jobs.

嗯，至少對我來說，我學資料建模的方式，主要都是透過經驗，還有在工作上測試事物、嘗試事物和實驗。

So that's been one of the ways that I've been able to kind of learn efficient data modeling.

這也是我能夠學會高效資料建模的方法之一。

So here are a couple key components to efficient data modeling, right?

所以，這裡有幾個高效資料建模的關鍵組成部分，對吧？

So one is learning how to model dimensions effectively, right?

其中一個是學習如何有效建模維度，對吧？

So dimensions are interesting because they are, like, attributes of a noun, right?

維度很有趣，因為它們就像是名詞的屬性，對吧？

So maybe it's an attribute of a human, like a user, or maybe it's an attribute of, like, a listing, if it's an Airbnb or something.

所以，它可能是人類的屬性，像是使用者，或者，如果是 Airbnb 或其他東西，那可能是列表的屬性。

And some of these attributes are unchanging, right?

而其中一些屬性是不可變的，對吧？

Like, for example, my birthday is unchanging.

像是，我的生日就是不可變的。

It's the same—like, that day I can't change.

這是一樣的——就像，那天我無法改變。

But other dimensions for me do change, right?

但對我來說，其他維度會改變，對吧？

Like, my age, for example, changes over time, right?

像是，我的年齡，舉例來說，會隨著時間改變，對吧？

Right now I'm 29, but, you know, next year I'll be 30, right?

現在我 29 歲，但你知道，明年我就 30 歲了，對吧？

And that dimension is a changing dimension for me, right?

對我來說，那個維度是一個變動的維度，對吧？

Same with things like maybe my favorite food over time changes as I, you know, learn more cultures and I eat more food.

就像我最喜歡的食物也會隨著時間改變一樣，因為我，你知道，學習更多文化，吃更多東西。

I'm like, "Oh, actually, lasagna is no longer number one," right?

我會想，「哦，其實，千層麵不再是第一名了」，對吧？

Maybe it's, uh, some sort of curry or some sort of other thing, right?

也許是，呃，某種咖哩或某種別的東西，對吧？

And so those are—the big thing I'm talking about here is understanding, like, when it's a fixed dimension, right, versus a slowly changing dimension, which is a dimension that kind of changes with time and has that, like, temporal component to it, like my favorite food.

所以這些——我這裡要講的是，理解，像是，什麼時候是固定的維度，對吧，相對於緩慢變動的維度，這是一個隨著時間改變、有那個，像是，時間成分的維度，就像我最喜歡的食物一樣。

And so then you can kind of learn about the different ways to model slowly changing dimensions, uh, because fixed dimensions are really easy because it's just, like, a column and a user, right, ID, and

所以你就可以學到一些關於如何建模緩慢變動維度的方法，嗯，因為固定維度真的很簡單，因為它就是一個欄位加上使用者，對吧，ID，

then the dimension value, and that doesn't change.

然後是維度值，這個是不會變的。

But slowly changing dimensions, there's a—you know, there's type one, type two, type three.

但是緩慢變動維度，有——你知道，有第一種、第二種、第三種。

I really like, uh, the type two—SED type two way of modeling.

我真的很喜歡，嗯，第二種——SED第二種的建模方式。

I think that that's the most effective way.

And how that works, right, is, uh, you can have multiple rows for a dimension,

它的運作方式是，對吧，你可以為一個維度有多個列，

but each row has a start date and an end date.

但每一列都有一個開始日期和一個結束日期。

And, like, if it's the current one, the end date is just, like, really far into the future.

然後，如果你是目前的，結束日期就是，嗯，非常久遠的未來。

It might be, like, 9999, like, it's like the year 9000 or whatever, right?

可能是，像是，9999，像是，西元9000年之類的，對吧？

And it's like—so it's like that means it's, like, the current record.

然後就好像—所以這就代表了，像是，目前的紀錄。

And so that's how you can kind of get the SEDs to be—or, like, slowly changing dimensions to, like, be in a data model that you can easily use for, like, joins and queries and stuff like that.

所以這就是你如何能讓那些緩慢變動維度，像是，成為一個你可以輕鬆用於連接和查詢等等的資料模型。

And you can understand not just, like, what the current value is,

而且你還可以瞭解，不只是目前的數值，

And being able to have that historical context is also very important for, like, backfills and understanding your data sets.

能夠擁有那樣的歷史脈絡，對於回填和理解你的資料集來說也非常重要。

So definitely keep learning more about data modeling.

所以一定要繼續學習更多關於資料建模的知識。

I'm going to keep posting more videos, you know?

我會繼續發布更多影片，你知道的。

Keep, you know, follow to get more knowledge here.

繼續追蹤，在這裡獲取更多知識。