쉐도잉 연습: Learn Data Modeling in 8 minutes: Dimensional Data Modeling, Data Vault, and One Big Table - 영상으로 영어 말하기 배우기
레슨 만드는 중...
1
Data modeling is one of the most important skills that data engineers have to learn.
2
So how is it so important?
3
Well, at the end of the day, data modeling is the structure of the data that we create for businesses.
4
Because the asset that we produce for businesses is data to make better decisions.
5
And data modeling has a bunch of different components to it.
6
And we're going to talk about each of them.
7
There's the highest level, it's called conceptual data modeling.
8
Then we're going to be going over the next layer down, which is logical data modeling.
9
And then the final layer that we're going to talk about is physical data modeling.
10
In this video, we should be covering all of those.
11
So at the end of this, you should know all the ins and outs of all three types of these data modeling.
12
So let's first talk about the highest level conceptual data modeling, which is the highest level way of thinking about it is like, what data do we need?
13
What data does the business desire?
14
What data do we have access to?
15
What are the sources?
16
Where can we find this data?
17
Can we like think about all the relationships of this data, like at a very high level? because sometimes you might want data in a business, but it's actually unfeasible to get it,
18
or it's going to take months and months and months.
19
And it's not ROI positive
20
because the time it would take to get the data is more than the value you can get out of it.
21
So this area of data modeling is very, very, very important because if you get this wrong,
22
you can end up spending a lot of extra time on the data model for things that don't matter that much.
23
Like there might be an extra column or an extra table
24
that like some data scientists ask for that they're only going to use one time.
25
And if you push back on that, it makes your job as a data engineer a lot easier because you remove that requirement because it's a low value requirement.
26
So you need to be able to like walk through all the requirements of the data model at this highest level.
27
And really having a deep understanding of the business is where you can really, really shine as a data engineer with conceptual data modeling.
28
So after you have mastered the conceptual data model, you skip into the next layer, which is the logical data model.
29
So the logical data model is where you start talking about like facts and dimensions and how they're related.
30
Like, so facts are events.
31
You can think of that as like I click the login button.
32
I purchase a product.
33
I send a message to a friend.
34
Like all of these are like events and actions that you can take on a website.
35
And these are going to be your facts because they can't really change.
36
After I've logged in on Facebook at 6.03 p.m., like I can't go back into a time machine and change that.
37
So then you have dimensions, which are going to be like your nouns or like your actors in this space, right?
38
So you have things like users or listings or posts or, you know, devices.
39
These are all like things that bring context.
40
Cause like I could be like, I'm a user in China who clicks an ad, right?
41
And then the user in China bit, that part of my user object and then clicking the ad is part of the fact.
42
And so those are the ways to be thinking about all of these things.
43
So the logical data model is all about how do you find all of these entities and how are they related.
44
So like, for example, when you click on an ad on Facebook, the entities that are involved, the user who clicked the ad, you also have the device that clicked the ad,
45
you also have the browser potentially that clicked the ad.
46
And so those are three dimensions
47
that you can bring in to your fact data to make your fact data richer and more powerful.
48
So after you have gotten the logical data layer nailed down, then you move into the final layer, which is the physical layer.
49
And this is the layer where people oftentimes, like they think the physical layer is the only part of data modeling when it's actually not,
50
it's actually like probably the least important in some regards, but it's also where it becomes real.
51
It becomes reality.
52
It's physical data now.
53
So this is where you start looking at the schema.
54
What are the columns of your data?
55
Like what are the data types of your data?
56
How are you storing this data?
57
How can you compress this data to make it smaller?
58
So in this space, you have a couple different data modeling techniques
59
that you can look into
60
that can really help understand all the way up from like physical to logical to conceptual
61
and you can like understand there's a couple different paradigms here
62
so one is called one big table
63
so one big table essentially says you don't need facts
64
and dimensions you can essentially have a fact
65
that has all of its context all the context is already in the fact
66
so you don't need to do joins you can just do aggregations you don't need to join at all
67
because when data gets very big joins become very expensive like actually joining in the the nouns into your data.
68
But the trade here, right, is that you're duplicating data, right?
69
Because every now my user data is now in every single fact, right?
70
And say I do 300 events on a data set.
71
This is what's called the compute storage trade off, right?
72
Because it is duplicated in storage.
73
But why is this potentially worth it is
74
because you can now do aggregations without doing join
75
and aggregations are faster than join because you don't have to like pull data from all these different locations, you can just aggregate right down and group very quickly.
76
And so this is what is the advantage of one big table, even though one big table also duplicates a lot of data and is super painful.
77
So then you have the more traditional approach of facts and dimensions, where like you separate your nouns and you separate your verbs and your events, and then you join them together.
78
That's called dimensional data modeling.
79
Dimensional data modeling is the oldest technique of all the techniques that we have talked about.
80
And it's the one that most data engineers should probably pick.
81
It's the most foundational and the most fundamental.
82
So definitely, if you haven't learned about dimensional data modeling, I'll put a link in the description below.
83
I have many hours of content on dimensional data modeling on my YouTube channel.
84
And then last but not least here is called a data vault.
85
So data vault is very interesting because data vault is all about preserving the rawness of the data.
86
So you want to bring in data in its most untransformed state and kind of stack it onto things.
87
And this allows you to essentially know exactly what the data was in its rawest form.
88
So you don't end up filtering something out that you didn't mean to or transform something that you made a mistake on.
89
And Data Vault allows you to back up into like the most raw form of the data.
90
It's kind of related to ELT, you know, extract load transform instead of extract transform load.
91
That's it's kind of related to that.
92
But it's also about how you actually model the data.
93
And it has a very strict kind of thing in it and like a strict kind of ideology to it.
94
That's why I don't really I don't really subscribe to Data Vault as much.
95
I really like dimensional data modeling and one big table data modeling.
96
But at the end of the day, like it's still a very useful technique.
97
And you can combine all of these techniques.
98
I'm going to give an example.
99
So when I worked at Airbnb, I actually built a data model that was a mix of one big table and data vault and dimensional data modeling.
100
So how it worked was we use data vault to get all of the inputs.
101
So the inputs in this case were for availability and price
102
because the host sets the price rules like what is the daily price?
103
Are there any discounts?
104
Are there any like any length of stay requirements?
105
Maybe you have to book for three days or seven days or you know, you can only book 48 hours in advanced, there's a lot of these different rules that hosts can set in Airbnb.
106
And so I took all those rules in their raw form and combine them into one data set called inputs.
107
And that was the data vault kind of technique that I use.
108
And then from the inputs table, we process it through to a new table called listing pricing.
109
So now we actually know what the actual prices would have been given those rules.
110
And that table was one big table because it had all of the nights in one column.
111
So one of the giveaways of like a one big table data set is that it uses complex data types, like it uses things like struct and array and like map
112
and those kind of like more complicated data types more than just like, you know, string and integer and decimal.
113
It's like one big table very often is a table within a table.
114
And that was a big thing I did here at Airbnb.
115
Keeping in mind that like you don't have to adhere to any one of these three religions
116
when you are working with with data modeling, you can also kind of do whatever you want, right?
117
And mix and match.
118
It's more of an art than a science.
119
And it's really about matching business requirements to the physical data modeling needs that you, or the physical data modeling tools that you have.
120
So yeah, I hope you found this content interesting and make sure to like, follow, and subscribe.
✨ 추천 영상
B21
220 문장
How Modern Data Teams Work (Engineer, Analyst, Scientist, Architect, ML)
Business EnglishUniversity StudentsWorking Adults
C14
123 문장
Introduction to Diminsionality Reduction (a.k.a. Manifold Learning)
Business EnglishUniversity StudentsWorking Adults
C1288 문장
Using Selection & Scan Processing tools for reverse engineering | Geomagic Design X
Business EnglishUniversity StudentsWorking Adults
B2155 문장
80% of Interior Design Basics in 10 minutes
Business EnglishUniversity StudentsWorking Adults
C15
34 문장
Marketing Research Essentials Intro, Models & Intelligence
Business EnglishUniversity StudentsWorking Adults
C13
53 문장
System Design Was HARD - Until You Knew the Trade-Offs
Business EnglishUniversity StudentsWorking Adults
C18
102 문장
World Models explained in 10min..
Business EnglishUniversity StudentsWorking Adults
C11
392 문장
Uber Data Engineering Mock Interview - Ride-Sharing Data Warehouse Schema
Business EnglishInterview EnglishUniversity Students
C11
50 문장
0.0.1 How to become a CAE Engineer CAE | What are the ways to become a CAE Engineer as a fresher
Business EnglishUniversity StudentsWorking Adults
C13
84 문장
What is the 8D report? (problem solving tools)
Business EnglishUniversity StudentsWorking Adults
C12
51 문장
Cloud Models - CompTIA Network+ N10-009 - 1.3
Business EnglishUniversity StudentsWorking Adults
C11
133 문장
Data Ingestion Best Practices! How to Ingest Data Efficiently at Enterprise Scale!
Business EnglishUniversity StudentsWorking Adults
이 레슨의 어휘와 말하기 포인트
이 영상에는 섀도잉할 문장 120개와 단어 1736개가 있습니다. 말하는 구간의 길이는 8:06입니다. 화자는 분당 약 214단어로 빠르게 말하므로 연음과 약하게 발음되는 소리가 많습니다. 단어의 87%가 영어에서 가장 많이 쓰이는 3,000단어에 속해 어휘가 쉬운 편입니다.
이 영상의 핵심 어휘
영상에 나오는 덜 흔한 단어 15개를 발음, 뜻과 함께 정리했습니다.
- layer /ˈleɪ̯ɚ/ (명사) — 층. A single thickness of some material covering a surface.
- vault /vɑlt/ (명사) — 궁륭. An arched masonry structure supporting and forming a ceiling, whether freestanding or forming part of a larger building.
- technique /tɛkˈniːk/ (명사) — 기술. The practical aspects of a given art, occupation etc.; formal requirements.
- requirement /ɹɪˈkwaɪɹmənt/ (명사) — 요구. A necessity or prerequisite; something required or obligatory. Its adpositions are generally of in relation to who or what has given it, on in relation to whom…
- dimension /daɪˈmɛn.ʃən/ (명사) — 치수. A single aspect of a given thing.
- transform /tɹænsˈfɔɹm/ (동사) — 변형시키다, 변형하다. To change greatly the appearance or form of.
- noun /naʊn/ (명사) — 명사. A word that functions as the name of a specific object or set of objects, such as person, animal, place, word, thing, phenomenon, substance, quality, or idea…
- duplicate /ˈd(j)uː.plɪˌkeɪt/ (동사) — 복사하다. To make a copy of.
- column /ˈkɒləm/ (명사) — 기둥. A solid upright structure designed usually to support a larger structure above it, such as a roof or horizontal beam, but sometimes for decoration.
- combine /kəmˈbaɪn/ (동사) — 조합하다, 결합하다. To bring (two or more things or activities) together; to unite.
- entity /ˈɛn.tə.ti/ [-tʰi] (명사) — 실재. That which has a distinct existence as an individual unit, often used for organizations which have no physical form.
- subscribe /səbˈskɹaɪb/ (동사) — 구독하다. To write (one’s name) at the bottom of a document; to sign (one's name).
- complicated /ˈkɑm.plɪˌkeɪ.tɪd/ (형용사) — 복잡하다. Difficult or convoluted.
- skill /skɪl/ (명사) — 기술, 스킬. A capacity to do something well; a technique, an ability, usually acquired or learned, as opposed to abilities that are regarded as innate.
- string /stɹɪŋ/ (명사) — 끈. A long, thin and flexible structure made from threads twisted together.
주의할 발음
화자는 don't, we're, can't 같은 축약형과 약화된 형태를 23번 사용합니다. 들리는 대로 짧게 발음하세요.
- “sh”와 “zh” 소리: dimensional /daɪˈmɛn.ʃə.nəl/, dimension /daɪˈmɛn.ʃən/, essentially /ɪˈsɛnʃəli/, potentially /pəˈtɛnʃ(ə)li/, shine /ʃaɪn/
- 긴 단어 — 강세 위치에 주의: dimensional /daɪˈmɛn.ʃə.nəl/, essentially /ɪˈsɛnʃəli/, potentially /pəˈtɛnʃ(ə)li/, entity /ˈɛn.tə.ti/ [-tʰi], complicated /ˈkɑm.plɪˌkeɪ.tɪd/
이 영상으로 연습하는 방법
- 먼저 말하지 않고 영상을 끝까지 듣고 모르는 단어를 적어 둡니다.
- 0.75배속으로 한 문장씩 섀도잉을 시작하고, 익숙해지면 보통 속도로 돌아갑니다.
- 자신의 목소리를 녹음해 원본과 비교하고, layer, vault, technique 같은 단어에 특히 주의합니다.
이 영상의 문법
화자가 가장 많이 쓰는 문형을 영상 속 실제 표현과 함께 정리했습니다.
| 문형 | 영상 속 표현 |
|---|---|
| 현재완료 have/has + 과거분사 — 과거의 일이 지금도 관련이 있을 때 | have mastered · I've logged · have gotten |
| 수동태 be + 과거분사 — 누가 하는지보다 무슨 일이 일어나는지에 초점 | are involved · is called · is duplicated |
| 관계절 who / which + 절 — 사람이나 사물에 대한 추가 정보 | down, which is · modeling, which is · layer, which is |
쉐도잉이란? 영어 실력을 빠르게 키우는 과학적 방법
쉐도잉(Shadowing)은 원래 전문 통역사 훈련을 위해 개발된 언어 학습 기법으로, 다언어 학자인 Dr. Alexander Arguelles에 의해 대중화된 방법입니다. 핵심 원리는 간단하지만 매우 강력합니다: 원어민의 영어를 들으면서 1~2초의 짧은 지연으로 즉시 소리 내어 따라 말하는 것——마치 '그림자(shadow)'처럼 화자를 따라가는 것입니다. 문법 공부나 수동적인 청취와 달리, 쉐도잉은 뇌와 입 근육이 동시에 실시간으로 영어를 처리하고 재현하도록 훈련합니다. 연구에 따르면 이 방법은 발음 정확도, 억양, 리듬, 연음, 청취력, 말하기 유창성을 크게 향상시킵니다. IELTS 스피킹 준비와 자연스러운 영어 소통을 원하는 분들에게 특히 효과적입니다.