쉐도잉 연습: Database vs Data Warehouse vs Data Lake | What is the Difference? - 영상으로 영어 말하기 배우기
레슨 만드는 중...
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What's going on, everybody?
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Welcome back to another video.
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Today, we're going to be taking a look at the differences between a database, a data warehouse, and a data lake.
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Now, when I was first starting out, I'd only ever heard of a database.
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And I think that's what most people are familiar with.
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But I had never heard of a data warehouse or a data lake.
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And so in this video, we're going to be walking through the differences between each one of them, as well as how they kind of connect with one another.
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So let's jump onto my screen and get started.
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All right, so we're going to be taking a look at a database, a data warehouse, and a data lake, but let's start with a database.
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Now, when someone says a database, typically they're referring to a relational database.
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Now, a relational database can capture and store data via an OLTP process, which stands for online transactional process.
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So when a company completes a transaction and sells an item, it'll record that within a database.
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And that data has the ability to be live real-time data.
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Data in a database is going to be stored in tables, which has columns and rows, and it's gonna be highly detailed, which means you're gonna be able to go in and see every single aspect of the data.
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And databases also have a really flexible schema, which means you can go in there
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and kind of change things as you go to make it work for what you need.
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Now, a data warehouse is also a database, just like we were looking at before, but it's gonna be used for analytical processing or OLAP.
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OLAP.
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OLAP stands for online analytical processing.
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It's created to basically analyze huge amounts of data.
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Now, if you notice on the last slide, there were these three databases and they were just kind of sitting there and they were storing the data.
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In this visualization that we have on the right, these three databases on the bottom are all aggregating and sending their data to this data warehouse via an ETL process,
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which is where it extracts the data, it transforms it and loads it exactly how they need it in this data warehouse.
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And that's how data is put into the data warehouse.
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It isn't getting it directly from the source, but it's being put into a database and via the ETL process
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is being updated as it goes or whenever the ETL process runs.
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A data warehouse will always have the historical data, but it won't always have the current data unless the ETL process is running every single day or very frequently.
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The data in the data warehouse is also a little bit different
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because we're doing this ETL process to get the data in there.
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We're not actually putting every single piece of data or every column and row in there.
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We're typically summarizing it and then putting it in there, which will allow us to process that data for our analytical purposes much faster.
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Now, a data warehouse is going to have a much more rigid schema, so you really need to plan ahead with how you're going to put your data into a data warehouse.
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It's not as flexible as just a database.
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So now let's look at some of the key differences between a database and a data warehouse.
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A database is going to be used for recording transactions, where a data warehouse is going to be used for analytics and reporting.
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A database is going to have fresh and detailed data, where a data warehouse is going to have summarized data.
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It's only going to be as fresh as the ETL process is created.
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A database is going to be a little bit slower for querying large amounts of data.
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And when you do query large amounts of data, it can actually slow down the processing of all those transactions.
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A data warehouse was designed for the exact opposite.
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It It was designed to be very fast at querying
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and not slow down any processes because it isn't part of that transaction processing at all.
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So now that we've looked at a database and a data warehouse, let's take a look at a data lake.
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A data lake was basically designed to capture any type of data that you could possibly want.
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It could be a video, a picture, an image, a document, a graph, anything you could imagine that you'd want to put in a database or store in some way,
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you can store it in a data lake.
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Now, there are a ton of use cases for a data lake, but I think people who work with machine learning and AI get to use it or benefit from it the most.
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They can use all that structured and unstructured data and create models to really use it in its raw form, where if you want to use it for analytical purposes, typically you're going to have to clean it up a little bit
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and do a little bit more work to actually make it usable.
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And so a data lake is just that.
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It's this lake where you can basically throw any type of data in there, but it's not always super usable because you're just putting it in there and it's raw form.
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If you want to use it for analytical purposes and reporting, most of the time, you're gonna wanna clean that up and put it into a database or a data warehouse.
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So now when we're looking at all three, they are all different and they're all used for different purposes.
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So no one option is better than another for your data.
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If you're using it just to record transactions, a database is what you should do.
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And if you have a large amount of data, that's just too much for your database to handle, Sounds like you might need a data warehouse.
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And if you have all this data, you have no idea what to do with, or it's unstructured, it's semi-structured data that you can't fit into a database, well, then I highly recommend using a data lake.
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There really is no one size fits all.
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All three of these can be options for different uses.
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And in fact, you can use all three within one company for just different things that your company needs.
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So I hope that that was helpful, learning the differences between a database, a data warehouse, and a data lake.
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Again, I had really never used a data warehouse or a data lake when I first got into analytics.
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But now that I've gotten hands-on experience with all of them, they're all really interesting, can be used for so many different things.
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So thank you so much for watching this video.
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I really appreciate it.
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If you liked this video, put your like and subscribe below, and I'll see you in the next video.
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이 레슨의 어휘와 말하기 포인트
이 C1 수준 말하기 레슨은 영상 “Database vs Data Warehouse vs Data Lake”을(를) 바탕으로 합니다. 가장 자주 반복되는 단어는 다음과 같습니다: database, warehouse, lake, process, Etl. 이 영상에는 섀도잉할 문장 66개와 단어 1174개가 있습니다. 말하는 구간의 길이는 5:10입니다. 화자는 분당 약 228단어로 빠르게 말하므로 연음과 약하게 발음되는 소리가 많습니다. 단어의 83%가 영어에서 가장 많이 쓰이는 3,000단어에 속합니다. 나머지는 연습 전에 미리 확인해 두세요.
이 영상의 핵심 어휘
영상에서 가장 어려운 단어 9개를 발음, 뜻과 함께 정리했습니다.
| 단어 | 발음 | 뜻 |
|---|---|---|
| warehouse 명사 | /ˈwɛə(ɹ)haʊs/ | 창고 |
| query 명사 | /ˈkwɪɹ.i/ | 질문 |
| schema 명사 | /ˈskiːmə/ | 스키마 |
| summarize 동사 | /ˈsʌməˌɹaɪz/ | 요약하다 |
| analytics 명사 | /ˌæn.əˈlɪt.ɪks/ | 해석학, 애널리틱스 |
| analyze 동사 | /ˈæn.əˌlaɪz/ | 분석하다 |
| subscribe 동사 | /səbˈskɹaɪb/ | 구독하다 |
| transform 동사 | /tɹænsˈfɔɹm/ | 변형시키다, 변형하다 |
| graph 명사 | /ɡɹæf/ | 그래프, 도표 |
이 영상의 문법
화자가 가장 많이 쓰는 문형을 영상 속 실제 표현과 함께 정리했습니다.
| 문형 | 영상 속 표현 |
|---|---|
| 수동태 be + 과거분사 — 누가 하는지보다 무슨 일이 일어나는지에 초점 | be stored · is put · being put |
| 관계절 who / which + 절 — 사람이나 사물에 대한 추가 정보 | tables, which has · schema, which means · there, which will |
| 현재완료 have/has + 과거분사 — 과거의 일이 지금도 관련이 있을 때 | we've looked · I've gotten |
주의할 발음
화자는 we're, you're, gonna 같은 축약형과 약화된 형태를 31번 사용합니다. 들리는 대로 짧게 발음하세요.
- “sh”와 “zh” 소리: transactional /trænˈzækʃ(ə)nəl/, visualization /ˌvɪʒ.ʊ.ə.laɪˈzeɪ.ʃən/
- 긴 단어 — 강세 위치에 주의: analytical /ˌæn.əˈlɪt.ɪ.kəl/, analytics /ˌæn.əˈlɪt.ɪks/, transactional /trænˈzækʃ(ə)nəl/, visualization /ˌvɪʒ.ʊ.ə.laɪˈzeɪ.ʃən/
한국어 화자가 어려워하는 소리:
- /f/ — ㅍ(/p/)으로 바꾸지 말고 윗니를 아랫입술에 대기: transform /tɹænsˈfɔɹm/, graph /ɡɹæf/
- /z/ — ㅈ이 아니라 성대를 울리는 /s/: summarize /ˈsʌməˌɹaɪz/, usable /ˈjuː.zə.bəl/, transactional /trænˈzækʃ(ə)nəl/, visualization /ˌvɪʒ.ʊ.ə.laɪˈzeɪ.ʃən/, analyze /ˈæn.əˌlaɪz/
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, warehouse, query, schema 같은 단어에 특히 주의합니다.
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.