跟讀練習: Database vs Data Warehouse vs Data Lake | What is the Difference? - 通過影片學習英語口語

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What's going on, everybody?

Welcome back to another video.

Today, we're going to be taking a look at the differences between a database, a data warehouse, and a data lake.

Now, when I was first starting out, I'd only ever heard of a database.

And I think that's what most people are familiar with.

But I had never heard of a data warehouse or a data lake.

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.

So let's jump onto my screen and get started.

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.

Now, when someone says a database, typically they're referring to a relational database.

Now, a relational database can capture and store data via an OLTP process, which stands for online transactional process.

So when a company completes a transaction and sells an item, it'll record that within a database.

And that data has the ability to be live real-time data.

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.

And databases also have a really flexible schema, which means you can go in there

and kind of change things as you go to make it work for what you need.

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.

OLAP.

OLAP stands for online analytical processing.

It's created to basically analyze huge amounts of data.

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.

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,

which is where it extracts the data, it transforms it and loads it exactly how they need it in this data warehouse.

And that's how data is put into the data warehouse.

It isn't getting it directly from the source, but it's being put into a database and via the ETL process

is being updated as it goes or whenever the ETL process runs.

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.

The data in the data warehouse is also a little bit different

because we're doing this ETL process to get the data in there.

We're not actually putting every single piece of data or every column and row in there.

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.

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.

It's not as flexible as just a database.

So now let's look at some of the key differences between a database and a data warehouse.

A database is going to be used for recording transactions, where a data warehouse is going to be used for analytics and reporting.

A database is going to have fresh and detailed data, where a data warehouse is going to have summarized data.

It's only going to be as fresh as the ETL process is created.

A database is going to be a little bit slower for querying large amounts of data.

And when you do query large amounts of data, it can actually slow down the processing of all those transactions.

A data warehouse was designed for the exact opposite.

It It was designed to be very fast at querying

and not slow down any processes because it isn't part of that transaction processing at all.

So now that we've looked at a database and a data warehouse, let's take a look at a data lake.

A data lake was basically designed to capture any type of data that you could possibly want.

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,

you can store it in a data lake.

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.

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

and do a little bit more work to actually make it usable.

And so a data lake is just that.

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.

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.

So now when we're looking at all three, they are all different and they're all used for different purposes.

So no one option is better than another for your data.

If you're using it just to record transactions, a database is what you should do.

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.

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.

There really is no one size fits all.

All three of these can be options for different uses.

And in fact, you can use all three within one company for just different things that your company needs.

So I hope that that was helpful, learning the differences between a database, a data warehouse, and a data lake.

Again, I had really never used a data warehouse or a data lake when I first got into analytics.

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.

So thank you so much for watching this video.

I really appreciate it.

If you liked this video, put your like and subscribe below, and I'll see you in the next video.

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本課的詞彙與口語要點

這節 C1 級別的口語課以影片“Database vs Data Warehouse vs Data Lake”為素材。影片中反覆出現的詞有：database, warehouse, lake, process, Etl。這段影片共有 66 個句子、1174 個單詞可供跟讀。講話部分時長為 5:10。說話人語速較快，每分鐘約 228 個詞，會出現較多連讀和弱讀。 83% 的單詞屬於英語最常用的 3,000 詞，其餘的詞建議在練習前先學一下。

影片中的重點詞彙

影片中最難的 13 個單詞，附發音和釋義：

單詞	發音	釋義
warehouse 名詞	/ˈwɛə(ɹ)haʊs/	倉庫 /倉庫
query 名詞	/ˈkwɪɹ.i/	問題 /問題, 質問 /質問
schema 名詞	/ˈskiːmə/	圖式 /圖式
summarize 動詞	/ˈsʌməˌɹaɪz/	摘要, 總結 /總結
usable 形容詞	/ˈjuː.zə.bəl/	可用
analytics 名詞	/ˌæn.əˈlɪt.ɪks/	分析學 /分析學
visualization 名詞	/ˌvɪʒ.ʊ.ə.laɪˈzeɪ.ʃən/	視覺化 /視覺化, 視覺化 /視覺化
rigid 形容詞	/ˈɹɪd͡ʒ.ɪd/	硬性, 僵硬
analyze 動詞	/ˈæn.əˌlaɪz/	分析
subscribe 動詞	/səbˈskɹaɪb/	簽署 /簽署
extract 名詞	/ˈɛkstɹækt/	精
transform 動詞	/tɹænsˈfɔɹm/	變換 /變換, 變形 /變形
graph 名詞	/ɡɹæf/	圖表 /圖表

影片中出現的短語動詞

單詞	釋義
slow down 動詞	減速 /減速, 放慢

影片中的語法

說話人最常用的結構，並附上影片中的原話：

結構	影片中的用法
被動語態 be + 過去分詞 — 強調發生了什麼，而不是誰做的	be stored · is put · being put
定語從句 who / which + 從句 — 補充說明人或事物	tables, which has · schema, which means · there, which will
現在完成時 have/has + 過去分詞 — 過去發生但與現在仍有關聯的事	we've looked · I've gotten

需要注意的發音

說話人用了 31 次縮略和弱讀形式，例如 we're, you're, gonna。請按聽到的簡短形式來說。

“sh” 和 “zh” 音: transactional /trænˈzækʃ(ə)nəl/, visualization /ˌvɪʒ.ʊ.ə.laɪˈzeɪ.ʃən/
長單詞——注意重音位置: analytical /ˌæn.əˈlɪt.ɪ.kəl/, analytics /ˌæn.əˈlɪt.ɪks/, transactional /trænˈzækʃ(ə)nəl/, visualization /ˌvɪʒ.ʊ.ə.laɪˈzeɪ.ʃən/

中文母語者容易讀錯的音：

詞尾子音 — 要讀清楚，後面不要加母音: warehouse /ˈwɛə(ɹ)haʊs/, summarize /ˈsʌməˌɹaɪz/, analytics /ˌæn.əˈlɪt.ɪks/, rigid /ˈɹɪd͡ʒ.ɪd/, analyze /ˈæn.əˌlaɪz/
/æ/ — 嘴張大，不要讀成 /e/: analytical /ˌæn.əˈlɪt.ɪ.kəl/, analytics /ˌæn.əˈlɪt.ɪks/, transactional /trænˈzækʃ(ə)nəl/, analyze /ˈæn.əˌlaɪz/, aggregate /ˈæɡ.ɹɪ.ɡət/

如何用這段影片練習

先完整聽一遍影片，不要開口，記下不認識的單詞。
先用 0.75 倍速逐句跟讀，熟練之後再回到正常速度。
錄下自己的聲音並與原聲對比，特別注意 warehouse, query, schema 這類單詞。

什麼是跟讀法？

跟讀法 (Shadowing) 是一種有科學依據的語言學習技巧，最初開發用於專業口譯員的培訓，並由多語言者Alexander Arguelles博士普及。這個方法簡單而強大：您在聽英語母語原聲的同時立即大聲重複——就像是一個延遲1-2秒緊跟說話者的影子。與被動聽力或語法練習不同，跟讀法強迫您的大腦和口腔肌肉同時處理並模模擬實的講話模式。研究表明它能顯著提高發音準確性，語調，節奏，連讀，聽力理解和口語流利度——使其成為雅思口語備考和真實英語交流最有效的方法之一。

影子跟讀法: 閱讀完整分步指南 →

跟讀練習: Database vs Data Warehouse vs Data Lake | What is the Difference? - 通過影片學習英語口語

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