シャドーイング練習: Database vs Data Warehouse vs Data Lake | What is the Difference? - 動画で英語スピーキングを学ぶ
レッスンを作成中...
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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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このレッスンの語彙とスピーキングのポイント
このC1レベルのスピーキングレッスンは、動画「Database vs Data Warehouse vs Data Lake」を教材にしています。 繰り返し出てくる語は次のとおりです：database, warehouse, lake, process, Etl。 この動画には、シャドーイング用の文が66文、単語が1174語あります。 音声の長さは5:10です。 話す速さは速く、1分あたり約228語です。音のつながりや弱く発音される音が多くなります。 単語の83%は英語の頻出3,000語に含まれます。残りは練習の前に確認しておきましょう。
この動画の重要語彙
動画の中で特に難しい単語13語を、発音と意味つきで紹介します。
| 単語 | 発音 | 意味 |
|---|---|---|
| warehouse 名詞 | /ˈwɛə(ɹ)haʊs/ | 倉庫 |
| query 名詞 | /ˈkwɪɹ.i/ | 質問 |
| schema 名詞 | /ˈskiːmə/ | スキーマ |
| summarize 動詞 | /ˈsʌməˌɹaɪz/ | 要約する |
| analytics 名詞 | /ˌæn.əˈlɪt.ɪks/ | 分析論, 分析学 |
| visualization 名詞 | /ˌvɪʒ.ʊ.ə.laɪˈzeɪ.ʃən/ | 映像化, 可視化 |
| rigid 形容詞 | /ˈɹɪd͡ʒ.ɪd/ | 硬い |
| analyze 動詞 | /ˈæn.əˌlaɪz/ | 分析する |
| aggregate 名詞 | /ˈæɡ.ɹɪ.ɡət/ | 総計, 合計 |
| subscribe 動詞 | /səbˈskɹaɪb/ | 購読する |
| extract 名詞 | /ˈɛkstɹækt/ | エキス, 越幾斯 |
| transform 動詞 | /tɹænsˈfɔɹm/ | 変形する, 変換する |
| graph 名詞 | /ɡɹæf/ | グラフ, 図表 |
動画に出てくる句動詞
| 単語 | 意味 |
|---|---|
| slow down 動詞 | 遅らせる |
この動画の文法
話し手がよく使っている文型を、動画の実際の表現とともに紹介します。
| 文型 | 動画での表現 |
|---|---|
| 受動態 be + 過去分詞 — 誰がするかより、何が起きるかに焦点を当てる | be stored · is put · being put |
| 関係詞節 who / which + 節 — 人や物について情報を加える | tables, which has · schema, which means · there, which will |
| 現在完了形 have/has + 過去分詞 — 過去の出来事が今も関係している | we've looked · I've gotten |
注意したい発音
話し手はwe're, you're, gonnaなど、短縮形や弱形を31回使っています。聞こえたとおりの短い形で発音しましょう。
- 「sh」と「zh」の音: transactional /trænˈzækʃ(ə)nəl/, visualization /ˌvɪʒ.ʊ.ə.laɪˈzeɪ.ʃən/
- 長い単語（アクセントの位置に注意）: analytical /ˌæn.əˈlɪt.ɪ.kəl/, analytics /ˌæn.əˈlɪt.ɪks/, transactional /trænˈzækʃ(ə)nəl/, visualization /ˌvɪʒ.ʊ.ə.laɪˈzeɪ.ʃən/
日本語話者が苦手な音：
- /f/ — 「フ」ではなく、上の歯と下唇で出す: transform /tɹænsˈfɔɹm/, graph /ɡɹæf/
- 子音の連続 — 間に母音を入れない: query /ˈkwɪɹ.i/, schema /ˈskiːmə/, analytics /ˌæn.əˈlɪt.ɪks/, transactional /trænˈzækʃ(ə)nəl/, columns /ˈkɑləmz/
この動画での練習方法
- まず声を出さずに動画を最後まで聞き、知らない単語をメモします。
- まず0.75倍速で一文ずつシャドーイングし、慣れてきたら通常の速度に戻します。
- 自分の声を録音して元の音声と比べます。warehouse, query, schemaなどの単語に特に注意しましょう。
シャドーイングとは？英語上達に効果的な理由
シャドーイング（Shadowing）は、もともとプロの通訳者養成プログラムで開発された言語学習法で、多言語習得者として知られるDr. Alexander Arguelles によって広く普及されました。方法はシンプルですが非常に効果的：ネイティブスピーカーの英語を聞きながら、1〜2秒の遅延で声に出してすぐに繰り返す——まるで「影（shadow）」のように話者を追いかけます。文法ドリルや受動的なリスニングと異なり、シャドーイングは脳と口の筋肉が同時にリアルタイムで英語を処理・再現することを強制します。研究により、発音精度、抑揚、リズム、連音、リスニング力、そして会話の流暢さが大幅に向上することが確認されています。IELTSスピーキング対策や自然な英語コミュニケーションを目指す方に特におすすめです。