शैडोइंग अभ्यास: Database vs Data Warehouse vs Data Lake | What is the Difference? - वीडियो के साथ अंग्रेजी बोलना सीखें
पाठ बनाया जा रहा है...
1
What's going on, everybody?
2
Welcome back to another video.
3
Today, we're going to be taking a look at the differences between a database, a data warehouse, and a data lake.
4
Now, when I was first starting out, I'd only ever heard of a database.
5
And I think that's what most people are familiar with.
6
But I had never heard of a data warehouse or a data lake.
7
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.
8
So let's jump onto my screen and get started.
9
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.
10
Now, when someone says a database, typically they're referring to a relational database.
11
Now, a relational database can capture and store data via an OLTP process, which stands for online transactional process.
12
So when a company completes a transaction and sells an item, it'll record that within a database.
13
And that data has the ability to be live real-time data.
14
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.
15
And databases also have a really flexible schema, which means you can go in there
16
and kind of change things as you go to make it work for what you need.
17
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.
18
OLAP.
19
OLAP stands for online analytical processing.
20
It's created to basically analyze huge amounts of data.
21
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.
22
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,
23
which is where it extracts the data, it transforms it and loads it exactly how they need it in this data warehouse.
24
And that's how data is put into the data warehouse.
25
It isn't getting it directly from the source, but it's being put into a database and via the ETL process
26
is being updated as it goes or whenever the ETL process runs.
27
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.
28
The data in the data warehouse is also a little bit different
29
because we're doing this ETL process to get the data in there.
30
We're not actually putting every single piece of data or every column and row in there.
31
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.
32
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.
33
It's not as flexible as just a database.
34
So now let's look at some of the key differences between a database and a data warehouse.
35
A database is going to be used for recording transactions, where a data warehouse is going to be used for analytics and reporting.
36
A database is going to have fresh and detailed data, where a data warehouse is going to have summarized data.
37
It's only going to be as fresh as the ETL process is created.
38
A database is going to be a little bit slower for querying large amounts of data.
39
And when you do query large amounts of data, it can actually slow down the processing of all those transactions.
40
A data warehouse was designed for the exact opposite.
41
It It was designed to be very fast at querying
42
and not slow down any processes because it isn't part of that transaction processing at all.
43
So now that we've looked at a database and a data warehouse, let's take a look at a data lake.
44
A data lake was basically designed to capture any type of data that you could possibly want.
45
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,
46
you can store it in a data lake.
47
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.
48
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
49
and do a little bit more work to actually make it usable.
50
And so a data lake is just that.
51
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.
52
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.
53
So now when we're looking at all three, they are all different and they're all used for different purposes.
54
So no one option is better than another for your data.
55
If you're using it just to record transactions, a database is what you should do.
56
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.
57
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.
58
There really is no one size fits all.
59
All three of these can be options for different uses.
60
And in fact, you can use all three within one company for just different things that your company needs.
61
So I hope that that was helpful, learning the differences between a database, a data warehouse, and a data lake.
62
Again, I had really never used a data warehouse or a data lake when I first got into analytics.
63
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.
64
So thank you so much for watching this video.
65
I really appreciate it.
66
If you liked this video, put your like and subscribe below, and I'll see you in the next video.
📺 उसी चैनल से
✨ अनुशंसित वीडियो
B21
42 वाक्य
Database vs Data Warehouse vs Data Lake | Explaining the Differences
Business EnglishUniversity StudentsWorking Adults
C1178 वाक्य
Data Presentation, Step-by-Step | Google Data Analytics Certificate
Business EnglishUniversity StudentsWorking Adults
C11
43 वाक्य
What is an Organization? - the Nature of Organizations
Business EnglishUniversity StudentsWorking Adults
C11
184 वाक्य
PLC Programming Tutorial - Ladder Logic #3: Math and Calculations
Business EnglishUniversity StudentsWorking Adults
C11
689 वाक्य
Database Sharding! Designing Data-Intensive Applications chapter 6
Business EnglishUniversity StudentsWorking Adults
C11
149 वाक्य
Inside a warehouse run by AI robots
Business EnglishUniversity StudentsWorking Adults
C11
339 वाक्य
Introduction to HPLC - Lecture 1: HPLC Basics
Business EnglishUniversity StudentsWorking Adults
C110
30 वाक्य
Research Source Mastery: How to Find & Verify Credible Data (2026)
Business EnglishUniversity StudentsWorking Adults
C12
64 वाक्य
P2 什么是机器学习【2024公认最好的 | 吴恩达机器学习 | 教程 | Machine Learning Specialization(超爽中英!)】
Business EnglishUniversity StudentsWorking Adults
C11
120 वाक्य
Learn Data Modeling in 8 minutes: Dimensional Data Modeling, Data Vault, and One Big Table
Business EnglishUniversity StudentsWorking Adults
C1260 वाक्य
Data Analyst Interview 2024 | SQL | POWER BI | Data Science | Mock Interview
Business EnglishInterview EnglishUniversity Students
C12
153 वाक्य
S3 Speech1 short
Business EnglishUniversity StudentsWorking Adults
Vocabulary and speaking notes for this lesson
This C1 speaking lesson is built on the video “Database vs Data Warehouse vs Data Lake”. The speaker keeps coming back to these words: database, warehouse, lake, process, Etl. This video has 66 sentences and 1174 words to shadow. The speech runs for 5:10. The speaker talks fast, about 228 words per minute, so expect linked and reduced sounds. 83% of the words are among the 3,000 most common in English; the rest is worth studying before you start.
Key vocabulary in this video
The 15 most advanced words in the video, with pronunciation and meaning:
| Word | Pronunciation | Meaning |
|---|---|---|
| warehouse noun | /ˈwɛə(ɹ)haʊs/ | A facility for storing large amounts of merchandise or products. |
| analytical adjective | /ˌæn.əˈlɪt.ɪ.kəl/ | Of or pertaining to analysis; resolving into elements or constituent parts |
| query noun | /ˈkwɪɹ.i/ | A question, an inquiry (US), an enquiry (UK). |
| relational adjective | Relating to relations. | |
| schema noun | /ˈskiːmə/ | An outline or image universally applicable to a general conception, under which it is likely to be presented to the mind (for example, a body schema). |
| summarize verb | /ˈsʌməˌɹaɪz/ | To prepare a summary of (something). |
| unstructured adjective | Lacking structure. | |
| usable adjective | /ˈjuː.zə.bəl/ | Capable of being used. |
| analytics noun | /ˌæn.əˈlɪt.ɪks/ | The principles governing any of various forms of analysis. |
| transactional adjective | /trænˈzækʃ(ə)nəl/ | Of, pertaining to or involving transactions. |
| visualization noun | /ˌvɪʒ.ʊ.ə.laɪˈzeɪ.ʃən/ | The act of visualizing, or something visualized. |
| rigid adjective | /ˈɹɪd͡ʒ.ɪd/ | Stiff, rather than flexible. |
| analyze verb | /ˈæn.əˌlaɪz/ | To subject to analysis. |
| aggregate noun | /ˈæɡ.ɹɪ.ɡət/ | A mass, assemblage, or sum of particulars; something consisting of elements but considered as a whole. |
| columns noun | /ˈkɑləmz/ | pattern which involves throwing props in the air alternately. |
Phrasal verbs you will hear
| Word | Meaning |
|---|---|
| slow down verb | To decelerate. |
| start out verb | To emerge suddenly; to jump out. |
| walk through verb | To explain (something) to (someone), step by step. |
इस वीडियो का व्याकरण
वक्ता जिन संरचनाओं का सबसे अधिक प्रयोग करता है, वीडियो के असली शब्दों के साथ:
| संरचना | वीडियो में |
|---|---|
| Passive voice be + past participle — ज़ोर इस पर कि क्या होता है, न कि कौन करता है | be stored · is put · being put |
| Relative clauses who / which + उपवाक्य — व्यक्ति या वस्तु के बारे में अतिरिक्त जानकारी | tables, which has · schema, which means · there, which will |
| Present perfect have/has + past participle — बीता हुआ काम जिसका असर अभी भी है | we've looked · I've gotten |
Pronunciation to watch
The speaker uses 31 contractions and reduced forms, such as we're, you're, gonna. Say them the short way, as you hear them.
- The “sh” and “zh” sounds: transactional /trænˈzækʃ(ə)nəl/, visualization /ˌvɪʒ.ʊ.ə.laɪˈzeɪ.ʃən/
- Long words — get the stress right: analytical /ˌæn.əˈlɪt.ɪ.kəl/, analytics /ˌæn.əˈlɪt.ɪks/, transactional /trænˈzækʃ(ə)nəl/, visualization /ˌvɪʒ.ʊ.ə.laɪˈzeɪ.ʃən/
How to practise with this video
- Listen to the whole video once without speaking and note the words you do not know.
- Start at 0.75× speed, shadow it sentence by sentence, then go back to normal speed once it feels easy.
- Record yourself and compare with the original, paying attention to words like warehouse, analytical, query.
शैडोइंग तकनीक क्या है?
शैडोइंग (Shadowing) एक विज्ञान-समर्थित भाषा सीखने की तकनीक है जो मूल रूप से पेशेवर दुभाषिया प्रशिक्षण के लिए विकसित की गई थी। विधि सरल लेकिन शक्तिशाली है: आप मूल अंग्रेज़ी ऑडियो सुनते हैं और तुरंत इसे ज़ोर से दोहराते हैं — जैसे वक्ता की छाया 1-2 सेकंड की देरी से। शोध से पता चलता है कि यह उच्चारण सटीकता, स्वर, लय, जुड़ी हुई ध्वनियाँ, सुनने की समझ और बोलने की प्रवाहशीलता में काफ़ी सुधार करता है।