KNOW the difference between Data Base // Data Warehouse // Data Lake (Easy Explanation👌)
ฝัง
- เผยแพร่เมื่อ 2 มิ.ย. 2024
- Confusing your data lakes with actual lakes? Not sure how much land you need for the data warehouse? You have come to the right place my friend.
In this concept buster video, let me explain what Data base, data warehouse and data lake are and how they connect / differ?
⏱In this video 👉
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0:00 - Intro & Databases
1:38 - Data warehouse
2:28 - Why can't we use DB for reporting?
3:09 - ETL, how data goes from DB to DWH
4:05 - What is a Data Lake?
5:46 - Examples of DB, DWH & DL
6:43 - How to access data in DB, DWH & DL?
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#DBvsDWH - วิทยาศาสตร์และเทคโนโลยี
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I have watched or read many explanation about the differences among these 3 terms, but so far this video is the simpliest yet cleariest and easiest to understand. Thanks a lot!!!
Wow.. thank you for that 😀
Exactly, this is how I feel. Thanks Chandoo.
I came to the comments to say the same thing! Thank you for this simple, illustrative explanation.
@@chandoo_ qqq
i very agree
There is no other video on youtube that explains DB/DW/DL this easy. Really appreciate the time and effort you put into making these videos.
I dont think any other video in the internet explains this difference as clearly as this video. Thank you brother. Keep posting more videos to educate us.
Simple to start with. No PPT slides, just notepad is enough to explain ❤️ Thank you bro. Keep up your good work 👍
This video did a great job of helping me learn the distinction between these 3 things. Love it!
Thank you Demetri... 😍
Super! In just 8 minutes, you have put such a clear picture of data base, data warehouse and data lake, that I can never forget and in future, any time I deal with these terminology, I have crystal clear idea of what am I dealing with! You are a GREAT teacher Chandoo and I really appreciate your effort!
As a person in this industry, this is the best video ever. Exceptionally clear.
I love this. This explains perfectly what I've been trying to explain at work. Instead of me keep arguing I am just going to show this video
I had no idea about data warehouse or data lakes. Thanks Chandoo for sharing your knowledge and the great breakdown of each.
I think because of your clear and concise points and humor, I learn more from you than other Excel tutorial channels.
Keep up the great work.
Aww.. that means a lot Patrick :)
Without any flattery- the BEST explanation of this topic I ever encountered on youtube!
One more comment for me.
The best, most simple, laconic, yet rich, explanation about the diffs of the terms.
Chandoo...you have a divine gift for explaining things so clearly. The drawings help so much too. I wish I had found your channel sooner.
Very nice for non data specialists. I was searching for basic explanation and that's what you gave me!
FANTASTIC explanation!
Now I realize I have created all three of these over the years. I wish I had understood these concepts better back in the day.
Glad it was helpful!
It’s a magic that I found you, thank you so much for explaining in simple words difficult at first glance things.
Wise men can explain sophisticated things in a way that a 5-year kid can easily learn! Congrats Wise Man!
😍 That is a beautiful compliment. Thanks Amir.
I have seen many videos but this explanation is very nice and clear
Thanks Chandu for making these concepts so simple to understand. Whenever I get confused I just refer to your videos for quick and accurate understanding of the concepts.
Awesome video of comparison/differences! The the explanation was very easy to follow! Thank you! Also the puns are hilarious! Keep the content coming.
Very good, thanks!
What I got is that it is more of a conceptual difference rather than technical, understanding that there must be some infrastructure nuances..
Clear, concise, and to the point. Thank you so much for sharing your knowledge!
Even a person who is at the earliest stages of his data career would understand this. Thanks a lot.
Short, sweet and right on point to help quick learning, you got a new sub!
Just found your channel. I’m sharing your videos with my team that is a bit behind on these concepts. Thanks!!
Just wanted say I love you so much and I appreciate your effort to make us educated on these type of stuff .
A super simple and understandable explanation! Thanks man! Thumbs up!
In a typical database there will be transactions taking place like insert of a table row, update of a table row, read of a table row that are in line with a set of business cases.
In a datawarehouse there will be analysis taking place to across multiple rows from multiple tables.
A data lake is where data goes to get drowned.
"A data lake is where data goes to get drowned." 😂😂😂
Thanks a lot! That was one of the best explanations I ever heard. Sometimes I think people want things to be difficult so they seem more intelligent...
You just won another fan and subscriber. Nice content, Chandoo. You humor is well dosed too.
Amazing clarification of the concepts of DB, DW and DL. Thanks Chandoo.
Hey Chandoo. Great video, but also I’m so happy to have found your channel and see you speak as over so many years your website has been solving my excel queries when I Google them.
OMG!!. Never knew this thing could be made this interesting. Great Humour! Subscribed !!
The best video that could explain differences between the systems. very informative and thank you so much
Thanks, Chandoo, for this humorous primer on these database buzzwords! Keep posting such conceptual nuggets in your signature style! 👍🏻
Great example, very well explained! Thank you for making this video
I love how this man explains things
Thanks Chandoo.
TH-cam algo was brilliant today suggesting me this goldmine!
Nice illustration , Since I entered into IT struggling a lot to understand between these 3 components. You have cleared all my doubts ... Thank you very much
I have always struggled to understand what a datawarehouse is but this video made it so simple to understand thank you
I liked your video because of your clear and concise points and humor. Keep up the great work.
This is the clearest and understandable in layperson’s term. Thank you!
Thank you very much for this illustrative explanation! Very easy to comprehend indeed!
Love this! Thanks for explaining it really really in easiest manner and choice of words.
Your video is the only one that made this clear to me.. thank you teacher!
You video is very helpful, it cleared my cloud about DB, DW, DL Thank you very much!
i really appreciate your effort and time which you put into your video.TBH your video is on the point and very interesting i never thought that someone explain these topic that much easily.May God Bless you and give you r more power so you make more video for us
The best video which explains behind the scenes....simple language and simple example....All the best for ur future endeavors...
AWESOME, SIMPLIFIED EXPLANATION THANK YOU SO MUCH.
Nice and Simple! Thank you for sharing!
Thanks, finally explaining it clearly and simply.
Man that was good way to explain stuff. I like it and understood in a better way.
Best Explanation on the internet!
Awesome, very clear explanations, much appreciated!
Great job, clear explanation and I also enjoy your humor. Would be great if you could create a video describing the difference between data scientist, engineer, analyst and architect. Kudos on your excellent work!
If you're starting in I.T. doing analysis type work, you'll start as an Analyst. This can be anything from reporting, automated feed maintenance/RCA, and even development. Most of the above 3 (maybe save for Data Scientist) start here.
Data Engineer is probably the most logical next step from analyst. You'll definitely be doing more development and analytical work as an analyst prior to this. This shifts your scope from retrieving data from a data warehouse/db/lake (lake is quite rare for a run of the mill analyst), to actually designing and some possible light architecting of table/schema structures for data to import into from other sources (typically starting as transactional information into a database from an app, or maybe an external source of some sort). Typically as an engineer you won't start on data warehouse modelling until you've had some experience with general transactional architecting/engineering since the data within a warehouse shouldn't be updated/deleted, only inserted. It will be deleted, possibly if you've archived it in some situation (like data that's over x-years old and based on specific policies), but even then it probably wouldn't be deleted. If the architecture allows, you may just duplicate the tables, or partition them in some way and then archive the older pages. They may also determine certain structural recommendations (rowstore vs columnstore table structures, for example, or using NoSQL vs relational databases), but usually it's in concert with an Architect if the process being designed is large enough, or has significant impact, especially in terms of performance. However, after discussions between Engineers and Architects, the Engineers (and to a lesser extent, Analysts) will IMPLEMENT the requisites of decided Architecture. Engineers are typically more hands on than Architects, but Archs may get their hands dirty if something is largely conceptual and they want to start plugging away earlier in the phase to ensure design solidity.
Data Architect is anything from designing the schema for your transactional infrastructure (your primary database), data warehouse, or even data lake, as well as helping navigate and determine how to import data into those repositories, as well as even more expansive things such as CI/CD pipelines, *maybe* networking tasks if you're familiar enough with that (usually system administrators do that, though), or even helping implement connection string/authentication against your cloud resource targets originating from nearly any source caller (on premises machine, like a developer computer, a VM hosting an app service, CI/CD agent, or a completely separate cloud service not native to your cloud service, even on a completely different domain or client server).
An Architect is going to be responsible for HOW disparate system objects are going to interact with each other and any potential issues given certain implementations or design sequences. Typically Architects are going to have some knowledge as to what different approaches are available and determine which makes sense given what's required for the need or problem that needs resolution. As an Architect you're not expected to know how to implement everything as if you were doing all the work yourself. However, having a basic understanding of the limitations of each element in the design will definitely help you determine which is possible and which may not be earlier in design phase, which helps mitigate wasted developer time later during spikes (Proof of Concept phases) and help with further engineering alignment tasks.
Most people consider scientists as the babies in the room because the data they require should be perfect in terms of not needing to accommodate any changes to their representations outside of any algorithmic modelling is concerned. It's entirely possible a Scientist will ask the Engineer to modify schema and data to accommodate some sort of analysis or data modelling they're trying to complete. It's not a-typical for an Engineer to work closely with a Scientist, but not typical for the Scientist to work with the Architect, aside from initial standing up of a new Data Warehouse or Data Lake. Typically the Engineer maintains or may make the every-day changes to those structures once the inputs/outputs/transformational processes have already been established. Scientists are typically Statisticians or anything having to do with applied mathematics. They will also typically work with code that isn't strictly SQL, such as Python, R, Power BI, DAX, (maybe MDX, but I think that's fallen largely by the way-side), etc...Scientists are tasked with supplying the answers to complex problems for the business using quantitative analysis. These are the people that determine what Ads you may see given your previous and most recent search history. Something you searched for 3 years ago may not be as relevant as something you searched for yesterday. That would be a typical example of what a Scientist may do. Also, Google translate, things like that will be developed by the Scientist, but the Architect will design the bridges to source that data whereas the Engineer will make that design a reality. The Analyst will make sure data makes sense as it starts trickling through the design process and if there's any issues, the Analyst and maybe working with the Engineer will troubleshoot the why/how and determine a fix where either of them may implement that fix to ensure it works as intended.
If you look at it as a decision tree, it may look something like:
Analyst > Engineer > Architect
Analyst > Engineer > Scientist
Analyst > Scientist (again, typically short cut by a Masters in Statistics or similar)
Hope that helps!
The best explanation that I heard. Thanks!
Brilliant.
Well explained, clear, simple and concise.
Thank you very much
Thank you so much for the time put in your videos! extremely helpful!
Thanks for sharing..
Excellent video, with great and user friendly explanation. Loved it
First time watching your video,I am unlucky because I didn't found till now,you are doing great job thank you so much.keep doing more...
Best video there is about the topic! 🎉
Thanks, man
Glad you liked it!
Thanks for the demo and info, have a great day
This was the best explanatory video that I came across!!
Glad it was helpful!
You're a natural! Thanks!
so clean thanks man!
perfectly explained . Thanks you
DL explained so simply - Thank you Chandoo
Very easy and clear explanation of DB/DW/DL :)
Explained very clear! Thank you
There was not ppt , just sweet and crisp explanation of the topic using a notebook. 👌🏽 Loved it.
Thank you Martin 😀
Many Thanks for the video 👍
I loved this video, very well done, clarified a lot of things in my mind, big thanks to you
This is the first video I saw on your channel and it made me instantly subscribe. Brilliant explanation.
Thank you and welcome aboard Abhishek.
Wow. It's easy to understand. You are a genius
This is an excellent video.
Loved this one, keep these coming
Simplicity at it's best!
Good explanation! Expecting more like this..
Very clear explanation! Thank you so much!!!
Thanks Man, I started learning big data concepts and this video is very useful for me
Thank you, that was a great explanation!
Just brillant and easy to understand
Thank you for explaining this so simple!
Excellent Thank you sir - I am non tech from product side but this makes it clear for me - THANKS Again
Now I understood, Thanks for great explanation.
Thanks, Man! Obrigado.
Simply superb... Greate explanation with general example..
Easy to understand! thank you!
Thanks a lot.. well depicted.. now it’s clear to me about the grey area.
Very simple yet effective articulation!!
really clear! thank you
Thank you so much for this simplistic explanation 😊👍👌
This was a great explanation in a simple, clear, and concise manner.
I dont think there are any other vedios in the internet that explains database/datawarehourse/datalake like you did..thanks for your explainations
Cripsly explained and to the point, Thanks
That was awesome. Thanks.
Wow, this video explanation is very easy to understand & really helpful. Just by watching this video 2-3 times, I already have a big picture about the data lake, database, data warehouse in my mind. Thank you so much for making the video!!!
Glad it was helpful!
this is great. thanks for the clear explanations
Just the way I like it, Barney style! Amazing job!!!
Thank you so much!
Well explained! Thank you