Database vs Data Warehouse vs Data Lake | What is the Difference?

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Information Database vs Data Warehouse vs Data Lake | What is the Difference?

Title	:	Database vs Data Warehouse vs Data Lake \| What is the Difference?
Lasting	:	5.22
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Views	:	459 rb

You missed Data Mart? are data mart and data lake same?
Comment from : Moon Baboon

Super useful video Much appreciated!
Comment from : Ariel Spalter

Thanks!
Comment from : Saif SpaceX

oh ok cool so like they're all still just a database lol
Comment from : m o

VS Databricks Lakehouse
Comment from : Comrade Vyke

Excellent explanation Thank you! Also wanted to know about Delta lake
Comment from : Umesh Jain

Thanks
Comment from : Banana George

thanks Alex, love your channel and very clear explanation of critical concepts :) - can you also cover data lakehouse?
Comment from : Anh Ho

thank you Sir Alex Great and concise video
Comment from : mzkhan1

"when someone says database , typically they're refering to relational database " FALSE what aboun non relational DB ??🤔
Comment from : Ufancha Lolo

And if you’re looking at doing Analytics AND ML on the same copy of data - you can use a Lakehouse Databricks is doing exactly that
Comment from : Denise

Nice simple video Good job Only negative comment is, there's no need to constantly show your face which blocks part of what you're trying to show - diagram, title, description etc
Comment from : Jamie

Very helpful video for basic understanding of differences between them, thank you
Comment from : Thrilled 2Bits

Data lake is where Scarlett Johansson go for swimming after becoming Lucy
Comment from : greendesertsnow

Many "super-scale" databases like Snowflake allow you to do Data Warehouse / Data Science type queries in a timely manner without hurting OLTP performance
Comment from : Doug Rosser

Very well explained!
Comment from : Naman Bhayani

Data Lake? Data swamp, more like
Comment from : Gary Rowe

in short: brbr- DATABASE: for transactionsbr- DATAWAREHOUSE: for analysisbr- DATA-LAKE: for everythingbrbrThe rest is only noisebrAHAHAH
Comment from : alfonso

Clear and short, thank you!
Comment from : Виталий Воропаев

Thanks for explaining, very clear now
Comment from : Durga Prasad Vadlamoodi

thank you great comparison and explanation
Comment from : Rich Bashaw

You say the same thing several times (like what OLAP is) and your picture is in the way of your slides
Comment from : anoxRJ

If i want to learn data warehouse or data lakebrCloud or big data is required or not i don't know please guide me
Comment from : vasanthkumar S

Hi Alex, Thanks for the video It is very clear One question, what about the schema for Data Lake ? Where it is stored ?
Comment from : Shibu George

Which vendor charges you for storage? If you’re data’s on a database in the cloud, is the database vendor charging you for storage, or is it the DWH vendor that has the storage fees? I’m thinking about a cloud environment not on prem
Comment from : KoolA1d

Oh, I invented the Data Lake w/ my file structuring (or lack thereof) on my personal laptop? 😭
Comment from : The Man Of The Hour Every Hour

Mi esposa dice que te pareces a Caillou 😂
Comment from : Saul Yarhi

thanks Alex
Comment from : BigDataLogin

Good content, but I did want to ask about what is happening with your audio Sounds over-processed so the lower levels are getting cut off You might be compressing it too aggresively
Comment from : Shawn Wildermuth

The key differences are: br br•Databases: Capture transactions, fresh detailed data, fast •Data Warehouses: Enable analytics and reporting, summarized historical data, fast querying •Data Lakes: Store many data types, unorganized raw data, enable machine learning
Comment from : Jin Xin

OLAP is OFFline analytical processing
Comment from : Doug Hills

Than you I am making a similar transition into becoming a Data Analyst so this background is extremely helpfulbr
Comment from : Simranpal Singh

A nice, clear presentation and nice explanations of the key terms Thanks !
Comment from : JHatLpool

This is why working at multiple companies/projects is always a good idea, otherwise you can end up in one silo as “thats the way the company it do it” and 5-10 years later you only have practical knowledge in one area
Comment from : milkboccle

Very well explained Thank you for sharing
Comment from : Alain B

Thanks!
Comment from : Peter Rhines

post a ksqldb course
Comment from : Krish Shiva

Great video! I just understood the differences between these key terms, thanks to your video Something I did not grab with a very long texts written for the same purpose Well done Alex 👏
Comment from : JENBA CS

Data ocean?
Comment from : bookzdotmedia Sola Fide

This was the best explanation in YouTube Thanks :)
Comment from : Mikko Tuomi

sos un crack
Comment from : Pizzu

Great video Subscribed
Comment from : T D’Ortenzio

Simple and easy to use Great voice and extremely friendly and humble
Comment from : Ndubisi Onuora

This is a great synopsis, is it possible for you to update to include data lakehouse
Comment from : Trisha Hunt

Nothing but awesome!! This is very nice Alex You won't ever know how much you have come through from me 👏
Comment from : From_My_End

Short, helpful, well explained Thanks!❤
Comment from : dominique ingrid

very helpful! thanks :)
Comment from : Harshal Gavali

Starting my data analyst journey on 1st of April 2023 through a tech academy I'm hoping for the best I can't wait to switch career from Law to tech
Comment from : Ify Ihesie

At my company the data warehouse sits in the data lake along side the raw and unstructured data
Comment from : Patrick Hogue

Database schema have to be just as rigid regardless of whether they are for OLTP or OLAP Period These aren’t no sql DBs
Comment from : AlphaBasic

Thanks please make a playlist for data warehouse and etl process
Comment from : Kamran Kiani

I think that a database is rather a basic element of both operational or data warehous databases Operational databases are used by multi-user applications as human ressources, payments, bookkeeping, logistics etc They are mostly of the relational type In a conventional environment a data warehouse consists as well of one or more relational databases loaded by ETL-jobs extracting, transforming and loading data from the operational databases into the data warehouse on a regular basis, at least once a day While operational databases just contain the data needed for daily business the data warehouse builds the long term data memory of the entire company The data in the data warehouse must be made accessible by business intelligence software to produce short and long term statistics and reports supporting the control of businessbrbrHaving the IT organised in the way descibed above the operational database(s) with their OLTP can be kept relatively small and technical processes of any kind run more quickly Many reporting abilities of business applications can be stripped off because most reporting could be done by the business intelligence software relying on the data in the data warehouse In other words: The evaluation of business processes does not need to rely on reporting functionalities of business applications and data mining could bring data of different sources together independent of the source having them createdbrbrA data lake is an unstructured collection of data in its original form It could be seen as the opposite of a data warehouse, but most companies have some data of which an ETL-process is not viable, eg correspondence and lots of descriptional documents
Comment from : Lars Wolschner

and is Data Lake same as Data Mart ?
Comment from : delicious_data

3:03 Don't forget one HUGE reason for data warehouse: Whatever your Data Analyst F's up on that side, the original data is safe (read-only before ETL)
Comment from : Steven dv

Great explanation! Thanks
Comment from : mehmet kaya

very nice content!
Comment from : Paulo Fernando De Mello

I wish Alex would make his own course Lol! Everything is always so easy to understand
Comment from : danicoleb5394

Awesome explanation thanks
Comment from : butter

Whoa, Alex with this clarity and instruction you're going to get my University phd instructors "Fired"
Comment from : n19ence

So is a Data Warehouse basicly PowerBI file with refreshed data And a Data Lake is a folder with all the raw data files??
Comment from : CruiserTech GT

Clear cut explanation!!! Thank you!!!
Comment from : Oscar Parada

NIFTY Options Live Trading and Options Training

There are so many big universities around the world, but this guy made it so clear for me You deserve to put your own University brother, thanks for enlightening me 🙏👍😊all the best 💐💐
Comment from : NIFTY Options Live Trading and Options Training

Very informative,simple, easy to understandthanks a lot
Comment from : Rucha J

Best explanation ever 👍
Comment from : Sübhi Sədiyev

Very well done Alex!
Comment from : Stephen Jones

Thanks, that was really well explained :)
Comment from : MeryemLux

@AlexTheAnalyst I wish to learn data pipeline creation using pythoncan you recommend me some tutorials?
Comment from : heynilesh_sup

Cool stuff, short and informative
Comment from : Dmytro B

Thank you
Comment from : Kasun Daminda

Thanks for your video, it is really clear and helpful information
Comment from : Shannis Yang

Super helpful thanks
Comment from : Shoto_UK

Thank for this Do you use it on your work? Is it important subject to learn before getting a Bi job? Any udemy courses that you recommend?
Comment from : yossi levi

Much appreciated brkeep up the great actions
Comment from : Majid Rasouli

Alex, which online courses do you recommend to get certification in data Warehouse, ETL? I've taken a few but none have provided information on this Thank you!!
Comment from : Nola_Belle

wow! thank you!!!
Comment from : Nikhil

Could you expand with a real-life example on what you mean by a "summarized version of each database" in result of the ETL process/upload to the data warehouse? What's the summarized output called, is it a specific file type?
Comment from : Drew

Understood the difference and I now know what I need for my own purposesbrbrAmazing content kudos 🥂
Comment from : Blank Devs

This is great!
Comment from : Kevin Mcinturf

Nice explantion
Comment from : Saul Burgos

Dope video, thanks!
Comment from : Manga art

Awesome explanation Thank you
Comment from : Navikak

helpful
Comment from : Fahad Nadeem

Everyone has a data lake fragmented in the form of scattered hard drives and web accounts
Comment from : Dave Dei

Thanks for the explanation but why would you put yourself over the slides to cover information
Comment from : Johann

Have to disagree with what you said about Databases Often times it's not easy/practical to alter these Especially true for MSSQL I'd also say that Security can benefit from a Data Lake even more than ML can Otherwise, this is a pretty cool overview
Comment from : Ryan Whiteman

Great explanation! Been struggling with understanding the differences, this really cleared it up!
Comment from : Jimmy Hildingsson

And Operational Data Store
Comment from : Paul Amarante

Interesting video, but your view/definition of database vs data warehouse is not accurate Database is just a collection of data, similarly a data warehouse is also a collection of data A database can be a data warehouse, but it can also contain operational data, which usually differs from a data warehouse Also a data warehouse is not only OLAP, as OLAP is a technology that can be implemented on top of a data warehouse or completely separate from a data warehouse I know it's complicated, but having been in this area of business for +30 years - you'll understand - that it truly IS complicated 🙂
Comment from : Jan Pedersen

bIts 2:00am - Why am I watching a video about data lakes?/b
Comment from : LLOOYYYDD!

Though often times by database people do mean a RDBMS (Relational Data Base Management System), and use SQL, a database is any organized data store and goes well beyond just relational data models One of the more common generic alternatives are hierarchical databases Some simple examples of that are xml with xquery or the windows registry I have even used what was tab delineated files and a file system hierarchy to do bioinformatics researchbrA second thing is that databases don't need to be persistent, ACID, or on disk The point being keep your mind open to things past RDBs A different data store and query system may provide a better solution to the problem at handbrIf you do use RDBs, please bring it to third normal form, preferably BCNF I cannot tell you the number of times I have come into a company to help solve a problem and it turns out that their data doesn't even fit first normal form
Comment from : B G

I think this guy's trying to look like me
Comment from : Walter White

The "transaction" in OLTP doesn't mean retail sale It means that a specific and complete interaction with the database has occurred Yes, it works like a retail sale, but using such an analogy tends to confuse people that are unfamiliar with databases
Comment from : Dustin