Datalake can contain unstructured, semistructured, and structured all kinds of data. purpose of datalake is to store raw data for various downstream teams. Whereas Data warehouse is for structured data only. It is a read-heavy system for analytical needs. ETL: is used where source data is not bulky and transformation logic is clear for a specific purpose, Only the transformed data needs to be loaded in the target system, enhanced security etc. ELT: is used where a huge volume of data is being processed, and multiple transformation logics will be applied to the raw data by multiple teams, in case of real-time analytics etc. Many to many relationships can be handled by introducing a bridge table between the tables. The bridge table will have 1:M relationship with the other tables.
not completely agree with the first difference you explained for ETL vs ELT, both can be used to load data warehouse or data lake, however, the decision on which one should be used is based upon the infrastructure requirement, in ELT you can leverage the power of your target data base for load as well as transformation but in case of ETL you must provision a third server/ compute machine only for the transformation purpose. Happy to read if you have any other view on it..!!
You are a good tutor and a request, why don't you post some tutorials on data modelling that would be helpful. Thank you for this video it is very helpful.
Well a Googler and professional data modelling trainer at Please check my linked page for my experiences. It will be really good if you post your questions and ask what exactly you are looking for.
I don’t feel that explanation was not clear TBH. But ma’am don’t you think that these questions are very simple? Is this truly the level of interviews?
Datalake can contain unstructured, semistructured, and structured all kinds of data.
purpose of datalake is to store raw data for various downstream teams.
Whereas Data warehouse is for structured data only. It is a read-heavy system for analytical needs.
ETL: is used where source data is not bulky and transformation logic is clear for a specific purpose, Only the transformed data needs to be loaded in the target system, enhanced security etc.
ELT: is used where a huge volume of data is being processed, and multiple transformation logics will be applied to the raw data by multiple teams, in case of real-time analytics etc.
Many to many relationships can be handled by introducing a bridge table between the tables. The bridge table will have 1:M relationship with the other tables.
Great example and shared insights! Very helpful!
Thank you for the nice short summary and your insights Pritha.
not completely agree with the first difference you explained for ETL vs ELT, both can be used to load data warehouse or data lake, however, the decision on which one should be used is based upon the infrastructure requirement, in ELT you can leverage the power of your target data base for load as well as transformation but in case of ETL you must provision a third server/ compute machine only for the transformation purpose.
Happy to read if you have any other view on it..!!
I agree with you.
correct
I love your gestures
Your explanation was so easy to understand like a friend explaining before exam!!!! Please make more videos.. Thank youuuuuu!!
Really helpful!
very good explanation
Please put more videos like this, it is very helpful for student like us.
Thanks :)
very helpful, thanks a lot
Good one ❤
Nice explanation
Can you please tell about the reverse engineering for XML or Oracle DB?
very nice
I can see explanations of 2 questions( i.e., Data Warehouse vs Data Lake and ELT VS ETL) , what is the 3rd data modeling question ?
Could you please explain the differences between different data models(Inmon,Kimball,3NF,Dimension Modelling,Data Vault).
Sure. In my next tutorial.
Nicely explained. Thank you. Please keep it up!
You are a good tutor and a request, why don't you post some tutorials on data modelling that would be helpful. Thank you for this video it is very helpful.
bane extralu ,,,,,,,
Can you please post more videos on data modeling. Thanks
Nivea body lotion 🧴
Could you please explain in detail, how to resolve many to many relationships? (I didn't get in this vodeo)
We solve many to many relationships by creating a bridge/ linked table which act as intermediate table between other tables.
Sure. Will explain in my next tutorial.
Haeyy
How to connect with you?
Mail me at tina.pritha@gmail.com. I will share my number over mail.
Awesome video পৃথা দি, প্লীজ ডেটা মডেল এর আরো use case video বানান
"basically" became Data Redundant .... try to use less.....
no clear explanation. She might be fresher.
Well a Googler and professional data modelling trainer at Please check my linked page for my experiences. It will be really good if you post your questions and ask what exactly you are looking for.
I don’t feel that explanation was not clear TBH. But ma’am don’t you think that these questions are very simple? Is this truly the level of interviews?