Difference between ETL vs ELT | Modern Data Architectures

Difference between ETL vs ELT 

 ETL (Extract, Transform, Load):

ETL which stands for extract transform load as the acronym implies in this scenario we first extract the data from a source system and then this is followed immediately by applying transformations aka business logic to that extracted data we then finally load that transform data into some final tables where it can be consumed by end users and reporting tools if you have something like power bi or tableau a key point in this

Requires a temporary storage location (staging area) to hold extracted data before transformation.

Common ETL tools include Microsoft SSIS, Informatica, and Talend.

ELT (Extract, Load, Transform):

Extract: Data is pulled from a source system.

Load: The data is loaded directly into a permanent destination (data lake).

Transform: Transformations are applied to the entire dataset in the destination.

Transformations can happen at any time, independent of extract and load.

No need for a temporary staging area.

not possible to crud operations :-they're not really well suited for a lot of truncating reloading or individual record updating

Common ELT tools include Fivetran, Stitch, Apache Kafka, AWS Firehose, and dbt.

More scalable approach for large data volumes.

Choosing ETL vs. ELT:

ELT is generally considered a more modern and scalable approach due to cheaper data storage and powerful cloud databases.

ETL is still widely used and can be a complex undertaking to change.