ETL vs ELT

 ETL vs ELT

⚡ETL is a traditional process for data transformation into a data warehouse (Cloud based or On-prem).
ELT is a modern process for any kind of structured and non-structured data transformation inside a cloud based data warehouse.

⚡In ETL, data moves from source to stage, then into data warehouse.

In ELT, data staging is not required. Data can directly move into data warehouse from source.

⚡ETL only transforms and loads the Data which you think is necessary when creating DWH. Hence Data migration is slower.

ELT loads all the Data immediately into DWH and users can decide later which Data to transform and analyze. Hence Data migration is faster.

⚡ETL is not normally a solution for Data lakes. It transforms data for integration with structured and relational DWH.

ELT offers a pipeline for Data lakes to ingest any kind of structured or unstructured data. Data could be transformed on need basis for analysis.

⚡ETL is best suited for dealing with smaller datasets.

ELT is best suited for dealing massive amount of structured or unstructured data

⚡Use cases for ELT:
Organizations with huge amount of Data
Organizations which require immediate access of data

⚡Uses cases for ETL:

Organizations which need to synchronize data from multiple sources
Organizations which need to migrate and update their data from legacy systems