ELT vs ETL

Name	ETL	ELT
History	Data warehouse cost is very expensive (millions of dollars). Data volume is still manageable. People are forced to practice waterfall development.	Cloud data warehouse drives the cost of storing and processing data down significantly (hundreds/thousands of dollars only). Data volume explode. Agile practices are possible.
Process	Raw data is transformed in a staging server. Only transformed data is loaded into the data warehouse. Transformations rely on the server’s processing power.	Raw data is loaded into the data warehouse. Transformations are done within the data warehouse. Results are also stored within the data warehouse. Transformations rely on data warehouse processing power.
Pros/Cons	Data warehouse only contains cleaned, transformed data ⇒ maximize utilization of data warehouse. Doesn’t work well when data volume increase ⇒ bottlenecks on the staging server. Usually take weeks/months to change process due to waterfall approach.	All data is stored in the cloud data warehouse ⇒ very easy to change up new data warehouse. Doesn’t need additional staging servers. Assuming a modern data warehouse, works well when data volume increases. Takes only days to transform/introduce new data.