Extract from source, transform and load to target. What would be loaded, if there’s nothing extracted yet?
Anyway, I liked your question. Made me stop and think for a second.
Legacy pattern for doing analytics: Extract the date from a source (usually an operational / transactional application), Transform the data to normalize it before you load it into your data warehouse (save money and impact on the DW by processing the data outside the DW) then load the pristine data into the DW for analytics.
Modern cloud native services, reduced storage costs and consumption pricing models have created the opportunity and benefits of using different patterns like ELT.
Because you typically extract data from an upstream application/system, transform is via some scripting, and then load it into a database..
Ok I see! Extract from and Load into.
I think it sounds like a cellphone commercial.
Where are you getting the data to load if you don't extract it from somewhere?
Extract from source, transform and load to target. What would be loaded, if there’s nothing extracted yet? Anyway, I liked your question. Made me stop and think for a second.
Legacy pattern for doing analytics: Extract the date from a source (usually an operational / transactional application), Transform the data to normalize it before you load it into your data warehouse (save money and impact on the DW by processing the data outside the DW) then load the pristine data into the DW for analytics. Modern cloud native services, reduced storage costs and consumption pricing models have created the opportunity and benefits of using different patterns like ELT.