Comparison
ELTvsReverse ETL
ELT
you dumped the source table into the warehouse untouched and did all the shaping afterwards, in SQL, where everyone could read it.
Extract from the source, load it raw, then transform inside the warehouse itself. Cheap elastic warehouse compute is what made this the default: the transform is now SQL in version control that any analyst can read, and the raw landing tables are still there when the definition changes. The bill moves with the work — you are now storing everything and paying to re-read it — which is why ELT shops end up caring about partitioning and incremental models far earlier than ETL shops did.
Full entry →Reverse ETL
the churn score computed in the warehouse gets pushed back into the CRM so a salesperson can see it in the tool they already use.
Moving modelled data out of the warehouse and back into operational systems — the CRM, the support desk, the ad platform. It exists because the warehouse is where the joined-up definition of a customer finally lives, and nobody in sales is going to open a BI tool. It quietly turns your analytics stack into a production dependency: a failed model run is now a wrong field in someone's sales workflow, with a rate limit and an API contract attached.
Full entry →