Reverse ETL

Reverse ETL moves data in the opposite direction from a conventional pipeline: instead of pulling data into a data warehouse, it takes already-modeled data out of the warehouse and syncs it into the operational tools that sales, marketing and support teams use, such as a CRM, helpdesk or ad platform.

It sits downstream of standard ETL or ELT pipelines, which bring data in and model it, and downstream of the transformation logic that computes metrics like customer scores or segments. Reverse ETL then pushes those computed fields back out on a schedule or trigger. This overlaps with what a customer data platform does for audience activation, but reverse ETL tools are generally warehouse-native and field-level, syncing arbitrary modeled columns rather than only customer profiles.

Reverse ETL matters because it lets the warehouse, where the most trustworthy version of a metric lives, become the source of truth for operational systems too, instead of each tool recalculating its own version. Common pitfalls include syncing fields that change faster than the sync schedule can keep up with, creating confusing lag between systems, and pushing so much data downstream that the destination tools become hard to govern.