How PostgreSQL Staging MERGE Works for CDC Final State
April 2026
Postgres as a CDC destination works best when staging is treated as part of correctness, not as disposable plumbing.
Short Answer
Postgres CDC final-state delivery usually relies on a staging-and-merge pattern: land changes safely, identify rows correctly, and reconcile into the table shape the warehouse should expose.
That makes staging part of the correctness model. It is not just a holding area between extraction and dbt.
Why Teams Struggle with This
Because Postgres is familiar, teams sometimes under-design the warehouse side. They assume a known database means the CDC contract will be obvious. It usually is not.
- A staging table can quietly preserve bad assumptions about row identity.
- Upserts look easy until the first replay reveals a weak key choice.
- Delete handling often becomes an application-specific cleanup problem.
- Teams can end up with a destination that is technically live but hard to trust.
How Skippr Handles It
Skippr helps by making the destination behavior explicit at the same time the source CDC settings are defined. That gives the team a better chance to review merge rules before they leak downstream.
It also makes the generated dbt project more useful, because the starting warehouse tables already reflect a clearer contract.
- Staging and reconciliation stay tied to the pipeline config.
- Business keys are defined close to where they are used.
- Replay handling is considered part of destination behavior.
- Generated dbt models can build from tables with cleaner state.
What the First Useful Version Looks Like
If Postgres is your destination, treat CDC as table design work as much as transport work.
That is what keeps the warehouse boring in the right way.