Reverse ETL

Reverse ETL inverts the typical data flow: instead of moving data into the warehouse, you move data out of it — into the operational systems that your team uses day-to-day.

Use cases

Warehouse source	Destination	Example
Snowflake	PostgreSQL	Sync enriched customer segments into an operational Postgres DB
BigQuery	MySQL	Push model predictions into a production recommendations DB
Snowflake or BigQuery	PostgreSQL / MySQL / MongoDB	Any computed view → a live operational destination

Other warehouse and SaaS destinations are on demand — see More connectors on demand.

Creating a Reverse ETL pipeline

Go to Jobs → New Job
Select Reverse ETL as the job type
Follow the same 4-step wizard as ETL:
- Source — choose your warehouse connection (Snowflake or BigQuery today)
- Destination — choose an operational system connection (e.g. PostgreSQL, MySQL, MongoDB)
- Tables — select which tables or views to push
- Settings — sync mode, destination write mode, schedule

Syncing from views and queries

For Reverse ETL, your “source” is typically not a raw table — it’s a transformed or aggregated view:

-- Create a view in Snowflake
CREATE OR REPLACE VIEW public.active_customer_segments AS
SELECT
  customer_id,
  segment,
  ltv_score,
  last_updated
FROM analytics.customer_model
WHERE is_active = true;

Select this view as your source table in Zwiron. The view runs fresh on every sync.

Write modes for Reverse ETL

Upsert is the most common write mode for Reverse ETL — it inserts new rows and updates existing ones based on the primary key, keeping the destination in sync with the warehouse output.

Scenario	Recommended write mode
Push all segments, update if changed	Upsert
Append only (event stream)	Append
Replace entire dataset each run	Overwrite

Schedule

Reverse ETL jobs should run on a schedule that matches how frequently your warehouse models are updated. If your dbt models run at 6 AM daily, schedule the Reverse ETL job to run at 6:30 AM.

What Reverse ETL is not

Reverse ETL is not a way to replicate the warehouse back to a second warehouse. For warehouse-to-warehouse syncing, use a standard ETL pipeline with a warehouse as source and destination.

Last modified on July 13, 2026

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Use cases

Creating a Reverse ETL pipeline

Syncing from views and queries

Write modes for Reverse ETL

Schedule

What Reverse ETL is not

​Use cases

​Creating a Reverse ETL pipeline

​Syncing from views and queries

​Write modes for Reverse ETL

​Schedule

​What Reverse ETL is not

Use cases

Creating a Reverse ETL pipeline

Syncing from views and queries

Write modes for Reverse ETL

Schedule

What Reverse ETL is not