Postgres to Snowflake sync at a price you can predict
The Postgres to Snowflake connector from Adapters syncs tables incrementally into your warehouse with schema mapping, type casting, and automatic retries, at a flat monthly price instead of per-row usage billing. It runs on a schedule, so PostgreSQL data stays current in Snowflake without a hand-built pipeline.
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Last updated August 2026
What running Postgres to Snowflake by hand costs you
- MAR-based pricing turns a busy month into a surprise invoice.
- Hand-rolled sync scripts fail silently over the weekend.
- Schema changes upstream break the load and nobody is alerted.
The field mapping, out of the box
These cables are pre-wired when you pick the pair. Rewire any of them, or add your own, in the same visual data mapping tool you use for every adapter.
Input / POSTGRES
Output / SNOWFLAKE
Transforms included
Integer cents cast to NUMBER(12,2) dollars; timestamps normalize to UTC; new columns can auto-create in Snowflake or hold for review.
Postgres to Snowflake: the connector options, and the four things that break
There is more than one thing called a Snowflake connector for PostgreSQL, and picking the wrong one costs a week. Below: what each option actually is, the PostgreSQL configuration log-based replication needs, the type conversions that quietly corrupt data, and the disk trap that takes production down. Snowflake documentation read 13 August 2026.
What people mean by a Snowflake connector for PostgreSQL
Three different things share the name. The Openflow Connector for PostgreSQL is Snowflake first-party, part of the Openflow ingestion service that went generally available for Snowflake Deployments on 4 November 2025, and it replicates Postgres tables into Snowflake using change data capture. A third-party connector from Fivetran, Airbyte, Estuary or Adapters does the same job on the vendor infrastructure with its own pricing model. The Snowflake Connector for Python is not a pipeline at all: it is a client driver so your own code can query Snowflake. Search results mix all three together, which is why the term is confusing.
What PostgreSQL configuration log-based replication needs
Any CDC-based connector, first-party or third-party, reads the write-ahead log through logical replication. That requires wal_level = logical, which has been available since PostgreSQL 11, plus enough replication slots and WAL senders for every consumer you plan to run. The Openflow connector creates its own slot with a name beginning snowflake_connector_ and advances it as it reads, which is what lets PostgreSQL trim the log and reclaim disk. On managed Postgres such as Amazon RDS or Aurora, these are parameter group settings and changing wal_level needs a restart, so plan that into the migration window rather than discovering it on the day.
The replication slot trap that fills your disk
This is the failure that takes a production database down, and it is worth understanding before you enable CDC. A replication slot makes PostgreSQL retain WAL data from the slot position until the consumer confirms it has read past it. If the connector is paused, stopped, or simply broken over a weekend, the slot stops advancing while writes keep arriving, and WAL grows until the volume is full. Two rules follow: do not leave a CDC connector paused on a high-traffic database, and when you remove a connector, drop its replication slot manually, because removing the connector does not remove the slot. Monitor slot lag as a first-class alert, not as something you check after an incident.
The type conversions that silently corrupt data
Snowflake NUMBER defaults to precision and scale (38,0), so a Postgres numeric money column lands with the cents truncated unless the target is declared as NUMBER(19,4) or similar. Timestamps are the second trap: timestamptz should map to TIMESTAMP_TZ, because TIMESTAMP_NTZ stores no zone and shifts every value by your offset. Postgres jsonb maps cleanly to VARIANT, capped at 128 MB per value. And identifier case is the one people hit first: Postgres folds unquoted identifiers to lowercase while Snowflake folds them to uppercase, so created_at becomes CREATED_AT and a quoted lowercase reference will not find it. Pick one convention and apply it across every table.
When change data capture is worth the configuration, and when it is not
CDC catches hard deletes and rows updated by any process, including ones that never touch an updated-at column. A watermark query, filtering on a timestamp since the last run, catches neither, but it needs no database configuration, no replication slot and no disk monitoring. The honest rule: use a watermark when the table is append-mostly and a missed delete does not change a reported number, and use CDC when deletes matter, when rows are updated in place by application logic, or when freshness has to be measured in minutes. Small dimension tables are usually cheapest to reload in full and not worth either mechanism.
How Adapters handles this lane, and where it does not fit
Adapters runs scheduled incremental syncs with visual field mapping, type casting you can see before the first run, retries, alerting on the run that did not happen, and per-record error logs, on a flat monthly price from $49 rather than a monthly-active-rows meter. That suits teams whose Postgres to Snowflake requirement is a defined set of tables on an hourly or daily cadence. It is not the right pick if you need sub-minute streaming freshness, in which case look at Snowpipe Streaming or a streaming CDC vendor, or if the wider project needs forty SaaS sources as well, where connector-catalog breadth matters more than a predictable bill.
How it goes live
Three steps, minutes end to end, covered by flat data integration pricing from $49 a month.
STEP 01
Pick the pair
Connect Postgres and Snowflake with scoped credentials. About a minute each.
STEP 02
Confirm the mapping
The cables above are pre-wired. Adjust any field, preview the transform on sample records, done.
STEP 03
Schedule the sync
Hourly down to every minute, with retries, alerting, and a full log on every run.
Postgres to Snowflake sync: common questions
How do I sync Postgres to Snowflake?
Read only the rows that changed since the last run using an indexed updated_at watermark, land them in a Snowflake staging table, then MERGE on the primary key. Advance the stored watermark after the merge commits, so a failed run repeats its window instead of skipping rows.
Is there a Snowflake connector for PostgreSQL?
Yes. This is a prebuilt Snowflake connector for PostgreSQL: pick the pair, confirm the field mapping, and schedule the sync. It owns the watermark bookkeeping, the staging table, the merge, and the retries, so there is no extraction script to maintain.
How do PostgreSQL data types map to Snowflake?
Integers become NUMBER, TEXT and VARCHAR become VARCHAR, BOOLEAN stays BOOLEAN, and JSONB becomes VARIANT so you can query keys directly. Decide NUMERIC deliberately: keep its declared precision inside Snowflake NUMBER rather than letting money columns land in FLOAT.
Can Snowflake query a Postgres database directly?
No. Snowflake has no federated driver that reads a PostgreSQL table in place, so something has to extract rows and load them. That is what this connector does on a schedule, which also keeps analyst queries off the database serving your application.
How do I handle deletes when replicating Postgres to Snowflake?
A watermark load cannot see a hard delete, because the row is gone and no updated_at changed. Soft-delete in Postgres with a deleted_at column, run logical replication which emits delete events, or reconcile primary keys between the two on a schedule and remove the orphans.
How does the Postgres to Snowflake sync work?
The Postgres to Snowflake connector from Adapters syncs tables incrementally into your warehouse with schema mapping, type casting, and automatic retries, at a flat monthly price instead of per-row usage billing. It runs on a schedule, so PostgreSQL data stays current in Snowflake without a hand-built pipeline.
Is there a prebuilt Postgres connector for Snowflake?
Yes. This Postgres to Snowflake connector ships prebuilt: the field mapping is wired the moment you pick the pair, transforms are included, and you can try it against sample records in the live demo. No code or engineering sprint required.
How much does the Postgres Snowflake integration cost?
Pricing is flat and monthly: Starter at $49, Growth at $149, Scale at $399. Every plan includes this pair, visual field mapping, and per-record logs. There are no per-task or per-row fees, so the bill stays the same as volume grows.
How often can Adapters sync Postgres to Snowflake?
Hourly on Starter, every 5 minutes on Growth, and down to every minute on Scale. Failed records retry automatically with backoff, and alerting plus a full per-record log come standard on every run.
Do I need to write code to connect Postgres and Snowflake?
No. Fields are auto-mapped the moment you pick the pair, and you can rewire any mapping visually before the first sync. Integer cents cast to NUMBER(12,2) dollars; timestamps normalize to UTC; new columns can auto-create in Snowflake or hold for review.
More pairs from the API connector library
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Postgres and Snowflake, finally in agreement
Map the pair once and let it sync on schedule. Flat price from $49 a month, no per-task fees.
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