How to Migrate PostgreSQL to Snowflake: Stitch + dbt Cloud vs. Skippr
May 2026
Technical comparison of Stitch + dbt Cloud versus Skippr for PostgreSQL-to-Snowflake migration, replication, and bronze-silver-gold delivery.
Why Teams Use Stitch + dbt Cloud for PostgreSQL to Snowflake
Stitch + dbt Cloud is a common answer when teams want to move PostgreSQL to Snowflake without building replication and transformation plumbing from scratch.
Stitch appeals to smaller teams that want simple SaaS replication into Snowflake and are comfortable adding a separate modeling layer afterward.
From an operator's perspective, the tradeoff is that ingestion and modeling still live in separate products. Stitch owns the sync path. The dbt layer owns the semantic path. The warehouse is where they meet, but the workflow still spans multiple surfaces.
Skippr takes a narrower but more integrated approach: if the end goal is PostgreSQL to Snowflake migration with bronze, silver, and gold output, the product should collapse more of that path into one project, one config file, and one execution path.
PostgreSQL to Snowflake Migration: Side-by-Side Setup
Step Stitch + dbt Cloud Skippr
**1. Connect Postgres**
Create a PostgreSQL integration in Stitch, configure extraction access, and choose the schemas and tables to replicate.
Run `skippr connect source postgres` or write one `source:` block in `skippr.yaml`. Turn on CDC with `cdc_enabled: true` when you want ongoing change capture.
**2. Connect Snowflake**
Create the Snowflake destination, define the raw schema or database, and validate the destination connection in Stitch.
Run `skippr connect warehouse snowflake` or write one `warehouse:` block. Bronze lands in the schema you set, typically `RAW`.
**3. Build silver**
Use the raw Stitch tables as the bronze layer, then connect dbt Cloud or dbt Core to define sources and create staging models.
`skippr run` discovers schemas, loads bronze tables, and generates the dbt project with silver staging models automatically.
**4. Build gold**
Build marts, tests, and incremental logic in dbt after raw replication is stable.
Skippr generates the starting dbt structure and materialises silver and gold schemas. You keep extending the generated dbt project in Git like normal.
**5. Orchestrate ongoing runs**
Coordinate Stitch replication and downstream dbt runs separately.
Schedule or trigger `skippr run`. The same command handles incremental sync plus dbt generation and validation.
**6. Number of surfaces to manage**
Stitch UI + Snowflake + dbt project + Git + scheduler.
One project directory, one config file, one execution path.
Short version: Stitch keeps the replication story simple; Skippr keeps the full warehouse build path simpler.
Config Surface: Stitch + dbt Cloud vs. Skippr
If you are evaluating the best way to move PostgreSQL data to Snowflake, the real question is not just "can it sync?" It is "how many systems do we have to touch before the warehouse is trustworthy?"
The left side below is representative rather than exhaustive, because much of Stitch + dbt Cloud is configured through a product UI or platform workspace. That is exactly the point: the workflow is spread across more operational surfaces.
Stitch + dbt Cloud surface
Skippr surface
`# Stitch connector settings source = postgres sync_mode = replication destination = snowflake destination_schema = RAW
dbt bootstrap
packages.yml packages:
- package: dbt-labs/codegen
generated and maintained separately
models/sources.yml models/staging/stg_orders.sql models/marts/fact_orders.sql
orchestration
Stitch sync -> dbt Cloud job`
`project: postgres_snowflake
warehouse: kind: snowflake database: ANALYTICS schema: RAW warehouse: COMPUTE_WH role: ACCOUNTADMIN
source: kind: postgres host: ${POSTGRES_HOST} port: 5432 user: ${POSTGRES_USER} password: ${POSTGRES_PASSWORD} database: app cdc_enabled: true
dbt: target_schema: postgres_snowflake
cdc: business_key_columns: - id`
With Skippr, secrets still live in environment variables, but the shape of the pipeline lives in one file. That is the simplification most technical buyers miss when they only compare connector screenshots.
Technical Walkthrough: Process vs. Process
Stitch + dbt Cloud process Skippr process
Create the Stitch account or workspace and the Snowflake destination.
Connect PostgreSQL and configure replication or CDC.
Wait for raw tables to land in
RAW.Create a dbt Cloud or dbt Core project and connect Git.
Generate or author source definitions and staging models.
Author gold marts and tests.
Schedule the connector sync and the downstream dbt run.
skippr init postgres-snowflakeskippr connect warehouse snowflake --database ANALYTICS --schema RAW --warehouse COMPUTE_WH --role ACCOUNTADMINskippr connect source postgres --host ... --database appSet
cdc_enabled: truewhen you want ongoing change capture.skippr doctorskippr runReview and extend the generated dbt project in Git.
What changes technically? With Skippr, schema discovery, raw loading, dbt project generation, and validation happen in one execution path. You still own the SQL that matters, but you do not have to hand-assemble the first production-shaped version of the pipeline.
Stitch is straightforward for raw replication, but the semantic warehouse layer still starts somewhere else. Skippr reduces that extra bootstrap work by generating the dbt foundation automatically.
What Actually Lands in Snowflake
Stitch is effective at raw PostgreSQL replication into Snowflake, but silver and gold still require a separate transformation workflow.
LayerWhere it lands with SkipprWhat it containsBronzeANALYTICS.RAWRaw extracted PostgreSQL tables in SnowflakeSilverANALYTICS.POSTGRES_SNOWFLAKE_SILVERGenerated staging models with typing, renaming, and cleanupGoldANALYTICS.POSTGRES_SNOWFLAKE_GOLDGenerated mart layer ready to extend for business metrics
Stitch handles ongoing replication, but the durability of bronze, silver, and gold remains split between replication tooling and downstream modeling. Skippr keeps those layers tied to the same project contract.
The dbt project is generated locally and remains yours. You can add tests, snapshots, incremental models, or custom gold marts exactly the way a serious analytics team expects.
What Skippr Removes From the Stack
For this use case, Skippr removes four categories of work:
- Separate product choreography: no handoff between an ingestion tool, platform workflow, or virtualization layer and a separate transformation contract just to keep one warehouse path fresh.
- Manual project bootstrap: no extra scaffolding step to get initial sources and staging models into place.
- Repeated configuration: source, destination, dbt naming, and CDC intent live together instead of being spread across dashboards and repo files.
- Extra operational reasoning: your team runs one command or one scheduled job, not a chain of connectors, triggers, jobs, and downstream handoffs.
That does not mean the gold layer becomes magic. Business logic still deserves human judgment. The point is that your team gets from source connection to a working Snowflake bronze/silver/gold warehouse much faster, so effort goes into metrics and model quality instead of tool coordination.
Which PostgreSQL to Snowflake Approach Should You Choose?
Choose Stitch + dbt if you want a lightweight SaaS replication tool and are comfortable adding the full transformation layer yourself.
Choose Skippr if you want the same Postgres to Snowflake outcome with fewer boundaries: one project, one config, one run path, generated dbt scaffolding, and direct control over how bronze, silver, and gold are produced.
If you want to see the exact flow, start with the Snowflake quick start and the PostgreSQL source connector docs, then run the pipeline end to end with one config and one command.