How to Migrate PostgreSQL to Snowflake: Matillion vs. Skippr

April 2026

Technical comparison of Matillion versus Skippr for PostgreSQL-to-Snowflake migration, orchestration, and bronze-silver-gold delivery.

Why Teams Use Matillion for PostgreSQL to Snowflake

Matillion is usually chosen by teams that want a broader data platform for PostgreSQL to Snowflake pipelines: connectors, jobs, orchestration, and transformation logic in one vendor ecosystem.

Matillion is attractive when teams already standardize on visual ELT workflows and want Snowflake-centric transformation jobs inside a mature commercial platform.

The tradeoff is operational surface area. Even when one vendor sells the whole platform, teams still end up managing connections, jobs, staging logic, orchestration, Snowflake conventions, and often a separate analytics repo for durable SQL and testing.

Skippr is simpler when the job is straightforward warehouse migration and modeling. Instead of maximizing platform flexibility, it minimizes the number of moving parts required to get raw tables, staging models, and mart-ready outputs into Snowflake.

PostgreSQL to Snowflake Migration: Side-by-Side Setup

Step Matillion Skippr

              **1. Connect Postgres**
              Create a PostgreSQL connection in Matillion, configure extraction components, and define the source tables or queries.
              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 environment, warehouse, and target schema, then wire the raw landing pattern into the Matillion project.
              Run `skippr connect warehouse snowflake` or write one `warehouse:` block. Bronze lands in the schema you set, typically `RAW`.
            
            
              **3. Build silver**
              Build orchestration and transformation jobs for staging, type casting, and cleanup, or hand off from raw tables into a dbt project.
              `skippr run` discovers schemas, loads bronze tables, and generates the dbt project with silver staging models automatically.
            
            
              **4. Build gold**
              Create marts in Matillion SQL transformations or maintain them in dbt if you want a code-first analytics layer.
              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**
              Use Matillion schedules and job dependencies to sequence loads and transforms.
              Schedule or trigger `skippr run`. The same command handles incremental sync plus dbt generation and validation.
            
            
              **6. Number of surfaces to manage**
              Matillion project + Snowflake + optional dbt repo + job scheduler.
              One project directory, one config file, one execution path.

Short version: Matillion centralizes more platform logic; Skippr minimizes the platform footprint needed for the same warehouse result.

Config Surface: Matillion 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 Matillion is configured through a product UI or platform workspace. That is exactly the point: the workflow is spread across more operational surfaces.

              Matillion surface
              Skippr surface

`# Matillion pipeline / project source = postgres destination = snowflake replication_mode = incremental_or_cdc

platform jobs

job_1 = load raw tables into RAW job_2 = stage / cleanse / type cast job_3 = build marts or hand off to dbt

orchestration

scheduler = Matillion pipelines`

`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

Matillion process Skippr process

• Create the Matillion project or workspace.

• Connect PostgreSQL and configure source access.

• Connect Snowflake and define the raw landing pattern.

• Build load, staging, and transformation jobs or workflows.

• Add scheduling, dependency order, and failure handling.

• Maintain SQL, marts, and tests in the platform or a separate analytics repo.

• skippr init postgres-snowflake

• skippr connect warehouse snowflake --database ANALYTICS --schema RAW --warehouse COMPUTE_WH --role ACCOUNTADMIN

• skippr connect source postgres --host ... --database app

• Set cdc_enabled: true when you want ongoing change capture.

• skippr doctor

• skippr run

• Review 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.

Matillion is job-oriented and flexible, which is useful for complex platform estates. Skippr is pipeline-oriented and narrower, which makes the PostgreSQL-to-Snowflake contract easier to reason about.

What Actually Lands in Snowflake

Matillion can land raw data and run transformations against Snowflake, but teams still have to decide how much logic belongs in visual jobs versus durable analytics code.

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

Matillion can orchestrate incremental or change-oriented loads, but the operational model is job-centric rather than a single migration contract. Skippr keeps ingestion, schema discovery, and dbt generation aligned in one run path.

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 Matillion if you want a broad commercial ELT platform with visual job design and you are comfortable managing the warehouse workflow as a set of platform jobs.

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.