Your data in a Snowflake warehouse you own, modeled for commerce on day one.
Polar runs on Snowflake. Every plan includes a dedicated warehouse that is yours to query, export, and build on, arriving pre-loaded with a governed eCommerce model (blended ROAS, true CAC, contribution margin, LTV) instead of empty tables you have to model yourself. Already run Snowflake or BigQuery? Connect it too, both directions.
/Setup Guide
How to Connect Snowflake to Polar.
A Polar account with Shopify connected, and access to the warehouse or data lake you want to connect, credentials or a share.
01 · What you need
A Polar account with Shopify
A Polar account with Shopify connected. Your dedicated Snowflake warehouse comes with it, already modeled.
Connect your data →Access to your warehouse
Credentials or a share for the warehouse or data lake you want to connect: Snowflake, BigQuery, Amazon S3, Azure Blob, or Google Cloud Storage.
Open Snowflake →One request in Connectors
Snowflake sits under Custom connections available in Connectors. Click Request and Polar's AI Data Engineer builds it, in minutes.
02 · Bring Snowflake into Polar
1
Request Snowflake in Connectors ~ minutes
Custom connections available.
In Polar, open Connectors. Snowflake sits under Custom connections available: click Request and Polar's AI Data Engineer builds it, in minutes.
2
We connect and map the source automatic
Your definitions stay intact.
Polar connects to your warehouse or data lake, maps the tables into the model, and validates the joins. Custom dimensions, custom metrics, and custom configuration keep your business definitions intact.
3
See it in your reports and your warehouse the payoff
In the model, and queryable.
Once the sync runs, your warehouse data behaves like any other source: it flows into your P&L, contribution margin, CAC, and any custom metric, and it lands in your Polar Snowflake warehouse where you can query it directly.
Tip: Send the table and column list with the request: it makes the mapping cleaner.
Side by side
A warehouse that arrives already modeled for commerce.
A warehouse is a world-class engine that still needs a data team to make it useful for commerce. Polar ships the commerce model with it.
Snowflake on its ownthe engine
+Snowflake with Polarthe engine, modeled
A world-class engine, and empty on day one. Every source has to be piped in and maintained.
The warehouse comes pre-loaded. Shopify, Meta, Google, Klaviyo, and your warehouse sources are already flowing in.
Blended ROAS, true CAC, contribution margin, and LTV cohorts do not exist until your team builds and maintains them.
The governed semantic layer ships with the eComm model built: about 80 percent of metrics inherited, blended ROAS, true CAC, contribution margin, LTV cohorts, with the other 20 percent your custom dimensions and definitions.
Every metric is defined in whatever query wrote it, so two dashboards can disagree.
Every metric is defined once, so the number is the same whether you read it in Polar, in SQL, or through Claude.
Getting to trustworthy commerce reporting is a data team and months of modeling.
Trustworthy commerce reporting on core data in about a day, not a quarter.
The Gap
The data goes in, and the model comes back out.
Your sources flow into the warehouse Polar provisions for you, then the modeled result comes back out three ways: query it directly, replicate it into your own warehouse, or ask it in plain language.
Your sources and warehouse
sales, product, warehouse tables