Snowflake integration

Snowflake in Polar: your models, governed and blended.

Point Polar at the datasets in your Snowflake catalog and their columns become governed metrics and dimensions, blended with Shopify, Meta and Google and served to every dashboard, report and AI answer. Read-only is enough, Polar never writes to your warehouse, and no one has to write SQL to use the models your data team already built.

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Connect Snowflake See the setup
Google Sheets → Polar

What you can bring in

Modeled datasets
"Can Polar read the models we already built?"
Point Polar at datasets in your catalog and their columns become queryable metrics and dimensions.
catalog -> metrics
Custom KPIs
"I have KPIs that live only in the warehouse."
Bring your modeled KPIs into governed reporting, blended with revenue, spend and orders.
your KPI, blended
Cost tables
"Our cost data sits in Snowflake."
Load cost tables so contribution margin and net profit are calculated on your real numbers.
real cost in margin
No re-modeling
"I don't want a duplicate pipeline."
Polar resolves your existing models into the semantic layer, no re-modeling, refreshed on every sync.
no duplicate pipeline
No SQL to use it
"Only my data team can query it today."
Once governed, anyone can use the metric in a dashboard, report or AI answer without writing SQL.
governed, self-serve
Query over MCP
"Can an AI read these models?"
The governed metrics are available over the Polar MCP, so an assistant reasons on them, sourced.
MCP-ready

Snowflake holds the models. Polar makes everyone use them.

Your data team modeled cohorts, KPIs and cost logic in Snowflake, but using them still means writing SQL or waiting on an analyst. Polar turns those models into governed metrics the whole team can use, blended with the rest of your stack.

Snowflake on its own

  • Using a model means writing SQL or queuing an analyst request.
  • Warehouse metrics live apart from your marketing and revenue data.
  • Definitions drift between the warehouse and the dashboards.
  • AI assistants can't reason on the models without bespoke pipes.

Snowflake with Polar

  • Modeled columns become governed metrics anyone can use, no SQL.
  • Blended with Shopify, Meta and Google in one semantic layer.
  • One definition, governed, served everywhere the same way.
  • The governed metrics are available over the Polar MCP, sourced.
Setup Guide

Expose Snowflake as a governed source.

01 · What you need
Polar

A Polar account with Shopify

A Polar account with Shopify connected. This connector plugs in right next to your stack.

Connect your data →

A read-only Snowflake role

A read-only role Polar can query, and the tables or models you want to expose. Polar never writes to your warehouse.

Open Snowflake →

The Polar team to map it

The AI data engineer maps your datasets into the semantic layer so their columns become governed metrics and dimensions.

02 · Expose Snowflake as a governed source
1

Grant a read-only role talk to Polar

Read-only is enough.
Create a read-only Snowflake role Polar can query and share the tables or models you want to expose. Polar reads only, it never writes back to your warehouse.
2

Map to the semantic layer built for you

Columns become governed fields.
The AI data engineer maps your datasets into your semantic layer, so each column resolves into a governed metric or dimension, blended with Shopify, Meta and Google.
3

Use it everywhere the payoff

No SQL required.
Your modeled datasets, cost tables and custom KPIs behave like any other source across dashboards, reports and AI answers, no re-modeling, no duplicate pipeline, no SQL.
Get started

The models your data team built, used by everyone.

Grant a read-only role, point Polar at your datasets, and let the semantic layer turn them into governed metrics no one has to query by hand.

Book a demo Connect your data ↗
The Gap

The connector is only half of it.

Once your Snowflake models and KPIs are clean and attributed inside Polar, you can use them wherever you already work. Ask Polar in plain language, pull them live through the Polar MCP, or schedule a snapshot into the report your team reads every morning.

Snowflake

models, cost tables, KPIs
Polar

Polar

governed, blended, no SQL

Ask Polar

Polar MCP

Scheduled reports

Featured Use Case

The warehouse, without the SQL tax.

The warehouse, without the SQL tax

The models your data team built in Snowflake are good, cohorts, landed-cost logic, custom KPIs. The problem is access: every time someone needs them, it is a SQL query or a ticket to the analyst, and the definition quietly drifts between the warehouse and the slide. Polar reads your datasets over a read-only role and resolves their columns into your semantic layer, so a modeled KPI becomes a governed metric that behaves exactly like Net Sales or Blended CAC. It blends with Shopify, Meta and Google, it shows up in dashboards and reports, and it answers to Ask Polar and the Polar MCP, all without re-modeling and without a second pipeline. Your data team stays the source of truth; everyone else just uses it.

What makes it real

Read-only, no writes. Polar queries a read-only role and never writes back to your warehouse.

One governed definition. Modeled columns resolve into the semantic layer, so the number means the same thing everywhere.

Ask Polar, in plain language. “Show my modeled contribution margin by channel next to revenue.” No SQL, no export.

See how the governed layer works
Other Use Cases

Other use cases

Use case 01

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Placeholder. The use case skill fills this. (rung: see it)

See the use case › Use case 02

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Placeholder. The use case skill fills this. (rung: report it)

See the use case › Use case 03

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Placeholder. The use case skill fills this. (rung: analyze it)

See the use case › Use case 04

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Placeholder. The use case skill fills this. (rung: decide from it)

See the use case › Use case 05

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Placeholder. The use case skill fills this. (rung: act on it)

See the use case ›
Your Prompt Library

Snowflake in plain language

A scannable wall of sample prompts for your Snowflake data inside Polar. Ask Polar, or Claude with the Polar MCP connected.
See it
Show my modeled KPI from Snowflake next to revenue this month.
List the Snowflake datasets exposed as governed metrics.
Break down my modeled cohorts by channel.
Analyze it
Blend my Snowflake cost table into contribution margin.
Compare my warehouse KPI to Polar's native metric for the same period.
Show net profit using my Snowflake cost logic.
Decide from it
Which product lines are most profitable using my modeled costs?
Summarize how my modeled KPIs moved since last month and why.
Where do my warehouse numbers and blended numbers disagree, and why?
Get started · read-only · governed everywhere

Your models, used by everyone.

Grant a read-only role, let Polar map your datasets into the semantic layer, and use your modeled metrics across dashboards, reports and AI answers, blended and without SQL.

Get started by booking a demo Connect your data in Polar
Phase 1 talk to PolarGrant a read-only roleShare the datasets or models you want Polar to read.
Phase 2 built for youMap to the semantic layerThe AI data engineer turns columns into governed metrics and dimensions.
Phase 3 the payoffUse it everywhereBlended, governed, self-serve, and available over the Polar MCP.