Databricks integration

Databricks in Polar: your models, governed and blended.

Point Polar at the datasets in your Databricks lakehouse 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, and no one has to write SQL to use the models your data team already built.

/ ⏎
Connect Databricks See the setup
Google Sheets → Polar

What you can bring in

Modeled datasets
"Read the models we built?"
Point Polar at your lakehouse and columns become queryable metrics and dimensions.
catalog -> metrics
Custom KPIs
"KPIs live only in the lakehouse."
Bring modeled KPIs into governed reporting, blended with revenue and spend.
your KPI, blended
Cost tables
"Our cost data sits in Databricks."
Load cost tables so margin and net profit are on your real numbers.
real cost in margin
No re-modeling
"No duplicate pipeline."
Polar resolves your models into the semantic layer, refreshed on every sync.
no duplicate pipeline
No SQL to use it
"Only data team can query."
Once governed, anyone uses the metric without writing SQL.
self-serve
Query over MCP
"Can an AI read it?"
Governed metrics available over the Polar MCP, sourced.
MCP-ready

Databricks holds the models. Polar makes everyone use them.

Your data team modeled KPIs and cost logic in Databricks, but using them still means SQL or an analyst request. Polar turns those models into governed metrics the whole team can use, blended with your stack.

Databricks on its own

  • Using a model means SQL or an analyst ticket.
  • Lakehouse metrics live apart from marketing and revenue.
  • Definitions drift between the lakehouse and dashboards.
  • AI can't reason on models without bespoke pipes.

Databricks with Polar

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

Expose Databricks 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 Databricks role

A read-only role Polar can query, and the tables or models you want to expose.

Open Databricks →

The Polar team to map it

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

02 · Expose Databricks as a governed source
1

Grant a read-only role talk to Polar

Read-only is enough.
Create a read-only Databricks role Polar can query and share the tables or models you want to expose.
2

Map to the semantic layer built for you

Columns become governed fields.
The AI data engineer maps your datasets into your semantic layer, blended with Shopify, Meta and Google.
3

Use it everywhere the payoff

No SQL required.
Your modeled datasets, cost tables and KPIs behave like any other source, 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.

Book a demo Connect your data ↗
The Gap

The connector is only half of it.

Once your Databricks models are connected to 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.

Databricks

models, cost tables, KPIs
Polar

Polar

governed, blended, no SQL

Ask Polar

Polar MCP

Scheduled reports

Featured Use Case

The lakehouse, without the SQL tax.

The lakehouse, without the SQL tax

The models your data team built in Databricks are good; the problem is access, every use is a SQL query or an analyst ticket, and the definition drifts between the lakehouse 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 like Net Sales or Blended CAC, blended with Shopify, Meta and Google, and answering to Ask Polar and the Polar MCP, without re-modeling or a second pipeline.

What makes it real

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

One governed definition. Modeled columns resolve into the semantic layer, the same 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

Placeholder

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

See the use case › Use case 02

Placeholder

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

See the use case › Use case 03

Placeholder

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

See the use case › Use case 04

Placeholder

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

See the use case › Use case 05

Placeholder

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

See the use case ›
Your Prompt Library

Databricks in plain language

A scannable wall of sample prompts for your Databricks data inside Polar. Ask Polar, or Claude with the Polar MCP connected.
See it
Show my modeled KPI from Databricks next to revenue.
List the Databricks datasets exposed as governed metrics.
Break down my modeled cohorts by channel.
Analyze it
Blend my Databricks cost table into contribution margin.
Compare my lakehouse KPI to Polar's native metric.
Show net profit using my Databricks cost logic.
Decide from it
Which product lines are most profitable using my modeled costs?
Summarize how my modeled KPIs moved since last month.
Where do lakehouse 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 Databricks datasets into the semantic layer, and use your modeled metrics everywhere, 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 layerColumns become governed metrics and dimensions.
Phase 3 the payoffUse it everywhereBlended, governed, self-serve, and over the Polar MCP.