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.

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Connect Databricks See the setup
Databricks → Polar

The Databricks models your data team built, usable by everyone.

Modeled datasets, custom KPIs and cost tables read from Databricks into Polar's semantic layer, so they become governed metrics nobody has to re-model or query by hand.
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
Setup Guide

How to Connect Databricks to Polar.

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

Polar's AI Data Engineer maps your datasets into the semantic layer so columns become governed metrics.

02 · Bring Databricks into Polar
1

Ask the AI Data Engineer for Databricks ~ minutes

Plain language, no code.
Describe what you need from Databricks in plain language. Polar's AI Data Engineer reads the Databricks API, writes the connector and pipes the data into your warehouse as governed data, in minutes. No code.
2

Map to the semantic layer automatic

Columns become governed fields.
Polar's 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 ↗
Side by side

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 ownon its own

+

Databricks with Polarblended

Using a model means SQL or an analyst ticket.
Modeled columns become governed metrics, no SQL.
Lakehouse metrics live apart from marketing and revenue.
Blended with Shopify, Meta and Google in one layer.
Definitions drift between the lakehouse and dashboards.
One definition, served everywhere the same way.
AI can't reason on models without bespoke pipes.
Governed metrics available over the Polar MCP.
The Gap

The Databricks 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 Databricks 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.

Read-only, no writes

No re-modeling

Polar queries a read-only role and never writes back to your lakehouse. Modeled columns resolve into the semantic layer, the same everywhere.

Ask Polar, in plain language

No SQL

“Show my modeled contribution margin by channel next to revenue.” No SQL, no export.

See how the governed layer works
Your Prompt Library

15 prompts a connected Databricks unlocks.

Your modeled datasets and cost tables as governed metrics, and where lakehouse and blended disagree.
Type
Audience
15 of 15
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 plain languageGrant 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.