Microsoft Azure Blob Storage integration

Azure Blob Storage in Polar: drop a file, get a governed metric.

Drop your offline sales, logistics, cost and proprietary datasets into an Azure Blob container and Polar turns them into governed metrics and dimensions, blended with Shopify, Meta and Google in one semantic layer your team and your AI can query. No pipeline to build, no reverse-ETL, no SQL to write in Polar.

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Connect Azure Blob Storage See the setup

Azure Blob Storage → Polar

The Microsoft Azure Blob Storage files your team already exports, live next to Shopify and ads.

Point Polar at a container in your Azure Blob account and each dataset you drop in becomes queryable metrics and dimensions, blended with revenue, spend and orders. No re-modeling, no duplicate pipeline, picked up on every scheduled refresh.

Custom Datasets

“Can I just drop in the files we already export?”

Any dataset you land in your Azure Blob container maps into Polar's semantic layer, so its columns become governed metrics and dimensions. Drop a new file and it flows in on the next refresh.

blob files -> metrics

Offline & Wholesale

“What about revenue that never touches Shopify?”

Wholesale, retail, marketplace and offline orders dropped as files land next to DTC, so the P&L reflects the whole business, not just the storefront.

offline + DTC, one P&L

Logistics & Cost

“Where do shipping and landed cost live?”

Bring 3PL, freight, COGS and landed-cost files into Polar and blend them with Shopify revenue and ad spend for real contribution margin, not gross revenue.

landed cost & freight

Proprietary Metrics

“My KPI isn't in any standard connector.”

Whatever your team computes and exports (custom LTV, unit economics, cost per SKU) becomes one governed Polar metric everyone reads the same way.

custom KPI, governed once

Product & Customer

“Can I enrich orders with my own data?”

Product master data, cost per SKU, customer segments and cohorts dropped into the container join to Shopify orders as dimensions you can slice by.

SKU & segment dimensions

Blended Context

“How does file data sit with everything else?”

Your dropped datasets live in the same semantic layer as Shopify, Meta, Google and Klaviyo, blended into Net Sales, MER, CAC and margin, and readable by Ask Polar and the MCP.

blob data in the blended layer

Setup Guide

How to Connect Microsoft Azure Blob Storage to Polar.

A Polar account with Shopify connected, read access to an Azure Blob container, and the datasets you want to expose. Azure Blob is a custom connection, so your Customer Success Manager maps each file into your semantic layer. Read-only is enough, Polar never writes back to your storage.

01 · What you need

A Polar account with Shopify

Azure Blob plugs in next to your store data, so your dropped datasets meet real revenue and spend automatically.

Connect your data →

Read access to a container

An Azure Blob container Polar can read, with a storage account key or SAS token scoped to it. Read-only is enough, Polar never writes back to your storage.

Azure Portal →

The datasets you want to expose

Drop the files you want in Polar (offline orders, cost tables, custom KPIs) into the container.Microsoft Azure Blob Storage sits under Custom connections available in Connectors. Click Request and Polar's AI Data Engineer builds it, in minutes.

02 · Connect it, the guided way

Add Azure Blob Storage from Connectors ~1 min

Custom connections available.

In Polar, open Connectors. Microsoft Azure Blob Storage sits under Custom connections available: click Request and Polar's AI Data Engineer builds it, in minutes.

Open Polar connectors

Grant read access and drop your datasets ~5 min

A read-only container and the files to expose.

Share read access to an Azure Blob container with a storage account key or SAS token, and drop the datasets you want in Polar into it.

Name the files and custom KPIs to bring in. Polar maps each into your semantic layer as governed metrics and dimensions, picked up on the next refresh.

Best practice: start with the one or two datasets you report on most (an offline sales file, a landed-cost table). Drop a new file anytime and it flows in without touching a pipeline.

See it in your reports the payoff

File data, blended and governed.

Your dropped datasets now power dashboards, reports and every Ask Polar answer, blended with Shopify, Meta and Google. They feed contribution margin, true CAC and MER, with no SQL to write in Polar.

Side by side

Microsoft Azure Blob Storage stores the data. Polar turns it into governed metrics.

Files land in Microsoft Azure Blob Storage and stay files: no metric definitions, no blend with your store, and every question needs someone to write the query. Polar ingests them into the same semantic layer as your orders and your spend.

Microsoft Azure Blob Storage on its ownon its own

Microsoft Azure Blob Storage with Polarblended

Data sits as files and tables, with no metric layer on top.

Ingested into Polar's semantic layer as governed metrics.

Blending it with orders and spend means writing SQL.

Blended with Shopify, ads and email without writing SQL.

Every new question is a new query and a new export.

Ask in plain language, or read it in a dashboard that refreshes.

No shared definition, so two teams arrive with two numbers.

One definition, so the number is the same everywhere.

Featured Use Case

The true-margin P&L your offline and cost files already hold.

Your offline sales and landed-cost data sit in files. Drop them into Azure Blob, blend them with Shopify revenue and ad spend in Polar, and every channel gets a real contribution margin, not a gross-revenue guess. No export step, no reverse-ETL, no SQL in Polar.

Contribution margin by channel● costs from Azure Blob

Channel

Revenue

COGS + spend

Margin

Meta paid

$182k

$146k

20%

Email / SMS

$94k

$51k

46%

Offline / retail

$120k

$101k

16%

Offline sales and landed cost dropped into Azure Blob, blended with Shopify and ads in Polar.

Mapped once

No AI needed

Map your Azure Blob cost file into the semantic layer once. Contribution margin then shows up in every dashboard, report and scheduled export automatically, no SQL, no rebuild. Drop a fresh file and it updates.

AI agent

Dynamic

Give an agent the Polar MCP. Ask it to rank channels by true contribution margin using your dropped cost files, and it queries the blended model in plain language, no SQL required.