Amazon S3 integration

Amazon S3 in Polar: drop a file, get a governed metric.

Drop any dataset into an S3 bucket and Polar turns it into governed metrics and dimensions, blended with Shopify, Meta and Google in one semantic layer. Offline and retail sales, 3PL and logistics costs, COGS tables, custom KPIs, any file your team already exports. No warehouse to run, no pipeline to build, no CSV to re-upload by hand.

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Connect Amazon S3 See the setup
Google Sheets → Polar

Any file in a bucket, live next to Shopify and ads.

Point Polar at a bucket and drop your files in it. Their columns become queryable metrics and dimensions, blended with revenue, spend and orders, and refreshed on every scheduled sync. No manual re-upload, no data warehouse required.
Custom Datasets
“Can I import data no standard connector has?”
Drop a file in your S3 bucket and its columns become extra metrics and dimensions in Polar, available on the next scheduled data refresh. Whatever your team already exports, Polar can read.
file -> metrics & dimensions
Offline & Retail
“What about revenue that never touches Shopify?”
Wholesale, retail, marketplace and offline orders you export to a CSV land next to DTC, so the P&L reflects the whole business, not just the storefront.
offline orders + DTC, one P&L
Cost & Logistics
“Where do COGS and 3PL costs live?”
Drop cost and logistics files (COGS, landed cost, 3PL and shipping fees) into the bucket and blend them with Shopify revenue and ad spend for real contribution margin, not gross revenue.
COGS, 3PL & landed cost
Custom KPIs
“My KPI isn't in any connector.”
Whatever your team computes and exports (custom LTV, unit economics, proprietary metrics) becomes one governed Polar metric everyone reads the same way, no re-modeling.
custom KPI, governed once
Flexible Modeling
“How do my columns become usable?”
Polar maps your file's columns to custom dimensions and metrics, so a raw export turns into a governed dataset you can slice by SKU, channel, region or segment.
columns mapped to your schema
Blended Context
“How does file data sit with the rest?”
Your dropped files 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.
files in the blended layer
Setup Guide

Connect Amazon S3 as a governed source.

A Polar account with Shopify connected, an S3 bucket Polar can read, and the files you want to expose. Polar's team maps the columns into your semantic layer. Read access is enough, Polar never writes to your bucket.
01 · What you need

A Polar account with Shopify

Amazon S3 plugs in next to your store data, so the files you drop meet real revenue and spend automatically.

Connect your data →

An S3 bucket Polar can read

A bucket, and read access for Polar to it. Think of it as a shared folder: you drop files in, Polar reads them. Read-only is enough, Polar never writes back to your bucket.

Amazon S3 →

The files you want to expose

Pick the datasets to bring in: offline orders, a cost or 3PL file, a custom-KPI export. As a custom data connection it's tailored to your files, so your CSM helps map each one into the semantic layer.

02 · Connect it, the guided way
1

Add Amazon S3 from Connectors ~1 min

Data Warehouses section, or search for it.
In Polar, open Connectors, and in the Data Warehouses section find Amazon S3 (or use the search bar). Because a bucket connection is tailored to your files, click Connect and your Customer Success Manager picks it up to set it up with you.
Open Polar connectors
2

Grant read access and drop your files ~5 min

A readable bucket and the datasets to expose.
1
Give Polar read access to your S3 bucket and drop the files you want in Polar. New files landing in the bucket sync on the next scheduled refresh.
2
Name the columns and custom KPIs to bring in. Polar maps each into your semantic layer as governed metrics and dimensions.
Best practice: start with one or two files you report on most (an offline-orders export, a cost file). Keep dropping new versions in the same bucket and Polar picks them up.
3

See it in your reports the payoff

File data, blended and governed.
Your dropped files 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 pipeline to maintain and no CSV to re-upload by hand.
Get started

Put your custom data where the rest of your stack already lives.

Connect once and every dashboard, report and AI answer reads the files you drop in S3, blended and governed with Shopify, ads and the rest of your data.

Book a demoConnect your data ↗
The Gap

Files in. Governed metrics out.

Amazon S3 feeds the files you drop in, offline orders, cost and logistics tables, custom KPIs, into Polar. Polar's semantic layer maps their columns into governed metrics and dimensions, blended with Shopify, Meta and Google, and served to every dashboard, report and AI answer, so no one has to run a warehouse or re-upload a CSV.

Amazon S3

files, offline orders, cost tables
Polar

Polar

Featured Use Case

The whole-business P&L, online and offline in one file drop.

Your retail, wholesale and offline sales live in a spreadsheet, not Shopify. Drop them in S3 and blend them with DTC revenue and ad spend in Polar for a P&L that reflects the whole business. No warehouse, no reverse-ETL, no manual re-upload.
Revenue by channel type● offline from S3
Channel
Revenue
Share
Margin
Shopify DTC
$182k
58%
31%
Retail (from S3)
$96k
30%
22%
Wholesale (from S3)
$38k
12%
16%
Offline and wholesale dropped in S3, blended with Shopify DTC in Polar.

Mapped once

No AI needed

Map your offline-sales file into the semantic layer once. Whole-business revenue and margin then show up in every dashboard, report and scheduled export automatically. Drop a fresh file and it just updates.

AI agent

Dynamic

Give an agent the Polar MCP. Ask it to split revenue and margin across DTC, retail and wholesale using your S3 files, and it queries the blended model in plain language, no SQL required.

Dig deeper into this use case
Other Use Cases

More Polar + Amazon S3 use cases

What teams run once the files they drop in S3 are governed and blended in Polar.
Use case 01

Whole-business P&L

Fold retail, wholesale and offline orders from your S3 files in next to DTC, so the P&L reflects every revenue line.

See the use case ›Use case 02

True contribution margin

Blend a COGS and 3PL cost file from S3 with Shopify revenue and ad spend for real margin by channel, SKU and campaign.

See the use case ›Use case 03

Custom KPI everywhere

Drop a KPI export once and let every team read the same governed number in dashboards and AI answers.

See the use case ›Use case 04

File-powered AI agent

Point an MCP agent at your semantic layer so anyone can ask questions of your dropped files in Slack, no SQL.

See the use case ›Use case 05

SKU-level profitability

Join a cost-per-SKU file from S3 to Shopify orders to see which products actually make money after ads.

See the use case ›Use case 06

Cohorts from your export

Bring your own customer segments and LTV cohorts as a file and slice blended metrics by them.

See the use case ›
Your Prompt Library

15 prompts a connected Amazon S3 unlocks.

Whole-business revenue, 3PL-adjusted margin, custom KPIs, offline orders. Once your files are in Polar, ask in plain language.
Type
Audience
15 of 15
Get started · connect once · stays in sync

Your data is already in a file. Polar lets everyone ask it.

The offline sales, cost files and custom KPIs your team exports shouldn't sit in a folder no one queries. Connect an S3 bucket to Polar and every file you drop becomes a governed metric, blended with Shopify, Meta and Google, and readable by your whole team and your AI, with no warehouse to run and no pipeline to maintain.

Get started by booking a demoConnect your data in Polar
Phase 1 ~5 minConnect your data in PolarConnect Shopify and your stack. Polar's semantic layer turns it into governed, blended metrics.
Phase 2 ~5 minAdd Amazon S3 and drop your filesGrant read access and drop the files to expose. Polar maps their columns into your semantic layer.
Phase 3 the payoffUse it everywhereYour file data powers contribution margin, true CAC and MER across dashboards, reports and AI answers.