Faire + Bruin

Source

Ingest Faire data into your warehouse with incremental loading, quality checks, and full lineage. Defined in YAML, version-controlled in Git.

For business teams

What you get

Revenue analytics, automated
Faire orders, refunds, and transactions flow into your warehouse. Build cohort analysis, LTV, and revenue models with clean data.
True ROAS across channels
Join Faire revenue with ad spend from Google, Facebook, and others. Know your real return — not what each ad platform claims.
Inventory monitoring
Quality checks flag low stock levels and stockout risks from Faire data. Operations gets alerts before customers notice.
Customer 360 view
Combine Faire purchase history with support tickets, NPS, and product usage. See the full customer picture.

For data & engineering teams

How it works

Incremental order sync
Only sync new and updated Faire orders. No full reloads, even for high-volume stores.
YAML-defined, Git-versioned
Your Faire pipeline is a YAML file. Review in PRs, deploy with CI/CD, roll back with git revert.
Order data validation
Quality checks catch negative totals, invalid statuses, and missing order IDs on every sync.
Multi-destination support
Land Faire data in BigQuery, Snowflake, Redshift, or DuckDB. Switch destinations by changing one line.

Before you start

Faire brand account with API access enabled

Step 1

Add your Faire connection

Connect using Faire API key and brand identifier. Add this to your Bruin environment file — credentials are stored securely and referenced by name in your pipeline YAML.

Parameters

api_keyFaire API key from your brand portal
brand_idYour brand identifier on Faire

connections:
  faire:
    type: faire
    uri: "faire://?api_key=<your-api-key>&brand_id=<your-brand-id>"

Step 2

Create your pipeline

Define a YAML asset that tells Bruin what to pull from Faire and where to land it. This file lives in your Git repo — reviewable, version-controlled, and deployable with CI/CD.

Available tables

ordersproductsbrandsretailersshipments

name: raw.faire_orders
type: ingestr

parameters:
  source_connection: faire
  source_table: 'orders'
  destination: bigquery

Step 3

Add quality checks

Add column-level and custom SQL checks to your Faire data. If a check fails, the pipeline stops — bad data never reaches downstream models or dashboards.

Catch negative order totals before they reach reports

Validate order statuses against accepted values

Ensure order IDs are unique — no duplicates

columns:
  - name: order_id
    checks:
      - name: not_null
      - name: unique
  - name: total_price
    checks:
      - name: not_null
  - name: status
    checks:
      - name: accepted_values
        value: ['pending', 'paid', 'shipped', 'delivered', 'cancelled']

custom_checks:
  - name: no negative order totals
    query: |
      SELECT COUNT(*) = 0
      FROM raw.faire_orders
      WHERE total_price < 0

Step 4

Run it

One command. Bruin connects to Faire, pulls data incrementally, runs your quality checks, and lands clean data in your warehouse. If a check fails, the pipeline stops — bad data never reaches downstream.

Backfill historical data with --start-date

Schedule with cron or trigger from CI/CD

Full lineage from Faire to your dashboards

$ bruin run .

Running pipeline...

  faire_orders
    ✓ Fetched 2,847 new records
    ✓ Quality: campaign_id not_null     PASSED
    ✓ Quality: spend not_null           PASSED
    ✓ Quality: no negative ad spend     PASSED
    ✓ Loaded into bigquery

  Completed in 12s

Other E-commerce Platform integrations

Amazon Seller Central

BigCommerce

Chargebee

ConvertKit

Drip

eBay

Ready to connect Faire?

Start for free, or book a demo to see how Bruin handles ingestion, quality, lineage, and scheduling for your entire data stack.

Book a demo Read the docs