eBay + Bruin

Source

Ingest eBay 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
eBay orders, refunds, and transactions flow into your warehouse. Build cohort analysis, LTV, and revenue models with clean data.
True ROAS across channels
Join eBay 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 eBay data. Operations gets alerts before customers notice.
Customer 360 view
Combine eBay 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 eBay orders. No full reloads, even for high-volume stores.
YAML-defined, Git-versioned
Your eBay 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 eBay data in BigQuery, Snowflake, Redshift, or DuckDB. Switch destinations by changing one line.

Before you start

eBay developer account

Application keys from Developer Program

User consent token for account data

Step 1

Add your eBay connection

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

Parameters

app_ideBay application ID (Client ID)
cert_ideBay certification ID (Client Secret)
user_tokeneBay user auth token

connections:
  ebay:
    type: ebay
    uri: "ebay://app_id:cert_id@user_token"

Step 2

Create your pipeline

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

Available tables

orderslistingstransactionsinventoryreturnsseller_metrics

name: raw.ebay_orders
type: ingestr

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

Step 3

Add quality checks

Add column-level and custom SQL checks to your eBay 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.ebay_orders
      WHERE total_price < 0

Step 4

Run it

One command. Bruin connects to eBay, 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 eBay to your dashboards

$ bruin run .

Running pipeline...

  ebay_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

Etsy

Ready to connect eBay?

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