Amazon Seller Data to BigQuery

We understand that some Amazon sellers need full control over their data. You want to run your own SQL queries, build custom models, and integrate Amazon data into your existing warehouse. That's exactly why we created Nova's ready-made Amazon raw data service: clean, normalized data delivered directly to BigQuery or Snowflake. In our experience, operational rigor (closing the loop on fees, returns, and inventory) outperforms tactical optimization here. In our experience, operational rigor (closing the loop on fees, returns, and inventory) outperforms tactical optimization here.

Building an Amazon data pipeline from scratch takes 12-18 months and costs $300K+ in engineering time. Most projects fail before they deliver anything useful. The Selling Partner API is a maze of 20+ endpoints, aggressive rate limits, and constant schema changes.

This guide covers three paths to getting Amazon seller data into BigQuery: the hard way (DIY with SP-API), the medium way (ETL tools), and the fast way (pre-built solutions like Nova). You'll learn what data is available, the real costs of each approach, and how to avoid the pitfalls that kill most Amazon data projects. If your team uses Snowflake instead, see our Snowflake guide.

Why Amazon Sellers Need BigQuery

BigQuery isn't just a database. It's Google's serverless data warehouse that can query terabytes in seconds. For Amazon sellers who need flexibility beyond Seller Central reporting, BigQuery delivers:

The sellers who get the most from BigQuery aren't the biggest. They're the ones who ask questions that Seller Central can't answer: "Which products are profitable after accounting for all fees and ad spend?" or "How does my conversion rate compare across marketplaces by day of week?"

The Amazon API Chaos: Why Building Your Own Pipeline Fails

Let's be honest about what "build it yourself" actually means. According to industry research, 80% of data projects fail to deliver business value. Amazon data pipelines are particularly brutal.

A realistic timeline for a DIY Amazon-to-BigQuery pipeline:

These numbers come from aggregators and brands who've tried it. The ones who succeed usually have dedicated data engineering teams of 3+ people. Solo sellers or small teams? The math doesn't work.

What Amazon Data You Can Export to BigQuery

Amazon's SP-API provides access to hundreds of data points across four main categories. With the right pipeline (or Nova), you get access to all of it:

3 Ways to Get Amazon Data into BigQuery

Your options range from full DIY to fully managed. Here's an honest comparison:

Option 1: Build with SP-API (Hard)

This is the path described above. You build everything from scratch using Amazon's Selling Partner API documentation.

Best for: Aggregators with $100M+ GMV and dedicated data engineering teams of 5+ people.

Option 2: ETL Tools Like Airbyte or Fivetran (Medium)

ETL (Extract, Transform, Load) tools provide pre-built connectors for common APIs. They handle authentication and basic data extraction.

Best for: Brands with existing data teams who want to accelerate the build without starting from zero.

Option 3: Pre-Built Solutions Like Nova (Easy)

Instead of building or configuring pipelines, you connect your Amazon accounts and receive clean, modeled data in BigQuery (or Snowflake) with hourly refresh cycles.

Best for: any seller who wants Amazon data in BigQuery without the engineering overhead. Especially agencies managing multiple brands and aggregators who need data fast.

Building Custom Reports in BigQuery with Amazon Data

Once your Amazon data is in BigQuery, you can run any SQL query. Here are examples of analyses that aren't possible in Seller Central:

Example 1: True P&L by SKU

This query calculates net profit per SKU by combining revenue, all Amazon fees, and advertising spend. Essential for profit analysis:

SELECT 
  sku,
  SUM(revenue) as gross_revenue,
  SUM(fba_fees + referral_fees + storage_fees) as total_amazon_fees,
  SUM(ad_spend) as advertising_cost,
  SUM(revenue) - SUM(fba_fees + referral_fees + storage_fees + ad_spend) as contribution_margin
FROM `your_project.amazon_data.daily_sku_metrics`
WHERE date >= DATE_SUB(CURRENT_DATE(), INTERVAL 30 DAY)
GROUP BY sku
ORDER BY contribution_margin DESC
LIMIT 50;

Example 2: Conversion Rate by Day of Week

Identify which days drive the highest conversion for bid adjustments:

SELECT 
  FORMAT_DATE('%A', date) as day_of_week,
  AVG(unit_session_percentage) as avg_conversion_rate,
  SUM(sessions) as total_sessions,
  SUM(units_ordered) as total_units
FROM `your_project.amazon_data.traffic_metrics`
WHERE date >= DATE_SUB(CURRENT_DATE(), INTERVAL 90 DAY)
GROUP BY day_of_week
ORDER BY avg_conversion_rate DESC;

Example 3: Cross-Marketplace Comparison

Compare performance across US, UK, DE, and other marketplaces. Critical for multi-marketplace sellers:

SELECT 
  marketplace,
  COUNT(DISTINCT asin) as active_asins,
  SUM(revenue_usd) as total_revenue_usd,
  AVG(profit_margin) as avg_profit_margin,
  SUM(ad_spend_usd) / SUM(revenue_usd) * 100 as tacos_percentage
FROM `your_project.amazon_data.marketplace_summary`
WHERE date >= DATE_SUB(CURRENT_DATE(), INTERVAL 30 DAY)
GROUP BY marketplace
ORDER BY total_revenue_usd DESC;

Connecting BigQuery to Looker Studio & Tableau

With Amazon data in BigQuery, connecting to visualization tools is straightforward:

Looker Studio (Free)

Google's free BI tool has native BigQuery integration:

1. Open Looker Studio and create a new report
2. Select BigQuery as your data source
3. Choose your project and dataset
4. Build visualizations using drag-and-drop

Looker Studio is ideal for teams who want collaborative dashboards without additional software costs. Check our Amazon Looker Studio Dashboard Guide for templates and best practices.

Tableau

Tableau offers more advanced visualizations and is preferred by enterprise teams. See our complete Amazon Tableau Dashboard Guide for templates and best practices. The BigQuery connector requires:

• Tableau Desktop or Tableau Server license
• BigQuery ODBC driver installation
• Service account credentials for authentication

Getting Started: Your Next Steps

Frequently Asked Questions

References

External resources referenced in this guide:

• Google BigQuery - Serverless data warehouse for analytics
• Amazon Selling Partner API Documentation - Official Amazon SP-API reference
• Google Looker Studio - Free business intelligence and dashboard tool
• Tableau BigQuery Documentation - Tableau connector setup guide
• Airbyte Amazon Connector - Open-source ETL tool for Amazon data
• Gartner Data Quality Research - Industry research on data project success rates
• Nova Data API - Pre-built Amazon data pipeline to BigQuery/Snowflake