Home > Destinations > BigQuery > Amazon Seller

Connect Amazon Seller to BigQuery

Connect Amazon Seller to Big Query and export selected accounts, metrics, and dimensions to your Google BigQuery project.

Configure the historical window, destination table, and recurring schedule without maintaining a Amazon Seller API pipeline.

Connect Amazon Seller to BigQuery

Used by the world's leading companies

Export Amazon Seller data to Big Query

Catchr handles extraction, normalization, and loading automatically.

Centralize

Centralize Amazon Seller data in BigQuery

Bring all your Amazon Seller Metrics data into your warehouse and make it available for analytics, machine learning, and internal applications.

Data Freshness

Keep your warehouse always up to date

Automatically sync new data from Amazon Seller so every dashboard, model, and report works with fresh information.

Data Freshness

Unify Amazon Seller with the rest of your business data

Join marketing, finance, CRM, and product data inside the same warehouse.

Amazon Seller metrics and dimensions available in Google Big Query

Access 50 metrics and 54 dimensions from Amazon Seller connectors including Ordered Product Sales Amount, Units Ordered, Sessions, Page Views, Buy Box Percentage, Refund Rate, Date, Product SKU, Product ASIN, Product Name, Sales Channel, Order Status and many more...

50

Metrics availables

54

Dimensions availables

Connect Amazon Seller to BigQuery in minutes

Move your data from Amazon Seller to BigQuery with a simple setup. Once connected, Catchr handles the ingestion automatically so your warehouse stays up to date without manual work.

Step one

Connect your Amazon Seller account

Authenticate your Amazon Seller account securely in Catchr. No custom scripts, API maintenance, or engineering work required.

Connect now
Client A · Connected sources
5 sources ready
Amazon Seller 3 advertising accounts Connected
Google Ads 2 advertising accounts Connected
Google Analytics 4 1 web property Connected
HubSpot 1 CRM portal Connected
Step Two

Choose the Big Query Destination

Select your Google Cloud project and BigQuery dataset. Catchr automatically prepares the destination and maps your data into analytics-ready tables.

Destination / Google BigQuery Required fields
Step three

Keep your warehouse in sync

Catchr continuously imports new and updated data into BigQuery.

Your warehouse stays fresh automatically, ready for SQL, dbt, BI tools, and AI workloads.

Destination / Google BigQuery Example
Project ID Required
agency-reporting-prod
Dataset ID Required
marketing_raw
Service-account JSON key Required
catchr-bigquery-writer.json
Destination table Per job
facebook_ads_daily
Date partition Optional
date
Recurring schedule Choose a cadence
Every 6 hours
Daily · 06:00
Weekly · Mon
First sync scheduled Selected fields · configured history
06:00 UTC

Review 30-day Amazon Seller sales performance in SQL.

Once the table is available, query the fields you selected and named. This example summarizes revenue, orders, units sold, and average order value by sales channel.

Amazon Seller.sql
BigQuery SQL
30-day window 
SELECT
  sales_channel,
  SUM(revenue) AS revenue,
  SUM(orders) AS orders,
  SUM(units_sold) AS units_sold,
  SAFE_DIVIDE(
    SUM(revenue),
    SUM(orders)
  ) AS average_order_value
FROM `your_project.marketing_raw.amazon_seller_orders`
WHERE order_date >= DATE_SUB(
  CURRENT_DATE(),
  INTERVAL 30 DAY
)
GROUP BY sales_channel
ORDER BY revenue DESC;
Illustrative SQL — adapt it to the fields and schema selected in your export. Standard SQL

Connect more table to your Amazon Seller data

Open the dedicated route for each platform. Its field catalog, reporting grain, historical constraints, and warehouse use cases should be specific to that source.

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Before you schedule the job.

Answers about available Meta Ads data, reporting cadence, history, table settings, attribution changes, and downstream joins.

How do I connect Amazon Seller to BigQuery?

Add Amazon Seller as a source in Catchr, choose BigQuery as the destination, select the fields to export, and authorize the target Google Cloud project and dataset.

What Amazon Seller data can I send to BigQuery?

Export stores, customers, products, variants, orders, order items, payments, discounts, refunds, inventory, and other available commerce records.

How often can Amazon Seller data be refreshed in BigQuery?

Schedule recurring refreshes in Catchr so your BigQuery tables stay updated automatically. Available frequencies depend on your plan, source limits, and reporting needs.

Can I combine Amazon Seller data with other sources in BigQuery?

Yes. Keep sources in separate tables, then use SQL views or models to join them on shared dimensions such as date, campaign, account, customer, product, or region.

What can I build with Amazon Seller data in BigQuery?

Build sales dashboards, customer and product analysis, cohort models, margin reporting, inventory monitoring, and unified commerce datasets.

Can the table be partitioned by date?

If your selected schema contains an eligible date field, you can configure that field as the optional date partition for the destination table. Catchr does not configure table clustering or design your downstream analytical models.

Can Catchr backfill history into BigQuery?


Yes. Configure the historical period supported by the source for the initial import, then keep the table updated with recurring exports. The available range is determined by the source API and the settings of your job, not an unlimited-history promise.

Still have a question ? 

Our teams is always here to responds to any question you could have about our data connector. 

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